A Novel Pyroptosis Prognostic Model Centers on NLRC4 in Colorectal Cancer

A Novel Pyroptosis Prognostic Model Centers on NLRC4 in Colorectal Cancer | 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 A Novel Pyroptosis Prognostic Model Centers on NLRC4 in Colorectal Cancer Yaoyao Zhuang, Yeqiong Xu, Yimai Deng, Huanhuan Chen, Chuandan Wan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7902247/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 Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, necessitating the discovery of novel biomarkers and therapeutic targets. This study investigates the role of pyroptosis-related genes (PRGs) in CRC through comprehensive bioinformatics analysis. We identified 202 PRGs from the MSigDB, Gene, and GeneCards databases, of which 159 were differentially expressed in CRC tissues compared to normal tissues. Functional enrichment analysis revealed significant involvement of these genes in pathways such as pyroptosis, cytokine production regulation, and inflammasome complex formation. The KEGG analysis highlighted pathways including NOD-like receptor signaling and necroptosis. A protein-protein interaction (PPI) network identified CASP1, NLRP3, PYCARD, NLRC4, and AIM2 as core genes, with subsequent validation showing decreased expression of CASP1, NLRP3, NLRC4, and AIM2 in CRC tissues. Immune infiltration analysis indicated that NLRP3, CASP1, AIM2, and NLRC4 are associated with immune cell infiltration in CRC. Diagnostic efficacy assessment using ROC curves demonstrated that NLRP3 and NLRC4 have excellent potential as diagnostic biomarkers. Prognostic analysis revealed that high expression of CASP1 correlates with favorable prognosis, while high NLRC4 expression is linked to poor prognosis. A predictive nomogram model incorporating NLRC4 and clinical indicators showed strong predictive capability for overall survival in CRC patients. Immunohistochemical validation confirmed the expression patterns of core genes in CRC tissues. This study underscores the potential of PRGs as biomarkers and therapeutic targets in CRC, offering insights into their role in tumorigenesis and immune modulation. Future research should focus on validating these findings in larger cohorts to enhance the clinical applicability of these biomarkers in CRC management. Colorectal carcinoma (CRC) pyroptosis immune infiltration diagnosis prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Colorectal carcinoma (CRC) is one of the most prevalent malignancies worldwide, with an increasing incidence and mortality rate, particularly in developed countries. It ranks as the second leading cause of cancer-related deaths globally, highlighting the urgent need for effective diagnostic and prognostic tools[ 1 ]. The complex interplay of genetic, environmental, and lifestyle factors contributes to the pathogenesis of CRC[ 2 ], making early detection and personalized treatment essential for improving patient outcomes. Current therapeutic strategies, including surgical interventions, chemotherapy, and radiation therapy, are often limited by their inability to effectively screen early-stage disease and provide personalized treatment options. This underscores the pressing need for the discovery of novel biomarkers and therapeutic targets that can enhance early detection and treatment efficacy[ 3 ]. Recent advances in biomolecular research have highlighted the significance of cell death mechanisms in cancer pathogenesis, particularly focusing on pyroptosis, a newly identified form of programmed cell death intertwined with inflammatory responses[ 4 ]. This process is mediated by gasdermin proteins, particularly gasdermin D (GSDMD), which upon cleavage by inflammatory caspases, forms pores in the cell membrane, leading to cell lysis and release of pro-inflammatory cytokines[ 5 ]. The mechanisms underlying pyroptosis are complex and involve various signaling pathways that can be broadly categorized into canonical and non-canonical pathways. The canonical pathway is initiated by the activation of the NLRP3 inflammasome, which leads to the activation of caspase-1. This enzyme GSDMD, resulting in the formation of pores in the cell membrane, cell swelling, and eventual rupture, releasing pro-inflammatory cytokines such as IL-1β and IL-18 into the extracellular space[ 6 ]. Conversely, the non-canonical pathway involves caspases 4, 5, and 11, which can directly cleave GSDMD without the need for inflammasome assembly. This distinction is critical, as it highlights the various stimuli that can trigger pyroptosis, ranging from pathogen infections to cellular stressors, and the resultant inflammatory response plays a significant role in both host defense and the pathology of various diseases, including cancer[ 7 ]. The relationship between pyroptosis and CRC is complex, as it can either inhibit tumor progression by inducing inflammatory responses that target cancer cells or promote tumor growth by creating a supportive microenvironment for cancer cell survival[ 8 ]. Pyroptosis can inhibit the initiation and progression of tumors by promoting an inflammatory microenvironment that is hostile to cancer cell survival. During the pyroptosis process, the release of pro-inflammatory cytokines can recruit immune cells to the tumor site and enhance the anti-tumor immune response. However, excessive pyroptosis can also contribute to tumorigenesis by creating a supportive environment for tumor growth through chronic inflammation[ 9 ]. For instance, the NLRP3 inflammasome has been shown to mediate pyroptosis in CRC cells, which can lead to the release of pro-inflammatory cytokines and influence tumor progression[ 5 ]. Nevertheless, novel therapeutic agents like FL118 have been identified that can induce pyroptosis in CRC cells, inhibiting their proliferation and metastasis through the activation of pyroptosis-related pathways[ 10 ]. Furthermore, the impact of pyroptosis on the tumor immune microenvironment in CRC is profound and multifaceted. Pyroptosis not only influences the survival and proliferation of tumor cells but also alters the immune landscape within the tumor. The inflammatory nature of pyroptosis leads to the release of cytokines and damage-associated molecular patterns (DAMPs), which can enhance the recruitment and activation of immune cells, thereby promoting a more robust anti-tumor response[ 10 ]. Conversely, the inflammatory milieu created by pyroptosis can also contribute to immune evasion mechanisms employed by tumors, leading to a complex interplay between tumor cells and immune components[ 11 ]. Therefore, understanding the dual role of pyroptosis in CRC could provide valuable insights into its potential as a therapeutic target and prognostic biomarker. Bioinformatics is an interdisciplinary subject combining mathematics, statistics, computer science and biology. It is mainly used to analyze and interpret biological data and is widely applied in fields such as gene analysis and drug development[ 12 ]. To investigate the potential association between PRGs and CRC, our study employs bioinformatics to construct a model delineating pyroptosis gene characteristics in CRC. This methodology facilitates the comprehensive analysis of large-scale genomic data, thereby revealing potential biomarkers and elucidating their underlying mechanisms of action[ 13 ]. Utilizing the genomic information of CRC cells and with the aid of bioinformatics tools, the key genetic mutations associated with CRC were identified. Through the analysis of large datasets from CRC patients, biomarkers related to the occurrence, development, prognosis and treatment response of CRC were identified. These biomarkers can be used for the early diagnosis, prognosis assessment and treatment guidance of CRC[ 14 ]. This approach boasts several advantages, including high throughput, sensitivity, and robust data analytical capabilities, allowing for an in-depth examination of pyroptosis-related gene expression patterns and their correlation with clinical outcomes[ 15 ]. In conclusion, the integration of bioinformatics in the study of PRGs presents a promising avenue for advancing our understanding of CRC. As we delve deeper into the molecular intricacies of this disease, the identification of novel biomarkers and therapeutic targets holds the potential to significantly alter the clinical landscape of CRC management. Future research should focus on validating these findings in larger, multicenter cohorts to ensure the applicability and robustness of the proposed strategies[ 16 ]. Prior studies have established a relationship between aberrant expression of PRGs and the initiation and advancement of tumors, suggesting that they may serve as potential biomarkers for CRC[ 15 ]. Therefore, the objective of this research is to explore the role of these genes in the development, immune infiltration, and clinical features of CRC through bioinformatics approaches. Additionally, immunohistochemistry was used to verify the expression patterns of the clinical relevance of the core pyroptosis genes in CRC and normal intestinal tissues. This evolving landscape of pyroptosis research highlights its potential as both a therapeutic target and a biomarker for CRC, which may contribute to the early detection and targeted treatment of CRC. Materials and Methods Acquisition of Pyroptosis-Related Gene Databases in CRC . In this study, bioinformatics methods were used to conduct correlation analysis between core pyroptosis genes and CRC. Figure 1 shows the detailed research process. PRGs were identified by retrieving information from several databases, including MSigDB ( https://www.gsea-msigdb.org/gsea/msigdb/human/geneset/REACTOME_PYROPTOSIS.html?keywords=pyroptosis )[ 17 ], Gene ( https://www.ncbi.nlm.nih.gov/gene/?term=pyroptosis )[ 18 ], and GeneCards ( https://www.genecards.org/Search/Keyword?queryString=pyroptosis )[ 19 ]. The results from these three databases were consolidated, and the expression levels of PRGs in tumor tissues were compared to those in normal tissues using the UALCAN database ( https://ualcan.path.uab.edu/analysis.html )[ 20 ]. Among these databases, MSigDB offers a wide range of annotated gene sets connected to various biological processes, signaling pathways, and gene expression profiles, making it a valuable resource for gene function research and bioinformatics analysis. The Gene database from NCBI serves as a fundamental source of gene information, while GeneCards is recognized for its comprehensiveness and authority. It encompasses multiple aspects, such as gene function, expression patterns, and disease associations, providing extensive gene annotation information for researchers. The combined use of these databases enables a systematic analysis of CRC-related genes and their roles in various biological processes. UALCAN is an online tool specifically designed for cancer data research, based on the TCGA database ( https://www.cancer.gov/ccg/research/genome-sequencing/tcga ), which facilitates statistical analysis and mining of cancer data. Xiantao Academic Tools. Xiantao Academic ( https://www.xiantaozi.com ) is a powerful bioinformatics analysis tool that provides a wide range of functions for bioinformatics analysis and data visualization[ 21 ]. This study utilized the basic drawing module, functional clustering module, interaction network module, and clinical significance module. The research data were sourced from the Xiantao Academic Cloud dataset, specifically from Colorectal Cancer/TCGA/TCGA - COADREAD/RNA Sequencing (RNAseq)/Spliced Transcripts Alignment to a Reference (STAR)/Transcripts Per Million (TPM). The raw data were filtered, which involved removing normal samples, samples without clinical information, and duplicate samples. Subsequently, data normalization was achieved through log2(value + 1) transformation, effectively enhancing the stability and comparability of subsequent analyses. The preprocessed dataset was ultimately used to construct an immune cell infiltration assessment model, decipher key molecular interaction networks, build a prognostic risk prediction system, and carry out multi - dimensional screening and validation of diagnostic biomarkers. Enrichment Analysis of PRGs in CRC . The principle is based on the known information of genes in biological processes (BPs), molecular functions (MFs) and cellular components (CCs). With the help of GO for gene function annotation and combined with the pathway information of KEGG, the list of differentially expressed genes was compared with the gene sets in the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The aim is to identify the gene functional categories or metabolic pathways that are significantly enriched under experimental conditions, and to deeply analyze the biological processes and signaling pathways involved in PRGs related to CRC. Using Xiantao Academic online software, version R (4.2.1), after ID conversion of the input list of pyroptosis genes related to CRC cells, GOKEGG enrichment analysis was performed using the clusterProfiler package[ 22 ], and then the enrichment analysis results were visualized using the ggplot2 package. The significance cut-off value of GOKEGG enrichment analysis is generally set as the adjusted p-value < 0.05. Acquisition of Pyroptosis Core Genes in CRC . The PPI network was constructed using the STRING database ( https://cn.string-db.org )[ 23 ]. Whereafter, the results were imported into Cytoscape software[ 24 ], and the cytoHubba plugin was used for visualization[ 25 ]. The top five core genes were screened out using the Maximum Clique Centrality (MCC) algorithm. The STRING database is a bioinformatics database and network resource that can not only collect and integrate the PPI data but also predict indirectly associated relationships through computational methods[ 23 ]. The parameters set in the STRING database include: the network type is the complete STRING network, the required confidence score is the highest confidence level (0.900), and the false discovery rate (FDR) stringency is medium (5%). Cytoscape is an open-source software for network visualization and analysis. It is often used to display and analyze the interaction relationships among biomolecules such as genes and proteins. In addition, it has a rich set of layout algorithms, which can automatically adjust the positions of nodes and edges according to the characteristics of the network, enabling a clear presentation of the network structure[ 24 ]. The PPI network can be intuitively presented through Cytoscape. Validation of the Expression of Pyroptosis Core Genes . Retrieve the expression data of core genes from CRC datasets in the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74602 )[ 26 ] and verify the reliability of the expression of core genes. Subsequently, use Xiantao Academic analysis tools to perform visualization analysis on the differential expression data of core genes. The GEO database is a public functional genomics data repository that provides abundant data resources and promotes the development of multiple aspects such as gene function research, disease diagnosis, and treatment[ 26 ]. Analysis of the Association among Diagnosis, Prognosis and Immune Infiltration of Pyroptosis Core Genes . To explore the correlation between immune cell infiltration, diagnosis and prognosis of pyroptosis core genes related to CRC, the TIMER 2.0 ( http://timer.cistrome.org/ )[ 27 ], Xiantao Academic website[ 21 ] and Kaplan - Meier plotter ( https://kmplot.com/analysis/ ) databases[ 28 ] were used for analysis respectively. The TIMER 2.0 database is a tumor immune analysis tool, which integrates gene expression data from multiple public databases. By analyzing the immune cell infiltration in different cancer types, it clarifies the interaction between the expression of core genes and the immune system, providing important information for cancer research. In this study, the Xiantao Academic tool was used to analyze the diagnostic effect of core genes in CRC. The RNAseq data of the STAR process in the TCGA - COADREAD project were downloaded and organized from the TCGA database ( https://portal.gdc.cancer.gov ), and the data in TPM format were extracted. The pROC package was used for receiver operating characteristic (ROC) analysis of the data, and the results were visualized with ggplot2. The pROC package will correct the outcome order of the data by default. The data filtering strategy was to remove samples without clinical information and duplicate samples, and the data processing method was log2(value + 1). The Kaplan - Meier plotter database is an online bioinformatics tool that integrates a large amount of gene expression data and clinical survival data of cancer patients, which is used to analyze the relationship between the expression level of specific genes and the survival prognosis of patients[ 28 ]. The Kaplan - Meier survival curve can intuitively show the change trend of the survival rate of CRC patients over time under the grouping of core gene expression levels. The parameter configuration is as follows: the "Auto select best cutoff" setting was used for patients, the survival was set as "OS", and the probe setting option was set as “only JetSet best probe set”. Construct a Prognostic Nomogram Model for Patients with CRC. The Nomogram graph represents the status of various variables in the multivariate regression model based on multivariate regression analysis. It integrates multiple predictive variables through a specific algorithm, predicts the probability of an event occurrence based on the total score, and presents it in a graphical way[ 29 ]. Through the Xiantao Academic website, download and organize the RNAseq data of the STAR process of the TCGA-COADREAD project from the TCGA database, extract the data in TPM format and clinical data[ 30 ]. Use R language Cox regression analysis to construct the Nomogram model, aiming to develop a clinical decision-making tool that can quantitatively predict the 1-year, 3-year or 5-year overall survival of CRC patients. By integrating the CRC TNM staging system[ 31 ], pathological staging information and core gene expression profile data, use the survival package and rms package in R language to complete the model construction. The study further establishes a calibration plot to evaluate the prediction consistency and validate the prediction efficacy of the Nomogram model. The data filtering strategy is to remove normal samples and samples without clinical information, and the data processing method is log2(value + 1). Evaluation of Tumor Immune Cell Infiltration . To assess the relationship between the risk model and immune cell characteristics, data was downloaded and organized from the TCGA database for the TCGA - COAD and TCGA - READ projects via the cloud - based data on the Xiantao Academic website. The RNAseq data processed by the STAR pipeline was extracted in TPM format along with the clinical data. Based on the single - sample gene set enrichment analysis (ssGSEA) algorithm provided by the R package - GSVA [1.46.0][ 32 ], the immune infiltration status of the corresponding cloud - based data was calculated using the markers of 24 immune cells[ 33 ]. Spearman correlation analysis was performed between the risk score values and the immune infiltrating cells, and the analysis results were visualized using a lollipop plot created by the ggplot2 package. The data filtering strategy involved removing normal samples, samples without clinical information, and duplicate samples. The data processing method was log2(value + 1). Protein Expression Analysis of Pyroptosis Core Genes in CRC. This study utilized the Human Protein Atlas (HPA)[ 34 ] ( https://www.proteinatlas.org/ ) database to analyze the differential expression of core genes in CRC and corresponding normal tissues. The HPA database is a comprehensive and large - scale database dedicated to mapping the expression and distribution of human proteins in cells, tissues, and organs. This database provides over 700 types of human protein antibodies, which can be matched with 400,000 high - resolution images[ 35 ]. Results Screening of PRGs in CRC. Relevant genes were retrieved by searching "pyroptosis" in the MSigDB, Gene, and GeneCards databases respectively. By integrating the search results from the three databases, 202 genes related to pyroptosis were identified. Detailed information is shown in Table 1. 