Prognosis analysis of pyroptosis- and aging-related genes in colorectal cancer based on bioinformatic analysis

Prognosis analysis of pyroptosis- and aging-related genes in colorectal cancer based on bioinformatic analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognosis analysis of pyroptosis- and aging-related genes in colorectal cancer based on bioinformatic analysis Tianyan Pan, Yongdong Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4185479/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Colorectal cancer (CRC) is the most prevalent gastrointestinal cancer worldwide. Our goal was to construct a model based on pyroptosis- and aging-related genes (PARGs) to predict CRC outcomes of colorectal cancer. Methods The Colon Adenocarcinoma/Rectal Adenocarcinoma Esophageal Carcinoma (COADREAD) dataset from the cancer genome atlas (TCGA) was obtained using R. Colorectal cancer-related datasets, namely, GSE74602, GSE87211, and GSE161158 were acquired from the Gene Expression Omnibus (GEO) database. PARGs were collected from various sources such as the GeneCards database, Molecular Signatures Database (MSigDB), and relevant literature. Differential expression analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed using R. Prognostic models were constructed utilizing LASSO (least absolute shrinkage and selection) regression analyses. Column line plots and calibration curve plots were generated using the R package. Immunohistochemical analyses were performed using the HPA (Human Protein Atlas) database. Results To obtain sets of genes related to both pyroptosis and aging (PARGs), we identified overlapping genes from two distinct datasets: one consisting of genes associated with pyroptosis (PRGs), and the other consisting of genes associated with aging (ARGs). We then created a risk signature that encompassed both pyroptosis and aging factors, which was further validated using diagnostic tools such as a Calibration Curve and decision curve analysis (DCA). The risk score derived from this signature significantly affects the overall survival of patients (CRC) patients. The stability and accuracy of this association were further confirmed using stratified survival analysis and DCA. Additionally, GSEA was performed to obtain results for both high-risk and low-risk groups. Conclusions CRC severity may be predicted using the PARGs signature, which is a reliable prognostic analysis model. Pyroptosis aging colorectal cancer (CRC) prognosis gene signature Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction The most recent statistics released by the International Agency for Research on Cancer (IARC) reveal that colorectal cancer (CRC) is the second most deadly and the third most common cancer in terms of its incidence rate [ 1 ] . As the world population continues to grow, deaths from colon and rectal cancers are projected to increase by 60.0% and 71.5%, respectively [ 2 ] . Despite appreciable advances in treatment, the prognosis of CRC remains poor due to metastasis, recurrence, and drug resistance [ 3 ] . More than 50% of individuals diagnosed with CRC have already reached an advanced stage with distant metastases, and those with distant metastases have a five-year survival rate of less than 10% [ 4 ] . The objective response rate (ORR) of immunotherapy-advantaged populations with defective mismatch repair (dMMR) or high microsatellite instability (MSI-H) is only–40–50% with single or combination drugs [ 5 , 6 ] . MSI-H colorectal cancer accounts for only 15% of the colorectal cancer population and 5% of distant metastatic colorectal cancers (mCRC) [ 7 ] . Hence, the prevalence of CRC, emphasizes the urgent need for a thorough understanding of the mechanisms that contribute to cancer progression. Furthermore, there is a vital need to discover novel predictive indicators, develop improved clinical prediction models, and investigate fresh therapeutic targets in the context of CRC. Gasdermins are responsible for the regulation of pyroptosis [ 8 ] , a form of Programmed Cell Death (PCD) that is associated with pro-inflammatory activity [ 9 ] . A recent study has identified four distinct molecular pathways involved in pyroptosis [ 10 ] . The first mechanism [ 11 ] involves the activation of caspase-1 by canonical inflammasomes. Once activated, caspase-1 divides the gasdermin family protein GSDMD, thereby initiating pyroptosis. This procedure also triggers the emission of IL-18 and IL-1β [ 11 ] . The second mechanism involves the activation of caspase-4/5/11 by non-canonical inflammasomes. Activation of caspase-4/5/11 activation results in the cleavage of GSDMD, leading to pyroptosis [ 11 ] . The intracellular cytosolic lipopolysaccharide (LPS) initiates the activation of caspase-4/5/11, which in turn fragments GSDMD into two parts: C-terminal and N-terminal [ 12 ] . The N-terminal fragment of GSDMD is then punctured at the cell membrane to initiate pyroptosis [ 13 ] . Activation of pro-apoptotic caspase-3 can also initiate pyroptosis, resulting in the cleavage of the gasdermin family protein GSDME from the cell membrane [ 14 ] . Moreover, the researchers established that granzymes, created by natural killer cells, can activate pyroptosis even in the absence of caspase action [ 15 , 16 ] . Granzyme is a collective term for a family of serine proteases that are mainly present in cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells. Proteases can selectively cleave substrates in target cells, which in turn triggers PCD [ 17 ] . Research has demonstrated that pyroptosis can either inhibit or stimulate tumor growth [ 18 , 19 ] , as the inflammatory reaction caused by pyroptosis attracts and stimulates immune cells, thus causing tumor removal. Conversely, this immune reaction can also contribute to tumor growth [ 20 ] . Studies suggest that chronic inflammation could contribute to CRC [ 21 ] , which is a leading cause of this type of cancer [ 22 ] . The growth of colorectal cells is affected by the combined beneficial and detrimental effects of pyroptosis. Colorectal cancer may advance due to the release of inflammatory components [ 10 ] and excessive amounts of GSDMC [ 23 ] during pyroptosis. GSDME-induced pyroptosis results in the release of high-mobility group box-1(HMGB1), which stimulates the ERK1/2 signaling cascade and further advances colorectal cancer [ 24 ] . In contrast, research has revealed that IL-1β and IL-18, which are produced during pyroptosis, may obstruct the development and advance of colorectal cancer [ 25 – 27 ] . Furthermore, it has been discovered that GSDMD has a similar effect [ 28 ] . Aging is a complex physiological phenomenon characterized by a progressive decline in the structure and function of organisms as they grow older, ultimately leading to death. Inflammation, immunosenescence, and cellular senescence are essential for this process [ 29 ] . Cellular senescence is a critical factor linking aging and cancer development [ 30 ] . Cellular senescence halts the growth of certain cells, thereby enabling the removal of damaged, inefficient, or surplus cells in certain situations. Senescent cells possess the capacity to secrete various substances such as chemokines, cytokines, and proteases, which is known as the senescence-associated secretory phenotype (SASP) [ 31 – 33 ] . Molecules associated with the SASP include the serum amyloid A (SAA) proteins SAA1 and SAA2 [ 34 ] , IL-1, IL-6, TNFs- [ 35 ] CXCL9, and VEGF [ 30 ] , among many others. Senescence is thought to serve as a safeguard against tumors by inducing senescence in cancer cells [ 36 ] . Despite this, various studies have shown that senescent cells can facilitate tumor growth through diverse pathways [ 37 , 38 ] , primarily through SASP factors that lead to cancer cell proliferation, evasion of apoptosis, angiogenesis induction, metastasis promotion, and inhibition of tumor immunity [ 32 , 36 , 37 , 39 ] . For instance, MMPs help cancer cells migrate by degrading the extracellular matrix [ 40 ] . In addition, senescent cancer cells can escape targeted therapy through transient senescence and can develop drug resistance [ 41 ] which may be associated with tumor recurrence. Studies have revealed that oncogenes such as RAS not only stimulate cell proliferation and survival but also cause cellular senescence, which can be seen in the early stages of tumors such as colon adenocarcinoma [ 42 ] . Based on these data, the removal of cellular senescence has become a significant topic in current research on cancer treatment. A recent study [ 43 ] indicated that caspase-4/11 noncanonical inflammasomes can lead to not only pyroptosis but also senescence. To induce caspase-4-dependent senescence through LPS/caspase-4 noncanonical inflammasomes, the presence of caspase-4, GSDMD, and P53 is essential. Caspase-4 is the only factor that is required for oncogene-induced senescence. The effectiveness of caspase-4 in triggering pyroptosis or senescence depends on its dose, and further investigation is necessary to understand these details. The results of this study also demonstrated that caspase-4 noncanonical inflammasomes are crucial in managing the SASP in OIS and have a significant influence on the control of cell senescence, the SASP, and pyroptosis. In 2022, Douglas Hanahan [ 44 ] acknowledged senescent cells were among the 14 hallmark features of tumors. They suggested that cellular senescence is a complementary mechanism to programmed cell death and is essential for tumor progression. There are several prognostic models for colorectal cancer, including senescence-related genes [ 45 ] , immune-related genes [ 46 ] , hypoxia-associated microRNAs [ 47 ] , autophagy-related genes [ 48 ] , and pyroptosis-related genes [ 49 ] . However, the impact of pyroptosis- and aging-related genes (PARGs) in patients with CRC is yet to be determined. Thanks to developments in bioinformatics and related areas, we can now explore the potential of PARGs to forecast colorectal cancer in patients and assess the influence of the immune microenvironment through bioinformatics analysis. This study aimed to examine PARGs expression in both healthy and colorectal cancer tissues, and evaluate its potential for predicting the outcome of colorectal cancer. By utilizing dissimilarly expressed PARGs, we devised a predictive model for colorectal cancer and identified possible new therapeutic goals. 2 Materials and methods 2.1 Data acquisition The expression matrix from TCGA-COADREAD (COADREAD: colon adenocarcinoma/rectum adenocarcinoma) dataset was obtained from the Cancer Genome Atlas (TCGA) database using TCGAbiolinks [ 50 ] R package. A comprehensive set of 698 data files with 647 colorectal cancer samples (classified as COADREAD) and 51 normal samples (classified as normal) were standardized to the FPKM format, and the related clinical data were obtained from the UCSC Xena database [ 51 ] ( http://genome.ucsc.edu ). TCGA-COADREAD dataset was normalized using the limma R package [ 52 ] . We acquired gene expression profile data and related clinical characteristics data for the COADREAD-associated datasets GSE74602 [ 53 ] , GSE87211 [ 54 ] , and GSE161158 [ 55 ] from the GEO database [ 56 ] , employing the R package GEOquery [ 57 ] . GSE74602, from Homo Sapiens, with data platform GPL6104, provides the microarray gene expression profile data of 30 colorectal cancer patient samples and 30 normal colon tissue samples. Colorectal cancer patient samples based on the data platform GPL13497 were found in the GSE87211 dataset, amounting to 203 samples in total. A dataset of microarray gene expression profiles of 250 patients with CRC, designated as GSE161158, was procured from Homo Sapiens on GPL570. This investigation included all samples, with GSE74602, GSE87211, and GSE161158 datasets serving as validation cohorts. The GeneCards database [ 58 ] ( https://www.genecards.org/ ) provides an extensive collection of information on human genes. We collected pyroptosis-related genes (PRGs) through the GeneCards database. We obtained a total of 200 PRGs using "Pyroptosis" as a search keyword and a relevance score > 0.2 as a screening criterion. We searched the Molecular Signatures Database (MSigDB) [ 59 ] website using "Pyroptosis" as a search keyword and collected two phenotypes from the REACTOME_MITOPHAGY.v7.5.1 reference gene set. Thirty PRGs were selected from the Intersection dataset. We obtained 33 PRGs from the literature [ 60 ] . A total of 225 PRGs were obtained by taking the merged set of PRGs from three sources. We used "Aging" as the search keyword, and relevance score > 0.2 as the filtering criterion to obtain a total of 400 ARGs. We collected 1028 ARGs from the intersection of two phenotypes in the REACTOME_MITOPHAGY.v7.5.1 reference gene set by using "Aging" as the search keyword on the MSigDB database. A total of 307 ARGs were obtained from the literature [ 61 ] . In total, 1594 ARGs were obtained from the merged set of ARGs from three sources. We intersected PRGs with ARGs to obtain 54 Pyroptosis and aging-related genes (PARGs) for inclusion in the subsequent analysis. Specific gene names are shown in Table S1. 2.2 Differential expression analysis To distinguish the genes, associated biological traits, and pathways unique to cancer and normal COADREAD groups, we standardized the data from TCGA-COADREAD, GSE74602, GSE8721, and GSE161158 using the limma R package. Subsequently, TCGA-COADREAD dataset was segregated into two distinct categories: patients with cancer and healthy individuals. TCGA-COADREAD dataset was analyzed at the gene level to determine the genes that were differentially expressed among the various groups. Genes exhibiting a significant difference in expression (P 1 and P.adj < 0.05 were upregulated, while those with logFC < -1 and P.adj < 0.05 were downregulated, and the results of the differential analysis were graphically depicted as volcanoes in the ggplot2 R package. We used PARGs to independently compare the upregulated and downregulated differentially expressed genes. The results were presented as a Venn diagram. The two partial intersections were merged into the pyroptosis- and aging-related differentially expressed genes (PARDEGs) of COADREAD, which included 18 PARDEGs: CTSG, VDR, CITED2, ELANE TREM2, FGF21, UCP1, IL17A, VEGFA, IL1A, TREM1, IL1B, DRD2, NOD2, PCSK9, CXCL8, CEBPB, and MMP1. Subsequently, TCGA-COADREAD, GSE74602, and GSE87211 datasets with differentially expressed genes were plotted as comparison plots. Grouped comparison maps were plotted with the DEGs in TCGA-COADREAD, GSE74602, and GSE87211, respectively. 2.3 GO-KEGG enrichment analysis Gene Ontology (GO) [ 62 ] is a frequently used approach for analyzing the functional enrichment of genes across various dimensions. This investigation typically involves three levels of gene activity: biological processes (BP), molecular functions (MF), and cellular components (CC). KEGG, the Kyoto Encyclopedia of Genes and Genomes [ 63 ] , is a widely used database for tracking data associated with genomes, pathways, ailments, and medications. Using ClusterProfiler [ 64 ] , GO functional analysis of PARDEGs was performed, and the results were depicted graphically with a p-value of 0.05. 2.4 GSEA Gene Set Enrichment Analysis (GSEA) [ 65 ] can be used to analyze the pattern of genes from a predetermined gene list that is ranked according to their correlation with a phenotype, allowing us to assess their impact on the phenotype. This research made use of the clusterProfiler package in R to compare the gene sets in cancer and normal samples from TCGA-COADREAD dataset. The GSEA parameters comprised 2020 as the seed, 10 as the minimum, and 500 as the maximum genes per gene set, as well as the Benjamini-Hochberg (BH) technique for P-value adjustment, with a total of 10,000 calculations. We sourced the gene set "c2.cp.v7.2. symbols" from the Molecular Signatures Database (MSigDB) and established a P-value of < 0.05, and FDR (q-value) of < 0.25 as the criteria for significant enrichment. 2.5 GSVA Gene Set Variation Analysis (GSVA) [ 66 ] is a nonparametric, unsupervised method used to assess the abundance of gene sets in microarray data. This was accomplished by converting the gene expression matrix between the samples into a gene set expression matrix. This technique was used to investigate whether certain pathways were represented more prominently in the various samples. We applied GSVA to TCGA-COADREAD dataset at the gene expression level using the gene set "h.all.v7.4. symbols.gmt) obtained from the MSigDB database. We were able to ascertain the differences in how the two sample sets were enriched using this method. The cutoff for terms that showed significant enrichment was determined to be a p-value of less than 0.05. 2.6 LASSO model To construct predictive models for the PARDEGs of colorectal cancer, we adopted a 10-fold cross-validation using a seed number of 2021. We ran a least absolute shrinkage and selection operator (LASSO) regression 1000 times to avoid over-fitting by employing a least absolute shrinkage and selection operator. The LASSO regression is a popular technique for developing predictive models as it incorporates a penalty element (the absolute value of the lambda slope) into the linear regression equation, which can help prevent overfitting and enhance the model's capacity to make broad-based predictions. We employed the LASSO regression prognostic model to graphically display the division of the cancer group samples into high and low-risk scores, together with their associated survival outcomes. A prognostic model was developed using least the application of LASSO regression analysis. The model formula is as follows: $$riskScore = \sum _{i}Coefficient \left({gene}_{i}\right)*mRNA Expression \left( {gene}_{i}\right)$$ 2.7 PPI networks Interactions between proteins are represented in protein-protein interaction networks, which are composed of various proteins that come together. The STRING database [ 67 ] is a useful tool for identifying known proteins and predicting their interactions. Genes, particularly those responsible for regulating the same biological operations, are intertwined. We established a protein-protein interaction network (PPI) to explore correlations between hub genes. The STRING database was employed with a focus on the human species, and networks of protein-protein interactions associated with PARDEGs were established with a minimum correlation coefficient of 0.400. Cytoscape [ 68 ] was employed to create representations of PPI network models. Additionally, the Maximal Clique Centrality (MCC) [ 69 ] algorithm from the cytoHubba [ 70 ] plug-in was used to assess and demonstrate the association between hub genes and proteins in the network. 2.8 Cox model To evaluate the potential of hub genes in predicting COADREAD, we used the expression data from these genes in TCGA-COADREAD dataset to conduct a Cox regression analysis involving multiple factors. A Cox regression model incorporating multiple variables was created and a forest plot was generated to illustrate the findings. We constructed a nomogram to predict the 1-, 3-, and 5-year survival rates of patients with COADREAD according to the outcomes of multifactorial Cox regression analysis. A nomogram is a chart that can be used to visually represent the correlations between different independent variables, enabling the evaluation of the impact of each variable on a multivariate regression model. A nomogram can be used to estimate the likelihood of an event occurring by determining the overall score. Calibration curves were constructed to evaluate the precision and discriminatory capabilities of the nomogram. This graph illustrates the compatibility between the true and predicted chances in different situations, primarily to determine whether the Cox regression model is applicable in the real world. We used the "rms" package in R to create column and calibration curve plots, and we also carried out decision curve analysis (DCA) to evaluate the nomogram model's predictive capability with regards to the 1-, 3-, and 5-year survival results of COADREAD patients. The R package ggDCA [ 71 ] enables easy development of DCA plots, which are useful tools for assessing clinical prognosis models, diagnostic tests, and molecular markers. 2.9 Immunohistochemical analysis IHC, also known as immunohistochemical analysis, involves the utilization of antibodies and antigens to identify and pinpoint specific antigens in cells and tissues. This is typically accomplished using a light microscope. We used the Human Protein Atlas (HPA) database [ 72 ] ( www.proteinatlas.org/ ) to perform immunohistochemical analysis of the genes specified by the Cox model in both COADREAD tumor specimens and ordinary colorectal tissues. By combining the information from the databases for both tissue types, we were able to display the findings of the immunohistochemical investigation, making it possible to compare the staining designs between them. 2.10 Statistical analysis All data processing and analyses were performed using R software (version 4.1.2). Continuous variables were represented using the mean and standard deviation. The Wilcoxon rank-sum test was used to compare two sets of continuous data and the independent Student’s t-test was used to measure the statistical significance of data that followed a normal distribution. The Kruskal-Wallis test was used to compare three or more groups. The chi-square test or Fisher’s exact test was used to assess and compare the statistical importance of categorical variables. The R package glmnet [ 73 ] was employed to perform the LASSO regression analysis. Spearman’s correlation analysis was performed to measure the connections between molecules unless otherwise mentioned. All P-values were evaluated for both directions, and a P-value of less than 0.05 was considered statistically significant. 3 Results 3.1 The detailed flowchart is shown in Fig. 1 Figure 1 The flowchart of this study TCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma. PARGs, Pyroptosis- and Aging- related genes. GO, Gene Ontology. KEGG, Kyoto Encyclopedia of Genes and Genomes. GSEA, Gene Set Enrichment Analysis. GSVA, Gene Set Variation Analysis. LASSO, least absolute shrinkage and selection operator. PPI, Protein-protein interaction. IHC, immunohistochemical analysis. 3.2 Differential expression analysis In the differential analysis of TCGA-COADREAD dataset, 55537 DEGs were identified, of which 14599 had a |logFC| greater than 1 and Padj of less than 0.05. There were 4266 and 10,333 differentially expressed genes with a negative logFC, which indicated higher expression in the colorectal cancer low-risk group and lower expression in the colorectal cancer high-risk group below the threshold. We visualized the volcano plot (Fig. 2A) and differential ranking plot (Fig. 2B) to demonstrate the outcomes of the differential assessment of this dataset. We combined PRGs and ARGs (Fig. 2C) to generate PARGs. Finally, we compared the upregulated differentially expressed genes with PARGs and identified four genes (CTSG, VDR, CITED2, and ELANE), which are represented in a Venn diagram (Fig. 2D). For the downregulated differentially expressed genes, intersection with ARGs yielded 14 genes (TREM2, FGF21, UCP1, IL17A, VEGFA, IL1A, TREM1, IL1B, DRD2, NOD2, PCSK9, CXCL8, CEBPB, and MMP1), and a Venn diagram (Fig. 2E) was used to illustrate the results. In Table 1 , we provide the gene names and expression data for the 18 PARDEGs. In Dataset GSE74602, sixteen distinct PARDEGs (CTSG, VDR, CITED2, TREM2, FGF21, UCP1, IL17A, VEGFA, IL1A, TREM1, IL1B, DRD2, NOD2, PCSK9, CEBPB, MMP1) were identified. In dataset GSE87211, seventeen genes were discovered, namely, CTSG, VDR, CITED2, ELANE, TREM2, FGF21, UCP1, IL17A, VEGFA, IL1A, TREM1, IL1B, DRD2, NOD2, PCSK9, CEBPB, and MMP1. We compared the expression levels of PARDEGs in TCGA-COADREAD, GSE 74602, and GSE87211 using grouped comparison plots (Fig. 2F-H), and determined whether the differences between them were statistically significant (P < 0.05). The GSE74602 and GSE87211 datasets demonstrated 12 genes that matched TCGA-COADREAD validation results, including CTSG, VDR, CITED2, ELANE, VEGFA, IL1A, TREM1, IL1B, NOD2, PCSK9, CEBPB, and MMP1, as shown in Figure (Fig. 2F-H). Table 1 List of description and expression difference of PARGs of differential expression analysis. Gene Symbol Description logFC P.Value adj.P CTSG cathepsin G 3.34413154 1.6521E-30 2.9674E-29 VDR vitamin D receptor 1.110243456 2.71598E-35 6.32369E-34 CITED2 Cbp/p300 interacting transactivator with Glu/Asp rich carboxy-terminal domain 2 1.756692626 2.82857E-80 5.79674E-78 ELANE elastase, neutrophil expressed 3.578797496 7.08607E-58 5.3411E-56 TREM2 triggering receptor expressed on myeloid cells 2 -1.145762154 4.92095E-08 1.82018E-07 FGF21 fibroblast growth factor 21 -2.46493427 2.54703E-08 9.72682E-08 UCP1 uncoupling protein 1 -2.112722822 1.82337E-05 5.0268E-05 IL17A interleukin 17A -2.640506888 1.59317E-15 1.11504E-14 VEGFA vascular endothelial growth factor A -1.522614701 2.7472E-54 1.73538E-52 IL1A interleukin 1 alpha -2.635870505 3.32149E-25 4.43041E-24 TREM1 triggering receptor expressed on myeloid cells 1 -2.638664774 6.77E-59 4.99E-57 IL1B interleukin 1 beta -2.635870505 3.32149E-25 4.43041E-24 DRD2 dopamine receptor D2 -3.249755965 2.637E-22 2.9465E-21 NOD2 nucleotide binding oligomerization domain containing 2 -1.465023815 7.23191E-23 8.34918E-22 PCSK9 proprotein convertase subtilisin/kexin type 9 -2.715151156 1.23554E-46 5.26298E-45 CXCL8 C-X-C motif chemokine ligand 8 -3.457782428 1.47291E-36 3.67163E-35 CEBPB CCAAT enhancer binding protein beta -1.578090596 1.89071E-38 5.2257E-37 MMP1 matrix metallopeptidase 1 -4.574778525 6.12923E-66 6.56802E-64 PARGs, Pyroptosis- and Ageing- related genes. Figure 2 Analysis of differentially expressed genes (DEGs). (A)Volcano plot of the distributions of all differentially expressed genes between cancer group (grouping: COADREAD) and normal group (grouping: Normal) for dataset TCGA-COADREAD. (B) Differential expression analysis differential ranking plot between cancer group (grouping: COADREAD) and normal group (grouping: Normal) for dataset TCGA-COADREAD. (C) Venn diagram showing the intersection of PRGs and ARGs. (D) Venn diagram display of the intersection of up-regulated differentially expressed genes with PARGs obtained from dataset TCGA-COADREAD. (E) Venn diagram display of the intersection of down-regulated differentially expressed genes with PARGs obtained from dataset TCGA-COADREAD. (F-H) ARGs in dataset TCGA-COADREAD, GSE 39582 and GSE113513 grouped comparison plots are shown. “ns” indicates lack of statistical significance (P > 0.05); asterisks denote statistically significant P values: * statistically significant(p ≤ 0.05), ** highly statistically significant(p ≤ 0.01), *** highly statistically significant(p ≤ 0.001). TCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma. PRGs, Pyroptosis related genes. ARGs, Aging related genes. PARGs, Pyroptosis- and Aging- related genes. 