Bioinformatics mining and experimental validation of prognostic biomarkers in colorectal cancer

Bioinformatics mining and experimental validation of prognostic biomarkers 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 Article Bioinformatics mining and experimental validation of prognostic biomarkers in colorectal cancer Feng Huang, Salah A. Alshehade, Wei Guo Zhao, Zhuo Ya Li, Jung Yin Fong, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4242994/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) is a prevalent malignancy with rising incidence and mortality rates. It is essential to identify potential prognostic gene biomarkers for CRC. We analyzed public datasets, revealing 5408 differentially expressed genes (DEGs) between CRC and adjacent normal tissues. Utilizing the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases, we identified 2779 up-regulated and 2629 down-regulated genes. Weighted gene co-expression network analysis (WGCNA) yielded the MEbrown module, containing 1639 genes highly correlated with CRC. A total of 926 differentially expressed CRC-related genes were screened for subsequent analysis. Then, a prognostic risk model with five characteristic genes ( TIMP1, PCOLCE2, MEIS2, HDC and CXCL13 ) was constructed. This model demonstrated strong predictive ability in the GSE32323 dataset and an internal test set. All five characteristic genes harbored predominantly missense mutations, with TIMP1 exhibiting the highest variant allele frequency. Functional enrichment analysis, including gene set enrichment analysis (GSEA) and histological expression analysis in the HPA database, highlighted the biological significance of TIMP1 in CRC. TIMP1 is upregulated in the tumor tissue and enriched in CRC-related pathways such as type I interferon receptor binding, oxidative phosphorylation, and Notch signaling pathways. Additionally, using siRNA technology, the impact of TIMP1 on cellular proliferation and apoptosis in CRC cell lines (HCT116 and HT29) was investigated, showing that TIMP1 knockdown significantly inhibited proliferation and promoted apoptosis. This study presents a novel prognostic risk model comprising five characteristic genes ( TIMP1, PCOLCE2, MEIS2, HDC and CXCL13 ) for CRC, which are strongly associated with overall survival in CRC patients with TIMP1 identified as having cancer-promoting properties in CRC. Our study suggests that TIMP1 holds promise as both a biomarker and a therapeutic target for CRC. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Biomarkers Colorectal cancer Prognostic biomarkers WGCNA Bioinformatics TIMP1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Colorectal cancer (CRC) is the third most common diagnosed and second most deadly of all cancers 1–3 . Nearly two million cases of CRC patients were diagnosed in 2022 according to data from the International Agency for Research on Cancer (IARC) (a division of the World Health Organization (WHO)). The CRC leads to 1.5 million people death each year 4–6 . At present, surgical resection is still the primary treatment for CRC, especially for patients with non-metastatic CRC, while chemotherapy is also an integral part of it 7–9 . Treatment and early diagnosis of CRC have improved significantly in recent years, but many patients are asymptomatic in the early stages, and 50–60% of patients with CRC often have multiple metastases. After receiving standard therapy, the 5-year survival rate for these patients is only 12–19%, and the recurrence rate is high 10 . Molecular studies have identified numerous genetic alterations that occur during colon carcinogenesis. However, the precise genetic changes responsible for the occurrence and progression of CRC are still poorly understood 11 . Therefore, it is vitally necessary to comprehend the molecular mechanisms of CRC development and identify biomarkers that may be used for improving the prognosis of CRC patients. In this study, we first downloaded RNA-seq data and clinical information regarding CRC from Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) databases. After integrated analysis of both two databases, we identified differentially expressed genes (DEGs) by the differential expression analysis. We obtained the CRC-related genes by weighted gene co-expression network analysis (WGCNA). Then, a prognostic risk model with five characteristic genes ( TIMP1, PCOLCE2, MEIS2, HDC and CXCL13 ) was constructed by univariate and multivariate Cox regression analyses, Least Absolute Shrinkage and Selection Operator (LASSO), which performed well in predicting overall survivals of CRC patients. These characteristic genes are highly related to overall survival. Tissue inhibitor matrix metalloproteinase 1 (TIMP1), belongs to the Tissue Inhibitor of Metalloproteinases family which included four identified members. Recent clinical studies have shown that the abnormal expression of TIMP1 is associated with an unfavorable prognosis in various types of tumors 12,13 . Considering the significant scientific interest in TIMP1 and to validate the accuracy of our prognostic genes, represented by TIMP1, we conducted a series of in vitro experiments to confirm its pro-oncogenic effects in CRC cells. Overall, the findings of the study provide a new foundation for prognostic analysis and insights into the molecular mechanisms underlying CRC. Results Acquirement of DE-CRC-related genes A total of 5408 DEGs (2779 genes up-regulated and 2629 genes down-regulated) were screened between the CRC/normal tissue samples in the TCGA-CRC dataset (Fig. 1 A- 1 B). Two outlier samples (TCGA-AA-3947-01A and TCGA-CM-4748-01A) were excluded from the sample clustering analysis (Fig. 1 C). A β value of 16 was chosen to ensure network conformity to a scale-free distribution (Fig. 1 D). With CRC as the trait, six modules were identified (Fig. 1 E). The heat map of module-trait relationships was obtained by Spearman analysis (Fig. 1 F). The MEbrown module, containing 1639 CRC-associated genes, was found to be most closely associated with CRC, with 926 of these genes showing differentially expressed (Fig. 1 G). Functional analysis and PPI network of 926 CRC-related genes Using the STRING database, we retrieved 923 out of 926 genes to construct the protein-protein interactions (PPI) network (Fig. 2 A). These genes were enriched to a total of 1022 GO entries and 27 KEGG pathways. According to the biological processes (BP) analysis, these genes were connected to cell adhesion and cell communication (Fig. 2 B). In the case of cellular component (CC), these genes were engaged in plasma and neuron part (Fig. 2 C). Regarding molecular function (MF), these genes were involved in transmembrane signaling receptor activity, metal ion transmembrane transporter activity, and G-protein coupled peptide receptor activity (Fig. 2 D). These intersecting genes were also enriched in cGMP-PKG signaling pathway, circadian rhythm, cAMP signaling pathway, and PI3K-Akt signaling pathway and other pathways related to CRC (Fig. 2 E). Great predictive capability of prognostic risk model The univariate Cox regression analysis was performed