202 genes were imported into the UALCAN database, and 159 differentially expressed genes (DEGs) were identified. Analysis revealed that compared with normal tissues, 67 genes were upregulated and 92 genes were downregulated in CRC. Table 2 shows the expression of PRGs in CRC tissues. Functional Enrichment Analysis of DEGs. In this study, the functional enrichment analysis of DEGs was carried out using the Xiantao Academic website. Enter the Xiantao Academic website, select "Bioinformatics Tools", choose "[GOKEGG] Analysis" under "Functional Clustering". Upload the up - regulated and down - regulated gene data respectively, verify, confirm, save the results, and download the GOKEGG results. The GOKEGG result data is then presented through the "[GOKEGG] bar chart". The main parameter ID list is selected as BP, CC, MF and KEGG separately to obtain the result of gene function enrichment analysis. The results of GO enrichment analysis were mainly concentrated in several key pathways: pyroptosis, regulation of cytokine production, response to lipopolysaccharide (LPS), inflammasome complex, cysteine-type endopeptidases involved in apoptotic process, amphisome, cell response to environmental stimuli, ubiquitin-like protein ligase binding, and phosphatidylserine binding. Other important pathways included: response to molecule of bacterial origin, pattern recognition receptor activity, protease binding, immune cell proliferation, ESCRT III complex, histone modification and RNA polymerase II − specific DNA − binding transcription factor binding. The results of KEGG enrichment analysis are mainly concentrated in the NOD-like receptor signaling pathway, necroptosis, IL-17 signaling pathway, C-type lectin receptor signaling pathway, TNF signaling pathway, lipid and atherosclerosis, platinum drug resistance, and microbial infection. The relevant enrichment analysis results are shown in Fig. 2 . The PPI Network Map and Core Genes Associated with Pyroptosis-Related DEGs. Access the STRING database, choose the Multiple proteins option, and enter the DEGs into the list of names field. Parameter configuration: Species: Homo sapiens, Network type: complete STRING network, minimum required interaction score: high confidence (0.7). Utilize Cytoscape software to visualize the data retrieved from the STRING database, thereby generating the DEGs PPI network diagram. The detailed outcomes of the PPI network are presented in Fig. 3 A. Subsequently, employ the "cytoHubba" function within Cytoscape software and identify the top 5 core genes using the MCC algorithm, which are CASP1, NLRP3, PYCARD, NLRC4, and AIM2. The intensity of red indicates the significance of the gene in the biological process. The deeper the red color corresponding to the gene, the greater its importance and influence on the biological phenotype. The precise results are illustrated in Fig. 3 B. Verification of Core Gene Expression. In this study, the GSE74602 dataset( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74602 ) was retrieved from the GEO database, and the expression data of CASP1, NLRP3, PYCARD, NLRC4, and AIM2 in normal tissues and CRC tissues were extracted. The above expression profile data were statistically analyzed using the Xiantao Academic tool. The results showed that compared with normal tissues, the expression levels of CASP1, NLRP3, NLRC4 and AIM2 in colon tumor tissues were significantly decreased, while there was no significant difference in PYCARD. Furthermore, the research results of the UALCAN database show that these core genes are also lowly expressed in CRC tissues, which is consistent with the research results of this dataset. Specific results as shown in Fig. 3 C. Correlation Between Immunity, Diagnostic Efficacy, and Survival Prognosis. We conducted an immune infiltration analysis of AIM2, CASP1, NLRP3, NLRC4, and PYCARD using the TIMER database, inputting each gene into the search bar separately. The parameters were set as follows: cancer type was COAD (colon adenocarcinoma), and immune infiltrates included B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and myeloid dendritic cell. The results from the TIMER database showed that NLRP3, CASP1, and AIM2 were positively correlated with the infiltration levels of CD4 + T cells, CD8 + T cells, macrophages, neutrophils, and myeloid dendritic cells. Specifically, NLRP3 exhibited a negative correlation with B cell infiltration, while CASP1 showed a positive correlation with B cell infiltration. AIM2 had no significant correlation with B cell infiltration. NLRC4 was positively correlated with the infiltration levels of CD8 + T cells, macrophages, neutrophils, and myeloid dendritic cells but negatively correlated with B cell infiltration. It showed no significant correlation with CD4 + T cell infiltration. PYCARD demonstrated no significant correlation with the infiltration levels of any of the immune cells mentioned above. These findings suggest that AIM2, CASP1, NLRP3 and NLRC4 are associated with immune cell infiltration in CRC, whereas PYCARD shows no such association (Fig. 4 ). Next, we evaluated the diagnostic potential of these core genes as biomarkers for CRC by generating ROC curves. The area under the ROC curve (AUC) is commonly used to assess diagnostic performance, with values ranging from 0.5 to 1. The AUC closer to 1 indicates better predictive accuracy, and the AUC > 0.700 suggests excellent diagnostic efficacy. The AUC values for each gene were as follows: NLRP3 (AUC = 0.766, CI = 0.714–0.817), NLRC4 (AUC = 0.720, CI = 0.658–0.782), AIM2 (AUC = 0.592, CI = 0.533–0.652), CASP1 (AUC = 0.584, CI = 0.521–0.647) and PYCARD (AUC = 0.487, CI = 0.425–0.549). These results indicate that NLRP3 and NLRC4 exhibit excellent diagnostic efficacy, suggesting their potential as diagnostic biomarkers for CRC. The detailed findings are shown in Fig. 5 A. Furthermore, analysis using the Kaplan-Meier Plotter database revealed that the expression levels of NLRC4, CASP1, and PYCARD were significantly associated with patient prognosis ( p < 0.05). Specifically, high expression of CASP1 and PYCARD was correlated with favorable prognosis, whereas high expression of NLRC4 was associated with poor prognosis. However, AIM2 and NLRP3 expression levels showed no clear correlation with prognosis. Detailed results are presented in Fig. 5 B. Constructing a Predictive Nomogram Model . This study integrated NLRC4 and clinical indicators (TNM stage, pathological stage) to develop a nomogram model for predicting the overall survival (OS) of CRC patients. The nomogram calculates personalized survival probabilities by summing the risk scores corresponding to each variable. Each variable is assigned a specific score, and the total score corresponds to the cumulative probability of OS, enabling a visual translation from individual characteristics to prognostic prediction. The consistency index (C-index) of the nomogram model for predicting OS in CRC patients was 0.708, indicating strong predictive capability (Fig. 5 C). The Calibration diagrams were established to predict the 1-year, 3-year, and 5-year OS, and the results showed that the predicted OS aligned well with the actual OS, demonstrating consistency and high clinical predictive reliability with minimal error (Fig. 5 D). Dual validation confirmed that the two models exhibited strong consistency between their predictive performance and observed outcomes, enabling accurate prediction of patient survival time. Immunohistochemical Results of the Core Genes . The protein expression levels of the core genes in CRC patient tissue samples and normal colon tissue samples were validated using the HPA database. The immunohistochemical results showed that, compared with normal tissues, NLRP3, AIM2, and PYCARD were highly expressed in CRC tissues, while CASP1 and NLRC4 were lowly expressed in CRC tissues (Fig. 6 ). Discussion CRC is one of the leading causes of cancer-related morbidity and mortality worldwide. It is characterized by a complex interplay of genetic, environmental, and lifestyle factors that contribute to its initiation and progression[ 36 ]. The disease often arises from adenomatous polyps, with the adenoma-carcinoma sequence being a well-established model for CRC development[ 37 ]. Early detection and intervention are critical for improving patient outcomes, as advanced stages of CRC are associated with a poor prognosis[ 38 ]. Given the increasing incidence of CRC, particularly among younger populations, there is an urgent need for novel diagnostic and therapeutic strategies to enhance early detection and improve survival rates[ 39 ]. In this study, we investigated PRGs in CRC to elucidate their potential roles in tumor biology and patient prognosis. We identified 202 genes associated with pyroptosis through comprehensive bioinformatics analyses, revealing 159 DEGs in CRC tissues compared to normal controls. Multiple functional enrichment analyses revealed that these DEGs are significantly associated with various biological processes and pathways, including inflammation and immune response, as well as cell death, such as pyroptosis, the NOD-like receptor signaling pathway, and cytokine production regulation. These pathways are crucial for understanding the molecular mechanisms by which pyroptosis promotes CRC tumor progression and immune evasion[ 40 ]. NOD-like receptors (NLRs) are intracellular immune sensors that detect pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs)[ 41 ]. By forming inflammasomes, they activate signaling pathways such as NF-κB, MAPK, and pyroptosis, leading to the release of inflammatory cytokines including TNF-α, IL-1β, and IL-18, thereby mediating a cascade of downstream immune and inflammatory responses[ 42 ]. NLRs, such as NLRP3 and NLRC4, regulate inflammatory homeostasis and influence the development of CRC. As key regulators of immune responses, NLRs normally help the body resist bacterial infections. However, their excessive activation may exacerbate intestinal inflammation and accelerate abnormal cell proliferation, thereby promoting CRC progression[ 43 ]. Additionally, we constructed a PPI network and identified five core genes that may serve as potential diagnostic and prognostic biomarkers. Our comprehensive analysis of the PPI network revealed the intricate interconnectivity among these hub genes, underscoring their synergistic role in the pathogenesis of CRC. Our findings provide insights into the molecular underpinnings of CRC and suggest that targeting pyroptosis could offer new therapeutic avenues for managing this disease. In addition, we identified and analyzed the role of PRGs in CRC, focusing on their expression patterns, functional enrichment, and potential as biomarkers for diagnosis and prognosis. Our findings revealed that several core genes, including CASP1, NLRP3, NLRC4, and AIM2, are significantly involved in immune cell infiltration and may influence the tumor microenvironment in CRC. Notably, the expression levels of these genes were found to correlate with immune cell types, such as CD8 + T cells, myeloid dendritic cell, neutrophils, and macrophages, suggesting that pyroptosis may play a crucial role in modulating immune responses within the tumor. Research indicates that aberrant alterations in PRGs are closely associated with the prognosis of CRC patients and the immune activation status of the tumor microenvironment (TME)[ 44 ]. The function of GSDMD in CRC is closely related to its intracellular localization. Membrane-localized GSDMD expression is strongly associated with immune response, while cytoplasmic GSDMD is linked to the tumor immune microenvironment and can improve patient prognosis. However, when GSDMD localizes to the nucleus of cancer cells, it promotes tumor invasion and metastasis[ 45 ]. The diagnostic efficacy of PRGs was assessed through ROC curve analyses, revealing that NLRP3 and NLRC4 exhibit excellent potential as biomarkers for CRC diagnosis, with AUC values exceeding the threshold 0.7 for excellent diagnostic performance. And, survival analyses indicated that high expression levels of CASP1 correlated with favorable patient prognosis, whereas elevated NLRC4 expression was linked to poor outcomes, underscoring the prognostic significance of these genes in CRC management. These associations