3.3 GO-KEGG enrichment analysis We constructed a bar chart (Fig. 3A) to illustrate the expression of the 18 PARDEGs in TCGA-COADREAD dataset. We conducted GO-KEGG enrichment analysis on the 18 PARDEGs (Table 2 ) to identify the biological processes (BP), molecular functions (MF), and cellular components (CC). We used GO-KEGG enrichment analysis to generate a network graph of BP, CC, MF, and KEGG for TCGA-COADREAD dataset, with joint logFC for PARDEGs, as shown in Fig. 3B. The logFC of the molecule was used to calculate the z-score for each entry, which is shown graphically using a chord plot (Fig. 3C) and a circle plot (Fig. 3D). The study revealed that PARDEGs were largely associated with temperature maintenance (GO:0001659), control of cytokine production (GO:0001819), sustaining multicellular organisms (GO:0048871), the inside of secretory granules (GO:0034774), the inside of azurophil granules (GO:0035578), the inside of cytoplasmic vesicles (GO:0060205), cytokine action (GO:0005125), glycosaminoglycan adhesion (GO:0005539), receptor ligand action (GO:0048018), Rheumatoid Arthritis (hsa05323), IL-17 pathway (hsa05321), and Tuberculosis (hsa05152). Table 2 Results of GO and KEGG Enrichment Analysis ONTOLOGY ID Description GeneRatio BgRatio pvalue p.adjust qvalue BP GO:0001659 temperature homeostasis 7/18 173/18670 1.52e-10 2.52e-07 1.06e-07 BP GO:0001819 positive regulation of cytokine production 8/18 464/18670 4.82e-09 3.23e-06 1.36e-06 BP GO:0048871 multicellular organismal homeostasis 8/18 485/18670 6.82e-09 3.23e-06 1.36e-06 BP GO:0050900 leukocyte migration 8/18 499/18670 8.52e-09 3.23e-06 1.36e-06 BP GO:0032103 positive regulation of response to external stimulus 7/18 323/18670 1.17e-08 3.23e-06 1.36e-06 CC GO:0034774 secretory granule lumen 3/18 321/19717 0.003 0.076 0.065 CC GO:0035578 azurophil granule lumen 2/18 91/19717 0.003 0.076 0.065 CC GO:0060205 cytoplasmic vesicle lumen 3/18 338/19717 0.003 0.076 0.065 CC GO:0031983 vesicle lumen 3/18 339/19717 0.003 0.076 0.065 MF GO:0005125 cytokine activity 5/18 220/17697 2.13e-06 1.24e-04 4.37e-05 MF GO:0005539 glycosaminoglycan binding 5/18 229/17697 2.59e-06 1.24e-04 4.37e-05 MF GO:0048018 receptor ligand activity 6/18 482/17697 5.56e-06 1.78e-04 6.24e-05 MF GO:0070851 growth factor receptor binding 4/18 134/17697 8.86e-06 2.13e-04 7.46e-05 MF GO:0004252 serine-type endopeptidase activity 4/18 160/17697 1.78e-05 3.43e-04 1.20e-04 KEGG hsa05323 Rheumatoid arthritis 6/17 93/8076 2.21e-08 1.93e-06 1.17e-06 KEGG hsa04657 IL-17 signaling pathway 5/17 94/8076 1.06e-06 4.63e-05 2.80e-05 KEGG hsa05321 Inflammatory bowel disease 4/17 65/8076 8.41e-06 2.44e-04 1.48e-04 KEGG hsa05152 Tuberculosis 5/17 180/8076 2.59e-05 5.63e-04 3.41e-04 KEGG hsa04933 AGE-RAGE signaling pathway in diabetic complications 4/17 100/8076 4.65e-05 8.10e-04 4.90e-04 GO, Gene Ontology. BP, Biological Process. CC, Cellular Component. MF, Molecular Function. KEGG, Kyoto Encyclopedia of Genes and Genomes. Figure 3 GO-KEGG enrichment analysis of the dataset TCGA-COADREAD. (A) The barplot displays GO-KEGG enrichment analysis results for PARDEGs (BP, CC, MF, KEGG). (B) Divergence network diagram showing the results of GO-KEGG enrichment analysis of PARDEGs. (C) Chord plot showing the results of GO-KEGG enrichment analysis of the dataset TCGA-COADREAD for PARDEGs of the joint logFC. The green dots represent specific genes and the purple circles represent specific pathways. (D) Circle plot showing the results of GO-KEGG enrichment analysis of the dataset TCGA-COADREAD for PARDEGs of the joint logFC. The purple dots represent up-regulated genes (logFC > 1) and the green dots represent down-regulated genes (logFC < -1).The screening criteria for GO-KEGG enrichment entries were P < 0.05 and FDR value (q.value) < 0.2. GO, Gene Ontology. BP, biological process. CC, cellular component. PARDEGs, Pyroptosis- and Aging- related Differentially Expressed Genes. 3.4 GSEA and GSVA GSEA was used to analyze the associations between the expression of all genes and related biological processes, affected cellular components, and executed molecular functions in TCGA-COADREAD dataset to assess the influence of gene expression levels on the differences between the COADREAD cancer and normal groups. P-values of less than 0.05 and FDR values (q.value) of less than 0.25 were considered indicative of a significant enrichment screening. Analysis of TCGA-COADREAD dataset revealed that multiple pathways, including resistin as an inflammation regulator (Fig. 4B), autophagy (Fig. 4C), glycolysis, and gluconeogenesis (Fig. 4D), and the role of the HSP70 pathway in modulating apoptosis (Fig. 4E), were significantly enriched in genes (Table 3 ). We present our findings in TCGA-COADREAD dataset by creating visual representations of mountains (Fig. 4A) and pathways (Fig. 4B-E). Table 3 Results of Combined Datasets GSEA ID setSize enrichmentScore NES pvalue p.adjust qvalues WP_RESISTIN_AS_A_REGULATOR_OF_INFLAMMATION 33 0.43547616 1.54621674 0.030837 0.09090979 0.05740866 WP_AUTOPHAGY 30 0.42571938 1.50380627 0.04365079 0.11439754 0.07224095 WP_GLYCOLYSIS_AND_GLUCONEOGENESIS 44 0.33117666 1.25108706 0.15533981 0.28425152 0.1795021 WP_APOPTOSIS_MODULATION_BY_HSP70 19 0.60073238 1.85915885 0.00363636 0.03591448 0.02267965 REACTOME_KERATINIZATION 215 -0.54275257 -2.10764977 0.00106952 0.03591448 0.02267965 REACTOME_G2_M_CHECKPOINTS 168 -0.4375401 -1.65067525 0.00110254 0.03591448 0.02267965 REACTOME_HATS_ACETYLATE_HISTONES 139 -0.49548866 -1.84359764 0.00111111 0.03591448 0.02267965 REACTOME_REPRODUCTION 143 -0.49336299 -1.83777081 0.00111607 0.03591448 0.02267965 GROSS_HYPOXIA_VIA_ELK3_DN 69 0.62999349 2.11091357 0.00146843 0.03953922 0.03159959 MENSE_HYPOXIA_UP 42 0.67727411 2.06922046 0.00159744 0.03953922 0.03159959 BROCKE_APOPTOSIS_REVERSED_BY_IL6 71 0.52335209 1.7571483 0.00738552 0.07736577 0.06183042 WP_SENESCENCE_AND_AUTOPHAGY_IN_CANCER 66 0.44080702 1.46191796 0.04654655 0.19930266 0.15928191 GSEA, Gene Set Enrichment Analysis We compared the expression of all genes in TCGA-COADREAD dataset using GSVA to evaluate variations in the hallmark gene set between the COADREAD cancer and normal groups. The data obtained from TCGA-COADREAD dataset indicated that several pathways, such as IL2 STAT5 signaling, apoptosis, hypoxia, and glycolysis, had significantly enriched gene profiles. After assessing the 44 hallmark gene sets, noteworthy disparities between the COADREAD cancer and normal groups were observed (p-value < 0.05, Fig. 4F, Table 4 ). We compared the representative hallmark gene sets and generated a graph to show the differences between groups (Fig. 4G). Table 4 GSVA analysis of dataset TCGA-COADREAD. ontology logFC AveExpr t P.Value adj.P HALLMARK_ADIPOGENESIS -0.527959232 -0.072147691 -14.78264416 1.96E-43 9.82E-42 HALLMARK_MYC_TARGETS_V2 0.854583441 0.017369086 14.57065456 2.19E-42 5.48E-41 HALLMARK_MYC_TARGETS_V1 0.744340126 0.007710798 13.56275337 1.60E-37 2.67E-36 HALLMARK_HEME_METABOLISM -0.466111592 -0.065907264 -13.42973383 6.79E-37 8.48E-36 HALLMARK_PANCREAS_BETA_CELLS -0.550855044 -0.045909656 -12.76822305 7.84E-34 7.84E-33 HALLMARK_E2F_TARGETS 0.736162689 0.00318997 12.47619596 1.64E-32 1.37E-31 HALLMARK_UNFOLDED_PROTEIN_RESPONSE 0.529483031 -0.017977879 12.0995204 7.78E-31 5.56E-30 HALLMARK_BILE_ACID_METABOLISM -0.434861256 -0.053540572 -11.48376507 3.60E-28 2.25E-27 HALLMARK_G2M_CHECKPOINT 0.624078712 -0.011162383 11.34810694 1.35E-27 7.52E-27 HALLMARK_DNA_REPAIR 0.461868068 -0.022384784 10.9964442 3.98E-26 1.99E-25 HALLMARK_KRAS_SIGNALING_DN -0.377246204 -0.044020506 -10.54647825 2.69E-24 1.22E-23 HALLMARK_MTORC1_SIGNALING 0.477122507 -0.028312327 10.39823332 1.05E-23 4.38E-23 HALLMARK_XENOBIOTIC_METABOLISM -0.368499235 -0.046846429 -10.15315597 9.66E-23 3.72E-22 HALLMARK_FATTY_ACID_METABOLISM -0.394084367 -0.028967167 -9.717404433 4.54E-21 1.62E-20 HALLMARK_MYOGENESIS -0.440182258 -0.040943765 -9.437514093 5.03E-20 1.68E-19 HALLMARK_COMPLEMENT -0.405575635 -0.032080661 -8.68853704 2.41E-17 7.53E-17 HALLMARK_PEROXISOME -0.327826326 -0.048675523 -8.522187976 8.99E-17 2.64E-16 HALLMARK_GLYCOLYSIS 0.302574608 -0.052239936 7.956315926 6.79E-15 1.89E-14 HALLMARK_KRAS_SIGNALING_UP -0.355809045 -0.033726964 -7.872425251 1.26E-14 3.32E-14 HALLMARK_OXIDATIVE_PHOSPHORYLATION -0.405994538 -0.021073592 -7.832246739 1.70E-14 4.24E-14 HALLMARK_UV_RESPONSE_DN -0.377341443 -0.039477708 -7.521452052 1.60E-13 3.81E-13 HALLMARK_WNT_BETA_CATENIN_SIGNALING 0.36427061 -0.043742995 7.382145161 4.27E-13 9.70E-13 HALLMARK_ANDROGEN_RESPONSE -0.343766179 -0.020921674 -7.268837254 9.37E-13 2.04E-12 HALLMARK_APICAL_SURFACE -0.312194556 -0.042647052 -6.873898277 1.34E-11 2.80E-11 HALLMARK_ESTROGEN_RESPONSE_LATE -0.243178736 -0.06054344 -6.559469349 1.02E-10 2.05E-10 HALLMARK_IL6_JAK_STAT3_SIGNALING -0.332179313 -0.024721835 -6.022871535 2.72E-09 5.23E-09 HALLMARK_PI3K_AKT_MTOR_SIGNALING -0.236320666 -0.06136432 -6.009484035 2.94E-09 5.45E-09 HALLMARK_ESTROGEN_RESPONSE_EARLY -0.229168303 -0.048207404 -5.728349787 1.48E-08 2.65E-08 HALLMARK_IL2_STAT5_SIGNALING -0.255964223 -0.036780853 -5.654659983 2.24E-08 3.87E-08 HALLMARK_APICAL_JUNCTION -0.261500256 -0.031185057 -5.624723208 2.65E-08 4.39E-08 HALLMARK_UV_RESPONSE_UP 0.205914642 -0.055631231 5.615077898 2.80E-08 4.39E-08 HALLMARK_ALLOGRAFT_REJECTION -0.317195761 -0.015436134 -5.614116799 2.81E-08 4.39E-08 HALLMARK_INFLAMMATORY_RESPONSE -0.278946708 -0.017744467 -5.163710446 3.13E-07 4.74E-07 HALLMARK_TGF_BETA_SIGNALING -0.249532394 -0.048248395 -4.961354905 8.72E-07 1.28E-06 HALLMARK_P53_PATHWAY 0.18388094 -0.065792794 4.869155925 1.38E-06 1.97E-06 HALLMARK_PROTEIN_SECRETION -0.216157519 -0.008472292 -4.294036713 1.99E-05 2.77E-05 HALLMARK_INTERFERON_GAMMA_RESPONSE -0.244894364 -0.02256085 -4.186345519 3.18E-05 4.30E-05 HALLMARK_ANGIOGENESIS 0.212539323 -0.031614118 3.653541343 0.000277225 0.00036477 HALLMARK_HEDGEHOG_SIGNALING -0.193756103 -0.03111402 -3.613325302 0.000323101 0.000414232 HALLMARK_APOPTOSIS -0.153182883 -0.040807553 -3.51141456 0.000473221 0.000591526 HALLMARK_HYPOXIA -0.145493452 -0.046050565 -3.313011799 0.000968621 0.001181245 HALLMARK_MITOTIC_SPINDLE 0.159273108 -0.03210883 3.279855768 0.00108806 0.001295309 HALLMARK_CHOLESTEROL_HOMEOSTASIS 0.129793123 -0.051712431 2.841518136 0.004615531 0.005366897 HALLMARK_COAGULATION -0.09512025 -0.026709223 -2.071228737 0.038689392 0.043965218 GSVA, Gene Set Variation Analysis. TCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma. Figure 4 GSEA and GSVA. (A) GSEA of dataset TCGA-COADREAD for the main 4 biological features. (B-E) Dataset TCGA-COADREAD in which genes are significantly enriched in the WP RESISTIN AS A REGULATOR OF INFLAMMATION (Fig. 4B), WP AUTOPHAGY (Fig. 4C), WP GLYCOLYSIS AND GLUCONEOGENESIS (Fig. 4D), WP APOPTOSIS MODULATION BY HSP70 (Fig. 4E), etc. (F.) Heat map display of functional scores in GSVA for dataset TCGA-COADREAD; (G) Heat map display of functional scores in GSVA for dataset TCGA-COADREAD. The significant enrichment screening criteria for GSEA enrichment analysis were P < 0.05 and FDR value (q.value) < 0.25. Asterisks denote statistically significant P values: * statistically significant(p ≤ 0.05), ** highly statistically significant(p ≤ 0.01), *** highly statistically significant(p ≤ 0.001). TCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma. GSEA, Gene Set Enrichment Analysis. GSVA, Gene Set Variation Analysis. 3.5 LASSO model To evaluate the potential of the 18 PARDEGs (CTSG, VDR, CITED2, ELANE, TREM2, FGF21, UCP1, IL17A, VEGFA, IL1A, TREM1, IL1B, DRD2, NOD2, PCSK9, CXCL8, CEBPB, MMP1) in TCGA-COADREAD dataset to be used for prediction, we employed LASSO regression analysis to generate a prognostic model (Fig. 5A,5C) with 7 key genes (NOD2, VEGFA, CEBPB, ELANE, FGF21, MMP1, TREM2). The LASSO model's Risk Score median was used to divide the cancer groups into two categories, with those deemed to be of high risk placed in the high-risk group, and those of low risk in the low-risk group. The LASSO risk factor map (Fig. 5B) was designed to show the results of the LASSO regression analysis through a visual representation of the high- and low-risk groups. We used statistical analysis to corroborate the LASSO prognostic model by examining the clinical data of patients with COADREAD drawn from TCGA-COADREAD dataset (Table 5 ). Moreover, the Risk Score for the cancer group samples in the GSE17536 dataset was established by employing the coefficients of the variables in the LASSO model, and the cancer group was split into high-risk (grouping: high) and low-risk (grouping: low) groups by taking the median of the Risk Score as the dividing line. Table 5 Patient Characteristics of COADREAD patients in the TCGA datasets. Characteristic levels Overall n 644 T stage, n (%) T1 20 (3.1%) T2 111 (17.3%) T3 436 (68%) T4 74 (11.5%) N stage, n (%) N0 368 (57.5%) N1 153 (23.9%) N2 119 (18.6%) M stage, n (%) M0 475 (84.2%) M1 89 (15.8%) Pathologic stage, n (%) Stage I 111 (17.8%) Stage II 238 (38.2%) Stage III 184 (29.5%) Stage IV 90 (14.4%) Gender, n (%) Female 301 (46.7%) Male 343 (53.3%) Age, n (%) 65 368 (57.1%) OS event, n (%) Alive 515 (80%) Dead 129 (20%) DSS event, n (%) Alive 544 (87.5%) Dead 78 (12.5%) PFI event, n (%) Alive 479 (74.4%) Dead 165 (25.6%) Age, median (IQR) 68 (58, 76) COADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma. TCGA, the cancer genome atlas. OS, overall survival. DSS, disease specific survival. PFI, progression free interval. IQR, interquartile range. We analyzed the levels of expression for NOD2, VEGFA, CEBPB, ELANE, FGF21, MMP1, and TREM2 in the datasets TCGA-COADREAD (Fig. 5D) and GSE161158(Fig. 5E) for high- and low-risk groups and compared the results to show whether there were any statistically significant differences (P < 0.05). The Figure(Fig. 5D,5E) demonstrates that The GSE161158 dataset contains two genes that match TCGA-COADREAD validation results: VEGFA and MMP1. The model formula is as follows: Figure 5 Construct prognostic model for PARDEGs and differential genetic analysis of high- and low-risk groups in COADREAD. (A) Lasso regression model of PARDEGs. The vertical coordinates of the LASSO regression diagnostic model represent the likelihood deviation values of the LASSO regression. The log(λ) value of the x-axis at the bottom of the plot represents by default the case after taking log of the lambda coefficient of the penalty term in the LASSO regression. the number of the x-axis at the top represents the number of variables with non-zero coefficients corresponding to each lambda. (B) The risk factor plot. The green points in the scatter plot portion represent deceased patients and the purple points represent surviving patients. (C) the trajectory plot of variables in the LASSO regression diagnostic model. (D-E) Comparative graphical presentation of the grouping of hub genes in the high- and low-risk groups in the dataset TCGA-COADREAD (D), GSE161158 (E). “ns” indicates lack of statistical significance (P > 0.05). asterisks denote statistically significant P values: * statistically significant(p ≤ 0.05), ** highly statistically significant(p ≤ 0.01), *** highly statistically significant(p ≤ 0.001). TCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma. LASSO, least absolute shrinkage and selection operator. 3.6 PPI We conducted a protein-protein interaction analysis of seven hub genes (NOD2, VEGFA, CEBPB, ELANE, FGF21, MMP1, and TREM2) using the STRING database, setting the biological species as humans and constructing a PPI network with the criterion of a minimum interrelationship coefficient of 0.400 or higher. Using Cytoscape software, we were able to graph the interactions (Fig. 6A) and observed that when the interrelationship coefficient was not less than 0.400, each hub gene was connected to at least one hub gene, with the exception of NOD2 and TREM2. VEGFA had the most connections with other genes in the group, with four core genes (VEGFA, CEBPB, ELANE, FGF21, and MMP1) as its primary linkages. We employed the MCC algorithm to determine the scores of the hub genes in the PPI network, which were linked to other PPI network nodes, and arranged the hub genes from the highest to lowest score using a gradient from red to yellow. The MCC algorithm yielded VEGFA as the first gene, as depicted in Fig. 6B. Furthermore, we used the GeneMANIA website to forecast and form a connection network (Fig. 6C) of functionally analogous genes among these seven key genes to analyze their co-expression, co-localization, and gene interactions. Finally, these seven core genes are displayed as their relevant proteins (Fig. 6D), including TREM2 and VEGFA on chromosome 6, MMP1 on chromosome 11, NOD2 on chromosome 16, ELANE, FGF21 on chromosome 19, and CEBPB on chromosome 20. Figure 6 PPI. (A) PPI network of hub genes. (B) The PPI network of hub genes in MCC algorithm, the color of the rectangle in the figure from yellow to red represents the gradual increase of the score. The interconfiguration network of A-B was collected in the STRING database and constructed in Cytoscape software with a minimum interaction score of 0.400. (C)The GeneMANIA website of hub genes predicts the interaction network of functionally similar genes of hub genes. The interconfiguration network was collected and derived from the GeneMANIA website, where black circles with white slashes represent input hub genes, other black circles without white slashes represent predicted functionally similar genes, purple lines represent co-expression, yellow lines represent Predicted, and red lines represent Physical interactions between genes. (D) The hub gene chromosome location diagram. PPI, protein-protein interaction. MCC, maximal clique centrality. 3.7 Clinical correlations and KM curve We examined the correlation between the seven hub genes and several clinical elements, such as T-stage, N-stage, M-stage, pathological stage, age, and survival indicators, including overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS), and the findings are illustrated in Figs. 7A-G. The results of the data indicated that NOD2 expression had a statistically significant correlation with the discrepancy between T1 and normal T-staging (P < 0.05); VEGFA expression was associated with the difference between survival and death in cases of PFI (P < 0.05); CEBPB expression was linked to the divergence between N0 and N1 in the clinical N-stage (P < 0.05); ELANE expression was related to the contrast between M0 and normal M-staging (P < 0.05); FGF21 expression was connected to the distinction between less than or equal to 65 years of age and normal (P < 0.05); MMP1 expression correlated with the contrast between survival and death in clinical OS cases (P < 0.05); and TREM2 expression was associated with the discrepancy between N0 and normal for clinical N staging (P < 0.05). We produced Kaplan-Meier graphs for the seven hub genes in the LASSO model considering the clinical subtype variables. Although the number of low FGF21 subgroups was less than three, it was not feasible to conduct high and low subgroup analyses. After examining the data from Figs. 7H-M, six hub genes (NOD2, VEGFA, CEBPB, ELANE, MMP1, and TREM2) were identified, and a P value of 0.05 was utilized to evaluate the importance of the associations between pertinent gene-subtype clinical parameters. Figure 7 Clinical correlations and KM curve. (A-H) Violin plots of correlation analysis among clinical subgroups for genes NOD2 (A), VEGFA (B), CEBPB (C), ELANE (D), FGF21 (E), MMP1 (F), and TREM2 (G). (H) KM plots for clinical correlation analysis of gene NOD2 in OS events. (I)KM plots for clinical correlation analysis of gene VEGFA in OS events. (J) KM plots for clinical correlation analysis of gene CEBPB in OS events. (K) KM plots for clinical correlation analysis of gene ELANE in OS events. (L) KM plots for clinical correlation analysis of gene MMP1 in OS events. (M) KM plots for clinical correlation analysis of gene TREM2 in OS events. Asterisks denote statistically significant P values: * statistically significant(p ≤ 0.05), ** highly statistically significant(p ≤ 0.01), *** highly statistically significant(p ≤ 0.001). TCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma. PARDEGs, Pyroptosis- and Aging- Related Differentially Expressed Genes. ROC, receiver operating characteristic curve. OS, overall survival. 3.8 Cox model We employed multivariate Cox regression analysis on TCGA-COADREAD dataset to corroborate our previously established LASSO regression prognostic model. The purpose of this analysis was to examine the connection between optimistic and pessimistic clinical manifestations of seven major genes (NOD2, VEGFA, CEBPB, ELANE, FGF21, MMP1, and TREM2). At first, we incorporated the expression levels of the seven key genes into a multivariate Cox regression model (shown in Table 6 ) and generated a forest plot (Fig. 8A) by using the multivariate Cox regression analysis. Additionally, we performed a nomogram analysis of the genes within the multivariate Cox regression model to evaluate their predictive strength, resulting in the formation of a nomogram (Fig. 8B). Moreover, we evaluated the accuracy of the nomograms based on the multivariate Cox regression model for one year (Fig. 8C), three years (Fig. 8D), and five years (Fig. 8E) by performing a prognostic calibration analysis and displaying the calibration curves. Examining the data, it is clear that the purple line denoting the three-year time span closely follows the gray ideal case line, suggesting that the model is more precise in forecasting results for a three-year duration than for one- or five-year durations. Subsequently, we used DCA to assess the practical value of our generated LASSO-Cox regression model for prognostication over the course of one year (Fig. 8F), three years (Fig. 8G), and five years (Fig. 8H). Examination of the data shows that the purple line representing the model always surpasses the x-values of the green line, suggesting overall positive results. The greatest variation is observed in five-year forecasts, whereas shorter-term forecasts, such as those for one or three years, are less variable. The results indicate that the model's accuracy in predicting outcomes is greater over a five-year period than over either a one-year or three-year period. Table 6 Cox regression to identify hub genes and clinical features associated with OS. Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value NOD2 643 Low 321 Reference High 322 0.867 (0.611–1.232) 0.426 VEGFC 643 Low 321 Reference High 322 1.466 (1.032–2.080) 0.032 1.666 (1.159–2.395) 0.006 CEBPB 643 Low 322 Reference High 321 1.373 (0.969–1.945) 0.075 ELANE 643 Low 322 Reference High 321 0.700 (0.493–0.995) 0.047 0.613 (0.426–0.881) 0.008 MMP1 643 Low 322 Reference High 321 0.727 (0.512–1.030) 0.073 TENM2 643 Low 322 Reference High 321 0.790 (0.558–1.118) 0.183 OS, overall survival. Figure 8 construct Cox model. (A-B) Forest plot (A)and nomogram (B) of multivariable Cox regression analysis of hub genes. (C-E) Calibration curve of the 1-year (C), 3-year (D), and 5-year (E) for the multifactor Cox regression model nomogram analysis. (F-G) DCA plots for the 1-year (F), 3-year (G), and 5-year (H) LASSO-Cox regression prognostic models. The probability threshold or Threshold Probability is represented by the x-axis in the DCA plots, while the net return is represented by the y-axis. DCA, decision curve analysis. LASSO, least absolute shrinkage and selection operator. 