using the training set and 15 genes were significantly associated with overall survival (OS) (Fig. 3 A). Next, the results were then analyzed by LASSO and multifactorial Cox analysis, which showed that TIMP1, PCOLCE2, MEIS2, HDC, and CXCL13 were the characteristic genes of CRC (Fig. 3 B- 3 C), and their positioning on the chromosome was shown in Fig. 3 D. Risk score = TIMP1 × 0.203931952 + PCOLCE2 × 0.09722934 + MEIS2 × 0.192140876 – HDC × 0.20005372 - CXCL13 × 0.14748485. According to median risk = 0.9711322, CRC patients were divided into high- and low-risk groups (Fig. 3 E). The Kaplan-Meier analysis showed that patients with high-risk scores had significantly lower OS than those with low-risk scores (Fig. 3 F). To further assess the validity of the risk signature, the ROC curve for OS was calculated, and the area and the curve (AUC) values at 1, 3, and 5 years were approximately 0.7 indicating better efficacy of the risk model (Fig. 3 G). This prognostic risk model still had strong predictive power in both the internal test set and the external GSE39582 dataset (Supplementary Fig. 1). In addition, the decision curve analysis (DCA) and principal component analysis (PCA) results of the prognostic risk model in the training set, internal test set, and external validation set demonstrated the strong predictive power of the model (Supplementary Fig. 2). These five characteristic genes were also differentially expressed in GSE32323, further demonstrating the superiority of the model (Fig. 3 H). Functional assessment of the nomogram model Clinical variables and risk scores from 606 CRC tissue samples were combined to perform univariate and multivariate Cox analyses (Table 1 and Fig. 4 A). The age, mismatch repair protein expression deficiency (MMR protein), lymphovascular infiltration, M staging, N staging, tumor stage, and risk score were a prognostic factor for CRCpatients. Construction of a nomogram model on the basis of independent prognostic factors (Fig. 4 B), it was found that the survival rate decreases as overall score increases. The correction curve value of this nomogram model approached 1, indicating that its prediction was true and reliable (Fig. 4 C). Moreover, the ROC curve values of nomogram model for 1, 3 and 5 years were all greater than 0.7, which indicated that the model had excellent prediction (Fig. 4 D). Table 1 The univariate analysis of independent prognostic models Clinical factor HR HR.95L HR.95H p age 1.03 1.01 1.05 0.000275 *** Loss Expression of MMR Proteins 0.23 0.07 0.73 0.012971 * Lymphatic invasion 1.89 1.29 2.77 0.001057 ** Risk score 1.52 1.33 1.74 8.35E-10 *** M stage 4.32 2.9 6.44 5.96E-13 *** N stage(reference: N0) N1 1.900343802 1.206960394 2.992067168 0.005568 ** N2 3.961218249 2.611668465 6.008132437 9.37E-11 *** T stage(reference:T0) T4 6.15 1.46 26 0.013490 * Tumor stage (reference: I) III 3.35 1.41 7.94 0.006098 ** IV 9.13 3.87 21.55 4.46E-07 *** Notes: HR: Hazards ration; * p < 0.05, ** p < 0.01, *** p < 0.001 Multiple analyses of characteristic genes indicated their importance in CRC Mutation analysis of all genes in the TCGA-CRC dataset revealed the highest proportion of missense mutations and the highest proportion of single nucleotide polymorphism (SNP) mutation patterns were C to T (Fig. 5 A). Although the top 10 mutated genes did not have the characteristic genes, the characteristic genes were all dominated by missense mutations (Fig. 5 B). The gene with the highest number of mutations was MEIS2, followed by HDC. The variant allele frequency (VAF) was highest for TIMP1 and lowest for HDC (Fig. 5 C). Hete Amp and Hete Del are basically the major forms of copy number variation of the characteristic genes (Fig. 5 D). Functional analysis of the prognostic gene TIMP1 and its expression at the histological level TIMP1 is an important prognostic marker for the progression and metastasis in different cancer 12,14,15 , and has been shown to influence several tumorigenic biological processes 16–18 . Moreover, TIMP1 plays a role in anti-tumor drugs resistance 15,16 . To identify enriched regulatory pathways and molecular functions associated with the prognostic gene TIMP1 in CRC, we conducted GSEA analysis on MSigdb, which is the gene set database. The type I interferon receptor binding, macroautophagy was the main enriched GO entry for TIMP1, and both entries were related to CRC (Fig. 6 A). Moreover, KEGG enrichment analysis showed that TIMP1 mainly enriched in CRC pathways such as oxidative phosphorylation and Notch signaling pathway (Fig. 6 B). In addition, we compared the immunohistochemical expression of the prognostic gene TIMP1 in CRC samples versus normal samples. It was clearly seen that TIMP1 was more expressed in the tumor tissue (Fig. 6 C- 6 D), which was consistent with the results of the previous expression analysis in GSE32323. We also performed functional and expression analysis for remaining characterized genes. The results were shown in Supplementary Fig. 3 and Supplementary Fig. 4. Knockdown of TIMP1 suppressed the colon cancer cell proliferation Although we have conducted a bioinformatics analysis that highlights the significant role of TIMP1 in the prognosis of CRC, our findings are consistent with previous studies indicating that TIMP1 expression is upregulated in colon cancer 19,20 . However, we still need to validate our results through further experiments. Moreover, a deeper exploration of the role of TIMP1 in CRC may pave the way for its use as a prognostic factor or a therapeutic target. Hence, we observed the function of TIMP1 on colorectal cancer cell proliferation and apoptosis in vitro . To confirm the effects of TIMP1 in human colon cancer cells, siRNA was used to knockdown TIMP1 in HCT116 and HT29 colon cancer cell lines which TIMP1 expression level was higher than other cell lines 14 . After transfection with si-TIMP1, the expression in mRNA level of TIMP1 gene was reduced in HCT116 and HT29 cells, indicating that both cells were successfully transfected (Fig. 7 A). Afterward, the MTT method was utilized to determine the cell viability. Following the transfection of si-TIMP1 into HCT116 and HT29 cells, the cell viability was observed to be inhibited as compared to the non-transfected group (Fig. 7 B, C). Moreover, we conducted colony formation assays to assess the role of TIMP1 in colon cell growth which showed that TIMP1-KD was associated with significantly impaired colony formation ability compared with that of Si-NC cells (Fig. 7 D, E). Based on these findings, it appears that TIMP1 plays a crucial role in colon cell proliferation. TIMP1 knockdown promoted apoptosis in vitro Real-time qPCR was used to detect anti-apoptotic and pro-apoptotic genes expression. The results revealed that the mRNA expression level of anti-apoptotic genes Bcl-2 and Survivin, which play an important role in apoptosis [ 20 ], were downregulated, while pro-apoptotic genes BAD and Bax were upregulated, respectively in HCT116 and HT29 cells when knockdown of TIMP1 (Fig. 8 A, B). Furthermore, the cell apoptosis was also studied using Annexin V-FITC Apoptosis Detection Kit in accordance