reveal the complex interplay between pyroptosis and tumor progression, where certain members of the pyroptosis pathway may serve as defenders against tumorigenesis, while others may inadvertently facilitate it[ 46 ]. The differential expression patterns of these core genes elucidate the multifaceted role of pyroptosis in the immune microenvironment and tumor biology, suggesting a nuanced understanding of their contributions to CRC pathology. Moreover, the development of a predictive nomogram model that integrates NLRC4 with clinical indicators underscores the practical implications of these biomarkers in clinical settings. With a C-index of 0.708, the nomogram demonstrates a robust predictive capability for overall survival in CRC, offering a personalized approach to patient management. Our findings highlight the potential of integrating PRGs into clinical practice, providing valuable insights for personalized treatment strategies and improving patient outcomes in CRC management. Future studies should focus on elucidating the mechanistic roles of these genes in pyroptosis and their implications for therapeutic interventions in CRC[ 47 ]. The IHC findings from our study reveal heightened expression of NLRP3 and AIM2 in CRC tissues, illustrating an inflammatory microenvironment, which may reflect ongoing pyroptotic processes. This is consistent with previous literature that suggests the retention of certain inflammasome-related proteins in malignant tissues may play a crucial role in tumor-promoting inflammation and immune evasion[ 48 , 49 ]. On the contrary, the observed downregulation of CASP1 and NLRC4 raises queries about their potential protective roles against tumorigenesis, as their diminished activity could facilitate tumor survival by undermining the host's innate immune responses[ 50 ]. These observations underscore the dual nature of pyroptosis in cancer, serving as both an inducer of inflammation and a potential promoter of tumor development under specific conditions. One limitation of this research study is the reliance on publicly available datasets, which may introduce inherent biases and variability in gene expression profiles due to differences in sample preparation, processing, and patient demographics. Although we performed a comprehensive analysis using multiple databases and validation cohorts, the generalizability of our findings may be constrained by the specific populations studied. Furthermore, while we identified core PRGs and their associations with clinical outcomes, the mechanistic roles of these genes in CRC remain to be elucidated through further functional validation studies. The complexity of tumor microenvironments and immune interactions also necessitates a more nuanced investigation to understand the multifaceted contributions of pyroptosis to cancer progression and therapeutic responses. Conclusion Our study highlights the significance of PRGs in CRC, particularly the core genes CASP1, NLRP3, NLRC4, and AIM2, as potential biomarkers for diagnosis and prognosis. The establishment of a predictive nomogram model underscores the clinical relevance of integrating these molecular markers with traditional factors to enhance patient stratification and treatment planning. While our findings provide valuable insights into the roles of pyroptosis in CRC, further research is essential to validate these biomarkers in larger cohorts and to explore their therapeutic implications in the management of this malignancy. Declarations Acknowledgements All authors extend their sincere gratitude for the project from Changshu Commission of Health Project and Changshu Science and Technology Project. Author Contributions Yaoyao Zhuang and Jiahuan Wu conceived the original idea and drafted the manuscript. Yaoyao Zhuang and Yeqiong Xu conducted literature research, compiled references, and prepared the figures. All authors contributed to critical revision of the manuscript, approved the final version, and agreed to be accountable for all aspects of the work, including ensuring the accuracy and integrity of any relevant portion. Additionally, all authors participated in editorial modifications of the manuscript. Yaoyao Zhuang and Yeqiong Xu contributed equally to this project and are co-first authors. Funding This work was supported by Changshu Science and Technology Project (CS202434, CY202330) and Changshu Commission of Health Project (CSWS202106). Data availability The datasets analyzed in this study (with accession numbers or direct web links) and the full names of the databases have been clearly stated in the main text. MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/human/geneset/REACTOME_PYROPTOSIS.html?keywords=pyroptosis) Gene (https://www.ncbi.nlm.nih.gov/gene/?term=pyroptosis) GeneCards (https://www.genecards.org/Search/Keyword?queryString=pyroptosis) GEO dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74602) Ethics approval and consent to Participate Not applicable. Consent to publish Not applicable. Conflict of Interest The authors declare no conflict of interest. References Long J, et al. 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Targeting tumor-derived NLRP3 reduces melanoma progression by limiting MDSCs expansion. Proc Natl Acad Sci U S A, 2021. 118(10). Wang Q, et al. AIM2 promotes renal cell carcinoma progression and sunitinib resistance through FOXO3a-ACSL4 axis-regulated ferroptosis. Int J Biol Sci. 2023;19(4):1266–83. Domblides C et al. Human NLRC4 expression promotes cancer survival and associates with type I interferon signaling and immune infiltration. J Clin Invest, 2024. 134(11). Tables Tables 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.jpg Table2.jpg Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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09:58:51","extension":"png","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80031,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7902247/v1/94d3d36120c34f26391ace88.png"},{"id":96970473,"identity":"af602fb8-e56f-4200-be6a-335c24cb2075","added_by":"auto","created_at":"2025-11-28 07:13:48","extension":"png","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7127997,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7902247/v1/8df4f4028f3eea8e535d76b8.png"},{"id":96970476,"identity":"00bc13dc-cea6-4389-ad32-556d3f662d97","added_by":"auto","created_at":"2025-11-28 07:13:48","extension":"xml","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106798,"visible":true,"origin":"","legend":"","description":"","filename":"ef92dfaa35dd4fada909fd720c2ae2df1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7902247/v1/7ef7f84ffca5717c8ad157a6.xml"},{"id":97138046,"identity":"fa8ef75d-5b69-4ec2-86ab-55c87ede8a8d","added_by":"auto","created_at":"2025-12-01 09:58:26","extension":"html","order_by":40,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":118435,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7902247/v1/5ae671bfe6b774297d5c902a.html"},{"id":97136976,"identity":"98bfee05-8704-48ee-910b-3a963dc65f90","added_by":"auto","created_at":"2025-12-01 09:57:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":630977,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchat\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7902247/v1/a4d100abc73e0085795166b4.jpg"},{"id":96970442,"identity":"f203e7bc-b8a6-4bb9-a859-b7da38654ed2","added_by":"auto","created_at":"2025-11-28 07:13:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":596819,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG enrichment analysis results. Functional enrichment results of (A-D) up-regulated and (E-H) down-regulated genes\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7902247/v1/d2d8c6e1abbda9c325c18400.jpg"},{"id":97138261,"identity":"985a270b-8973-42c1-9b45-ef83490ad88d","added_by":"auto","created_at":"2025-12-01 09:58:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":303145,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and expression validation of pyroptosis-related core genes. (A) The PPI network map. (B) Core genes. (C)Verification of core genes differential expression\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7902247/v1/ee080a2378db8f8cef5ca88e.jpg"},{"id":96970459,"identity":"ff980bac-4ac6-49d3-81e6-c89c07d878ab","added_by":"auto","created_at":"2025-11-28 07:13:47","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4599366,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis of CASP1, NLRP3, PYCARD, NLRC4, and AIM2\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7902247/v1/b34ced678e2aeb7c7a12d0bf.jpg"},{"id":96970443,"identity":"b56a375d-92b0-44be-ba59-b267a74ad08f","added_by":"auto","created_at":"2025-11-28 07:13:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":490947,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic efficacy, survival analysis, and predictive prognostic modeling of core genes. (A) ROC diagnostic curves of CASP1, NLRP3, PYCARD, NLRC4, and AIM2. (B) Survival analysis of CASP1, NLRP3, PYCARD, NLRC4, and AIM2. (C) The nomogram of NLRC4. (D) Nomogram’s calibration diagram\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7902247/v1/85539dc011205924458905e8.jpg"},{"id":96970454,"identity":"20966352-7bf6-4faf-9a2a-a329d11fa59b","added_by":"auto","created_at":"2025-11-28 07:13:47","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":11137739,"visible":true,"origin":"","legend":"\u003cp\u003eImmunohistochemical results of CASP1, NLRP3, PYCARD, NLRC4, and AIM2 in CRC tissues and normal tissues. (A) CRC tumor tissues. (B) Normal tissues\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7902247/v1/956e7a7206ee250e48b41d94.jpg"},{"id":99686921,"identity":"aced8903-2f59-43d3-87bb-6b6560390e08","added_by":"auto","created_at":"2026-01-07 09:40:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":65249390,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7902247/v1/4e3f82d5-78c5-47a3-b1cc-002cfc4b7fd9.pdf"},{"id":97138161,"identity":"605dba90-58e1-47c8-836c-1a731602cba4","added_by":"auto","created_at":"2025-12-01 09:58:33","extension":"jpg","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":249645,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7902247/v1/74afadf53cd8078cd59ac8dc.jpg"},{"id":97136989,"identity":"a4c1d910-5676-4008-8485-36ceed4d23a0","added_by":"auto","created_at":"2025-12-01 09:57:15","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":160906,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7902247/v1/9c0f9b322fe25b0df8ec5eae.