3.9 Immunohistochemical analysis Immunohistochemical techniques were utilized to study the expression of 5 genes (NOD2, CEBPB, ELANE, FGF21, TREM2) in COADREAD tumor tissues and normal colorectal tissues, taking into consideration 7 hub genes, with the help of the HPA database. The staining process required the use of DAB (3,3-diaminobenzidine) and counterstaining was performed with hematoxylin. Analysis of NOD2 expression indicated that the gene concentration was significantly higher in the COADREAD tumor tissue (COADREAD tissue, Fig. 9B) than in the normal colorectal tissue (normal colorectal tissues, Fig. 9A). CEBPB expression was significantly more elevated in COADREAD tissue (Fig. 9D) when compared to normal colorectal tissue (Fig. 9C). ELANE expression was higher in normal colorectal tissue than in COADREAD tumor tissue in both normal colorectal tissue (Fig. 9E) and COADREAD tissue (Fig. 9F). FGF21 expression was more pronounced in COADREAD tumor tissues (COADREAD tissue, Fig. 9H) in regular colorectal tissues (normal colon tissue, Fig. 9G). TREM2 expression was more pronounced in COADREAD tissue (Fig. 9J) when compared to normal colorectal tissue (Fig. 9I). Figure 9 Immunohistochemical analysis. (A-B) gene NOD2 immunohistochemistry analysis in COADREAD tissue(B)and Normal colorectal tissues (A). (C-D) gene CEBPB immunohistochemistry analysis in COADREAD tissue(D)and Normal colorectal tissues (C). (E-F) gene ELANE immunohistochemistry analysis in COADREAD tissue(F)and Normal colorectal tissues (E). (G-H) gene FGF21 immunohistochemistry analysis in COADREAD tissue(H)and normal Normal colorectal tissues (G). (I-J) gene TREM2 immunohistochemistry analysis in COADREAD tissue(J)and Normal colorectal tissues (I). All the data were obtained from the HPA database. COADREAD, colon adenocarcinoma/rectum adenocarcinoma. HPA, human protein atlas. 4 Discussion IARC's most recent data indicates that CRC has the third highest rate of occurrence worldwide and is the second leading cause of death [ 1 ] . New discoveries are being made in pyroptosis, aging, tumorigenesis, and growth, and oncology studies are exploring the possibilities of inducing pyroptosis and senescence, as well as eliminating senescent cancer cells. However, the prognostic value of PARGs in determining the outcome of colorectal cancer remains unclear. Pyroptosis and senescence are integral components of colorectal progression. Inflammation is caused by both pyroptosis and senescence, with pyroptosis regulating senescence at the cellular level [ 74 ] and triggering immunogenic cell death (ICD) [ 75 , 76 ] and senescence in cancer cells, causing the release of large quantities of cytokines and other cell components. Research on different ailments has revealed that pyroptosis can lead to cellular senescence [ 77 ] , and that cells exhibiting senescence characteristics are more susceptible to pyroptosis [ 78 ] . Evidence suggests that chemotherapy stimulates pyroptosis in cancer cells [ 79 ] , which may explain why chemotherapy combined with immunotherapy is often more effective against certain tumors. Recent research revealed that in certain forms of cancer, cellular demise can occur through a process of scorching caused by caspase-3/GSDME, which correlates with the expression of GSDME [ 80 ] . This process is distinct from apoptosis, which is considered the sole cause of cell death in aged cells. This study showed that senescent cancer cells can trigger an immune response not only by drawing macrophages and NK cells to eliminate them [ 81 ] , but also by encouraging immunogenic cell death in cancer cells by boosting MHC1 expression, amplifying the activity of dendritic cells and CD8 + T-cells [ 82 ] . Furthermore, researchers have discovered that tumor progression can be hindered and postponed by injecting senescent cancer cells. Research has made progress in the area of pyroptosis induced by nanomaterial-transporting drugs (see the review published by Meng et al. [ 83 ] ) and apoptogens [ 84 ] , with the pyroptosis of cancer cells being seen as a potential new therapeutic option [ 85 ] . This investigation identified seven colorectal cancer pyroptosis- and aging-related hub genes; however, due to the limited number of samples, prognostic models were created with the other six hub genes (NOD2, VEGFA, CEBPB, ELANE, FGF21, MMP1, and TREM2). The most noteworthy variations were observed for VEGFA and MMP1. The results of this study are in agreement with the fact that VEGFA [ 85 , 86 ] is connected to tumor angiogenesis and vasculogenic mimicry (VM), both of which are linked to a poor prognosis in colorectal cancer. A previous study demonstrated that the inhibition of tumor growth can be achieved by focusing on and neutralizing VEGFA [ 87 ] . In 2004, the US Food and Drug Administration (FDA) granted approval for bevacizumab, an inhibitor of VEGFA, as the initial therapy for CRC [ 88 ] . MMP-1 is a member of the Matrix Metalloproteinases (MMP) family and has a particular focus in the intestines [ 89 ] . This protein can break down collagen types I, II, III, VII, VIII, and X, and gelatin. Examination of MMP-1 gene expression revealed higher levels in colorectal cancer patients than in healthy individual tissues [ 40 , 90 ] . Eiro [ 91 ] and their colleagues discovered that the MMP-1 gene was expressed at higher levels in serrated, villous and tubular villous adenomas (i.e. polyps that have a high risk of turning into CRC). Previous studies conducted in the past have demonstrated that high MMP-1 expression is associated with an increased risk of invasion, advanced metastasis, lymph node metastasis, and shorter overall survival in CRC [ 92 , 93 ] . Wang et al. investigated the effects of MMP-1 on CRC progression. Research has shown that by reducing MMP-1 expression, the development of CRC in both laboratory experiments and living organisms can be restrained due to the suppression of the PI3K/Akt/c-myc signaling pathway and EMT [ 40 ] . These findings imply that MMP1 is linked to a favorable prognosis in CRC, which contradicts the outcomes of previous studies [ 40 , 89 – 93 ] . A research study [ 94 ] revealed that there may be a correlation between clinical staging and MMP1 expression in the samples taken, as MMP1 was found to be lower in liver metastases than in primary CRC. Moreover, patients with stage III colon cancer without MMP1 expression had a shorter time to distant metastasis and shorter overall survival, which is in line with the findings of this study. Further investigations into the relationship between MMP1 and CRC should be substantiated by multiple clinical and laboratory studies. The GO and KEGG enrichment analysis results indicated that PARDEGs were mainly found in biological processes such as temperature homeostasis (GO:0001659), positive regulation of cytokine production (GO:0001819), multicellular organismal homeostasis (GO:0048871), lumen of secretory granules (GO:0034774), lumen of azurophil granules (GO:0035578) and lumen of cytoplasmic vesicles (GO. 0060205), cytokine activity (GO:0005125), glycosaminoglycan binding (GO:0005539), receptor ligand activity (GO:0048018), rheumatoid arthritis (hsa05323), IL-17 signaling pathway (hsa05321), and tuberculosis (hsa05152). GSEA enrichment analysis demonstrated a substantial increase in genes in TCGA-COADREAD dataset that are regulated by WP resistin pathways, such as inflammation (Fig. 4B), autophagy (Fig. 4C), glycolysis and gluconeogenesis (Fig. 4D), and HSP70-mediated apoptosis modulation (Fig. 4E). GSVA revealed a statistically significant overrepresentation of PARDEGs in TCGA-COADREAD dataset within the HALLMARK IL2 STAT5 signaling, apoptosis, hypoxia, and glycolysis pathways. This study generated column line plots (nomogram plots) that were more accurate in predicting outcomes at 3-year intervals than at 1-year or 5-year intervals. The LASSO-Cox regression prognostic model demonstrated remarkable accuracy in predicting clinical utility over a 5-year period, outperforming the 1-year and 3-year models. Despite the success of this study in forming a reliable prognostic model, this was not corroborated by subsequent experiments. No clinical correlation was present for further examination. The utilization of numerous datasets throughout the investigation could result in interbatch disparities that are not feasible to avoid or eradicate during assessment. Moreover, this examination had a restricted sample size. These findings could have been distorted due to the natural inclination to select cases. Further research involving both in vivo and in vitro experiments, as well as extensive observational studies, must be conducted to validate our results. To summarize, our research produced a prognostic model for colorectal cancer that is based on six pyroptosis- and aging-related genes, and the results demonstrate that it is reliable in predicting the overall survival of patients with COAD. This study could potentially pave the way for novel advancements in CRC research. Despite its merits, this study has some limitations. We drew our data from open sources and retrospectively obtained samples for the study. This study was conducted with a limited number of participants. Consequently, the outcomes could have been affected by an innate inclination towards certain cases. Further investigations, such as more extensive studies and experiments, both in the laboratory and in living organisms, are needed to support our conclusions. Abbreviations CRC, Colorectal cancer. PARGs, pyroptosis- and aging-related genes. COADREAD, The Colon Adenocarcinoma/Rectal Adenocarcinoma. TCGA, the cancer genome atlas. GEO, Gene Expression Omnibus. GO, Gene Ontology. KEGG, Kyoto Encyclopedia of Genes and Genomes. GSEA, Gene Set Enrichment Analysis. LASSO, least absolute shrinkage and selection. HPA, Human Protein Atlas. DCA, decision curve analysis. IARC, International Agency for Research on Cancer. ORR, objective response rate. dMMR, defective mismatch repair. MSI-H, high microsatellite instability. mCRC, metastatic colorectal cancers. PCD, Programmed Cell Death. SASP, senescence-associated secretory phenotype. BP, biological processes. MF, molecular functions. CC, cellular components. MCC, Maximal Clique Centrality. OS, overall survival. PFI, progression-free interval. DSS, disease-specific survival. ICD, immunogenic cell death. IQR, interquartile range. Declarations Availability of Data and Materials The datasets utilized in this study were downloaded from the following online tools, and the analysis results were also presented based on these datasets. TCGA(https://portal.gdc.cancer.gov/)，UCSC Xena database （http://genome.ucsc.edu, GEO (https://www.ncbi.nlm.nih.gov/geo/), GeneCards (https://www.genecards.org/), MSigDB( https://www.gsea-msigdb.org/gsea/msigdb/index.jsp), STRING database (https://cn.string-db.org/), HPA (https://www.proteinatlas.org/). Ethics approval and consent to participate This study does not contain any studies with humanparticipants or animals performed by any of the authors. Acknowledgments The authors would like to express their gratitude to Helix Life (https://www.helixlife.cn/class) for providing guidance on data analysis and language editing, and extend thanks to all the scholars who have made continuous efforts in cancer research. Publisher’s note The ideas and opinions expressed in this article are solely those of the authors and do not necessarily reflect the views of their affiliated organizations, the publisher, the editors, or the reviewers. The publisher does not guarantee or approve of any product or statement made by its manufacturer that is assessed in this article. Author Contribution TP aided in the review of articles, the composition of manuscripts, the editing of intellectual content, and were in charge of gathering data and providing an English interpretation, and evaluating quality. YJ served as the primary contact and was responsible for evaluating the quality of the content and making necessary revisions. The article was collaboratively written by all authors and they all agreed to the final version. References SUNG H, FERLAY J, SIEGEL R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. CA Cancer J Clin, 2021, 71(3): 209-49. ARAGHI M, SOERJOMATARAM I, JENKINS M, et al. Global trends in colorectal cancer mortality: projections to the year 2035 [J]. Int J Cancer, 2019, 144(12): 2992-3000. DHILLON S. Regorafenib: A Review in Metastatic Colorectal Cancer [J]. Drugs, 2018, 78(11): 1133-44. MCQUADE R M, STOJANOVSKA V, BORNSTEIN J C, et al. Colorectal Cancer Chemotherapy: The Evolution of Treatment and New Approaches [J]. Curr Med Chem, 2017, 24(15): 1537-57. OVERMAN M J, LONARDI S, WONG K Y M, et al. Durable Clinical Benefit With Nivolumab Plus Ipilimumab in DNA Mismatch Repair-Deficient/Microsatellite Instability-High Metastatic Colorectal Cancer [J]. J Clin Oncol, 2018, 36(8): 773-9. OVERMAN M J, MCDERMOTT R, LEACH J L, et al. Nivolumab in patients with metastatic DNA mismatch repair-deficient or microsatellite instability-high colorectal cancer (CheckMate 142): an open-label, multicentre, phase 2 study [J]. Lancet Oncol, 2017, 18(9): 1182-91. 曹鹏, 邱萌. 微卫星稳定型结直肠癌免疫治疗研究进展及热点 [J]. 实用肿瘤杂志, 2022, 37(02): 99-106. KOVACS S B, MIAO E A. Gasdermins: Effectors of Pyroptosis [J]. Trends Cell Biol, 2017, 27(9): 673-84. FANG Y, TIAN S, PAN Y, et al. Pyroptosis: A new frontier in cancer [J]. Biomed Pharmacother, 2020, 121(109595. LU L, ZHANG Y, TAN X, et al. Emerging mechanisms of pyroptosis and its therapeutic strategy in cancer [J]. Cell Death Discov, 2022, 8(1): 338. SHI J, GAO W, SHAO F. Pyroptosis: Gasdermin-Mediated Programmed Necrotic Cell Death [J]. Trends Biochem Sci, 2017, 42(4): 245-54. SHI J, ZHAO Y, WANG Y, et al. Inflammatory caspases are innate immune receptors for intracellular LPS [J]. Nature, 2014, 514(7521): 187-92. AGLIETTI R A, ESTEVEZ A, GUPTA A, et al. GsdmD p30 elicited by caspase-11 during pyroptosis forms pores in membranes [J]. Proc Natl Acad Sci U S A, 2016, 113(28): 7858-63. TAABAZUING C Y, OKONDO M C, BACHOVCHIN D A. Pyroptosis and Apoptosis Pathways Engage in Bidirectional Crosstalk in Monocytes and Macrophages [J]. Cell Chem Biol, 2017, 24(4): 507-14 e4. ZHANG Z, ZHANG Y, XIA S, et al. Gasdermin E suppresses tumour growth by activating anti-tumour immunity [J]. Nature, 2020, 579(7799): 415-20. [J]. VOSKOBOINIK I, WHISSTOCK J C, TRAPANI J A. Perforin and granzymes: function, dysfunction and human pathology [J]. Nat Rev Immunol, 2015, 15(6): 388-400. RUAN J, WANG S, WANG J. Mechanism and regulation of pyroptosis-mediated in cancer cell death [J]. Chem Biol Interact, 2020, 323(109052. ZHENG Z, LI G. Mechanisms and Therapeutic Regulation of Pyroptosis in Inflammatory Diseases and Cancer [J]. Int J Mol Sci, 2020, 21(4): WANG M, JIANG S, ZHANG Y, et al. The Multifaceted Roles of Pyroptotic Cell Death Pathways in Cancer [J]. Cancers (Basel), 2019, 11(9): SHAWKI S, ASHBURN J, SIGNS S A, et al. Colon Cancer: Inflammation-Associated Cancer [J]. Surg Oncol Clin N Am, 2018, 27(2): 269-87. JAFARI D, NOORBAKHSH F, DELAVARI A, et al. Expression level of long noncoding RNA NKILAmiR103-miR107 inflammatory axis and its clinical significance as potential biomarker in patients with colorectal cancer [J]. J Res Med Sci, 2020, 25(41. MIGUCHI M, HINOI T, SHIMOMURA M, et al. Gasdermin C Is Upregulated by Inactivation of Transforming Growth Factor beta Receptor Type II in the Presence of Mutated Apc, Promoting Colorectal Cancer Proliferation [J]. PLoS One, 2016, 11(11): e0166422. TAN G, HUANG C, CHEN J, et al. HMGB1 released from GSDME-mediated pyroptotic epithelial cells participates in the tumorigenesis of colitis-associated colorectal cancer through the ERK1/2 pathway [J]. J Hematol Oncol, 2020, 13(1): 149. DUPAUL-CHICOINE J, YERETSSIAN G, DOIRON K, et al. Control of intestinal homeostasis, colitis, and colitis-associated colorectal cancer by the inflammatory caspases [J]. Immunity, 2010, 32(3): 367-78. ZAKI M H, BOYD K L, VOGEL P, et al. The NLRP3 inflammasome protects against loss of epithelial integrity and mortality during experimental colitis [J]. Immunity, 2010, 32(3): 379-91. ZAKI M H, VOGEL P, BODY-MALAPEL M, et al. IL-18 production downstream of the Nlrp3 inflammasome confers protection against colorectal tumor formation [J]. J Immunol, 2010, 185(8): 4912-20. WU L S, LIU Y, WANG X W, et al. LPS Enhances the Chemosensitivity of Oxaliplatin in HT29 Cells via GSDMD-Mediated Pyroptosis [J]. Cancer Manag Res, 2020, 12(10397-409. DODIG S, CEPELAK I, PAVIC I. Hallmarks of senescence and aging [J]. Biochem Med (Zagreb), 2019, 29(3): 030501. CAI Y, SONG W, LI J, et al. The landscape of aging [J]. Sci China Life Sci, 2022, 65(12): 2354-454. GORGOULIS V, ADAMS P D, ALIMONTI A, et al. Cellular Senescence: Defining a Path Forward [J]. Cell, 2019, 179(4): 813-27. FAGET D V, REN Q, STEWART S A. Unmasking senescence: context-dependent effects of SASP in cancer [J]. Nat Rev Cancer, 2019, 19(8): 439-53. [J]. [J]. WAN M, GRAY-GAILLARD E F, ELISSEEFF J H. Cellular senescence in musculoskeletal homeostasis, diseases, and regeneration [J]. Bone Res, 2021, 9(1): 41. LEE S, SCHMITT C A. The dynamic nature of senescence in cancer [J]. Nat Cell Biol, 2019, 21(1): 94-101. WANG B, KOHLI J, DEMARIA M. Senescent Cells in Cancer Therapy: Friends or Foes? [J]. Trends Cancer, 2020, 6(10): 838-57. KOWALD A, PASSOS J F, KIRKWOOD T B L. On the evolution of cellular senescence [J]. Aging Cell, 2020, 19(12): e13270. HE S, SHARPLESS N E. Senescence in Health and Disease [J]. Cell, 2017, 169(6): 1000-11. WANG K, ZHENG J, YU J, et al. Knockdown of MMP‑1 inhibits the progression of colorectal cancer by suppressing the PI3K/Akt/c‑myc signaling pathway and EMT [J]. Oncol Rep, 2020, 43(4): 1103-12. DE BLANDER H, MOREL A P, SENARATNE A P, et al. Cellular Plasticity: A Route to Senescence Exit and Tumorigenesis [J]. Cancers (Basel), 2021, 13(18): SIEBEN C J, STURMLECHNER I, VAN DE SLUIS B, et al. Two-Step Senescence-Focused Cancer Therapies [J]. Trends Cell Biol, 2018, 28(9): 723-37. FERNANDEZ-DURAN I, QUINTANILLA A, TARRATS N, et al. Cytoplasmic innate immune sensing by the caspase-4 non-canonical inflammasome promotes cellular senescence [J]. Cell Death Differ, 2022, 29(6): 1267-82. HANAHAN D. Hallmarks of Cancer: New Dimensions [J]. Cancer Discov, 2022, 12(1): 31-46. [J]. HU X, LI Y Q, MA X J, et al. A Risk Signature With Inflammatory and T Immune Cells Infiltration in Colorectal Cancer Predicting Distant Metastases and Efficiency of Chemotherapy [J]. Front Oncol, 2019, 9(704. [J]. ZHOU Z, MO S, DAI W, et al. Development and Validation of an Autophagy Score Signature for the Prediction of Post-operative Survival in Colorectal Cancer [J]. Front Oncol, 2019, 9(878. ZHENG C, TAN Z. A novel identified pyroptosis-related prognostic signature of colorectal cancer [J]. Math Biosci Eng, 2021, 18(6): 8783-96. COLAPRICO A, SILVA T C, OLSEN C, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data [J]. Nucleic Acids Res, 2016, 44(8): e71. GOLDMAN M J, CRAFT B, HASTIE M, et al. Visualizing and interpreting cancer genomics data via the Xena platform [J]. Nat Biotechnol, 2020, 38(6): 675-8. RITCHIE M E, PHIPSON B, WU D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies [J]. Nucleic Acids Res, 2015, 43(7): e47. SHI J, BAO M, WANG W, et al. Integrated Profiling Identifies PLOD3 as a Potential Prognostic and Immunotherapy Relevant Biomarker in Colorectal Cancer [J]. Front Immunol, 2021, 12(722807. HU Y, GAEDCKE J, EMONS G, et al. Colorectal cancer susceptibility loci as predictive markers of rectal cancer prognosis after surgery [J]. Genes Chromosomes Cancer, 2018, 57(3): 140-9. SZEGLIN B C, WU C, MARCO M R, et al. A SMAD4-modulated gene profile predicts disease-free survival in stage II and III colorectal cancer [J]. Cancer Rep (Hoboken), 2022, 5(1): e1423. BARRETT T, WILHITE S E, LEDOUX P, et al. NCBI GEO: archive for functional genomics data sets--update [J]. Nucleic Acids Res, 2013, 41(Database issue): D991-5. DAVIS S, MELTZER P S. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor [J]. Bioinformatics, 2007, 23(14): 1846-7. STELZER G, ROSEN N, PLASCHKES I, et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses [J]. Curr Protoc Bioinformatics, 2016, 54(1.30.1-1..3. LIBERZON A, BIRGER C, THORVALDSDOTTIR H, et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection [J]. Cell Syst, 2015, 1(6): 417-25. WU J, ZHU Y, LUO M, et al. Comprehensive Analysis of Pyroptosis-Related Genes and Tumor Microenvironment Infiltration Characterization in Breast Cancer [J]. Front Immunol, 2021, 12(748221. HE J, LI X. Identification and Validation of Aging-Related Genes in Idiopathic Pulmonary Fibrosis [J]. Front Genet, 2022, 13(780010. Gene Ontology Consortium: going forward [J]. Nucleic Acids Research, 2014, 43(D1): D1049-D56. KANEHISA M, GOTO S. KEGG: kyoto encyclopedia of genes and genomes [J]. Nucleic Acids Res, 2000, 28(1): 27-30. YU G, WANG L G, HAN Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters [J]. OMICS, 2012, 16(5): 284-7. SUBRAMANIAN A, TAMAYO P, MOOTHA V K, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles [J]. Proc Natl Acad Sci U S A, 2005, 102(43): 15545-50. HäNZELMANN S, CASTELO R, GUINNEY J. GSVA: gene set variation analysis for microarray and RNA-seq data [J]. BMC Bioinformatics, 2013, 14(7. SZKLARCZYK D, GABLE A L, LYON D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets [J]. Nucleic Acids Res, 2019, 47(D1): D607-D13. SHANNON P, MARKIEL A, OZIER O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks [J]. Genome Res, 2003, 13(11): 2498-504. YANG X, LI Y, LV R, et al. Study on the Multitarget Mechanism and Key Active Ingredients of Herba Siegesbeckiae and Volatile Oil against Rheumatoid Arthritis Based on Network Pharmacology [J]. Evid Based Complement Alternat Med, 2019, 2019(8957245. CHIN C H, CHEN S H, WU H H, et al. cytoHubba: identifying hub objects and sub-networks from complex interactome [J]. BMC Syst Biol, 2014, 8 Suppl 4(Suppl 4): S11. TATARANNI T, PICCOLI C. Dichloroacetate (DCA) and Cancer: An Overview towards Clinical Applications [J]. Oxid Med Cell Longev, 2019, 2019(8201079. COLWILL K, GRäSLUND S. A roadmap to generate renewable protein binders to the human proteome [J]. Nat Methods, 2011, 8(7): 551-8. ENGEBRETSEN S, BOHLIN J. Statistical predictions with glmnet [J]. Clin Epigenetics, 2019, 11(1): 123. JIN H, XIE W, HE M, et al. Pyroptosis and Sarcopenia: Frontier Perspective of Disease Mechanism [J]. Cells, 2022, 11(7): DING B, SHENG J, ZHENG P, et al. Biodegradable Upconversion Nanoparticles Induce Pyroptosis for Cancer Immunotherapy [J]. Nano Lett, 2021, 21(19): 8281-9. KOLB J P, OGUIN T H, 3RD, OBERST A, et al. Programmed Cell Death and Inflammation: Winter Is Coming [J]. Trends Immunol, 2017, 38(10): 705-18. ZHAO P, YUE Z, NIE L, et al. Hyperglycaemia-associated macrophage pyroptosis accelerates periodontal inflamm-aging [J]. J Clin Periodontol, 2021, 48(10): 1379-92. FALI T, FABRE-MERSSEMAN V, YAMAMOTO T, et al. Elderly human hematopoietic progenitor cells express cellular senescence markers and are more susceptible to pyroptosis [J]. JCI Insight, 2018, 3(13): TAN Y, CHEN Q, LI X, et al. Pyroptosis: a new paradigm of cell death for fighting against cancer [J]. J Exp Clin Cancer Res, 2021, 40(1): 153. JIANG M, QI L, LI L, et al. The caspase-3/GSDME signal pathway as a switch between apoptosis and pyroptosis in cancer [J]. Cell Death Discov, 2020, 6(112. PRATA L G P L, OVSYANNIKOVA I G, TCHKONIA T, et al. Senescent cell clearance by the immune system: Emerging therapeutic opportunities [J]. Seminars in Immunology, 2018, 40( MARIN I B O, GARCIA-GARIJO A, SIROIS I, CABALLE A, ZARZUELA E, RUANO I, STEPHAN-OTTO ATTOLINI C, PRATS N, LOPEZ-DOMINGUEZ JA, KOVATCHEVA M, GARRALDA E, MUNOZ J, CARON E, ABAD M, GROS A, PIETROCOLA F, SERRANO M. Cellular senescence is immunogenic and promotes anti-tumor immunity [J]. Cancer Discov, 2022, MENG Q, DING B, MA P, et al. Interrelation between Programmed Cell Death and Immunogenic Cell Death: Take Antitumor Nanodrug as an Example [J]. Small Methods, 2023, e2201406. LIU Z, LI Y, ZHU Y, et al. Apoptin induces pyroptosis of colorectal cancer cells via the GSDME-dependent pathway [J]. Int J Biol Sci, 2022, 18(2): 717-30. ANDREOZZI M, QUAGLIATA L, GSPONER J R, et al. VEGFA gene locus analysis across 80 human tumour types reveals gene amplification in several neoplastic entities [J]. Angiogenesis, 2014, 17(3): 519-27. LIU X, HE H, ZHANG F, et al. m6A methylated EphA2 and VEGFA through IGF2BP2/3 regulation promotes vasculogenic mimicry in colorectal cancer via PI3K/AKT and ERK1/2 signaling [J]. Cell Death Dis, 2022, 13(5): 483. [J]. [J]. PEZESHKIAN Z, NOBILI S, PEYRAVIAN N, et al. Insights into the Role of Matrix Metalloproteinases in Precancerous Conditions and in Colorectal Cancer [J]. Cancers (Basel), 2021, 13(24): JONSSON A, FALK P, ANGENETE E, et al. Plasma MMP-1 Expression as a Prognostic Factor in Colon Cancer [J]. J Surg Res, 2021, 266(254-60. EIRO N, GONZALEZ L O, CID S, et al. Matrix metalloproteases expression in different histological types of colorectal polyps [J]. Rev Esp Enferm Dig, 2017, 109(6): 414-20. LIANG Y, LV Z, HUANG G, et al. Prognostic significance of abnormal matrix collagen remodeling in colorectal cancer based on histologic and bioinformatics analysis [J]. Oncol Rep, 2020, 44(4): 1671-85. SUNAMI E, TSUNO N, OSADA T, et al. MMP-1 is a prognostic marker for hematogenous metastasis of colorectal cancer [J]. Oncologist, 2000, 5(2): 108-14. WONG J C, CHAN S K, SCHAEFFER D F, et al. Absence of MMP2 expression correlates with poor clinical outcomes in rectal cancer, and is distinct from MMP1-related outcomes in colon cancer [J]. Clin Cancer Res, 2011, 17(12): 4167-76. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4185479","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286494984,"identity":"29034b8f-0a22-40b5-b9b3-2924f7261e5b","order_by":0,"name":"Tianyan Pan","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Tianyan","middleName":"","lastName":"Pan","suffix":""},{"id":286494985,"identity":"b1e0efa7-0b63-4114-a258-293a7c01b5e7","order_by":1,"name":"Yongdong Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoklEQVRIiWNgGAWjYJACgwQGGx5+/gbStKTJSM44QJpFh20MGhKIVMvffvhBwcMd53kMGA4wfviYQ4QWiTNpBgaJZ27zmDM3MEvO3EaEFgMJHgaDxLbbPJYNB9iYeUnQco7H4EACaVoOkKAF4pe2ZB7JGQebifMLMMSeGf5ss7Pn528++OEjMVqAgM0AQjM2EKceCJgfEK10FIyCUTAKRiYAAMDWMe7j2n5VAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China","correspondingAuthor":true,"prefix":"","firstName":"Yongdong","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2024-03-29 03:44:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4185479/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4185479/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54178656,"identity":"c5c840ae-210d-4d36-8f08-c659b5a21134","added_by":"auto","created_at":"2024-04-05 16:14:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":735203,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe flowchart of this study\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4185479/v1/93ae806d83e94486ff9e2765.jpg"},{"id":54178662,"identity":"2e566c01-38bc-458e-aa24-b4fb017492bb","added_by":"auto","created_at":"2024-04-05 16:14:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1120570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of differentially expressed genes (DEGs).\u003c/strong\u003e(A)Volcano plot of the distributions of all differentially expressed genes between cancer group (grouping: COADREAD) and normal group (grouping: Normal) for dataset TCGA-COADREAD. (B) Differential expression analysis differential ranking plot between cancer group (grouping: COADREAD) and normal group (grouping: Normal) for dataset TCGA-COADREAD. (C) Venn diagram showing the intersection of PRGs and ARGs. (D) Venn diagram display of the intersection of up-regulated differentially expressed genes with PARGs obtained from dataset TCGA-COADREAD. (E) Venn diagram display of the intersection of down-regulated differentially expressed genes with PARGs obtained from dataset TCGA-COADREAD. (F-H) ARGs in dataset TCGA-COADREAD, GSE 39582 and GSE113513 grouped comparison plots are shown. “ns” indicates lack of statistical significance (P \u0026gt;0.05); asterisks denote statistically significant P values: * statistically significant(p≤0.05), ** highly statistically significant(p≤0.01), *** highly statistically significant(p≤0.001). TCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma. PRGs, Pyroptosis related genes. ARGs, Aging related genes. PARGs, Pyroptosis- and Aging- related genes.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4185479/v1/5ebff9fc70758a2157d33fc2.jpg"},{"id":54178655,"identity":"0a228eeb-7d18-49cf-b2b6-b91a45fc73d6","added_by":"auto","created_at":"2024-04-05 16:14:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1221212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO-KEGG enrichment analysis of the dataset TCGA-COADREAD. \u003c/strong\u003e(A) The barplot displays GO-KEGG enrichment analysis results for PARDEGs (BP, CC, MF, KEGG). (B) Divergence network diagram showing the results of GO-KEGG enrichment analysis of PARDEGs. (C) Chord plot showing the results of GO-KEGG enrichment analysis of the dataset TCGA-COADREAD for PARDEGs of the joint logFC. The green dots represent specific genes and the purple circles represent specific pathways. (D) Circle plot showing the results of GO-KEGG enrichment analysis of the dataset TCGA-COADREAD for PARDEGs of the joint logFC. The purple dots represent up-regulated genes (logFC \u0026gt; 1) and the green dots represent down-regulated genes (logFC \u0026lt; -1).The screening criteria for GO-KEGG enrichment entries were P \u0026lt; 0.05 and FDR value (q.value) \u0026lt; 0.2. GO, Gene Ontology. BP, biological process. CC, cellular component. PARDEGs, Pyroptosis- and Aging- related Differentially Expressed Genes.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4185479/v1/6af5a37f3619815c78f5453a.jpg"},{"id":54178661,"identity":"2db9ecbd-0bf7-4688-adc7-3915410dc9fb","added_by":"auto","created_at":"2024-04-05 16:14:44","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2001628,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSEA and GSVA.\u003c/strong\u003e (A) GSEA of dataset TCGA-COADREAD for the main 4 biological features. (B-E) Dataset TCGA-COADREAD in which genes are significantly enriched in the WP RESISTIN AS A REGULATOR OF INFLAMMATION (Fig4B), WP AUTOPHAGY (Fig4C), WP GLYCOLYSIS AND GLUCONEOGENESIS (Fig4D), WP APOPTOSIS MODULATION BY HSP70 (Fig4E), etc. (F.) Heat map display of functional scores in GSVA for dataset TCGA-COADREAD; (G) Heat map display of functional scores in GSVA for dataset TCGA-COADREAD. The significant enrichment screening criteria for GSEA enrichment analysis were P \u0026lt; 0.05 and FDR value (q.value) \u0026lt;0.25. Asterisks denote statistically significant P values: * statistically significant(p≤0.05), ** highly statistically significant(p≤0.01), *** highly statistically significant(p≤0.001). TCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma. GSEA, Gene Set Enrichment Analysis. GSVA, Gene Set Variation Analysis.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4185479/v1/13ada80b83a0dc4d5eefae28.jpg"},{"id":54178665,"identity":"bc9f3a1f-95ac-485f-ae97-8468596bb197","added_by":"auto","created_at":"2024-04-05 16:14:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1072603,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruct prognostic model for PARDEGs and differential genetic analysis of high- and low-risk groups in COADREAD.\u003c/strong\u003e (A) Lasso regression model of PARDEGs. The vertical coordinates of the LASSO regression diagnostic model represent the likelihood deviation values of the LASSO regression. The log(λ) value of the x-axis at the bottom of the plot represents by default the case after taking log of the lambda coefficient of the penalty term in the LASSO regression. the number of the x-axis at the top represents the number of variables with non-zero coefficients corresponding to each lambda. (B) The risk factor plot. The green points in the scatter plot portion represent deceased patients and the purple points represent surviving patients. (C) the trajectory plot of variables in the LASSO regression diagnostic model. (D-E) Comparative graphical presentation of the grouping of hub genes in the high- and low-risk groups in the dataset TCGA-COADREAD (D), GSE161158 (E). “ns” indicates lack of statistical significance (P \u0026gt;0.05). asterisks denote statistically significant P values: * statistically significant(p≤0.05), ** highly statistically significant(p≤0.01), *** highly statistically significant(p≤0.001). TCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma. LASSO, least absolute shrinkage and selection operator.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4185479/v1/2d77eb6a44391775bf3b8585.jpg"},{"id":54178658,"identity":"d872f4a2-5f8e-41ff-b0b0-85f886b1b3d5","added_by":"auto","created_at":"2024-04-05 16:14:43","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":936951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI.\u003c/strong\u003e (A) PPI network of hub genes. (B) The PPI network of hub genes in MCC algorithm, the color of the rectangle in the figure from yellow to red represents the gradual increase of the score. The interconfiguration network of A-B was collected in the STRING database and constructed in Cytoscape software with a minimum interaction score of 0.400. (C)The GeneMANIA website of hub genes predicts the interaction network of functionally similar genes of hub genes. The interconfiguration network was collected and derived from the GeneMANIA website, where black circles with white slashes represent input hub genes, other black circles without white slashes represent predicted functionally similar genes, purple lines represent co-expression, yellow lines represent Predicted, and red lines represent Physical interactions between genes. (D) The hub gene chromosome location diagram. PPI, protein-protein interaction. MCC, maximal clique centrality.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4185479/v1/7636c20833166990afee0803.jpg"},{"id":54178659,"identity":"1e25983e-9173-4d0f-9953-aad84f2800f4","added_by":"auto","created_at":"2024-04-05 16:14:44","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1041900,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical correlations and KM curve.\u003c/strong\u003e (A-H) Violin plots of correlation analysis among clinical subgroups for genes NOD2 (A), VEGFA (B), CEBPB (C), ELANE (D), FGF21 (E), MMP1 (F), and TREM2 (G). (H) KM plots for clinical correlation analysis of gene NOD2 in OS events. (I)KM plots for clinical correlation analysis of gene VEGFA in OS events. (J) KM plots for clinical correlation analysis of gene CEBPB in OS events. (K) KM plots for clinical correlation analysis of gene ELANE in OS events. (L) KM plots for clinical correlation analysis of gene MMP1 in OS events. (M) KM plots for clinical correlation analysis of gene TREM2 in OS events. Asterisks denote statistically significant P values: * statistically significant(p≤0.05), ** highly statistically significant(p≤0.01), *** highly statistically significant(p≤0.001). TCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma. PARDEGs, Pyroptosis- and Aging- Related Differentially Expressed Genes. ROC, receiver operating characteristic curve. OS, overall survival.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4185479/v1/3e21d1f103c61d7131a0e18d.jpg"},{"id":54178657,"identity":"edd05a83-3b67-4af2-9b89-223ed07f1226","added_by":"auto","created_at":"2024-04-05 16:14:43","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":761723,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003econstruct Cox model. \u003c/strong\u003e(A-B) Forest plot (A)and nomogram (B) of multivariable Cox regression analysis of hub genes. (C-E) Calibration curve of the 1-year (C), 3-year (D), and 5-year (E) for the multifactor Cox regression model nomogram analysis. (F-G) DCA plots for the 1-year (F), 3-year (G), and 5-year (H) LASSO-Cox regression prognostic models. The probability threshold or Threshold Probability is represented by the x-axis in the DCA plots, while the net return is represented by the y-axis. DCA, decision curve analysis. LASSO, least absolute shrinkage and selection operator.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4185479/v1/5ff132a1102a7b829346335d.jpg"},{"id":54178663,"identity":"0db0f929-8181-460d-a774-0165d9039163","added_by":"auto","created_at":"2024-04-05 16:14:44","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2206290,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunohistochemical analysis.\u003c/strong\u003e (A-B) gene NOD2 immunohistochemistry analysis in COADREAD tissue(B)and Normal colorectal tissues (A). (C-D) gene CEBPB immunohistochemistry analysis in COADREAD tissue(D)and Normal colorectal tissues(C). (E-F) gene ELANE immunohistochemistry analysis in COADREAD tissue(F)and Normal colorectal tissues (E). (G-H) gene FGF21 immunohistochemistry analysis in COADREAD tissue(H)and normal Normal colorectal tissues (G). (I-J) gene TREM2 immunohistochemistry analysis in COADREAD tissue(J)and Normal colorectal tissues (I). All the data were obtained from the HPA database. COADREAD, colon adenocarcinoma/rectum adenocarcinoma. HPA, human protein atlas.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4185479/v1/d95355b17e199bc74021e364.jpg"},{"id":57750732,"identity":"a43504e7-388a-41a1-8070-6866e1cc66a2","added_by":"auto","created_at":"2024-06-05 07:01:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12498517,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4185479/v1/121ed20a-5755-4b6b-bafd-38b0b91475e3.pdf"},{"id":54178664,"identity":"1523396c-34bb-4eab-9657-ef1d42c97d46","added_by":"auto","created_at":"2024-04-05 16:14:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17252,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4185479/v1/70a7277ec23363d1da50ca15.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognosis analysis of pyroptosis- and aging-related genes in colorectal cancer based on bioinformatic analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe most recent statistics released by the International Agency for Research on Cancer (IARC) reveal that colorectal cancer (CRC) is the second most deadly and the third most common cancer in terms of its incidence rate\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. As the world population continues to grow, deaths from colon and rectal cancers are projected to increase by 60.0% and 71.5%, respectively\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Despite appreciable advances in treatment, the prognosis of CRC remains poor due to metastasis, recurrence, and drug resistance\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. More than 50% of individuals diagnosed with CRC have already reached an advanced stage with distant metastases, and those with distant metastases have a five-year survival rate of less than 10%\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The objective response rate (ORR) of immunotherapy-advantaged populations with defective mismatch repair (dMMR) or high microsatellite instability (MSI-H) is only\u0026ndash;40\u0026ndash;50% with single or combination drugs\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. MSI-H colorectal cancer accounts for only 15% of the colorectal cancer population and 5% of distant metastatic colorectal cancers (mCRC)\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Hence, the prevalence of CRC, emphasizes the urgent need for a thorough understanding of the mechanisms that contribute to cancer progression. Furthermore, there is a vital need to discover novel predictive indicators, develop improved clinical prediction models, and investigate fresh therapeutic targets in the context of CRC.\u003c/p\u003e \u003cp\u003eGasdermins are responsible for the regulation of pyroptosis\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, a form of Programmed Cell Death (PCD) that is associated with pro-inflammatory activity\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. A recent study has identified four distinct molecular pathways involved in pyroptosis\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The first mechanism\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e involves the activation of caspase-1 by canonical inflammasomes. Once activated, caspase-1 divides the gasdermin family protein GSDMD, thereby initiating pyroptosis. This procedure also triggers the emission of IL-18 and IL-1β\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The second mechanism involves the activation of caspase-4/5/11 by non-canonical inflammasomes. Activation of caspase-4/5/11 activation results in the cleavage of GSDMD, leading to pyroptosis\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The intracellular cytosolic lipopolysaccharide (LPS) initiates the activation of caspase-4/5/11, which in turn fragments GSDMD into two parts: C-terminal and N-terminal\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The N-terminal fragment of GSDMD is then punctured at the cell membrane to initiate pyroptosis\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Activation of pro-apoptotic caspase-3 can also initiate pyroptosis, resulting in the cleavage of the gasdermin family protein GSDME from the cell membrane\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Moreover, the researchers established that granzymes, created by natural killer cells, can activate pyroptosis even in the absence of caspase action\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Granzyme is a collective term for a family of serine proteases that are mainly present in cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells. Proteases can selectively cleave substrates in target cells, which in turn triggers PCD\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Research has demonstrated that pyroptosis can either inhibit or stimulate tumor growth\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, as the inflammatory reaction caused by pyroptosis attracts and stimulates immune cells, thus causing tumor removal. Conversely, this immune reaction can also contribute to tumor growth\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Studies suggest that chronic inflammation could contribute to CRC\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, which is a leading cause of this type of cancer\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The growth of colorectal cells is affected by the combined beneficial and detrimental effects of pyroptosis. Colorectal cancer may advance due to the release of inflammatory components\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e and excessive amounts of GSDMC\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e during pyroptosis. GSDME-induced pyroptosis results in the release of high-mobility group box-1(HMGB1), which stimulates the ERK1/2 signaling cascade and further advances colorectal cancer\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. In contrast, research has revealed that IL-1β and IL-18, which are produced during pyroptosis, may obstruct the development and advance of colorectal cancer\u003csup\u003e[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Furthermore, it has been discovered that GSDMD has a similar effect\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAging is a complex physiological phenomenon characterized by a progressive decline in the structure and function of organisms as they grow older, ultimately leading to death. Inflammation, immunosenescence, and cellular senescence are essential for this process\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Cellular senescence is a critical factor linking aging and cancer development\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Cellular senescence halts the growth of certain cells, thereby enabling the removal of damaged, inefficient, or surplus cells in certain situations. Senescent cells possess the capacity to secrete various substances such as chemokines, cytokines, and proteases, which is known as the senescence-associated secretory phenotype (SASP)\u003csup\u003e[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Molecules associated with the SASP include the serum amyloid A (SAA) proteins SAA1 and SAA2\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, IL-1, IL-6, TNFs-\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003eCXCL9, and VEGF\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, among many others. Senescence is thought to serve as a safeguard against tumors by inducing senescence in cancer cells\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Despite this, various studies have shown that senescent cells can facilitate tumor growth through diverse pathways\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e, primarily through SASP factors that lead to cancer cell proliferation, evasion of apoptosis, angiogenesis induction, metastasis promotion, and inhibition of tumor immunity\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. For instance, MMPs help cancer cells migrate by degrading the extracellular matrix\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. In addition, senescent cancer cells can escape targeted therapy through transient senescence and can develop drug resistance\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e which may be associated with tumor recurrence. Studies have revealed that oncogenes such as RAS not only stimulate cell proliferation and survival but also cause cellular senescence, which can be seen in the early stages of tumors such as colon adenocarcinoma\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Based on these data, the removal of cellular senescence has become a significant topic in current research on cancer treatment.\u003c/p\u003e \u003cp\u003eA recent study\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e indicated that caspase-4/11 noncanonical inflammasomes can lead to not only pyroptosis but also senescence. To induce caspase-4-dependent senescence through LPS/caspase-4 noncanonical inflammasomes, the presence of caspase-4, GSDMD, and P53 is essential. Caspase-4 is the only factor that is required for oncogene-induced senescence. The effectiveness of caspase-4 in triggering pyroptosis or senescence depends on its dose, and further investigation is necessary to understand these details. The results of this study also demonstrated that caspase-4 noncanonical inflammasomes are crucial in managing the SASP in OIS and have a significant influence on the control of cell senescence, the SASP, and pyroptosis. In 2022, Douglas Hanahan\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e acknowledged senescent cells were among the 14 hallmark features of tumors. They suggested that cellular senescence is a complementary mechanism to programmed cell death and is essential for tumor progression. There are several prognostic models for colorectal cancer, including senescence-related genes\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e, immune-related genes\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e, hypoxia-associated microRNAs\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e, autophagy-related genes\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e, and pyroptosis-related genes\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. However, the impact of pyroptosis- and aging-related genes (PARGs) in patients with CRC is yet to be determined. Thanks to developments in bioinformatics and related areas, we can now explore the potential of PARGs to forecast colorectal cancer in patients and assess the influence of the immune microenvironment through bioinformatics analysis. This study aimed to examine PARGs expression in both healthy and colorectal cancer tissues, and evaluate its potential for predicting the outcome of colorectal cancer. By utilizing dissimilarly expressed PARGs, we devised a predictive model for colorectal cancer and identified possible new therapeutic goals.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Data acquisition\u003c/h2\u003e\n\u003cp\u003eThe expression matrix from TCGA-COADREAD (COADREAD: colon adenocarcinoma/rectum adenocarcinoma) dataset was obtained from the Cancer Genome Atlas (TCGA) database using TCGAbiolinks\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e R package. A comprehensive set of 698 data files with 647 colorectal cancer samples (classified as COADREAD) and 51 normal samples (classified as normal) were standardized to the FPKM format, and the related clinical data were obtained from the UCSC Xena database\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genome.ucsc.edu\u003c/span\u003e\u003c/span\u003e). TCGA-COADREAD dataset was normalized using the limma R package\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe acquired gene expression profile data and related clinical characteristics data for the COADREAD-associated datasets GSE74602\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e, GSE87211\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e, and GSE161158\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e from the GEO database\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e, employing the R package GEOquery\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e. GSE74602, from Homo Sapiens, with data platform GPL6104, provides the microarray gene expression profile data of 30 colorectal cancer patient samples and 30 normal colon tissue samples. Colorectal cancer patient samples based on the data platform GPL13497 were found in the GSE87211 dataset, amounting to 203 samples in total. A dataset of microarray gene expression profiles of 250 patients with CRC, designated as GSE161158, was procured from Homo Sapiens on GPL570. This investigation included all samples, with GSE74602, GSE87211, and GSE161158 datasets serving as validation cohorts.\u003c/p\u003e\n\u003cp\u003eThe GeneCards database\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003c/span\u003e) provides an extensive collection of information on human genes. We collected pyroptosis-related genes (PRGs) through the GeneCards database. We obtained a total of 200 PRGs using \"Pyroptosis\" as a search keyword and a relevance score\u0026thinsp;\u0026gt;\u0026thinsp;0.2 as a screening criterion. We searched the Molecular Signatures Database (MSigDB)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e website using \"Pyroptosis\" as a search keyword and collected two phenotypes from the REACTOME_MITOPHAGY.v7.5.1 reference gene set. Thirty PRGs were selected from the Intersection dataset. We obtained 33 PRGs from the literature\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. A total of 225 PRGs were obtained by taking the merged set of PRGs from three sources. We used \"Aging\" as the search keyword, and relevance score\u0026thinsp;\u0026gt;\u0026thinsp;0.2 as the filtering criterion to obtain a total of 400 ARGs. We collected 1028 ARGs from the intersection of two phenotypes in the REACTOME_MITOPHAGY.v7.5.1 reference gene set by using \"Aging\" as the search keyword on the MSigDB database. A total of 307 ARGs were obtained from the literature\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e. In total, 1594 ARGs were obtained from the merged set of ARGs from three sources. We intersected PRGs with ARGs to obtain 54 Pyroptosis and aging-related genes (PARGs) for inclusion in the subsequent analysis. Specific gene names are shown in Table S1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Differential expression analysis\u003c/h2\u003e\n\u003cp\u003eTo distinguish the genes, associated biological traits, and pathways unique to cancer and normal COADREAD groups, we standardized the data from TCGA-COADREAD, GSE74602, GSE8721, and GSE161158 using the limma R package. Subsequently, TCGA-COADREAD dataset was segregated into two distinct categories: patients with cancer and healthy individuals. TCGA-COADREAD dataset was analyzed at the gene level to determine the genes that were differentially expressed among the various groups. Genes exhibiting a significant difference in expression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were identified. Genes with logFC\u0026thinsp;\u0026gt;\u0026thinsp;1 and P.