with the manufacturer's instructions. The apoptosis cells were measured by staining with Annexin V-FITC along with Propidium Iodide. Cells were detected using an inverted fluorescence microscope. The result was consistent with real-time PCR results. Compared with Si-NC cells, there are more apoptotic cells in Si-TIMP1 group (Fig. 8 C). Discussion CRC is a leading cause of cancer-related mortality globally. It ranks third in terms of incidence rate among all malignant tumors worldwide 1 . The prognosis of advanced CRC is poor. it is crucial to explore and validate new molecular markers for CRC diagnosis. In this study, we integrated analyzed RNA-seq data and clinical information from GEO and TCGA databases. A total of 5408 DEGs consisting of 2779 upregulated DEGs and 2629 downregulated DEGs were identified between CRC tissues and normal tissues in the TCGA-CRC dataset. Based on WGCNA, we screened the modules related to CRC. The MEbrown module was the most associated with CRC and contained 1639 CRC-associated genes, of which 926 CRC-associated genes were differentially expressed. Subsequently, we conducted a great predictive capability of prognostic risk model. By LASSO and multifactorial Cox analysis, we obtained five characteristic genes of CRC, including TIMP1, PCOLCE2, MEIS2, HDC, and CXCL13 . The Kaplan-Meier analysis showed that patients with high-risk scores had significantly lower OS than those with low-risk scores. The time-dependent ROC analysis for OS obtained an AUC of 0.7 which indicated relatively high specificity and sensitivity of the prognostic signature for CRC. This prognostic risk model still had strong predictive power in both the internal dataset and the GSE39582 external dataset. In addition, the DCA and PCA results demonstrated the strong predictive power of the prognostic risk model in the training set, internal dataset, and external validation set. These five characteristic genes were also differentially expressed in GSE32323 dataset, further demonstrating the superiority of the model. The functional enrichment analysis demonstrated that the DEGs were involved in some biological processes, such as cell adhesion, cell communication. In accordance with previous research, these biological processes play crucial role in CRC tumorigenesis and development 21 . KEGG analysis on DE-CRC-related genes revealed that they were also enriched in cGMP-PKG signaling pathway, circadian rhythm, cAMP signaling pathway, and PI3K-Akt signaling pathway related to CRC, the results were consistent with previous knowledge. According to Zhan Ma et al, PHLDA2 regulates epithelial-mesenchymal transition (EMT) and autophagy in CRC via the PI3K/AKT signaling pathway 3 . Si-Yang Li et al has demonstrated that Diosgenin suppresses CRC cells through cAMP/PKA/CREB pathway 22 . Our research revealed that these DEGs may be implicated in the tumorigenesis and development of CRC. Univariate, multivariate Cox regression, and LASSO analyses all show that TIMP1, PCOLCE2, MEIS2, HDC, and CXCL13 are significantly related to CRC patient’s survival. According the previous studies, PCOLCE2 encodes a functional procollagen c-protease enhancer, and it can promote the enzymatic cleavage of type I procollagen to produce mature structured fibrils 23 . Our findings are consistent with previous research, Chen et al. developed a prognostic gene signature made up by 9 genes, including PCOLCE2 and T1MP1, and they accurately predicted the overall survival in CRC patients 24 . Although the specific mechanism of PCOLCE2 in CRC is less known, according to recent research, PCOLCE2 has been identified as the main gene driving the development of endometrial cancer, and our findings are supported by this research 25 . MEIS2 is a member of the MEIS protein family that regulates neural crest and limb development 26 , and it has been implicated in the development of human cancer 27 . Recent research showed that in prostate cancer and ovarian cancer, the degree of MEIS2 protein expression was related to the development of clinically metastatic illness and the absence of biochemical recurrence 28,29 . Ziang Wan et al. has firstly demonstrated that the MEIS2 promotes cell migration and invasion in CRC, and acts as a promoter of metastasis in CRC 30 . Histamine dihydrochloride (HDC) is an inhibitor of NOX2-derived ROS 31 , and exerts anti-cancer efficacy in experimental tumor models. Hanna et al. propose that anti-tumor properties of HDC may comprise the targeting of MDSCs 32 . In addition, Chen et al. demonstrated that HDC + granulocytic myeloid cells influence CD8 + T cells both directly and indirectly through modulating Tregs, and which hence appear to play crucial roles in suppressing tumoricidal immunity in murine colon cancer 33 . CXCL13, a homeostatic chemokine, is secreted by the stromal cells in the B-cell area of the secondary lymphoid tissues. CXCL13 plays a significant part in the growth of tumor 34 . The previous study demonstrates that CXCL13 can promote prostate cancer cell proliferation through JNK signalling and invasion through activation of ERK 35 . The same findings as our study, Qi XW et al, indicate that CXCR5 and CXCL13 appear to be independent predictors of survival for patients with CRC 36 . Senlin Zhao et al demonstrated that polarized M2 macrophages could induce premetastatic niche formation and promote CRLM by secreting CXCL13, which activated a CXCL13/CXCR5/NFκB/p65/miR-934 positive feedback loop in CRC cells 37 . Among these genes, TIMP1 attract our attention. TIMP1 encodes a 931 base-pair mRNA and a 207 amino acid protein. This protein may inhibit the proteolytic action of matrix metalloproteinases (MMPs), which are thought to be crucial for the tumor invasion and development of metastatic disease 38,39 . T1MP1 has been showed that its expression is upregulated in colon cancer 19,20 , and it also plays an important role in the regulation of cell proliferation and anti-apoptotic function 40,41 . A previous study indicated that TIMP1 is a key role in promoting progression and metastasis of human colon cancer, and function as a potential prognostic indicator for colon cancer 14 . Through in vitro experiments, we verified the inhibitory effect of blocking TIMP1 expression on growth of colorectal cancer cells. Moreover, we investigated the apoptotic effect of TIMP1 on CRC cells, TIMP1 knocked down can promote apoptosis of CRC cells. In summary, we preliminarily investigated the biofunctions of TIMP1 in CRC, which shed lights on CRC treatment. However, it is important to note that there are limitations to this study. Firstly, to confirm the biological functions of TIMP1 in CRC, in vivo experiments are required. Secondly, it is worth mentioning that the study did not investigate whether a TIMP1 inducer can reverse the stimulative effect of TIMP1 on the growth and apoptosis of colorectal cancer cells. Conclusion In this study, we developed a new prognostic risk model for CRC, which was validated using both the GSE32323 dataset and the internal dataset, confirming its predictive validity. The genes TIMP1, PCOLCE2, MEIS2, HDC, and CXCL13 were identified