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Pyroptosis Prognostic Model Centers on NLRC4 in Colorectal Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal carcinoma (CRC) is one of the most prevalent malignancies worldwide, with an increasing incidence and mortality rate, particularly in developed countries. It ranks as the second leading cause of cancer-related deaths globally, highlighting the urgent need for effective diagnostic and prognostic tools[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The complex interplay of genetic, environmental, and lifestyle factors contributes to the pathogenesis of CRC[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], making early detection and personalized treatment essential for improving patient outcomes. Current therapeutic strategies, including surgical interventions, chemotherapy, and radiation therapy, are often limited by their inability to effectively screen early-stage disease and provide personalized treatment options. This underscores the pressing need for the discovery of novel biomarkers and therapeutic targets that can enhance early detection and treatment efficacy[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent advances in biomolecular research have highlighted the significance of cell death mechanisms in cancer pathogenesis, particularly focusing on pyroptosis, a newly identified form of programmed cell death intertwined with inflammatory responses[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This process is mediated by gasdermin proteins, particularly gasdermin D (GSDMD), which upon cleavage by inflammatory caspases, forms pores in the cell membrane, leading to cell lysis and release of pro-inflammatory cytokines[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The mechanisms underlying pyroptosis are complex and involve various signaling pathways that can be broadly categorized into canonical and non-canonical pathways. The canonical pathway is initiated by the activation of the NLRP3 inflammasome, which leads to the activation of caspase-1. This enzyme GSDMD, resulting in the formation of pores in the cell membrane, cell swelling, and eventual rupture, releasing pro-inflammatory cytokines such as IL-1β and IL-18 into the extracellular space[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Conversely, the non-canonical pathway involves caspases 4, 5, and 11, which can directly cleave GSDMD without the need for inflammasome assembly. This distinction is critical, as it highlights the various stimuli that can trigger pyroptosis, ranging from pathogen infections to cellular stressors, and the resultant inflammatory response plays a significant role in both host defense and the pathology of various diseases, including cancer[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe relationship between pyroptosis and CRC is complex, as it can either inhibit tumor progression by inducing inflammatory responses that target cancer cells or promote tumor growth by creating a supportive microenvironment for cancer cell survival[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Pyroptosis can inhibit the initiation and progression of tumors by promoting an inflammatory microenvironment that is hostile to cancer cell survival. During the pyroptosis process, the release of pro-inflammatory cytokines can recruit immune cells to the tumor site and enhance the anti-tumor immune response. However, excessive pyroptosis can also contribute to tumorigenesis by creating a supportive environment for tumor growth through chronic inflammation[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For instance, the NLRP3 inflammasome has been shown to mediate pyroptosis in CRC cells, which can lead to the release of pro-inflammatory cytokines and influence tumor progression[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Nevertheless, novel therapeutic agents like FL118 have been identified that can induce pyroptosis in CRC cells, inhibiting their proliferation and metastasis through the activation of pyroptosis-related pathways[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, the impact of pyroptosis on the tumor immune microenvironment in CRC is profound and multifaceted. Pyroptosis not only influences the survival and proliferation of tumor cells but also alters the immune landscape within the tumor. The inflammatory nature of pyroptosis leads to the release of cytokines and damage-associated molecular patterns (DAMPs), which can enhance the recruitment and activation of immune cells, thereby promoting a more robust anti-tumor response[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Conversely, the inflammatory milieu created by pyroptosis can also contribute to immune evasion mechanisms employed by tumors, leading to a complex interplay between tumor cells and immune components[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, understanding the dual role of pyroptosis in CRC could provide valuable insights into its potential as a therapeutic target and prognostic biomarker.\u003c/p\u003e\u003cp\u003eBioinformatics is an interdisciplinary subject combining mathematics, statistics, computer science and biology. It is mainly used to analyze and interpret biological data and is widely applied in fields such as gene analysis and drug development[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. To investigate the potential association between PRGs and CRC, our study employs bioinformatics to construct a model delineating pyroptosis gene characteristics in CRC. This methodology facilitates the comprehensive analysis of large-scale genomic data, thereby revealing potential biomarkers and elucidating their underlying mechanisms of action[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Utilizing the genomic information of CRC cells and with the aid of bioinformatics tools, the key genetic mutations associated with CRC were identified. Through the analysis of large datasets from CRC patients, biomarkers related to the occurrence, development, prognosis and treatment response of CRC were identified. These biomarkers can be used for the early diagnosis, prognosis assessment and treatment guidance of CRC[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This approach boasts several advantages, including high throughput, sensitivity, and robust data analytical capabilities, allowing for an in-depth examination of pyroptosis-related gene expression patterns and their correlation with clinical outcomes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In conclusion, the integration of bioinformatics in the study of PRGs presents a promising avenue for advancing our understanding of CRC. As we delve deeper into the molecular intricacies of this disease, the identification of novel biomarkers and therapeutic targets holds the potential to significantly alter the clinical landscape of CRC management. Future research should focus on validating these findings in larger, multicenter cohorts to ensure the applicability and robustness of the proposed strategies[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrior studies have established a relationship between aberrant expression of PRGs and the initiation and advancement of tumors, suggesting that they may serve as potential biomarkers for CRC[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, the objective of this research is to explore the role of these genes in the development, immune infiltration, and clinical features of CRC through bioinformatics approaches. Additionally, immunohistochemistry was used to verify the expression patterns of the clinical relevance of the core pyroptosis genes in CRC and normal intestinal tissues. This evolving landscape of pyroptosis research highlights its potential as both a therapeutic target and a biomarker for CRC, which may contribute to the early detection and targeted treatment of CRC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eAcquisition of Pyroptosis-Related Gene Databases in CRC\u003c/b\u003e. In this study, bioinformatics methods were used to conduct correlation analysis between core pyroptosis genes and CRC. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the detailed research process. PRGs were identified by retrieving information from several databases, including MSigDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/human/geneset/REACTOME_PYROPTOSIS.html?keywords=pyroptosis\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/human/geneset/REACTOME_PYROPTOSIS.html?keywords=pyroptosis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], Gene (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gene/?term=pyroptosis\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gene/?term=pyroptosis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and GeneCards (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/Search/Keyword?queryString=pyroptosis\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/Search/Keyword?queryString=pyroptosis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The results from these three databases were consolidated, and the expression levels of PRGs in tumor tissues were compared to those in normal tissues using the UALCAN database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ualcan.path.uab.edu/analysis.html\u003c/span\u003e\u003cspan address=\"https://ualcan.path.uab.edu/analysis.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Among these databases, MSigDB offers a wide range of annotated gene sets connected to various biological processes, signaling pathways, and gene expression profiles, making it a valuable resource for gene function research and bioinformatics analysis. The Gene database from NCBI serves as a fundamental source of gene information, while GeneCards is recognized for its comprehensiveness and authority. It encompasses multiple aspects, such as gene function, expression patterns, and disease associations, providing extensive gene annotation information for researchers. The combined use of these databases enables a systematic analysis of CRC-related genes and their roles in various biological processes. UALCAN is an online tool specifically designed for cancer data research, based on the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/ccg/research/genome-sequencing/tcga\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/ccg/research/genome-sequencing/tcga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which facilitates statistical analysis and mining of cancer data.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eXiantao Academic Tools.\u003c/b\u003e Xiantao Academic (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.xiantaozi.com\u003c/span\u003e\u003cspan address=\"https://www.xiantaozi.