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were upregulated, while those with logFC \u0026lt; -1 and P.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were downregulated, and the results of the differential analysis were graphically depicted as volcanoes in the ggplot2 R package. We used PARGs to independently compare the upregulated and downregulated differentially expressed genes. The results were presented as a Venn diagram. The two partial intersections were merged into the pyroptosis- and aging-related differentially expressed genes (PARDEGs) of COADREAD, which included 18 PARDEGs: CTSG, VDR, CITED2, ELANE TREM2, FGF21, UCP1, IL17A, VEGFA, IL1A, TREM1, IL1B, DRD2, NOD2, PCSK9, CXCL8, CEBPB, and MMP1. Subsequently, TCGA-COADREAD, GSE74602, and GSE87211 datasets with differentially expressed genes were plotted as comparison plots. Grouped comparison maps were plotted with the DEGs in TCGA-COADREAD, GSE74602, and GSE87211, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 GO-KEGG enrichment analysis\u003c/h2\u003e\n\u003cp\u003eGene Ontology (GO)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e is a frequently used approach for analyzing the functional enrichment of genes across various dimensions. This investigation typically involves three levels of gene activity: biological processes (BP), molecular functions (MF), and cellular components (CC). KEGG, the Kyoto Encyclopedia of Genes and Genomes\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e, is a widely used database for tracking data associated with genomes, pathways, ailments, and medications. Using ClusterProfiler\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/sup\u003e, GO functional analysis of PARDEGs was performed, and the results were depicted graphically with a p-value of 0.05.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 GSEA\u003c/h2\u003e\n\u003cp\u003eGene Set Enrichment Analysis (GSEA)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/sup\u003e can be used to analyze the pattern of genes from a predetermined gene list that is ranked according to their correlation with a phenotype, allowing us to assess their impact on the phenotype. This research made use of the clusterProfiler package in R to compare the gene sets in cancer and normal samples from TCGA-COADREAD dataset. The GSEA parameters comprised 2020 as the seed, 10 as the minimum, and 500 as the maximum genes per gene set, as well as the Benjamini-Hochberg (BH) technique for P-value adjustment, with a total of 10,000 calculations. We sourced the gene set \"c2.cp.v7.2. symbols\" from the Molecular Signatures Database (MSigDB) and established a P-value of \u0026lt;\u0026thinsp;0.05, and FDR (q-value) of \u0026lt;\u0026thinsp;0.25 as the criteria for significant enrichment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e2.5 GSVA\u003c/h2\u003e\n\u003cp\u003eGene Set Variation Analysis (GSVA)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/sup\u003e is a nonparametric, unsupervised method used to assess the abundance of gene sets in microarray data. This was accomplished by converting the gene expression matrix between the samples into a gene set expression matrix. This technique was used to investigate whether certain pathways were represented more prominently in the various samples. We applied GSVA to TCGA-COADREAD dataset at the gene expression level using the gene set \"h.all.v7.4. symbols.gmt) obtained from the MSigDB database. We were able to ascertain the differences in how the two sample sets were enriched using this method. The cutoff for terms that showed significant enrichment was determined to be a p-value of less than 0.05.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e2.6 LASSO model\u003c/h2\u003e\n\u003cp\u003eTo construct predictive models for the PARDEGs of colorectal cancer, we adopted a 10-fold cross-validation using a seed number of 2021. We ran a least absolute shrinkage and selection operator (LASSO) regression 1000 times to avoid over-fitting by employing a least absolute shrinkage and selection operator. The LASSO regression is a popular technique for developing predictive models as it incorporates a penalty element (the absolute value of the lambda slope) into the linear regression equation, which can help prevent overfitting and enhance the model's capacity to make broad-based predictions. We employed the LASSO regression prognostic model to graphically display the division of the cancer group samples into high and low-risk scores, together with their associated survival outcomes. A prognostic model was developed using least the application of LASSO regression analysis. The model formula is as follows:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$riskScore = \\sum _{i}Coefficient \\left({gene}_{i}\\right)*mRNA Expression \\left( {gene}_{i}\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e2.7 PPI networks\u003c/h2\u003e\n\u003cp\u003eInteractions between proteins are represented in protein-protein interaction networks, which are composed of various proteins that come together. The STRING database\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/sup\u003e is a useful tool for identifying known proteins and predicting their interactions. Genes, particularly those responsible for regulating the same biological operations, are intertwined. We established a protein-protein interaction network (PPI) to explore correlations between hub genes. The STRING database was employed with a focus on the human species, and networks of protein-protein interactions associated with PARDEGs were established with a minimum correlation coefficient of 0.400. Cytoscape\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/sup\u003e was employed to create representations of PPI network models. Additionally, the Maximal Clique Centrality (MCC)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/sup\u003e algorithm from the cytoHubba\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/sup\u003e plug-in was used to assess and demonstrate the association between hub genes and proteins in the network.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e2.8 Cox model\u003c/h2\u003e\n\u003cp\u003eTo evaluate the potential of hub genes in predicting COADREAD, we used the expression data from these genes in TCGA-COADREAD dataset to conduct a Cox regression analysis involving multiple factors. A Cox regression model incorporating multiple variables was created and a forest plot was generated to illustrate the findings. We constructed a nomogram to predict the 1-, 3-, and 5-year survival rates of patients with COADREAD according to the outcomes of multifactorial Cox regression analysis. A nomogram is a chart that can be used to visually represent the correlations between different independent variables, enabling the evaluation of the impact of each variable on a multivariate regression model. A nomogram can be used to estimate the likelihood of an event occurring by determining the overall score. Calibration curves were constructed to evaluate the precision and discriminatory capabilities of the nomogram. This graph illustrates the compatibility between the true and predicted chances in different situations, primarily to determine whether the Cox regression model is applicable in the real world. We used the \"rms\" package in R to create column and calibration curve plots, and we also carried out decision curve analysis (DCA) to evaluate the nomogram model's predictive capability with regards to the 1-, 3-, and 5-year survival results of COADREAD patients. The R package ggDCA\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/sup\u003e enables easy development of DCA plots, which are useful tools for assessing clinical prognosis models, diagnostic tests, and molecular markers.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e2.9 Immunohistochemical analysis\u003c/h2\u003e\n\u003cp\u003eIHC, also known as immunohistochemical analysis, involves the utilization of antibodies and antigens to identify and pinpoint specific antigens in cells and tissues. This is typically accomplished using a light microscope. We used the Human Protein Atlas (HPA) database\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://genome.ucsc.edu\" target=\"_blank\"\u003ewww.proteinatlas.org/\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e) to perform immunohistochemical analysis of the genes specified by the Cox model in both COADREAD tumor specimens and ordinary colorectal tissues. By combining the information from the databases for both tissue types, we were able to display the findings of the immunohistochemical investigation, making it possible to compare the staining designs between them.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e\n\u003cp\u003eAll data processing and analyses were performed using R software (version 4.1.2). Continuous variables were represented using the mean and standard deviation. The Wilcoxon rank-sum test was used to compare two sets of continuous data and the independent Student\u0026rsquo;s t-test was used to measure the statistical significance of data that followed a normal distribution. The Kruskal-Wallis test was used to compare three or more groups. The chi-square test or Fisher\u0026rsquo;s exact test was used to assess and compare the statistical importance of categorical variables. The R package glmnet\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e]\u003c/sup\u003e was employed to perform the LASSO regression analysis. Spearman\u0026rsquo;s correlation analysis was performed to measure the connections between molecules unless otherwise mentioned. All P-values were evaluated for both directions, and a P-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 The detailed flowchart is shown in Fig.\u0026nbsp;1\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;1 The flowchart of this study\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma. PARGs, Pyroptosis- and Aging- related genes. GO, Gene Ontology. KEGG, Kyoto Encyclopedia of Genes and Genomes. GSEA, Gene Set Enrichment Analysis. GSVA, Gene Set Variation Analysis. LASSO, least absolute shrinkage and selection operator. PPI, Protein-protein interaction. IHC, immunohistochemical analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Differential expression analysis\u003c/h2\u003e\n \u003cp\u003eIn the differential analysis of TCGA-COADREAD dataset, 55537 DEGs were identified, of which 14599 had a |logFC| greater than 1 and Padj of less than 0.05. There were 4266 and 10,333 differentially expressed genes with a negative logFC, which indicated higher expression in the colorectal cancer low-risk group and lower expression in the colorectal cancer high-risk group below the threshold. We visualized the volcano plot (Fig.\u0026nbsp;2A) and differential ranking plot (Fig.\u0026nbsp;2B) to demonstrate the outcomes of the differential assessment of this dataset. We combined PRGs and ARGs (Fig.\u0026nbsp;2C) to generate PARGs. Finally, we compared the upregulated differentially expressed genes with PARGs and identified four genes (CTSG, VDR, CITED2, and ELANE), which are represented in a Venn diagram (Fig.\u0026nbsp;2D). For the downregulated differentially expressed genes, intersection with ARGs yielded 14 genes (TREM2, FGF21, UCP1, IL17A, VEGFA, IL1A, TREM1, IL1B, DRD2, NOD2, PCSK9, CXCL8, CEBPB, and MMP1), and a Venn diagram (Fig.\u0026nbsp;2E) was used to illustrate the results. In Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, we provide the gene names and expression data for the 18 PARDEGs. In Dataset GSE74602, sixteen distinct PARDEGs (CTSG, VDR, CITED2, TREM2, FGF21, UCP1, IL17A, VEGFA, IL1A, TREM1, IL1B, DRD2, NOD2, PCSK9, CEBPB, MMP1) were identified. In dataset GSE87211, seventeen genes were discovered, namely, CTSG, VDR, CITED2, ELANE, TREM2, FGF21, UCP1, IL17A, VEGFA, IL1A, TREM1, IL1B, DRD2, NOD2, PCSK9, CEBPB, and MMP1. We compared the expression levels of PARDEGs in TCGA-COADREAD, GSE 74602, and GSE87211 using grouped comparison plots (Fig.\u0026nbsp;2F-H), and determined whether the differences between them were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The GSE74602 and GSE87211 datasets demonstrated 12 genes that matched TCGA-COADREAD validation results, including CTSG, VDR, CITED2, ELANE, VEGFA, IL1A, TREM1, IL1B, NOD2, PCSK9, CEBPB, and MMP1, as shown in Figure (Fig.\u0026nbsp;2F-H).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eList of description and expression difference of PARGs of differential expression analysis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene Symbol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elogFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP.Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eadj.P\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCTSG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecathepsin G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.34413154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6521E-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9674E-29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evitamin D receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.110243456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.71598E-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.32369E-34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCITED2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCbp/p300 interacting transactivator with Glu/Asp rich carboxy-terminal domain 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.756692626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.82857E-80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.79674E-78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eELANE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eelastase, neutrophil expressed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.578797496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.08607E-58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.3411E-56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTREM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etriggering receptor expressed on myeloid cells 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.145762154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.92095E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82018E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFGF21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efibroblast growth factor 21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.46493427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.54703E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.72682E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUCP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003euncoupling protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.112722822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82337E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0268E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL17A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003einterleukin 17A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.640506888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59317E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11504E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVEGFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evascular endothelial growth factor A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.522614701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7472E-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.73538E-52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003einterleukin 1 alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.635870505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.32149E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.43041E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTREM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etriggering receptor expressed on myeloid cells 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.638664774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.77E-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.99E-57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003einterleukin 1 beta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.635870505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.32149E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.43041E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edopamine receptor D2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.249755965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.637E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9465E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNOD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enucleotide binding oligomerization domain containing 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.465023815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.23191E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.34918E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCSK9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eproprotein convertase subtilisin/kexin type 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.715151156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23554E-46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.26298E-45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCXCL8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-X-C motif chemokine ligand 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.457782428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47291E-36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.67163E-35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCEBPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCAAT enhancer binding protein beta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.578090596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.89071E-38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2257E-37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ematrix metallopeptidase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.574778525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.12923E-66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.56802E-64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003ePARGs, Pyroptosis- and Ageing- related genes.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;2 Analysis of differentially expressed genes (DEGs).\u003c/strong\u003e (A)Volcano plot of the distributions of all differentially expressed genes between cancer group (grouping: COADREAD) and normal group (grouping: Normal) for dataset TCGA-COADREAD. (B) Differential expression analysis differential ranking plot between cancer group (grouping: COADREAD) and normal group (grouping: Normal) for dataset TCGA-COADREAD. (C) Venn diagram showing the intersection of PRGs and ARGs. (D) Venn diagram display of the intersection of up-regulated differentially expressed genes with PARGs obtained from dataset TCGA-COADREAD. (E) Venn diagram display of the intersection of down-regulated differentially expressed genes with PARGs obtained from dataset TCGA-COADREAD. (F-H) ARGs in dataset TCGA-COADREAD, GSE 39582 and GSE113513 grouped comparison plots are shown. \u0026ldquo;ns\u0026rdquo; indicates lack of statistical significance (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05); asterisks denote statistically significant P values: * statistically significant(p\u0026thinsp;\u0026le;\u0026thinsp;0.05), ** highly statistically significant(p\u0026thinsp;\u0026le;\u0026thinsp;0.01), *** highly statistically significant(p\u0026thinsp;\u0026le;\u0026thinsp;0.001). TCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma. PRGs, Pyroptosis related genes. ARGs, Aging related genes. PARGs, Pyroptosis- and Aging- related genes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 GO-KEGG enrichment analysis\u003c/h2\u003e\n \u003cp\u003eWe constructed a bar chart (Fig.\u0026nbsp;3A) to illustrate the expression of the 18 PARDEGs in TCGA-COADREAD dataset. We conducted GO-KEGG enrichment analysis on the 18 PARDEGs (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) to identify the biological processes (BP), molecular functions (MF), and cellular components (CC). We used GO-KEGG enrichment analysis to generate a network graph of BP, CC, MF, and KEGG for TCGA-COADREAD dataset, with joint logFC for PARDEGs, as shown in Fig.\u0026nbsp;3B. The logFC of the molecule was used to calculate the z-score for each entry, which is shown graphically using a chord plot (Fig.\u0026nbsp;3C) and a circle plot (Fig.\u0026nbsp;3D). The study revealed that PARDEGs were largely associated with temperature maintenance (GO:0001659), control of cytokine production (GO:0001819), sustaining multicellular organisms (GO:0048871), the inside of secretory granules (GO:0034774), the inside of azurophil granules (GO:0035578), the inside of cytoplasmic vesicles (GO:0060205), cytokine action (GO:0005125), glycosaminoglycan adhesion (GO:0005539), receptor ligand action (GO:0048018), Rheumatoid Arthritis (hsa05323), IL-17 pathway (hsa05321), and Tuberculosis (hsa05152).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of GO and KEGG Enrichment Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eONTOLOGY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGeneRatio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBgRatio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep.adjust\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eqvalue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0001659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etemperature homeostasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173/18670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.52e-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06e-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0001819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epositive regulation of cytokine production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e464/18670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.82e-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.23e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0048871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emulticellular organismal homeostasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e485/18670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.82e-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.23e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0050900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eleukocyte migration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e499/18670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.52e-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.23e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0032103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epositive regulation of response to external stimulus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323/18670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.23e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0034774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esecretory granule lumen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321/19717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0035578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eazurophil granule lumen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91/19717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0060205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecytoplasmic vesicle lumen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e338/19717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0031983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evesicle lumen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e339/19717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecytokine activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e220/17697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.37e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eglycosaminoglycan binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229/17697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.59e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.37e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0048018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ereceptor ligand activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e482/17697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.56e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.24e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0070851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egrowth factor receptor binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134/17697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.86e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.46e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0004252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eserine-type endopeptidase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160/17697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.43e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa05323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRheumatoid arthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6/17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93/8076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa04657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-17 signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5/17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94/8076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.63e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.80e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa05321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflammatory bowel disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4/17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65/8076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.41e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.44e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa05152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTuberculosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5/17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180/8076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.59e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.63e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.41e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehsa04933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAGE-RAGE signaling pathway in diabetic complications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4/17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100/8076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.65e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.10e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.90e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eGO, Gene Ontology. BP, Biological Process. CC, Cellular Component. MF, Molecular Function. KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;3 GO-KEGG enrichment analysis of the dataset TCGA-COADREAD.