as playing crucial roles in CRC patients. Given the significance of TIMP1 in the field of oncology, we conducted in vitro experiments to investigate its biofunctions in CRC. Our findings have shown that TIMP1 promotes the proliferation of colorectal cancer cells, and silencing TIMP1 inhibited cell proliferation while promoting apoptosis in CRC cell lines. These results provide valuable insights for future research aiming to leverage prognostic genes to combat cancer and evaluate prognosis. Materials and methods Acquirement of the data of the CRC patients The TCGA-CRC dataset (training cohort) including RNA-seq data and clinical information of 51 normal tissue samples and 606 CRC tissue samples with survival information, was downloaded from the TCGA database ( https://portal.gdc.cancer.gov/ ). Two independent cohorts (GSE39582 and GSE32323) were acquired from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). GSE39582 dataset, including a total of 556 CRC samples containing survival information, were used as the validation set. GSE32323 dataset, which consisted of 17 CRC tissue samples and 17 normal tissue samples, was utilized to analyze the expression of the characteristic genes. Identification of differentially expressed CRC-related genes (DE-CRC-related genes) The "Deseq2" package 42 was used for differential expression analysis to obtain the differentially expressed genes (DEGs) between the normal and CRC tissue samples. The “ggplot2” and “pheatmap” packages were adopted to plot the volcano map and heatmap to visualize DEGs, respectively. Gene modules with comparable expression patterns have been identified using weighted gene co-expression network analysis (WGCNA) 43 , and the relationship between modules and particular traits has been examined. In this study, we used the R package "WGCNA" to create a gene co-expression network for CRC in the TCGA-CRC dataset. For the network construction to be reliable, abnormal samples were first eliminated. Then the appropriate threshold for network construction was selected, and a minimum number of genes per module of 30. The link between clinical traits (CRC and normal tissues samples) and module attributes was examined using the Pearson correlation analysis (MES), and the module with the highest correlation was identified as the module most closely related to CRC. The "VennDiagram" package 44 was applied to visualize the DE-CRC-related genes. Functional analysis and protein-protein interactions (PPI) network of DE-CRC-related genes Based on DE-CRC-related genes, the STRING database ( http://string-db.org ) was applied to establish the PPI network's structure. Then the DAVID database was adopted to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on these DE-CRC-related genes and visualize the results by the R package "ggplot2". Construction and validation of the prognostic risk model By using univariate Cox analysis of DE-CRC-related genes in the training set, the prognosis-related genes were acquired ( P < 0.03). Subsequently, the most predictive characteristic genes were identified by the least absolute shrinkage and selection operator (LASSO) 45 analysis and multivariate Cox analysis then sequentially. The R package "biomaRt" 46 was applied to visualize the localization of the characteristic genes on the chromosomes. Subsequently, risk score of each CRC patient was calculated based on the formula: Risk score = \(\sum _{1}^{n}coef\left({gene}_{i}\right)*expr\left({gene}_{i}\right)\) The training set of CRC patients was divided into two groups based on the median risk score. The difference in overall survival (OS) between the two groups was then displayed using Kaplan-Meier (K-M) curves. The "timeROC" package 47 was applied to display the receiver operating characteristic (ROC) curves to perform an assessment of the prognostic capability of the prognostic model. Meanwhile, the stability of this prognostic risk model was investigated in the external GSE71014 dataset and the internal validation set. In addition, decision curve analysis (DCA) run by the "DecisionCurve" package and principal component analysis (PCA) were used to further assess the predictive power of the prognostic risk model. Finally, whether these characteristic genes were differentially expressed in GSE32323 was investigated. Assessment of the nomogram model To determine if clinicopathological characteristics (age, mismatch repair protein expression deficiency, lymphovascular infiltration, M staging, N staging, T staging, and tumor stage) and risk scores were independent predictive factors for CRC patients, univariate and multifactorial Cox analyses were performed. The "rms" package was adopted to construct the nomogram to predict survival probability based on independent prognostic criteria. Both the calibration and the ROC curves were adopted to validate whether the nomogram can be used as an optimal model for clinical decision-making. The analysis of characteristic genes The mutation frequencies of all genes in the TCGA-CRC dataset were analyzed using the maftools package. The expression of characteristic genes in CRC/normal tissues was assessed by the Human Protein Atlas (HPA) database. The copy number variation (CNV) status of characteristic genes was also analyzed through the gene set cancer analysis (GSCA) database. Explore the biological functions enriched by genes associated with characteristic genes through gene set enrichment analysis (GSEA). Cell culture HCT116 and HT29 human colorectal cancer cell lines were obtained from American Type Culture Collection (ATCC, Manassas, VA, USA) and were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM; Gibco, Thermo Fisher Scientific, USA) with 10% fetal bovine serum, and 1% penicillin and streptomycin. All cells were incubated in a humidified atmosphere of 5% CO 2 at 37 ℃. 2.8 Cell transfection The control siRNA, and TIMP1 siRNA were all constructed by Servicebio Technology Company. Approximately 0.8 × 10 4 cells were inoculated into 96-well culture plates. After cell adhesion, the culture medium was replaced with 100 µl serum-free medium. Subsequently, in serum-free medium, siRNA-TIMP1 or siRNA-NC were mixed with 3 µl Lipofectamine transfection reagent (13778030, Thermo Fisher Scientific, USA) to form transfection complexes. The mixture was dropped into the corresponding wells and incubated for 6 h. Each well was supplemented with 400 µl of FBS containing 10% medium and incubated for 24 h. The silencing effect of TIMP1 was analyzed by RT-qPCR to detect the mRNA expression level of TIMP1 in both cells. Si-RNA-TIMP1 target sequence: Sense: 5′⁃CCAGCGUUAUGAGAUCAAGAUTT⁃3′; Antisense: 5′⁃AUCUUGAUCUCAUAACGCUGGTT⁃3′; siRNA-Control Target: Sense: 5′⁃CCGGTTCTCCGAACGTGTCAC⁃3’; Antisense: 5′⁃AATTCAAAAATTCTCCGAACGT⁃3′; MTT assay HCT116 and HT29 Cells were digested using trypsin, resuspended with fresh complete medium, and counted 5 × 10 3 cells per well were inoculated in 96-well cell plates for 24 h. At each detection point, 10 µl MTT reaction solution (298-93-1, Solarbio, China) was added to each well. Then, the plates were incubated in the incubator for 2 