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a powerful bioinformatics analysis tool that provides a wide range of functions for bioinformatics analysis and data visualization[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This study utilized the basic drawing module, functional clustering module, interaction network module, and clinical significance module. The research data were sourced from the Xiantao Academic Cloud dataset, specifically from Colorectal Cancer/TCGA/TCGA - COADREAD/RNA Sequencing (RNAseq)/Spliced Transcripts Alignment to a Reference (STAR)/Transcripts Per Million (TPM). The raw data were filtered, which involved removing normal samples, samples without clinical information, and duplicate samples. Subsequently, data normalization was achieved through log2(value\u0026thinsp;+\u0026thinsp;1) transformation, effectively enhancing the stability and comparability of subsequent analyses. The preprocessed dataset was ultimately used to construct an immune cell infiltration assessment model, decipher key molecular interaction networks, build a prognostic risk prediction system, and carry out multi - dimensional screening and validation of diagnostic biomarkers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEnrichment Analysis of PRGs in CRC\u003c/b\u003e. The principle is based on the known information of genes in biological processes (BPs), molecular functions (MFs) and cellular components (CCs). With the help of GO for gene function annotation and combined with the pathway information of KEGG, the list of differentially expressed genes was compared with the gene sets in the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The aim is to identify the gene functional categories or metabolic pathways that are significantly enriched under experimental conditions, and to deeply analyze the biological processes and signaling pathways involved in PRGs related to CRC. Using Xiantao Academic online software, version R (4.2.1), after ID conversion of the input list of pyroptosis genes related to CRC cells, GOKEGG enrichment analysis was performed using the clusterProfiler package[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and then the enrichment analysis results were visualized using the ggplot2 package. The significance cut-off value of GOKEGG enrichment analysis is generally set as the adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAcquisition of Pyroptosis Core Genes in CRC\u003c/b\u003e. The PPI network was constructed using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Whereafter, the results were imported into Cytoscape software[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and the cytoHubba plugin was used for visualization[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The top five core genes were screened out using the Maximum Clique Centrality (MCC) algorithm. The STRING database is a bioinformatics database and network resource that can not only collect and integrate the PPI data but also predict indirectly associated relationships through computational methods[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The parameters set in the STRING database include: the network type is the complete STRING network, the required confidence score is the highest confidence level (0.900), and the false discovery rate (FDR) stringency is medium (5%). Cytoscape is an open-source software for network visualization and analysis. It is often used to display and analyze the interaction relationships among biomolecules such as genes and proteins. In addition, it has a rich set of layout algorithms, which can automatically adjust the positions of nodes and edges according to the characteristics of the network, enabling a clear presentation of the network structure[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The PPI network can be intuitively presented through Cytoscape.\u003c/p\u003e\u003cp\u003e\u003cb\u003eValidation of the Expression of Pyroptosis Core Genes\u003c/b\u003e. Retrieve the expression data of core genes from CRC datasets in the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74602\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74602\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and verify the reliability of the expression of core genes. Subsequently, use Xiantao Academic analysis tools to perform visualization analysis on the differential expression data of core genes. The GEO database is a public functional genomics data repository that provides abundant data resources and promotes the development of multiple aspects such as gene function research, disease diagnosis, and treatment[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of the Association among Diagnosis, Prognosis and Immune Infiltration of Pyroptosis Core Genes\u003c/b\u003e. To explore the correlation between immune cell infiltration, diagnosis and prognosis of pyroptosis core genes related to CRC, the TIMER 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], Xiantao Academic website[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and Kaplan - Meier plotter (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kmplot.com/analysis/\u003c/span\u003e\u003cspan address=\"https://kmplot.com/analysis/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] were used for analysis respectively. The TIMER 2.0 database is a tumor immune analysis tool, which integrates gene expression data from multiple public databases. By analyzing the immune cell infiltration in different cancer types, it clarifies the interaction between the expression of core genes and the immune system, providing important information for cancer research. In this study, the Xiantao Academic tool was used to analyze the diagnostic effect of core genes in CRC. The RNAseq data of the STAR process in the TCGA - COADREAD project were downloaded and organized from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the data in TPM format were extracted. The pROC package was used for receiver operating characteristic (ROC) analysis of the data, and the results were visualized with ggplot2. The pROC package will correct the outcome order of the data by default. The data filtering strategy was to remove samples without clinical information and duplicate samples, and the data processing method was log2(value\u0026thinsp;+\u0026thinsp;1). The Kaplan - Meier plotter database is an online bioinformatics tool that integrates a large amount of gene expression data and clinical survival data of cancer patients, which is used to analyze the relationship between the expression level of specific genes and the survival prognosis of patients[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The Kaplan - Meier survival curve can intuitively show the change trend of the survival rate of CRC patients over time under the grouping of core gene expression levels. The parameter configuration is as follows: the \"Auto select best cutoff\" setting was used for patients, the survival was set as \"OS\", and the probe setting option was set as \u0026ldquo;only JetSet best probe set\u0026rdquo;.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruct a Prognostic Nomogram Model for Patients with CRC.\u003c/b\u003e The Nomogram graph represents the status of various variables in the multivariate regression model based on multivariate regression analysis. It integrates multiple predictive variables through a specific algorithm, predicts the probability of an event occurrence based on the total score, and presents it in a graphical way[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Through the Xiantao Academic website, download and organize the RNAseq data of the STAR process of the TCGA-COADREAD project from the TCGA database, extract the data in TPM format and clinical data[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Use R language Cox regression analysis to construct the Nomogram model, aiming to develop a clinical decision-making tool that can quantitatively predict the 1-year, 3-year or 5-year overall survival of CRC patients. By integrating the CRC TNM staging system[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], pathological staging information and core gene expression profile data, use the survival package and rms package in R language to complete the model construction. The study further establishes a calibration plot to evaluate the prediction consistency and validate the prediction efficacy of the Nomogram model. The data filtering strategy is to remove normal samples and samples without clinical information, and the data processing method is log2(value\u0026thinsp;+\u0026thinsp;1).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEvaluation of Tumor Immune Cell Infiltration\u003c/b\u003e. To assess the relationship between the risk model and immune cell characteristics, data was downloaded and organized from the TCGA database for the TCGA - COAD and TCGA - READ projects via the cloud - based data on the Xiantao Academic website. The RNAseq data processed by the STAR pipeline was extracted in TPM format along with the clinical data. Based on the single - sample gene set enrichment analysis (ssGSEA) algorithm provided by the R package - GSVA [1.46.0][\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], the immune infiltration status of the corresponding cloud - based data was calculated using the markers of 24 immune cells[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Spearman correlation analysis was performed between the risk score values and the immune infiltrating cells, and the analysis results were visualized using a lollipop plot created by the ggplot2 package. The data filtering strategy involved removing normal samples, samples without clinical information, and duplicate samples. The data processing method was log2(value\u0026thinsp;+\u0026thinsp;1).\u003c/p\u003e\u003cp\u003e\u003cb\u003eProtein Expression Analysis of Pyroptosis Core Genes in CRC.\u003c/b\u003e This study utilized the Human Protein Atlas (HPA)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database to analyze the differential expression of core genes in CRC and corresponding normal tissues. The HPA database is a comprehensive and large - scale database dedicated to mapping the expression and distribution of human proteins in cells, tissues, and organs. This database provides over 700 types of human protein antibodies, which can be matched with 400,000 high - resolution images[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eScreening of PRGs in CRC.\u003c/b\u003e Relevant genes were retrieved by searching \"pyroptosis\" in the MSigDB, Gene, and GeneCards databases respectively. By integrating the search results from the three databases, 202 genes related to pyroptosis were identified. Detailed information is shown in Table\u0026nbsp;1. 202 genes were imported into the UALCAN database, and 159 differentially expressed genes (DEGs) were identified. Analysis revealed that compared with normal tissues, 67 genes were upregulated and 92 genes were downregulated in CRC. Table\u0026nbsp;2 shows the expression of PRGs in CRC tissues.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional Enrichment Analysis of DEGs.\u003c/b\u003e In this study, the functional enrichment analysis of DEGs was carried out using the Xiantao Academic website. Enter the Xiantao Academic website, select \"Bioinformatics Tools\", choose \"[GOKEGG] Analysis\" under \"Functional Clustering\". Upload the up - regulated and down - regulated gene data respectively, verify, confirm, save the results, and download the GOKEGG results. The GOKEGG result data is then presented through the \"[GOKEGG] bar chart\". The main parameter ID list is selected as BP, CC, MF and KEGG separately to obtain the result of gene function enrichment analysis.