\u003c/strong\u003e (A) The barplot displays GO-KEGG enrichment analysis results for PARDEGs (BP, CC, MF, KEGG). (B) Divergence network diagram showing the results of GO-KEGG enrichment analysis of PARDEGs. (C) Chord plot showing the results of GO-KEGG enrichment analysis of the dataset TCGA-COADREAD for PARDEGs of the joint logFC. The green dots represent specific genes and the purple circles represent specific pathways. (D) Circle plot showing the results of GO-KEGG enrichment analysis of the dataset TCGA-COADREAD for PARDEGs of the joint logFC. The purple dots represent up-regulated genes (logFC\u0026thinsp;\u0026gt;\u0026thinsp;1) and the green dots represent down-regulated genes (logFC \u0026lt; -1).The screening criteria for GO-KEGG enrichment entries were P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR value (q.value)\u0026thinsp;\u0026lt;\u0026thinsp;0.2. GO, Gene Ontology. BP, biological process. CC, cellular component. PARDEGs, Pyroptosis- and Aging- related Differentially Expressed Genes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 GSEA and GSVA\u003c/h2\u003e\n \u003cp\u003eGSEA was used to analyze the associations between the expression of all genes and related biological processes, affected cellular components, and executed molecular functions in TCGA-COADREAD dataset to assess the influence of gene expression levels on the differences between the COADREAD cancer and normal groups. P-values of less than 0.05 and FDR values (q.value) of less than 0.25 were considered indicative of a significant enrichment screening. Analysis of TCGA-COADREAD dataset revealed that multiple pathways, including resistin as an inflammation regulator (Fig.\u0026nbsp;4B), autophagy (Fig.\u0026nbsp;4C), glycolysis, and gluconeogenesis (Fig.\u0026nbsp;4D), and the role of the HSP70 pathway in modulating apoptosis (Fig.\u0026nbsp;4E), were significantly enriched in genes (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). We present our findings in TCGA-COADREAD dataset by creating visual representations of mountains (Fig.\u0026nbsp;4A) and pathways (Fig.\u0026nbsp;4B-E).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of Combined Datasets GSEA\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"5\" align=\"left\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003esetSize\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eenrichmentScore\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eNES\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ep.adjust\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eqvalues\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eWP_RESISTIN_AS_A_REGULATOR_OF_INFLAMMATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.43547616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e1.54621674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.030837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.09090979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05740866\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eWP_AUTOPHAGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.42571938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e1.50380627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.04365079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.11439754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07224095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eWP_GLYCOLYSIS_AND_GLUCONEOGENESIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.33117666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e1.25108706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.15533981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.28425152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1795021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eWP_APOPTOSIS_MODULATION_BY_HSP70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.60073238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e1.85915885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.00363636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.03591448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02267965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREACTOME_KERATINIZATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e-0.54275257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e-2.10764977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.00106952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.03591448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.02267965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREACTOME_G2_M_CHECKPOINTS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e-0.4375401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e-1.65067525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.00110254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.03591448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.02267965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREACTOME_HATS_ACETYLATE_HISTONES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e-0.49548866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e-1.84359764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.00111111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.03591448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.02267965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREACTOME_REPRODUCTION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e-0.49336299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e-1.83777081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.00111607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.03591448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.02267965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGROSS_HYPOXIA_VIA_ELK3_DN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.62999349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e2.11091357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.00146843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.03953922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03159959\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMENSE_HYPOXIA_UP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.67727411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e2.06922046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.00159744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.03953922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03159959\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBROCKE_APOPTOSIS_REVERSED_BY_IL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.52335209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e1.7571483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.00738552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.07736577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06183042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eWP_SENESCENCE_AND_AUTOPHAGY_IN_CANCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.44080702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e1.46191796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.04654655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.19930266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15928191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"19\"\u003eGSEA, Gene Set Enrichment Analysis\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWe compared the expression of all genes in TCGA-COADREAD dataset using GSVA to evaluate variations in the hallmark gene set between the COADREAD cancer and normal groups. The data obtained from TCGA-COADREAD dataset indicated that several pathways, such as IL2 STAT5 signaling, apoptosis, hypoxia, and glycolysis, had significantly enriched gene profiles. After assessing the 44 hallmark gene sets, noteworthy disparities between the COADREAD cancer and normal groups were observed (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;4F, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). We compared the representative hallmark gene sets and generated a graph to show the differences between groups (Fig.\u0026nbsp;4G).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGSVA analysis of dataset TCGA-COADREAD.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eontology\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elogFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAveExpr\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP.Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eadj.P\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_ADIPOGENESIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.527959232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.072147691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-14.78264416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.96E-43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.82E-42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_MYC_TARGETS_V2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.854583441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017369086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.57065456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.19E-42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.48E-41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_MYC_TARGETS_V1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.744340126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007710798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.56275337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.60E-37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.67E-36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_HEME_METABOLISM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.466111592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.065907264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-13.42973383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.79E-37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.48E-36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_PANCREAS_BETA_CELLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.550855044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.045909656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-12.76822305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.84E-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.84E-33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_E2F_TARGETS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.736162689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00318997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.47619596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64E-32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37E-31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_UNFOLDED_PROTEIN_RESPONSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.529483031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.017977879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.0995204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.78E-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.56E-30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_BILE_ACID_METABOLISM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.434861256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.053540572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-11.48376507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.60E-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.25E-27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_G2M_CHECKPOINT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.624078712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.011162383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.34810694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35E-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.52E-27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_DNA_REPAIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.461868068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.022384784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.9964442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.98E-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_KRAS_SIGNALING_DN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.377246204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.044020506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-10.54647825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.69E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_MTORC1_SIGNALING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.477122507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.028312327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.39823332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.38E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_XENOBIOTIC_METABOLISM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.368499235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.046846429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-10.15315597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.66E-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.72E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_FATTY_ACID_METABOLISM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.394084367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.028967167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.717404433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.54E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62E-20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_MYOGENESIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.440182258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.040943765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.437514093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.03E-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_COMPLEMENT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.405575635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.032080661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.68853704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.41E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.53E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_PEROXISOME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.327826326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.048675523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-8.522187976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.99E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.64E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_GLYCOLYSIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.302574608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.052239936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.956315926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.79E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.89E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_KRAS_SIGNALING_UP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.355809045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.033726964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.872425251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.32E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_OXIDATIVE_PHOSPHORYLATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.405994538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.021073592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.832246739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.24E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_UV_RESPONSE_DN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.377341443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.039477708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.521452052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.60E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.81E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_WNT_BETA_CATENIN_SIGNALING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36427061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.043742995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.382145161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.27E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.70E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_ANDROGEN_RESPONSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.343766179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.020921674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.268837254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.37E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_APICAL_SURFACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.312194556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.042647052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.873898277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.80E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_ESTROGEN_RESPONSE_LATE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.243178736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06054344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.559469349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_IL6_JAK_STAT3_SIGNALING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.332179313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.024721835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.022871535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.72E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.23E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_PI3K_AKT_MTOR_SIGNALING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.236320666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06136432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.009484035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.94E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.45E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_ESTROGEN_RESPONSE_EARLY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.229168303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.048207404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.728349787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_IL2_STAT5_SIGNALING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.255964223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.036780853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.654659983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.24E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.87E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_APICAL_JUNCTION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.261500256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.031185057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.624723208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.39E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_UV_RESPONSE_UP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.205914642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.055631231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.615077898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.80E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.39E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_ALLOGRAFT_REJECTION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.317195761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.015436134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.614116799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.81E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.39E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_INFLAMMATORY_RESPONSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.278946708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.017744467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.163710446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.13E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.74E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_TGF_BETA_SIGNALING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.249532394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.048248395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.961354905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.72E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_P53_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18388094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.065792794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.869155925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.97E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_PROTEIN_SECRETION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.216157519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.008472292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.294036713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.77E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_INTERFERON_GAMMA_RESPONSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.244894364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02256085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.186345519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.18E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.30E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_ANGIOGENESIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.212539323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.031614118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.653541343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000277225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00036477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_HEDGEHOG_SIGNALING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.193756103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03111402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.613325302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000323101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000414232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_APOPTOSIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.153182883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.040807553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.51141456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000473221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000591526\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_HYPOXIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.145493452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.046050565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.313011799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000968621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001181245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_MITOTIC_SPINDLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.159273108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03210883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.279855768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00108806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001295309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_CHOLESTEROL_HOMEOSTASIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.129793123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.051712431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.841518136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004615531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005366897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHALLMARK_COAGULATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09512025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.026709223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.071228737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038689392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043965218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eGSVA, Gene Set Variation Analysis. TCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;4 GSEA and GSVA.