h. Subsequently, 100 µl DMSO was added in each well after discarding the supernatant. The absorbance at 570 nm was measured by a microplate reader (BMG Spectrostar, BMG Labtech, Germany). The results were analyzed by Prism 9.0 (GraphPad, La Jolla, CA, USA). Colony forming assay Approximately 500 cells were seeded in six-well plates and cultured at 37°C under a humidified atmosphere containing 5% CO 2 , and the medium was changed every 72 h. After 12 days, the cells were fixed with 4% paraformaldehyde for 15 minutes and stained with 0.1% crystalline violet staining solution (G1062, Solarbio, China) for 15 minutes. Colonies were then photographed. All experiments were performed in triplicate. ImageJ software was utilized to count the number of stained colonies 48 . Quantitative Real-time PCR (RT-qPCR) Total RNA was extracted from each group of cells using FastPure Cell/Tissue Total RNA Isolation Kit V2 (V2, Vazyme, China) according to the manufacturer’s instructions. Reverse transcription was performed using a reverse transcription kit (K1622, Thermo Fisher Scientific, USA). Gene expression was measured using a Taqman Probe (USP26, Applied Biosystems, Thermo Fisher Scientific, USA) by a QuantStudio™ 5 (Thermo Fisher Scientific, USA) fluorescence PCR instrument. Relative mRNA values were calculated by the ΔΔCt method. GAPDH was used as an internal standardized control. Annexin V/PI Assay for the Detection of Cell Apoptosis HCT116and HT29 cells were cultured in confocal dishes (2 × 10 5 cells/dish). After 24h, the cells were attached to the dishes. The intervention was performed for 24h, then cells were washed with PBS two times. The Annexin V-FITC Apoptosis Detection Kit (KTA0002-100T, Abbkine, USA) was used to perform apoptosis analysis according to the instructions. Annexin V-FITC and Propidium iodide staining was used to detect apoptotic cells. After incubation for 15 min, the stained cells were detected under confocal microscope. Statistical analysis All statistical analyses were carried out with GraphPad Prism 9.0 (GraphPad, La Jolla, USA). Numerical data are displayed as the mean ± standard deviation (SD). All experiments and analyses were performed in triplicate to ensure accuracy and reliability. The statistical significance of the differences was analyzed by Student’s t test, unpaired t test, and one-way ANOVA according to the test of homogeneity of variances. Statistical significance was set at *, P < 0.05; **, P < 0.01; and ***, P < 0.001. Declarations Conflicts of Interest The authors declare no conflicts of interest. Author Contribution MS, HF conceived and designed the study. HF, WGZ, ZYL,FJY performed the literature search and manuscript writing; HF, SA wrote the original draft; SA, MJ, CTN, PNO,KV and MAA provided input as content experts; MS, HF, MAA and BMM reviewed and proofread the writing. All authors have contributed, read, and approved the final manuscript. Data Availability All the data are available within the article. References Dekker, E., Tanis, P. J., Vleugels, J. L. A., Kasi, P. M. & Wallace, M. B. Colorectal cancer. Lancet 394, 1467–1480, doi: 10.1016/s0140-6736(19)32319-0 (2019). Schreuders, E. H. et al. Colorectal cancer screening: a global overview of existing programmes. Gut 64, 1637–1649, doi: 10.1136/gutjnl-2014-309086 (2015). Ma, Z., Lou, S. & Jiang, Z. PHLDA2 regulates EMT and autophagy in colorectal cancer via the PI3K/AKT signaling pathway. Aging (Albany NY) 12, 7985–8000, doi: 10.18632/aging.103117 (2020). Cancer, I. A. f. R. o. Colorectal cancer , (2022). Xi, Y. & Xu, P. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4242994","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":293957443,"identity":"8bfdcfca-dc52-46f7-ad9f-c4e9811552ec","order_by":0,"name":"Feng Huang","email":"","orcid":"","institution":"UCSI University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Huang","suffix":""},{"id":293957444,"identity":"b515c6ac-6d53-4bc5-b6b2-2efeaa2ee53b","order_by":1,"name":"Salah A. Alshehade","email":"","orcid":"","institution":"Universiti of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Salah","middleName":"A.","lastName":"Alshehade","suffix":""},{"id":293957445,"identity":"76dc7aa1-b419-4e83-9ede-12d457524d38","order_by":2,"name":"Wei Guo Zhao","email":"","orcid":"","institution":"Zhongshan City People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"Guo","lastName":"Zhao","suffix":""},{"id":293957446,"identity":"7392d0b4-97c9-4666-aa21-c45361b6717e","order_by":3,"name":"Zhuo Ya Li","email":"","orcid":"","institution":"Zhongshan City People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhuo","middleName":"Ya","lastName":"Li","suffix":""},{"id":293957447,"identity":"8ac7eb34-52b1-4206-8b86-99018e74223f","order_by":4,"name":"Jung Yin Fong","email":"","orcid":"","institution":"UCSI University","correspondingAuthor":false,"prefix":"","firstName":"Jung","middleName":"Yin","lastName":"Fong","suffix":""},{"id":293957448,"identity":"21b96c7d-c69c-449e-96b0-c5dac135e9e3","order_by":5,"name":"Patrick Nwabueaze Okechukwu","email":"","orcid":"","institution":"UCSI University","correspondingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"Nwabueaze","lastName":"Okechukwu","suffix":""},{"id":293957449,"identity":"086d2e96-739e-4f54-9914-474667ce8bab","order_by":6,"name":"Chin Tat Ng","email":"","orcid":"","institution":"University Kebangsaan Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Chin","middleName":"Tat","lastName":"Ng","suffix":""},{"id":293957450,"identity":"ffda1b2c-6773-46c0-970a-19862c1616c8","order_by":7,"name":"Karthikkumar Venkatachalam","email":"","orcid":"","institution":"University of Oklahoma Health Sciences Center","correspondingAuthor":false,"prefix":"","firstName":"Karthikkumar","middleName":"","lastName":"Venkatachalam","suffix":""},{"id":293957451,"identity":"fe9d4fa7-40e5-44d1-832b-4409f7ec73c9","order_by":8,"name":"Małgorzata Jeleń","email":"","orcid":"","institution":"Medical University of Silesia","correspondingAuthor":false,"prefix":"","firstName":"Małgorzata","middleName":"","lastName":"Jeleń","suffix":""},{"id":293957452,"identity":"0bb12327-746d-47b2-b98a-6115f404825f","order_by":9,"name":"Beata Morak Mlodawsak","email":"","orcid":"","institution":"Medical University of Silesia","correspondingAuthor":false,"prefix":"","firstName":"Beata","middleName":"Morak","lastName":"Mlodawsak","suffix":""},{"id":293957453,"identity":"cbd1981b-8346-4457-a825-520fdd98698d","order_by":10,"name":"Mohammed Abdullah Alshawsh","email":"","orcid":"","institution":"Universiti of Malaya","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"Abdullah","lastName":"Alshawsh","suffix":""},{"id":293957454,"identity":"30f80b35-d246-480e-97e2-04f3586ebc0e","order_by":11,"name":"Malarvili Selvaraja","email":"data:image/png;base64,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","orcid":"","institution":"UCSI University","correspondingAuthor":true,"prefix":"","firstName":"Malarvili","middleName":"","lastName":"Selvaraja","suffix":""}],"badges":[],"createdAt":"2024-04-09 15:33:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4242994/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4242994/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55513387,"identity":"f8f61ff1-ff24-4c44-adc2-aa47eb0fcb87","added_by":"auto","created_at":"2024-04-29 12:54:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":920185,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of the differentially expressed genes and CRC-related genes in the TCGA cohort.