\u003c/p\u003e\u003cp\u003eThe results of GO enrichment analysis were mainly concentrated in several key pathways: pyroptosis, regulation of cytokine production, response to lipopolysaccharide (LPS), inflammasome complex, cysteine-type endopeptidases involved in apoptotic process, amphisome, cell response to environmental stimuli, ubiquitin-like protein ligase binding, and phosphatidylserine binding. Other important pathways included: response to molecule of bacterial origin, pattern recognition receptor activity, protease binding, immune cell proliferation, ESCRT III complex, histone modification and RNA polymerase II\u0026thinsp;\u0026minus;\u0026thinsp;specific DNA\u0026thinsp;\u0026minus;\u0026thinsp;binding transcription factor binding. The results of KEGG enrichment analysis are mainly concentrated in the NOD-like receptor signaling pathway, necroptosis, IL-17 signaling pathway, C-type lectin receptor signaling pathway, TNF signaling pathway, lipid and atherosclerosis, platinum drug resistance, and microbial infection. The relevant enrichment analysis results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe PPI Network Map and Core Genes Associated with Pyroptosis-Related DEGs.\u003c/b\u003e Access the STRING database, choose the Multiple proteins option, and enter the DEGs into the list of names field. Parameter configuration: Species: Homo sapiens, Network type: complete STRING network, minimum required interaction score: high confidence (0.7). Utilize Cytoscape software to visualize the data retrieved from the STRING database, thereby generating the DEGs PPI network diagram. The detailed outcomes of the PPI network are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. Subsequently, employ the \"cytoHubba\" function within Cytoscape software and identify the top 5 core genes using the MCC algorithm, which are CASP1, NLRP3, PYCARD, NLRC4, and AIM2. The intensity of red indicates the significance of the gene in the biological process. The deeper the red color corresponding to the gene, the greater its importance and influence on the biological phenotype. The precise results are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVerification of Core Gene Expression.\u003c/b\u003e In this study, the GSE74602 dataset(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74602\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74602\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was retrieved from the GEO database, and the expression data of CASP1, NLRP3, PYCARD, NLRC4, and AIM2 in normal tissues and CRC tissues were extracted. The above expression profile data were statistically analyzed using the Xiantao Academic tool. The results showed that compared with normal tissues, the expression levels of CASP1, NLRP3, NLRC4 and AIM2 in colon tumor tissues were significantly decreased, while there was no significant difference in PYCARD. Furthermore, the research results of the UALCAN database show that these core genes are also lowly expressed in CRC tissues, which is consistent with the research results of this dataset. Specific results as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation Between Immunity, Diagnostic Efficacy, and Survival Prognosis.\u003c/b\u003e We conducted an immune infiltration analysis of AIM2, CASP1, NLRP3, NLRC4, and PYCARD using the TIMER database, inputting each gene into the search bar separately. The parameters were set as follows: cancer type was COAD (colon adenocarcinoma), and immune infiltrates included B cells, CD8\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, macrophages, neutrophils, and myeloid dendritic cell. The results from the TIMER database showed that NLRP3, CASP1, and AIM2 were positively correlated with the infiltration levels of CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, macrophages, neutrophils, and myeloid dendritic cells. Specifically, NLRP3 exhibited a negative correlation with B cell infiltration, while CASP1 showed a positive correlation with B cell infiltration. AIM2 had no significant correlation with B cell infiltration. NLRC4 was positively correlated with the infiltration levels of CD8\u0026thinsp;+\u0026thinsp;T cells, macrophages, neutrophils, and myeloid dendritic cells but negatively correlated with B cell infiltration. It showed no significant correlation with CD4\u0026thinsp;+\u0026thinsp;T cell infiltration. PYCARD demonstrated no significant correlation with the infiltration levels of any of the immune cells mentioned above. These findings suggest that AIM2, CASP1, NLRP3 and NLRC4 are associated with immune cell infiltration in CRC, whereas PYCARD shows no such association (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNext, we evaluated the diagnostic potential of these core genes as biomarkers for CRC by generating ROC curves. The area under the ROC curve (AUC) is commonly used to assess diagnostic performance, with values ranging from 0.5 to 1. The AUC closer to 1 indicates better predictive accuracy, and the AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.700 suggests excellent diagnostic efficacy. The AUC values for each gene were as follows: NLRP3 (AUC\u0026thinsp;=\u0026thinsp;0.766, CI\u0026thinsp;=\u0026thinsp;0.714\u0026ndash;0.817), NLRC4 (AUC\u0026thinsp;=\u0026thinsp;0.720, CI\u0026thinsp;=\u0026thinsp;0.658\u0026ndash;0.782), AIM2 (AUC\u0026thinsp;=\u0026thinsp;0.592, CI\u0026thinsp;=\u0026thinsp;0.533\u0026ndash;0.652), CASP1 (AUC\u0026thinsp;=\u0026thinsp;0.584, CI\u0026thinsp;=\u0026thinsp;0.521\u0026ndash;0.647) and PYCARD (AUC\u0026thinsp;=\u0026thinsp;0.487, CI\u0026thinsp;=\u0026thinsp;0.425\u0026ndash;0.549). These results indicate that NLRP3 and NLRC4 exhibit excellent diagnostic efficacy, suggesting their potential as diagnostic biomarkers for CRC. The detailed findings are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA.\u003c/p\u003e\u003cp\u003eFurthermore, analysis using the Kaplan-Meier Plotter database revealed that the expression levels of NLRC4, CASP1, and PYCARD were significantly associated with patient prognosis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, high expression of CASP1 and PYCARD was correlated with favorable prognosis, whereas high expression of NLRC4 was associated with poor prognosis. However, AIM2 and NLRP3 expression levels showed no clear correlation with prognosis. Detailed results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstructing a Predictive Nomogram Model\u003c/b\u003e. This study integrated NLRC4 and clinical indicators (TNM stage, pathological stage) to develop a nomogram model for predicting the overall survival (OS) of CRC patients. The nomogram calculates personalized survival probabilities by summing the risk scores corresponding to each variable. Each variable is assigned a specific score, and the total score corresponds to the cumulative probability of OS, enabling a visual translation from individual characteristics to prognostic prediction. The consistency index (C-index) of the nomogram model for predicting OS in CRC patients was 0.708, indicating strong predictive capability (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The Calibration diagrams were established to predict the 1-year, 3-year, and 5-year OS, and the results showed that the predicted OS aligned well with the actual OS, demonstrating consistency and high clinical predictive reliability with minimal error (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Dual validation confirmed that the two models exhibited strong consistency between their predictive performance and observed outcomes, enabling accurate prediction of patient survival time.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eImmunohistochemical Results of the Core Genes\u003c/b\u003e. The protein expression levels of the core genes in CRC patient tissue samples and normal colon tissue samples were validated using the HPA database. The immunohistochemical results showed that, compared with normal tissues, NLRP3, AIM2, and PYCARD were highly expressed in CRC tissues, while CASP1 and NLRC4 were lowly expressed in CRC tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCRC is one of the leading causes of cancer-related morbidity and mortality worldwide. It is characterized by a complex interplay of genetic, environmental, and lifestyle factors that contribute to its initiation and progression[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The disease often arises from adenomatous polyps, with the adenoma-carcinoma sequence being a well-established model for CRC development[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Early detection and intervention are critical for improving patient outcomes, as advanced stages of CRC are associated with a poor prognosis[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Given the increasing incidence of CRC, particularly among younger populations, there is an urgent need for novel diagnostic and therapeutic strategies to enhance early detection and improve survival rates[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In this study, we investigated PRGs in CRC to elucidate their potential roles in tumor biology and patient prognosis. We identified 202 genes associated with pyroptosis through comprehensive bioinformatics analyses, revealing 159 DEGs in CRC tissues compared to normal controls. Multiple functional enrichment analyses revealed that these DEGs are significantly associated with various biological processes and pathways, including inflammation and immune response, as well as cell death, such as pyroptosis, the NOD-like receptor signaling pathway, and cytokine production regulation. These pathways are crucial for understanding the molecular mechanisms by which pyroptosis promotes CRC tumor progression and immune evasion[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. NOD-like receptors (NLRs) are intracellular immune sensors that detect pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs)[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. By forming inflammasomes, they activate signaling pathways such as NF-κB, MAPK, and pyroptosis, leading to the release of inflammatory cytokines including TNF-α, IL-1β, and IL-18, thereby mediating a cascade of downstream immune and inflammatory responses[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. NLRs, such as NLRP3 and NLRC4, regulate inflammatory homeostasis and influence the development of CRC. As key regulators of immune responses, NLRs normally help the body resist bacterial infections. However, their excessive activation may exacerbate intestinal inflammation and accelerate abnormal cell proliferation, thereby