\u003c/strong\u003e (A) GSEA of dataset TCGA-COADREAD for the main 4 biological features. (B-E) Dataset TCGA-COADREAD in which genes are significantly enriched in the WP RESISTIN AS A REGULATOR OF INFLAMMATION (Fig.\u0026nbsp;4B), WP AUTOPHAGY (Fig.\u0026nbsp;4C), WP GLYCOLYSIS AND GLUCONEOGENESIS (Fig.\u0026nbsp;4D), WP APOPTOSIS MODULATION BY HSP70 (Fig.\u0026nbsp;4E), etc. (F.) Heat map display of functional scores in GSVA for dataset TCGA-COADREAD; (G) Heat map display of functional scores in GSVA for dataset TCGA-COADREAD. The significant enrichment screening criteria for GSEA enrichment analysis were P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR value (q.value)\u0026thinsp;\u0026lt;\u0026thinsp;0.25. Asterisks denote statistically significant P values: * statistically significant(p\u0026thinsp;\u0026le;\u0026thinsp;0.05), ** highly statistically significant(p\u0026thinsp;\u0026le;\u0026thinsp;0.01), *** highly statistically significant(p\u0026thinsp;\u0026le;\u0026thinsp;0.001). TCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma. GSEA, Gene Set Enrichment Analysis. GSVA, Gene Set Variation Analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 LASSO model\u003c/h2\u003e\n \u003cp\u003eTo evaluate the potential of the 18 PARDEGs (CTSG, VDR, CITED2, ELANE, TREM2, FGF21, UCP1, IL17A, VEGFA, IL1A, TREM1, IL1B, DRD2, NOD2, PCSK9, CXCL8, CEBPB, MMP1) in TCGA-COADREAD dataset to be used for prediction, we employed LASSO regression analysis to generate a prognostic model (Fig.\u0026nbsp;5A,5C) with 7 key genes (NOD2, VEGFA, CEBPB, ELANE, FGF21, MMP1, TREM2). The LASSO model\u0026apos;s Risk Score median was used to divide the cancer groups into two categories, with those deemed to be of high risk placed in the high-risk group, and those of low risk in the low-risk group. The LASSO risk factor map (Fig.\u0026nbsp;5B) was designed to show the results of the LASSO regression analysis through a visual representation of the high- and low-risk groups. We used statistical analysis to corroborate the LASSO prognostic model by examining the clinical data of patients with COADREAD drawn from TCGA-COADREAD dataset (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Moreover, the Risk Score for the cancer group samples in the GSE17536 dataset was established by employing the coefficients of the variables in the LASSO model, and the cancer group was split into high-risk (grouping: high) and low-risk (grouping: low) groups by taking the median of the Risk Score as the dividing line.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatient Characteristics of COADREAD patients in the TCGA datasets.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elevels\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e436 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e368 (57.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153 (23.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e475 (84.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathologic stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111 (17.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238 (38.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184 (29.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e301 (46.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343 (53.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;=65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e276 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e368 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS event, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e515 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDSS event, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e544 (87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePFI event, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e479 (74.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165 (25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (58, 76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eCOADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma. TCGA, the cancer genome atlas. OS, overall survival. DSS, disease specific survival. PFI, progression free interval. IQR, interquartile range.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWe analyzed the levels of expression for NOD2, VEGFA, CEBPB, ELANE, FGF21, MMP1, and TREM2 in the datasets TCGA-COADREAD (Fig.\u0026nbsp;5D) and GSE161158(Fig.\u0026nbsp;5E) for high- and low-risk groups and compared the results to show whether there were any statistically significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The Figure(Fig.\u0026nbsp;5D,5E) demonstrates that The GSE161158 dataset contains two genes that match TCGA-COADREAD validation results: VEGFA and MMP1. The model formula is as follows:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" height=\"301\" width=\"401\"\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;5 Construct prognostic model for PARDEGs and differential genetic analysis of high- and low-risk groups in COADREAD.\u003c/strong\u003e (A) Lasso regression model of PARDEGs. The vertical coordinates of the LASSO regression diagnostic model represent the likelihood deviation values of the LASSO regression. The log(\u0026lambda;) value of the x-axis at the bottom of the plot represents by default the case after taking log of the lambda coefficient of the penalty term in the LASSO regression. the number of the x-axis at the top represents the number of variables with non-zero coefficients corresponding to each lambda. (B) The risk factor plot. The green points in the scatter plot portion represent deceased patients and the purple points represent surviving patients. (C) the trajectory plot of variables in the LASSO regression diagnostic model. (D-E) Comparative graphical presentation of the grouping of hub genes in the high- and low-risk groups in the dataset TCGA-COADREAD (D), GSE161158 (E). \u0026ldquo;ns\u0026rdquo; indicates lack of statistical significance (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). asterisks denote statistically significant P values: * statistically significant(p\u0026thinsp;\u0026le;\u0026thinsp;0.05), ** highly statistically significant(p\u0026thinsp;\u0026le;\u0026thinsp;0.01), *** highly statistically significant(p\u0026thinsp;\u0026le;\u0026thinsp;0.001). TCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma. LASSO, least absolute shrinkage and selection operator.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 PPI\u003c/h2\u003e\n \u003cp\u003eWe conducted a protein-protein interaction analysis of seven hub genes (NOD2, VEGFA, CEBPB, ELANE, FGF21, MMP1, and TREM2) using the STRING database, setting the biological species as humans and constructing a PPI network with the criterion of a minimum interrelationship coefficient of 0.400 or higher. Using Cytoscape software, we were able to graph the interactions (Fig.\u0026nbsp;6A) and observed that when the interrelationship coefficient was not less than 0.400, each hub gene was connected to at least one hub gene, with the exception of NOD2 and TREM2. VEGFA had the most connections with other genes in the group, with four core genes (VEGFA, CEBPB, ELANE, FGF21, and MMP1) as its primary linkages. We employed the MCC algorithm to determine the scores of the hub genes in the PPI network, which were linked to other PPI network nodes, and arranged the hub genes from the highest to lowest score using a gradient from red to yellow. The MCC algorithm yielded VEGFA as the first gene, as depicted in Fig.\u0026nbsp;6B. Furthermore, we used the GeneMANIA website to forecast and form a connection network (Fig.\u0026nbsp;6C) of functionally analogous genes among these seven key genes to analyze their co-expression, co-localization, and gene interactions. Finally, these seven core genes are displayed as their relevant proteins (Fig.\u0026nbsp;6D), including TREM2 and VEGFA on chromosome 6, MMP1 on chromosome 11, NOD2 on chromosome 16, ELANE, FGF21 on chromosome 19, and CEBPB on chromosome 20.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;6 PPI.\u003c/strong\u003e (A) PPI network of hub genes. (B) The PPI network of hub genes in MCC algorithm, the color of the rectangle in the figure from yellow to red represents the gradual increase of the score. The interconfiguration network of A-B was collected in the STRING database and constructed in Cytoscape software with a minimum interaction score of 0.400. (C)The GeneMANIA website of hub genes predicts the interaction network of functionally similar genes of hub genes. The interconfiguration network was collected and derived from the GeneMANIA website, where black circles with white slashes represent input hub genes, other black circles without white slashes represent predicted functionally similar genes, purple lines represent co-expression, yellow lines represent Predicted, and red lines represent Physical interactions between genes. (D) The hub gene chromosome location diagram. PPI, protein-protein interaction. MCC, maximal clique centrality.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Clinical correlations and KM curve\u003c/h2\u003e\n \u003cp\u003eWe examined the correlation between the seven hub genes and several clinical elements, such as T-stage, N-stage, M-stage, pathological stage, age, and survival indicators, including overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS), and the findings are illustrated in Figs.\u0026nbsp;7A-G. The results of the data indicated that NOD2 expression had a statistically significant correlation with the discrepancy between T1 and normal T-staging (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); VEGFA expression was associated with the difference between survival and death in cases of PFI (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); CEBPB expression was linked to the divergence between N0 and N1 in the clinical N-stage (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); ELANE expression was related to the contrast between M0 and normal M-staging (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); FGF21 expression was connected to the distinction between less than or equal to 65 years of age and normal (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); MMP1 expression correlated with the contrast between survival and death in clinical OS cases (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); and TREM2 expression was associated with the discrepancy between N0 and normal for clinical N staging (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We produced Kaplan-Meier graphs for the seven hub genes in the LASSO model considering the clinical subtype variables. Although the number of low FGF21 subgroups was less than three, it was not feasible to conduct high and low subgroup analyses. After examining the data from Figs.\u0026nbsp;7H-M, six hub genes (NOD2, VEGFA, CEBPB, ELANE, MMP1, and TREM2) were identified, and a P value of 0.05 was utilized to evaluate the importance of the associations between pertinent gene-subtype clinical parameters.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;7 Clinical correlations and KM curve.\u003c/strong\u003e (A-H) Violin plots of correlation analysis among clinical subgroups for genes NOD2 (A), VEGFA (B), CEBPB (C), ELANE (D), FGF21 (E), MMP1 (F), and TREM2 (G). (H) KM plots for clinical correlation analysis of gene NOD2 in OS events. (I)KM plots for clinical correlation analysis of gene VEGFA in OS events. (J) KM plots for clinical correlation analysis of gene CEBPB in OS events. (K) KM plots for clinical correlation analysis of gene ELANE in OS events. (L) KM plots for clinical correlation analysis of gene MMP1 in OS events. (M) KM plots for clinical correlation analysis of gene TREM2 in OS events. Asterisks denote statistically significant P values: * statistically significant(p\u0026thinsp;\u0026le;\u0026thinsp;0.05), ** highly statistically significant(p\u0026thinsp;\u0026le;\u0026thinsp;0.01), *** highly statistically significant(p\u0026thinsp;\u0026le;\u0026thinsp;0.001). TCGA, the cancer genome atlas. COADREAD, colon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma. PARDEGs, Pyroptosis- and Aging- Related Differentially Expressed Genes. ROC, receiver operating characteristic curve. OS, overall survival.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8 Cox model\u003c/h2\u003e\n \u003cp\u003eWe employed multivariate Cox regression analysis on TCGA-COADREAD dataset to corroborate our previously established LASSO regression prognostic model. The purpose of this analysis was to examine the connection between optimistic and pessimistic clinical manifestations of seven major genes (NOD2, VEGFA, CEBPB, ELANE, FGF21, MMP1, and TREM2). At first, we incorporated the expression levels of the seven key genes into a multivariate Cox regression model (shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) and generated a forest plot (Fig.\u0026nbsp;8A) by using the multivariate Cox regression analysis. Additionally, we performed a nomogram analysis of the genes within the multivariate Cox regression model to evaluate their predictive strength, resulting in the formation of a nomogram (Fig.\u0026nbsp;8B). Moreover, we evaluated the accuracy of the nomograms based on the multivariate Cox regression model for one year (Fig.\u0026nbsp;8C), three years (Fig.\u0026nbsp;8D), and five years (Fig.\u0026nbsp;8E) by performing a prognostic calibration analysis and displaying the calibration curves. Examining the data, it is clear that the purple line denoting the three-year time span closely follows the gray ideal case line, suggesting that the model is more precise in forecasting results for a three-year duration than for one- or five-year durations. Subsequently, we used DCA to assess the practical value of our generated LASSO-Cox regression model for prognostication over the course of one year (Fig.\u0026nbsp;8F), three years (Fig.\u0026nbsp;8G), and five years (Fig.\u0026nbsp;8H). Examination of the data shows that the purple line representing the model always surpasses the x-values of the green line, suggesting overall positive results. The greatest variation is observed in five-year forecasts, whereas shorter-term forecasts, such as those for one or three years, are less variable. The results indicate that the model\u0026apos;s accuracy in predicting outcomes is greater over a five-year period than over either a one-year or three-year period.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCox regression to identify hub genes and clinical features associated with OS.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTotal(N)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNOD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.867 (0.611\u0026ndash;1.232)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVEGFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.466 (1.032\u0026ndash;2.080)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.666 (1.159\u0026ndash;2.395)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCEBPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.373 (0.969\u0026ndash;1.945)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eELANE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.700 (0.493\u0026ndash;0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.613 (0.426\u0026ndash;0.881)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.727 (0.512\u0026ndash;1.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTENM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.790 (0.558\u0026ndash;1.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eOS, overall survival.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;8 construct Cox model.\u003c/strong\u003e (A-B) Forest plot (A)and nomogram (B) of multivariable Cox regression analysis of hub genes. (C-E) Calibration curve of the 1-year (C), 3-year (D), and 5-year (E) for the multifactor Cox regression model nomogram analysis. (F-G) DCA plots for the 1-year (F), 3-year (G), and 5-year (H) LASSO-Cox regression prognostic models. The probability threshold or Threshold Probability is represented by the x-axis in the DCA plots, while the net return is represented by the y-axis. DCA, decision curve analysis. LASSO, least absolute shrinkage and selection operator.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e3.9 Immunohistochemical analysis\u003c/h2\u003e\n \u003cp\u003eImmunohistochemical techniques were utilized to study the expression of 5 genes (NOD2, CEBPB, ELANE, FGF21, TREM2) in COADREAD tumor tissues and normal colorectal tissues, taking into consideration 7 hub genes, with the help of the HPA database. The staining process required the use of DAB (3,3-diaminobenzidine) and counterstaining was performed with hematoxylin. Analysis of NOD2 expression indicated that the gene concentration was significantly higher in the COADREAD tumor tissue (COADREAD tissue, Fig.\u0026nbsp;9B) than in the normal colorectal tissue (normal colorectal tissues, Fig.\u0026nbsp;9A). CEBPB expression was significantly more elevated in COADREAD tissue (Fig.\u0026nbsp;9D) when compared to normal colorectal tissue (Fig.\u0026nbsp;9C). ELANE expression was higher in normal colorectal tissue than in COADREAD tumor tissue in both normal colorectal tissue (Fig.\u0026nbsp;9E) and COADREAD tissue (Fig.\u0026nbsp;9F). FGF21 expression was more pronounced in COADREAD tumor tissues (COADREAD tissue, Fig.\u0026nbsp;9H) in regular colorectal tissues (normal colon tissue, Fig.\u0026nbsp;9G). TREM2 expression was more pronounced in COADREAD tissue (Fig.\u0026nbsp;9J) when compared to normal colorectal tissue (Fig.\u0026nbsp;9I).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;9 Immunohistochemical analysis.\u003c/strong\u003e (A-B) gene NOD2 immunohistochemistry analysis in COADREAD tissue(B)and Normal colorectal tissues (A). (C-D) gene CEBPB immunohistochemistry analysis in COADREAD tissue(D)and Normal colorectal tissues (C). (E-F) gene ELANE immunohistochemistry analysis in COADREAD tissue(F)and Normal colorectal tissues (E). (G-H) gene FGF21 immunohistochemistry analysis in COADREAD tissue(H)and normal Normal colorectal tissues (G). (I-J) gene TREM2 immunohistochemistry analysis in COADREAD tissue(J)and Normal colorectal tissues (I). All the data were obtained from the HPA database. COADREAD, colon adenocarcinoma/rectum adenocarcinoma. HPA, human protein atlas.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIARC's most recent data indicates that CRC has the third highest rate of occurrence worldwide and is the second leading cause of death\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. New discoveries are being made in pyroptosis, aging, tumorigenesis, and growth, and oncology studies are exploring the possibilities of inducing pyroptosis and senescence, as well as eliminating senescent cancer cells. However, the prognostic value of PARGs in determining the outcome of colorectal cancer remains unclear. Pyroptosis and senescence are integral components of colorectal progression. Inflammation is caused by both pyroptosis and senescence, with pyroptosis regulating senescence at the cellular level\u003csup\u003e[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]\u003c/sup\u003e and triggering immunogenic cell death (ICD)\u003csup\u003e[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/sup\u003e and senescence in cancer cells, causing the release of large quantities of cytokines and other cell components. Research on different ailments has revealed that pyroptosis can lead to cellular senescence\u003csup\u003e[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]\u003c/sup\u003e, and that cells exhibiting senescence characteristics are more susceptible to pyroptosis\u003csup\u003e[\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]\u003c/sup\u003e. Evidence suggests that chemotherapy stimulates pyroptosis in cancer cells\u003csup\u003e[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]\u003c/sup\u003e, which may explain why chemotherapy combined with immunotherapy is often more effective against certain tumors. Recent research revealed that in certain forms of cancer, cellular demise can occur through a process of scorching caused by caspase-3/GSDME, which correlates with the expression of GSDME\u003csup\u003e[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]\u003c/sup\u003e. This process is distinct from apoptosis, which is considered the sole cause of cell death in aged cells. This study showed that senescent cancer cells can trigger an immune response not only by drawing macrophages and NK cells to eliminate them\u003csup\u003e[\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]\u003c/sup\u003e, but also by encouraging immunogenic cell death in cancer cells by boosting MHC1 expression, amplifying the activity of dendritic cells and CD8\u0026thinsp;+\u0026thinsp;T-cells\u003csup\u003e[\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]\u003c/sup\u003e. Furthermore, researchers have discovered that tumor progression can be hindered and postponed by injecting senescent cancer cells. Research has made progress in the area of pyroptosis induced by nanomaterial-transporting drugs (see the review published by Meng et al.\u003csup\u003e[\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]\u003c/sup\u003e) and apoptogens\u003csup\u003e[\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]\u003c/sup\u003e, with the pyroptosis of cancer cells being seen as a potential new therapeutic option\u003csup\u003e[\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis investigation identified seven colorectal cancer pyroptosis- and aging-related hub genes; however, due to the limited number of samples, prognostic models were created with the other six hub genes (NOD2, VEGFA, CEBPB, ELANE, FGF21, MMP1, and TREM2). The most noteworthy variations were observed for VEGFA and MMP1. The results of this study are in agreement with the fact that VEGFA\u003csup\u003e[\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]\u003c/sup\u003e is connected to tumor angiogenesis and vasculogenic mimicry (VM), both of which are linked to a poor prognosis in colorectal cancer. A previous study demonstrated that the inhibition of tumor growth can be achieved by focusing on and neutralizing VEGFA\u003csup\u003e[\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]\u003c/sup\u003e. In 2004, the US Food and Drug Administration (FDA) granted approval for bevacizumab, an inhibitor of VEGFA, as the initial therapy for CRC\u003csup\u003e[\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]\u003c/sup\u003e. MMP-1 is a member of the Matrix Metalloproteinases (MMP) family and has a particular focus in the intestines\u003csup\u003e[\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]\u003c/sup\u003e. This protein can break down collagen types I, II, III, VII, VIII, and X, and gelatin. Examination of MMP-1 gene expression revealed higher levels in colorectal cancer patients than in healthy individual tissues\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]\u003c/sup\u003e. Eiro\u003csup\u003e[\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]\u003c/sup\u003e and their colleagues discovered that the MMP-1 gene was expressed at higher levels in serrated, villous and tubular villous adenomas (i.e. polyps that have a high risk of turning into CRC). Previous studies conducted in the past have demonstrated that high MMP-1 expression is associated with an increased risk of invasion, advanced metastasis, lymph node metastasis, and shorter overall survival in CRC\u003csup\u003e[\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]\u003c/sup\u003e. Wang et al. investigated the effects of MMP-1 on CRC progression. Research has shown that by reducing MMP-1 expression, the development of CRC in both laboratory experiments and living organisms can be restrained due to the suppression of the PI3K/Akt/c-myc signaling pathway and EMT\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. These findings imply that MMP1 is linked to a favorable prognosis in CRC, which contradicts the outcomes of previous studies\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan additionalcitationids=\"CR90 CR91 CR92\" citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]\u003c/sup\u003e. A research study\u003csup\u003e[\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]\u003c/sup\u003e revealed that there may be a correlation between clinical staging and MMP1 expression in the samples taken, as MMP1 was found to be lower in liver metastases than in primary CRC. Moreover, patients with stage III colon cancer without MMP1 expression had a shorter time to distant metastasis and shorter overall survival, which is in line with the findings of this study. Further investigations into the relationship between MMP1 and CRC should be substantiated by multiple clinical and laboratory studies.