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Volcano plot, is used to identify differentially expressed genes between tumor and normal tissues;\u003c/p\u003e\n\u003cp\u003eB. Heatmap of differentially expressed genes；\u003c/p\u003e\n\u003cp\u003eC. Diagram of sample clustering；\u003c/p\u003e\n\u003cp\u003eD. Chart of genes scale-free distribution;\u003c/p\u003e\n\u003cp\u003eE. Diagram of modules;\u003c/p\u003e\n\u003cp\u003eF. The heat map of module-trait relationships. The MEbrown module is a key module related to CRC;\u003c/p\u003e\n\u003cp\u003eG. The expression trend of 926 differentially expressed CRC-related genes.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4242994/v1/078f7d38fb6001259ad845b5.png"},{"id":55514454,"identity":"0d657b29-0d4c-4722-a64b-8ff29b1723b7","added_by":"auto","created_at":"2024-04-29 13:02:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":804688,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of all differentially expressed genes in colorectal cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. PPI network; B. Go analysis in biological process of DE-CRC-related genes;\u003c/p\u003e\n\u003cp\u003eC. Go analysis in cellular component of DE-CRC-related genes;\u003c/p\u003e\n\u003cp\u003eD. Go analysis in molecular function of DE-CRC-related genes;\u003c/p\u003e\n\u003cp\u003eE. KEGG pathway analysis of DE-CRC-related genes.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4242994/v1/cb5b102a6e708296c88cd5c1.png"},{"id":55513392,"identity":"f2299324-ac9c-442c-910f-731aaeec7918","added_by":"auto","created_at":"2024-04-29 12:54:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":388973,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and Evaluation of the prognostic risk model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. The univariate Cox regression analysis prognostic gene screening;\u003c/p\u003e\n\u003cp\u003eB. LASSO analysis prognostic gene screening;\u003c/p\u003e\n\u003cp\u003eC. The multivariate Cox analysis prognostic gene screening;\u003c/p\u003e\n\u003cp\u003eD. Prognostic gene chromosome localization; E. Risk Score and Prognosis Chart;\u003c/p\u003e\n\u003cp\u003eF. Survival analysis of high and low risk groups; G. The ROC curve analysis.\u003c/p\u003e\n\u003cp\u003eH. Differential expression of prognostic genes in the GSE32323 dataset\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4242994/v1/553ca73c9f06a31ea13dee03.png"},{"id":55513388,"identity":"fe630a79-b332-45e3-affd-abf995c8cff8","added_by":"auto","created_at":"2024-04-29 12:54:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":830105,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional assessment of the prognostic risk model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Independent prognostic model forest diagram;\u003c/p\u003e\n\u003cp\u003eB. Prognostic model nomogram;\u003c/p\u003e\n\u003cp\u003eC. calibration curve;\u003c/p\u003e\n\u003cp\u003eD. The ROC curves for predicting OS in CRC patients.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4242994/v1/602b36bacba3ccc1db23db6f.png"},{"id":55515379,"identity":"3978afcb-c110-4841-975c-59f17ce4933d","added_by":"auto","created_at":"2024-04-29 13:10:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":854482,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultiple analyses of characteristic genes in CRC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Mutation analysis of all genes in the TCGA-CRC dataset;\u003c/p\u003e\n\u003cp\u003eB. Mutation status of five prognostic genes;\u003c/p\u003e\n\u003cp\u003eC. Variant Allele frequency of prognostic genes;\u003c/p\u003e\n\u003cp\u003eD. Analysis of copy number variation (CNV) status of prognostic genes by GSCA database\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4242994/v1/1caf7dcb02cd2ae2737e4dee.png"},{"id":55515380,"identity":"171df3fe-9eb4-4382-af7e-77b8013def45","added_by":"auto","created_at":"2024-04-29 13:10:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1103110,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional analyses and histological expression of TIMP1 in colorectal cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Go analysis items; B. KEGG pathways;\u003c/p\u003e\n\u003cp\u003eC. TIMP1 Expression in Normal Tissue; D. TIMP1 Expression in Tumor Tissue\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4242994/v1/ca7022ad800c71cd18ab5bd5.png"},{"id":55513394,"identity":"79f09698-06c8-4a10-9cd2-2455348f5899","added_by":"auto","created_at":"2024-04-29 12:54:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1066215,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe biological function of TIMP1 in proliferation in colorectal cancer cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Evaluation of silencing efficiency of siRNA in colorectal cancer cell lines;\u003c/p\u003e\n\u003cp\u003eB-C. MTT assays are used to assess the influence of blocking TIMP1 expressions on proliferative abilities of HCT116 and HT29 cells;\u003c/p\u003e\n\u003cp\u003eD-E. The impact of TIMP1 on proliferation in HCT116 and HT29 cells via cell clone assays.\u003c/p\u003e\n\u003cp\u003esiRNAs, short interfering RNAs; Control represents blank control group, and NC represents negative control group; ** means \u003cem\u003ep\u003c/em\u003e value \u0026lt;0.01; *** means \u003cem\u003ep\u003c/em\u003e value \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4242994/v1/5434fec419b375d7d45abce1.png"},{"id":55513395,"identity":"61eb3c08-fcda-465d-a470-345e5b5c1dde","added_by":"auto","created_at":"2024-04-29 12:54:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":578469,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe biological function of TIMP1 in apoptosis in colorectal cancer cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA-B. The expressions in mRNA level of anti-apoptotic and pro-apoptotic genes in HCT116 and HT29 cells;\u003c/p\u003e\n\u003cp\u003eC. Apoptosis detection assay with Annexin-V/PI when knocking down TIMP1 gene.\u003c/p\u003e\n\u003cp\u003e*** means \u003cem\u003ep\u003c/em\u003e value \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4242994/v1/813d94d0dcb4a1f4377cf79e.png"},{"id":61761914,"identity":"46369ee8-2329-4e99-8873-b9704551f134","added_by":"auto","created_at":"2024-08-05 09:35:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7536362,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4242994/v1/683e8679-c875-458c-908c-5ba86dc94b7f.pdf"},{"id":55514456,"identity":"63e31f33-26bd-47ad-8276-f14b27232dee","added_by":"auto","created_at":"2024-04-29 13:02:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2034319,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4242994/v1/02af2a844a47e71753995906.