promoting CRC progression[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, we constructed a PPI network and identified five core genes that may serve as potential diagnostic and prognostic biomarkers. Our comprehensive analysis of the PPI network revealed the intricate interconnectivity among these hub genes, underscoring their synergistic role in the pathogenesis of CRC. Our findings provide insights into the molecular underpinnings of CRC and suggest that targeting pyroptosis could offer new therapeutic avenues for managing this disease. In addition, we identified and analyzed the role of PRGs in CRC, focusing on their expression patterns, functional enrichment, and potential as biomarkers for diagnosis and prognosis. Our findings revealed that several core genes, including CASP1, NLRP3, NLRC4, and AIM2, are significantly involved in immune cell infiltration and may influence the tumor microenvironment in CRC. Notably, the expression levels of these genes were found to correlate with immune cell types, such as CD8\u0026thinsp;+\u0026thinsp;T cells, myeloid dendritic cell, neutrophils, and macrophages, suggesting that pyroptosis may play a crucial role in modulating immune responses within the tumor. Research indicates that aberrant alterations in PRGs are closely associated with the prognosis of CRC patients and the immune activation status of the tumor microenvironment (TME)[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The function of GSDMD in CRC is closely related to its intracellular localization. Membrane-localized GSDMD expression is strongly associated with immune response, while cytoplasmic GSDMD is linked to the tumor immune microenvironment and can improve patient prognosis. However, when GSDMD localizes to the nucleus of cancer cells, it promotes tumor invasion and metastasis[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe diagnostic efficacy of PRGs was assessed through ROC curve analyses, revealing that NLRP3 and NLRC4 exhibit excellent potential as biomarkers for CRC diagnosis, with AUC values exceeding the threshold 0.7 for excellent diagnostic performance. And, survival analyses indicated that high expression levels of CASP1 correlated with favorable patient prognosis, whereas elevated NLRC4 expression was linked to poor outcomes, underscoring the prognostic significance of these genes in CRC management. These associations reveal the complex interplay between pyroptosis and tumor progression, where certain members of the pyroptosis pathway may serve as defenders against tumorigenesis, while others may inadvertently facilitate it[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The differential expression patterns of these core genes elucidate the multifaceted role of pyroptosis in the immune microenvironment and tumor biology, suggesting a nuanced understanding of their contributions to CRC pathology. Moreover, the development of a predictive nomogram model that integrates NLRC4 with clinical indicators underscores the practical implications of these biomarkers in clinical settings. With a C-index of 0.708, the nomogram demonstrates a robust predictive capability for overall survival in CRC, offering a personalized approach to patient management. Our findings highlight the potential of integrating PRGs into clinical practice, providing valuable insights for personalized treatment strategies and improving patient outcomes in CRC management. Future studies should focus on elucidating the mechanistic roles of these genes in pyroptosis and their implications for therapeutic interventions in CRC[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe IHC findings from our study reveal heightened expression of NLRP3 and AIM2 in CRC tissues, illustrating an inflammatory microenvironment, which may reflect ongoing pyroptotic processes. This is consistent with previous literature that suggests the retention of certain inflammasome-related proteins in malignant tissues may play a crucial role in tumor-promoting inflammation and immune evasion[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. On the contrary, the observed downregulation of CASP1 and NLRC4 raises queries about their potential protective roles against tumorigenesis, as their diminished activity could facilitate tumor survival by undermining the host's innate immune responses[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. These observations underscore the dual nature of pyroptosis in cancer, serving as both an inducer of inflammation and a potential promoter of tumor development under specific conditions. One limitation of this research study is the reliance on publicly available datasets, which may introduce inherent biases and variability in gene expression profiles due to differences in sample preparation, processing, and patient demographics. Although we performed a comprehensive analysis using multiple databases and validation cohorts, the generalizability of our findings may be constrained by the specific populations studied. Furthermore, while we identified core PRGs and their associations with clinical outcomes, the mechanistic roles of these genes in CRC remain to be elucidated through further functional validation studies. The complexity of tumor microenvironments and immune interactions also necessitates a more nuanced investigation to understand the multifaceted contributions of pyroptosis to cancer progression and therapeutic responses.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study highlights the significance of PRGs in CRC, particularly the core genes CASP1, NLRP3, NLRC4, and AIM2, as potential biomarkers for diagnosis and prognosis. The establishment of a predictive nomogram model underscores the clinical relevance of integrating these molecular markers with traditional factors to enhance patient stratification and treatment planning. While our findings provide valuable insights into the roles of pyroptosis in CRC, further research is essential to validate these biomarkers in larger cohorts and to explore their therapeutic implications in the management of this malignancy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors extend their sincere gratitude for the project from Changshu Commission of Health Project and Changshu Science and Technology Project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYaoyao Zhuang and Jiahuan Wu conceived the original idea and drafted the manuscript. Yaoyao Zhuang and Yeqiong Xu conducted literature research, compiled references, and prepared the figures. All authors contributed to critical revision of the manuscript, approved the final version, and agreed to be accountable for all aspects of the work, including ensuring the accuracy and integrity of any relevant portion. Additionally, all authors participated in editorial modifications of the manuscript. Yaoyao Zhuang and Yeqiong Xu contributed equally to this project and are co-first authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Changshu Science and Technology Project (CS202434, CY202330) and Changshu Commission of Health Project (CSWS202106).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study (with accession numbers or direct web links) and the full names of the databases have been clearly stated in the main text.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMSigDB\u003c/strong\u003e(https://www.gsea-msigdb.org/gsea/msigdb/human/geneset/REACTOME_PYROPTOSIS.html?keywords=pyroptosis)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene\u0026nbsp;\u003c/strong\u003e(https://www.ncbi.nlm.nih.gov/gene/?term=pyroptosis)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneCards\u003c/strong\u003e(https://www.genecards.org/Search/Keyword?queryString=pyroptosis)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGEO dataset\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74602)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to Participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLong J, et al. 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J Clin Invest, 2024. 134(11).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the Supplementary Files section.\u003c/p\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":"Colorectal carcinoma (CRC), pyroptosis, immune infiltration, diagnosis, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-7902247/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7902247/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eColorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, necessitating the discovery of novel biomarkers and therapeutic targets. This study investigates the role of pyroptosis-related genes (PRGs) in CRC through comprehensive bioinformatics analysis. We identified 202 PRGs from the MSigDB, Gene, and GeneCards databases, of which 159 were differentially expressed in CRC tissues compared to normal tissues. Functional enrichment analysis revealed significant involvement of these genes in pathways such as pyroptosis, cytokine production regulation, and inflammasome complex formation. The KEGG analysis highlighted pathways including NOD-like receptor signaling and necroptosis. A protein-protein interaction (PPI) network identified CASP1, NLRP3, PYCARD, NLRC4, and AIM2 as core genes, with subsequent validation showing decreased expression of CASP1, NLRP3, NLRC4, and AIM2 in CRC tissues. Immune infiltration analysis indicated that NLRP3, CASP1, AIM2, and NLRC4 are associated with immune cell infiltration in CRC. Diagnostic efficacy assessment using ROC curves demonstrated that NLRP3 and NLRC4 have excellent potential as diagnostic biomarkers. Prognostic analysis revealed that high expression of CASP1 correlates with favorable prognosis, while high NLRC4 expression is linked to poor prognosis. A predictive nomogram model incorporating NLRC4 and clinical indicators showed strong predictive capability for overall survival in CRC patients. Immunohistochemical validation confirmed the expression patterns of core genes in CRC tissues. This study underscores the potential of PRGs as biomarkers and therapeutic targets in CRC, offering insights into their role in tumorigenesis and immune modulation. Future research should focus on validating these findings in larger cohorts to enhance the clinical applicability of these biomarkers in CRC management.\u003c/p\u003e","manuscriptTitle":"A Novel Pyroptosis Prognostic Model Centers on NLRC4 in Colorectal Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 07:13:42","doi":"10.21203/rs.3.rs-7902247/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":"19b7d01a-af51-49a7-a6df-9d2e9e1f4c41","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T09:38:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-28 07:13:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7902247","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7902247","identity":"rs-7902247","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}