\u003c/p\u003e \u003cp\u003eThe GO and KEGG enrichment analysis results indicated that PARDEGs were mainly found in biological processes such as temperature homeostasis (GO:0001659), positive regulation of cytokine production (GO:0001819), multicellular organismal homeostasis (GO:0048871), lumen of secretory granules (GO:0034774), lumen of azurophil granules (GO:0035578) and lumen of cytoplasmic vesicles (GO. 0060205), cytokine activity (GO:0005125), glycosaminoglycan binding (GO:0005539), receptor ligand activity (GO:0048018), rheumatoid arthritis (hsa05323), IL-17 signaling pathway (hsa05321), and tuberculosis (hsa05152). GSEA enrichment analysis demonstrated a substantial increase in genes in TCGA-COADREAD dataset that are regulated by WP resistin pathways, such as inflammation (Fig.\u0026nbsp;4B), autophagy (Fig.\u0026nbsp;4C), glycolysis and gluconeogenesis (Fig.\u0026nbsp;4D), and HSP70-mediated apoptosis modulation (Fig.\u0026nbsp;4E). GSVA revealed a statistically significant overrepresentation of PARDEGs in TCGA-COADREAD dataset within the HALLMARK IL2 STAT5 signaling, apoptosis, hypoxia, and glycolysis pathways. This study generated column line plots (nomogram plots) that were more accurate in predicting outcomes at 3-year intervals than at 1-year or 5-year intervals. The LASSO-Cox regression prognostic model demonstrated remarkable accuracy in predicting clinical utility over a 5-year period, outperforming the 1-year and 3-year models.\u003c/p\u003e \u003cp\u003eDespite the success of this study in forming a reliable prognostic model, this was not corroborated by subsequent experiments. No clinical correlation was present for further examination. The utilization of numerous datasets throughout the investigation could result in interbatch disparities that are not feasible to avoid or eradicate during assessment. Moreover, this examination had a restricted sample size. These findings could have been distorted due to the natural inclination to select cases. Further research involving both in vivo and in vitro experiments, as well as extensive observational studies, must be conducted to validate our results.\u003c/p\u003e \u003cp\u003eTo summarize, our research produced a prognostic model for colorectal cancer that is based on six pyroptosis- and aging-related genes, and the results demonstrate that it is reliable in predicting the overall survival of patients with COAD. This study could potentially pave the way for novel advancements in CRC research. Despite its merits, this study has some limitations. We drew our data from open sources and retrospectively obtained samples for the study. This study was conducted with a limited number of participants. Consequently, the outcomes could have been affected by an innate inclination towards certain cases. Further investigations, such as more extensive studies and experiments, both in the laboratory and in living organisms, are needed to support our conclusions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCRC, Colorectal cancer. PARGs, pyroptosis- and aging-related genes. COADREAD, The Colon Adenocarcinoma/Rectal Adenocarcinoma. TCGA, the cancer genome atlas. GEO, Gene Expression Omnibus. GO, Gene Ontology. KEGG, Kyoto Encyclopedia of Genes and Genomes. GSEA, Gene Set Enrichment Analysis. LASSO, least absolute shrinkage and selection. HPA, Human Protein Atlas. DCA, decision curve analysis. IARC, International Agency for Research on Cancer. ORR, objective response rate. dMMR, defective mismatch repair. MSI-H, high microsatellite instability. mCRC, metastatic colorectal cancers. PCD, Programmed Cell Death. SASP, senescence-associated secretory phenotype. BP, biological processes. MF, molecular functions. CC, cellular components. MCC, Maximal Clique Centrality. OS, overall survival. PFI, progression-free interval. DSS, disease-specific survival. ICD, immunogenic cell death. IQR, interquartile range.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e\n\u003cp\u003eThe datasets utilized in this study were downloaded from the following online tools, and the analysis results were also presented based on these datasets. TCGA(https://portal.gdc.cancer.gov/)，UCSC Xena database （http://genome.ucsc.edu, GEO (https://www.ncbi.nlm.nih.gov/geo/), GeneCards (https://www.genecards.org/), MSigDB( https://www.gsea-msigdb.org/gsea/msigdb/index.jsp), STRING database (https://cn.string-db.org/), HPA (https://www.proteinatlas.org/).\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study does not contain any studies with humanparticipants or animals performed by any of the authors.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThe authors would like to express their gratitude to Helix Life (https://www.helixlife.cn/class) for providing guidance on data analysis and language editing, and extend thanks to all the scholars who have made continuous efforts in cancer research.\u003c/p\u003e\n\u003ch2\u003ePublisher\u0026rsquo;s note\u003c/h2\u003e\n\u003cp\u003eThe ideas and opinions expressed in this article are solely those of the authors and do not necessarily reflect the views of their affiliated organizations, the publisher, the editors, or the reviewers. The publisher does not guarantee or approve of any product or statement made by its manufacturer that is assessed in this article.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eTP aided in the review of articles, the composition of manuscripts, the editing of intellectual content, and were in charge of gathering data and providing an English interpretation, and evaluating quality. YJ served as the primary contact and was responsible for evaluating the quality of the content and making necessary revisions. The article was collaboratively written by all authors and they all agreed to the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSUNG H, FERLAY J, SIEGEL R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. CA Cancer J Clin, 2021, 71(3): 209-49.\u003c/li\u003e\n\u003cli\u003eARAGHI M, SOERJOMATARAM I, JENKINS M, et al. Global trends in colorectal cancer mortality: projections to the year 2035 [J]. Int J Cancer, 2019, 144(12): 2992-3000.\u003c/li\u003e\n\u003cli\u003eDHILLON S. Regorafenib: A Review in Metastatic Colorectal Cancer [J]. Drugs, 2018, 78(11): 1133-44.\u003c/li\u003e\n\u003cli\u003eMCQUADE R M, STOJANOVSKA V, BORNSTEIN J C, et al. Colorectal Cancer Chemotherapy: The Evolution of Treatment and New Approaches [J]. Curr Med Chem, 2017, 24(15): 1537-57.\u003c/li\u003e\n\u003cli\u003eOVERMAN M J, LONARDI S, WONG K Y M, et al. Durable Clinical Benefit With Nivolumab Plus Ipilimumab in DNA Mismatch Repair-Deficient/Microsatellite Instability-High Metastatic Colorectal Cancer [J]. J Clin Oncol, 2018, 36(8): 773-9.\u003c/li\u003e\n\u003cli\u003eOVERMAN M J, MCDERMOTT R, LEACH J L, et al. Nivolumab in patients with metastatic DNA mismatch repair-deficient or microsatellite instability-high colorectal cancer (CheckMate 142): an open-label, multicentre, phase 2 study [J]. Lancet Oncol, 2017, 18(9): 1182-91.\u003c/li\u003e\n\u003cli\u003e曹鹏, 邱萌. 微卫星稳定型结直肠癌免疫治疗研究进展及热点 [J]. 实用肿瘤杂志, 2022, 37(02): 99-106.\u003c/li\u003e\n\u003cli\u003eKOVACS S B, MIAO E A. Gasdermins: Effectors of Pyroptosis [J]. Trends Cell Biol, 2017, 27(9): 673-84.\u003c/li\u003e\n\u003cli\u003eFANG Y, TIAN S, PAN Y, et al. Pyroptosis: A new frontier in cancer [J]. Biomed Pharmacother, 2020, 121(109595.\u003c/li\u003e\n\u003cli\u003eLU L, ZHANG Y, TAN X, et al. Emerging mechanisms of pyroptosis and its therapeutic strategy in cancer [J]. Cell Death Discov, 2022, 8(1): 338.\u003c/li\u003e\n\u003cli\u003eSHI J, GAO W, SHAO F. Pyroptosis: Gasdermin-Mediated Programmed Necrotic Cell Death [J]. Trends Biochem Sci, 2017, 42(4): 245-54.\u003c/li\u003e\n\u003cli\u003eSHI J, ZHAO Y, WANG Y, et al. Inflammatory caspases are innate immune receptors for intracellular LPS [J]. Nature, 2014, 514(7521): 187-92.\u003c/li\u003e\n\u003cli\u003eAGLIETTI R A, ESTEVEZ A, GUPTA A, et al. GsdmD p30 elicited by caspase-11 during pyroptosis forms pores in membranes [J]. Proc Natl Acad Sci U S A, 2016, 113(28): 7858-63.\u003c/li\u003e\n\u003cli\u003eTAABAZUING C Y, OKONDO M C, BACHOVCHIN D A. Pyroptosis and Apoptosis Pathways Engage in Bidirectional Crosstalk in Monocytes and Macrophages [J]. Cell Chem Biol, 2017, 24(4): 507-14 e4.\u003c/li\u003e\n\u003cli\u003eZHANG Z, ZHANG Y, XIA S, et al. Gasdermin E suppresses tumour growth by activating anti-tumour immunity [J]. Nature, 2020, 579(7799): 415-20.\u003c/li\u003e\n\u003cli\u003e\u0026lt;granzyme a from cytotoxic lymphocytes cleaves .pdf\u0026gt; [J].\u003c/li\u003e\n\u003cli\u003eVOSKOBOINIK I, WHISSTOCK J C, TRAPANI J A. Perforin and granzymes: function, dysfunction and human pathology [J]. Nat Rev Immunol, 2015, 15(6): 388-400.\u003c/li\u003e\n\u003cli\u003eRUAN J, WANG S, WANG J. Mechanism and regulation of pyroptosis-mediated in cancer cell death [J]. Chem Biol Interact, 2020, 323(109052.\u003c/li\u003e\n\u003cli\u003eZHENG Z, LI G. Mechanisms and Therapeutic Regulation of Pyroptosis in Inflammatory Diseases and Cancer [J]. Int J Mol Sci, 2020, 21(4):\u003c/li\u003e\n\u003cli\u003eWANG M, JIANG S, ZHANG Y, et al. The Multifaceted Roles of Pyroptotic Cell Death Pathways in Cancer [J]. Cancers (Basel), 2019, 11(9):\u003c/li\u003e\n\u003cli\u003eSHAWKI S, ASHBURN J, SIGNS S A, et al. Colon Cancer: Inflammation-Associated Cancer [J]. Surg Oncol Clin N Am, 2018, 27(2): 269-87.\u003c/li\u003e\n\u003cli\u003eJAFARI D, NOORBAKHSH F, DELAVARI A, et al. Expression level of long noncoding RNA NKILAmiR103-miR107 inflammatory axis and its clinical significance as potential biomarker in patients with colorectal cancer [J]. J Res Med Sci, 2020, 25(41.\u003c/li\u003e\n\u003cli\u003eMIGUCHI M, HINOI T, SHIMOMURA M, et al. Gasdermin C Is Upregulated by Inactivation of Transforming Growth Factor beta Receptor Type II in the Presence of Mutated Apc, Promoting Colorectal Cancer Proliferation [J]. PLoS One, 2016, 11(11): e0166422.\u003c/li\u003e\n\u003cli\u003eTAN G, HUANG C, CHEN J, et al. HMGB1 released from GSDME-mediated pyroptotic epithelial cells participates in the tumorigenesis of colitis-associated colorectal cancer through the ERK1/2 pathway [J]. J Hematol Oncol, 2020, 13(1): 149.\u003c/li\u003e\n\u003cli\u003eDUPAUL-CHICOINE J, YERETSSIAN G, DOIRON K, et al. Control of intestinal homeostasis, colitis, and colitis-associated colorectal cancer by the inflammatory caspases [J]. Immunity, 2010, 32(3): 367-78.\u003c/li\u003e\n\u003cli\u003eZAKI M H, BOYD K L, VOGEL P, et al. The NLRP3 inflammasome protects against loss of epithelial integrity and mortality during experimental colitis [J]. Immunity, 2010, 32(3): 379-91.\u003c/li\u003e\n\u003cli\u003eZAKI M H, VOGEL P, BODY-MALAPEL M, et al. IL-18 production downstream of the Nlrp3 inflammasome confers protection against colorectal tumor formation [J]. J Immunol, 2010, 185(8): 4912-20.\u003c/li\u003e\n\u003cli\u003eWU L S, LIU Y, WANG X W, et al. LPS Enhances the Chemosensitivity of Oxaliplatin in HT29 Cells via GSDMD-Mediated Pyroptosis [J]. Cancer Manag Res, 2020, 12(10397-409.\u003c/li\u003e\n\u003cli\u003eDODIG S, CEPELAK I, PAVIC I. Hallmarks of senescence and aging [J]. Biochem Med (Zagreb), 2019, 29(3): 030501.\u003c/li\u003e\n\u003cli\u003eCAI Y, SONG W, LI J, et al. The landscape of aging [J]. Sci China Life Sci, 2022, 65(12): 2354-454.\u003c/li\u003e\n\u003cli\u003eGORGOULIS V, ADAMS P D, ALIMONTI A, et al. Cellular Senescence: Defining a Path Forward [J]. Cell, 2019, 179(4): 813-27.\u003c/li\u003e\n\u003cli\u003eFAGET D V, REN Q, STEWART S A. Unmasking senescence: context-dependent effects of SASP in cancer [J]. Nat Rev Cancer, 2019, 19(8): 439-53.\u003c/li\u003e\n\u003cli\u003e\u0026lt;senescence and the sasp many therap source jodie birch genes dev 2020 so 2020.pdf\u0026gt; [J].\u003c/li\u003e\n\u003cli\u003e\u0026lt;the innate immune sensor toll-like receptor 2 .pdf\u0026gt; [J].\u003c/li\u003e\n\u003cli\u003eWAN M, GRAY-GAILLARD E F, ELISSEEFF J H. Cellular senescence in musculoskeletal homeostasis, diseases, and regeneration [J]. Bone Res, 2021, 9(1): 41.\u003c/li\u003e\n\u003cli\u003eLEE S, SCHMITT C A. The dynamic nature of senescence in cancer [J]. Nat Cell Biol, 2019, 21(1): 94-101.\u003c/li\u003e\n\u003cli\u003eWANG B, KOHLI J, DEMARIA M. Senescent Cells in Cancer Therapy: Friends or Foes? [J]. Trends Cancer, 2020, 6(10): 838-57.\u003c/li\u003e\n\u003cli\u003eKOWALD A, PASSOS J F, KIRKWOOD T B L. On the evolution of cellular senescence [J]. Aging Cell, 2020, 19(12): e13270.\u003c/li\u003e\n\u003cli\u003eHE S, SHARPLESS N E. Senescence in Health and Disease [J]. Cell, 2017, 169(6): 1000-11.\u003c/li\u003e\n\u003cli\u003eWANG K, ZHENG J, YU J, et al. Knockdown of MMP‑1 inhibits the progression of colorectal cancer by suppressing the PI3K/Akt/c‑myc signaling pathway and EMT [J]. Oncol Rep, 2020, 43(4): 1103-12.\u003c/li\u003e\n\u003cli\u003eDE BLANDER H, MOREL A P, SENARATNE A P, et al. Cellular Plasticity: A Route to Senescence Exit and Tumorigenesis [J]. Cancers (Basel), 2021, 13(18):\u003c/li\u003e\n\u003cli\u003eSIEBEN C J, STURMLECHNER I, VAN DE SLUIS B, et al. Two-Step Senescence-Focused Cancer Therapies [J]. Trends Cell Biol, 2018, 28(9): 723-37.\u003c/li\u003e\n\u003cli\u003eFERNANDEZ-DURAN I, QUINTANILLA A, TARRATS N, et al. Cytoplasmic innate immune sensing by the caspase-4 non-canonical inflammasome promotes cellular senescence [J]. Cell Death Differ, 2022, 29(6): 1267-82.\u003c/li\u003e\n\u003cli\u003eHANAHAN D. Hallmarks of Cancer: New Dimensions [J]. Cancer Discov, 2022, 12(1): 31-46.\u003c/li\u003e\n\u003cli\u003e\u0026lt;the aging related risk signature in source aging albany ny em em.pdf\u0026gt; [J].\u003c/li\u003e\n\u003cli\u003eHU X, LI Y Q, MA X J, et al. A Risk Signature With Inflammatory and T Immune Cells Infiltration in Colorectal Cancer Predicting Distant Metastases and Efficiency of Chemotherapy [J]. Front Oncol, 2019, 9(704.\u003c/li\u003e\n\u003cli\u003e\u0026lt;prognostic value of a hypoxia-related microrna signature in patients with colorectal ].pdf\u0026gt; [J].\u003c/li\u003e\n\u003cli\u003eZHOU Z, MO S, DAI W, et al. Development and Validation of an Autophagy Score Signature for the Prediction of Post-operative Survival in Colorectal Cancer [J]. Front Oncol, 2019, 9(878.\u003c/li\u003e\n\u003cli\u003eZHENG C, TAN Z. A novel identified pyroptosis-related prognostic signature of colorectal cancer [J]. Math Biosci Eng, 2021, 18(6): 8783-96.\u003c/li\u003e\n\u003cli\u003eCOLAPRICO A, SILVA T C, OLSEN C, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data [J]. Nucleic Acids Res, 2016, 44(8): e71.\u003c/li\u003e\n\u003cli\u003eGOLDMAN M J, CRAFT B, HASTIE M, et al. Visualizing and interpreting cancer genomics data via the Xena platform [J]. Nat Biotechnol, 2020, 38(6): 675-8.\u003c/li\u003e\n\u003cli\u003eRITCHIE M E, PHIPSON B, WU D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies [J]. Nucleic Acids Res, 2015, 43(7): e47.\u003c/li\u003e\n\u003cli\u003eSHI J, BAO M, WANG W, et al. Integrated Profiling Identifies PLOD3 as a Potential Prognostic and Immunotherapy Relevant Biomarker in Colorectal Cancer [J]. Front Immunol, 2021, 12(722807.\u003c/li\u003e\n\u003cli\u003eHU Y, GAEDCKE J, EMONS G, et al. Colorectal cancer susceptibility loci as predictive markers of rectal cancer prognosis after surgery [J]. Genes Chromosomes Cancer, 2018, 57(3): 140-9.\u003c/li\u003e\n\u003cli\u003eSZEGLIN B C, WU C, MARCO M R, et al. A SMAD4-modulated gene profile predicts disease-free survival in stage II and III colorectal cancer [J]. Cancer Rep (Hoboken), 2022, 5(1): e1423.\u003c/li\u003e\n\u003cli\u003eBARRETT T, WILHITE S E, LEDOUX P, et al. NCBI GEO: archive for functional genomics data sets--update [J]. Nucleic Acids Res, 2013, 41(Database issue): D991-5.\u003c/li\u003e\n\u003cli\u003eDAVIS S, MELTZER P S. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor [J]. Bioinformatics, 2007, 23(14): 1846-7.\u003c/li\u003e\n\u003cli\u003eSTELZER G, ROSEN N, PLASCHKES I, et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses [J]. Curr Protoc Bioinformatics, 2016, 54(1.30.1-1..3.\u003c/li\u003e\n\u003cli\u003eLIBERZON A, BIRGER C, THORVALDSDOTTIR H, et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection [J]. Cell Syst, 2015, 1(6): 417-25.\u003c/li\u003e\n\u003cli\u003eWU J, ZHU Y, LUO M, et al. Comprehensive Analysis of Pyroptosis-Related Genes and Tumor Microenvironment Infiltration Characterization in Breast Cancer [J]. Front Immunol, 2021, 12(748221.\u003c/li\u003e\n\u003cli\u003eHE J, LI X. Identification and Validation of Aging-Related Genes in Idiopathic Pulmonary Fibrosis [J]. Front Genet, 2022, 13(780010.\u003c/li\u003e\n\u003cli\u003eGene Ontology Consortium: going forward [J]. Nucleic Acids Research, 2014, 43(D1): D1049-D56.\u003c/li\u003e\n\u003cli\u003eKANEHISA M, GOTO S. KEGG: kyoto encyclopedia of genes and genomes [J]. Nucleic Acids Res, 2000, 28(1): 27-30.\u003c/li\u003e\n\u003cli\u003eYU G, WANG L G, HAN Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters [J]. OMICS, 2012, 16(5): 284-7.\u003c/li\u003e\n\u003cli\u003eSUBRAMANIAN A, TAMAYO P, MOOTHA V K, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles [J]. Proc Natl Acad Sci U S A, 2005, 102(43): 15545-50.\u003c/li\u003e\n\u003cli\u003eH\u0026auml;NZELMANN S, CASTELO R, GUINNEY J. GSVA: gene set variation analysis for microarray and RNA-seq data [J]. BMC Bioinformatics, 2013, 14(7.\u003c/li\u003e\n\u003cli\u003eSZKLARCZYK D, GABLE A L, LYON D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets [J]. Nucleic Acids Res, 2019, 47(D1): D607-D13.\u003c/li\u003e\n\u003cli\u003eSHANNON P, MARKIEL A, OZIER O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks [J]. Genome Res, 2003, 13(11): 2498-504.\u003c/li\u003e\n\u003cli\u003eYANG X, LI Y, LV R, et al. Study on the Multitarget Mechanism and Key Active Ingredients of Herba Siegesbeckiae and Volatile Oil against Rheumatoid Arthritis Based on Network Pharmacology [J]. Evid Based Complement Alternat Med, 2019, 2019(8957245.\u003c/li\u003e\n\u003cli\u003eCHIN C H, CHEN S H, WU H H, et al. cytoHubba: identifying hub objects and sub-networks from complex interactome [J]. BMC Syst Biol, 2014, 8 Suppl 4(Suppl 4): S11.\u003c/li\u003e\n\u003cli\u003eTATARANNI T, PICCOLI C. Dichloroacetate (DCA) and Cancer: An Overview towards Clinical Applications [J]. Oxid Med Cell Longev, 2019, 2019(8201079.\u003c/li\u003e\n\u003cli\u003eCOLWILL K, GR\u0026auml;SLUND S. A roadmap to generate renewable protein binders to the human proteome [J]. Nat Methods, 2011, 8(7): 551-8.\u003c/li\u003e\n\u003cli\u003eENGEBRETSEN S, BOHLIN J. Statistical predictions with glmnet [J]. Clin Epigenetics, 2019, 11(1): 123.\u003c/li\u003e\n\u003cli\u003eJIN H, XIE W, HE M, et al. Pyroptosis and Sarcopenia: Frontier Perspective of Disease Mechanism [J]. Cells, 2022, 11(7):\u003c/li\u003e\n\u003cli\u003eDING B, SHENG J, ZHENG P, et al. Biodegradable Upconversion Nanoparticles Induce Pyroptosis for Cancer Immunotherapy [J]. Nano Lett, 2021, 21(19): 8281-9.\u003c/li\u003e\n\u003cli\u003eKOLB J P, OGUIN T H, 3RD, OBERST A, et al. Programmed Cell Death and Inflammation: Winter Is Coming [J]. Trends Immunol, 2017, 38(10): 705-18.\u003c/li\u003e\n\u003cli\u003eZHAO P, YUE Z, NIE L, et al. Hyperglycaemia-associated macrophage pyroptosis accelerates periodontal inflamm-aging [J]. J Clin Periodontol, 2021, 48(10): 1379-92.\u003c/li\u003e\n\u003cli\u003eFALI T, FABRE-MERSSEMAN V, YAMAMOTO T, et al. Elderly human hematopoietic progenitor cells express cellular senescence markers and are more susceptible to pyroptosis [J]. JCI Insight, 2018, 3(13):\u003c/li\u003e\n\u003cli\u003eTAN Y, CHEN Q, LI X, et al. Pyroptosis: a new paradigm of cell death for fighting against cancer [J]. J Exp Clin Cancer Res, 2021, 40(1): 153.\u003c/li\u003e\n\u003cli\u003eJIANG M, QI L, LI L, et al. The caspase-3/GSDME signal pathway as a switch between apoptosis and pyroptosis in cancer [J]. Cell Death Discov, 2020, 6(112.\u003c/li\u003e\n\u003cli\u003ePRATA L G P L, OVSYANNIKOVA I G, TCHKONIA T, et al. Senescent cell clearance by the immune system: Emerging therapeutic opportunities [J]. Seminars in Immunology, 2018, 40(\u003c/li\u003e\n\u003cli\u003eMARIN I B O, GARCIA-GARIJO A, SIROIS I, CABALLE A, ZARZUELA E, RUANO I, STEPHAN-OTTO ATTOLINI C, PRATS N, LOPEZ-DOMINGUEZ JA, KOVATCHEVA M, GARRALDA E, MUNOZ J, CARON E, ABAD M, GROS A, PIETROCOLA F, SERRANO M. Cellular senescence is immunogenic and promotes anti-tumor immunity [J]. Cancer Discov, 2022,\u003c/li\u003e\n\u003cli\u003eMENG Q, DING B, MA P, et al. Interrelation between Programmed Cell Death and Immunogenic Cell Death: Take Antitumor Nanodrug as an Example [J]. Small Methods, 2023, e2201406.\u003c/li\u003e\n\u003cli\u003eLIU Z, LI Y, ZHU Y, et al. Apoptin induces pyroptosis of colorectal cancer cells via the GSDME-dependent pathway [J]. Int J Biol Sci, 2022, 18(2): 717-30.\u003c/li\u003e\n\u003cli\u003eANDREOZZI M, QUAGLIATA L, GSPONER J R, et al. VEGFA gene locus analysis across 80 human tumour types reveals gene amplification in several neoplastic entities [J]. Angiogenesis, 2014, 17(3): 519-27.\u003c/li\u003e\n\u003cli\u003eLIU X, HE H, ZHANG F, et al. m6A methylated EphA2 and VEGFA through IGF2BP2/3 regulation promotes vasculogenic mimicry in colorectal cancer via PI3K/AKT and ERK1/2 signaling [J]. Cell Death Dis, 2022, 13(5): 483.\u003c/li\u003e\n\u003cli\u003e\u0026lt;inhibition of vascular endothelial (1).pdf\u0026gt; [J].\u003c/li\u003e\n\u003cli\u003e\u0026lt;bevacizumab plus irinotecan fluorouracil (1).pdf\u0026gt; [J].\u003c/li\u003e\n\u003cli\u003ePEZESHKIAN Z, NOBILI S, PEYRAVIAN N, et al. Insights into the Role of Matrix Metalloproteinases in Precancerous Conditions and in Colorectal Cancer [J]. Cancers (Basel), 2021, 13(24):\u003c/li\u003e\n\u003cli\u003eJONSSON A, FALK P, ANGENETE E, et al. Plasma MMP-1 Expression as a Prognostic Factor in Colon Cancer [J]. J Surg Res, 2021, 266(254-60.\u003c/li\u003e\n\u003cli\u003eEIRO N, GONZALEZ L O, CID S, et al. Matrix metalloproteases expression in different histological types of colorectal polyps [J]. Rev Esp Enferm Dig, 2017, 109(6): 414-20.\u003c/li\u003e\n\u003cli\u003eLIANG Y, LV Z, HUANG G, et al. Prognostic significance of abnormal matrix collagen remodeling in colorectal cancer based on histologic and bioinformatics analysis [J]. Oncol Rep, 2020, 44(4): 1671-85.\u003c/li\u003e\n\u003cli\u003eSUNAMI E, TSUNO N, OSADA T, et al. MMP-1 is a prognostic marker for hematogenous metastasis of colorectal cancer [J]. Oncologist, 2000, 5(2): 108-14.\u003c/li\u003e\n\u003cli\u003eWONG J C, CHAN S K, SCHAEFFER D F, et al. Absence of MMP2 expression correlates with poor clinical outcomes in rectal cancer, and is distinct from MMP1-related outcomes in colon cancer [J]. Clin Cancer Res, 2011, 17(12): 4167-76.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pyroptosis, aging, colorectal cancer (CRC), prognosis, gene signature","lastPublishedDoi":"10.21203/rs.3.rs-4185479/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4185479/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eColorectal cancer (CRC) is the most prevalent gastrointestinal cancer worldwide. Our goal was to construct a model based on pyroptosis- and aging-related genes (PARGs) to predict CRC outcomes of colorectal cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe Colon Adenocarcinoma/Rectal Adenocarcinoma Esophageal Carcinoma (COADREAD) dataset from the cancer genome atlas (TCGA) was obtained using R. Colorectal cancer-related datasets, namely, GSE74602, GSE87211, and GSE161158 were acquired from the Gene Expression Omnibus (GEO) database. PARGs were collected from various sources such as the GeneCards database, Molecular Signatures Database (MSigDB), and relevant literature. Differential expression analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed using R. Prognostic models were constructed utilizing LASSO (least absolute shrinkage and selection) regression analyses. Column line plots and calibration curve plots were generated using the R package. Immunohistochemical analyses were performed using the HPA (Human Protein Atlas) database.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTo obtain sets of genes related to both pyroptosis and aging (PARGs), we identified overlapping genes from two distinct datasets: one consisting of genes associated with pyroptosis (PRGs), and the other consisting of genes associated with aging (ARGs). We then created a risk signature that encompassed both pyroptosis and aging factors, which was further validated using diagnostic tools such as a Calibration Curve and decision curve analysis (DCA). The risk score derived from this signature significantly affects the overall survival of patients (CRC) patients. The stability and accuracy of this association were further confirmed using stratified survival analysis and DCA. Additionally, GSEA was performed to obtain results for both high-risk and low-risk groups.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCRC severity may be predicted using the PARGs signature, which is a reliable prognostic analysis model.\u003c/p\u003e","manuscriptTitle":"Prognosis analysis of pyroptosis- and aging-related genes in colorectal cancer based on bioinformatic analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 16:14:36","doi":"10.21203/rs.3.rs-4185479/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8235089b-c874-4632-bf54-f31c70de7480","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-02T04:14:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-05 16:14:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4185479","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4185479","identity":"rs-4185479","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}