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bioinformatics mining and experimental validation of prognostic biomarkers in colorectal cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is the third most common diagnosed and second most deadly of all cancers \u003csup\u003e1\u0026ndash;3\u003c/sup\u003e. Nearly two million cases of CRC patients were diagnosed in 2022 according to data from the International Agency for Research on Cancer (IARC) (a division of the World Health Organization (WHO)). The CRC leads to 1.5\u0026nbsp;million people death each year \u003csup\u003e4\u0026ndash;6\u003c/sup\u003e. At present, surgical resection is still the primary treatment for CRC, especially for patients with non-metastatic CRC, while chemotherapy is also an integral part of it \u003csup\u003e7\u0026ndash;9\u003c/sup\u003e. Treatment and early diagnosis of CRC have improved significantly in recent years, but many patients are asymptomatic in the early stages, and 50\u0026ndash;60% of patients with CRC often have multiple metastases. After receiving standard therapy, the 5-year survival rate for these patients is only 12\u0026ndash;19%, and the recurrence rate is high \u003csup\u003e10\u003c/sup\u003e. Molecular studies have identified numerous genetic alterations that occur during colon carcinogenesis. However, the precise genetic changes responsible for the occurrence and progression of CRC are still poorly understood \u003csup\u003e11\u003c/sup\u003e. Therefore, it is vitally necessary to comprehend the molecular mechanisms of CRC development and identify biomarkers that may be used for improving the prognosis of CRC patients.\u003c/p\u003e \u003cp\u003eIn this study, we first downloaded RNA-seq data and clinical information regarding CRC from Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) databases. After integrated analysis of both two databases, we identified differentially expressed genes (DEGs) by the differential expression analysis. We obtained the CRC-related genes by weighted gene co-expression network analysis (WGCNA). Then, a prognostic risk model with five characteristic genes (\u003cem\u003eTIMP1, PCOLCE2, MEIS2, HDC and CXCL13\u003c/em\u003e) was constructed by univariate and multivariate Cox regression analyses, Least Absolute Shrinkage and Selection Operator (LASSO), which performed well in predicting overall survivals of CRC patients. These characteristic genes are highly related to overall survival.\u003c/p\u003e \u003cp\u003eTissue inhibitor matrix metalloproteinase 1 (TIMP1), belongs to the Tissue Inhibitor of Metalloproteinases family which included four identified members. Recent clinical studies have shown that the abnormal expression of TIMP1 is associated with an unfavorable prognosis in various types of tumors \u003csup\u003e12,13\u003c/sup\u003e. Considering the significant scientific interest in TIMP1 and to validate the accuracy of our prognostic genes, represented by TIMP1, we conducted a series of \u003cem\u003ein vitro\u003c/em\u003e experiments to confirm its pro-oncogenic effects in CRC cells. Overall, the findings of the study provide a new foundation for prognostic analysis and insights into the molecular mechanisms underlying CRC.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAcquirement of DE-CRC-related genes\u003c/h2\u003e \u003cp\u003eA total of 5408 DEGs (2779 genes up-regulated and 2629 genes down-regulated) were screened between the CRC/normal tissue samples in the TCGA-CRC dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Two outlier samples (TCGA-AA-3947-01A and TCGA-CM-4748-01A) were excluded from the sample clustering analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). A β value of 16 was chosen to ensure network conformity to a scale-free distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). With CRC as the trait, six modules were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). The heat map of module-trait relationships was obtained by Spearman analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). The MEbrown module, containing 1639 CRC-associated genes, was found to be most closely associated with CRC, with 926 of these genes showing differentially expressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eFunctional analysis and PPI network of 926 CRC-related genes\u003c/h2\u003e \u003cp\u003eUsing the STRING database, we retrieved 923 out of 926 genes to construct the protein-protein interactions (PPI) network (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). These genes were enriched to a total of 1022 GO entries and 27 KEGG pathways. According to the biological processes (BP) analysis, these genes were connected to cell adhesion and cell communication (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In the case of cellular component (CC), these genes were engaged in plasma and neuron part (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Regarding molecular function (MF), these genes were involved in transmembrane signaling receptor activity, metal ion transmembrane transporter activity, and G-protein coupled peptide receptor activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). These intersecting genes were also enriched in cGMP-PKG signaling pathway, circadian rhythm, cAMP signaling pathway, and PI3K-Akt signaling pathway and other pathways related to CRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGreat predictive capability of prognostic risk model\u003c/h2\u003e \u003cp\u003eThe univariate Cox regression analysis was performed using the training set and 15 genes were significantly associated with overall survival (OS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Next, the results were then analyzed by LASSO and multifactorial Cox analysis, which showed that \u003cem\u003eTIMP1, PCOLCE2, MEIS2, HDC, and CXCL13\u003c/em\u003e were the characteristic genes of CRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), and their positioning on the chromosome was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD. Risk score\u0026thinsp;=\u0026thinsp;TIMP1 \u0026times; 0.203931952\u0026thinsp;+\u0026thinsp;PCOLCE2 \u0026times; 0.09722934\u0026thinsp;+\u0026thinsp;MEIS2 \u0026times; 0.192140876 \u0026ndash; HDC \u0026times; 0.20005372 - CXCL13 \u0026times; 0.14748485. According to median risk\u0026thinsp;=\u0026thinsp;0.9711322, CRC patients were divided into high- and low-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The Kaplan-Meier analysis showed that patients with high-risk scores had significantly lower OS than those with low-risk scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). To further assess the validity of the risk signature, the ROC curve for OS was calculated, and the area and the curve (AUC) values at 1, 3, and 5 years were approximately 0.7 indicating better efficacy of the risk model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). This prognostic risk model still had strong predictive power in both the internal test set and the external GSE39582 dataset (Supplementary Fig.\u0026nbsp;1). In addition, the decision curve analysis (DCA) and principal component analysis (PCA) results of the prognostic risk model in the training set, internal test set, and external validation set demonstrated the strong predictive power of the model (Supplementary Fig.\u0026nbsp;2). These five characteristic genes were also differentially expressed in GSE32323, further demonstrating the superiority of the model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFunctional assessment of the nomogram model\u003c/h2\u003e \u003cp\u003eClinical variables and risk scores from 606 CRC tissue samples were combined to perform univariate and multivariate Cox analyses (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The age, mismatch repair protein expression deficiency (MMR protein), lymphovascular infiltration, M staging, N staging, tumor stage, and risk score were a prognostic factor for CRCpatients. Construction of a nomogram model on the basis of independent prognostic factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), it was found that the survival rate decreases as overall score increases. The correction curve value of this nomogram model approached 1, indicating that its prediction was true and reliable (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Moreover, the ROC curve values of nomogram model for 1, 3 and 5 years were all greater than 0.7, which indicated that the model had excellent prediction (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe univariate analysis of independent prognostic models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR.95L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR.95H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000275\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoss Expression of MMR Proteins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012971\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphatic invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001057\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.35E-10\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.96E-13\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage(reference: N0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.900343802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.206960394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.992067168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005568\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.961218249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.611668465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.008132437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.37E-11\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage(reference:T0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013490\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor stage\u003c/p\u003e \u003cp\u003e(reference: I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006098\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.46E-07\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: HR: Hazards ration; *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMultiple analyses of characteristic genes indicated their importance in CRC\u003c/h2\u003e \u003cp\u003eMutation analysis of all genes in the TCGA-CRC dataset revealed the highest proportion of missense mutations and the highest proportion of single nucleotide polymorphism (SNP) mutation patterns were C to T (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Although the top 10 mutated genes did not have the characteristic genes, the characteristic genes were all dominated by missense mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The gene with the highest number of mutations was MEIS2, followed by HDC. The variant allele frequency (VAF) was highest for TIMP1 and lowest for HDC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Hete Amp and Hete Del are basically the major forms of copy number variation of the characteristic genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional analysis of the prognostic gene TIMP1 and its expression at the histological level\u003c/h2\u003e \u003cp\u003eTIMP1 is an important prognostic marker for the progression and metastasis in different cancer\u003csup\u003e12,14,15\u003c/sup\u003e, and has been shown to influence several tumorigenic biological processes\u003csup\u003e16\u0026ndash;18\u003c/sup\u003e. Moreover, TIMP1 plays a role in anti-tumor drugs resistance\u003csup\u003e15,16\u003c/sup\u003e. To identify enriched regulatory pathways and molecular functions associated with the prognostic gene TIMP1 in CRC, we conducted GSEA analysis on MSigdb, which is the gene set database. The type I interferon receptor binding, macroautophagy was the main enriched GO entry for TIMP1, and both entries were related to CRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Moreover, KEGG enrichment analysis showed that TIMP1 mainly enriched in CRC pathways such as oxidative phosphorylation and Notch signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). In addition, we compared the immunohistochemical expression of the prognostic gene TIMP1 in CRC samples versus normal samples. It was clearly seen that TIMP1 was more expressed in the tumor tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), which was consistent with the results of the previous expression analysis in GSE32323. We also performed functional and expression analysis for remaining characterized genes. The results were shown in Supplementary Fig.\u0026nbsp;3 and Supplementary Fig.\u0026nbsp;4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eKnockdown of TIMP1 suppressed the colon cancer cell proliferation\u003c/h2\u003e \u003cp\u003eAlthough we have conducted a bioinformatics analysis that highlights the significant role of TIMP1 in the prognosis of CRC, our findings are consistent with previous studies indicating that TIMP1 expression is upregulated in colon cancer\u003csup\u003e19,20\u003c/sup\u003e. However, we still need to validate our results through further experiments. Moreover, a deeper exploration of the role of TIMP1 in CRC may pave the way for its use as a prognostic factor or a therapeutic target. Hence, we observed the function of TIMP1 on colorectal cancer cell proliferation and apoptosis \u003cem\u003ein vitro\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eTo confirm the effects of TIMP1 in human colon cancer cells, siRNA was used to knockdown TIMP1 in HCT116 and HT29 colon cancer cell lines which TIMP1 expression level was higher than other cell lines\u003csup\u003e14\u003c/sup\u003e. After transfection with si-TIMP1, the expression in mRNA level of TIMP1 gene was reduced in HCT116 and HT29 cells, indicating that both cells were successfully transfected (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Afterward, the MTT method was utilized to determine the cell viability. Following the transfection of si-TIMP1 into HCT116 and HT29 cells, the cell viability was observed to be inhibited as compared to the non-transfected group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, C). Moreover, we conducted colony formation assays to assess the role of TIMP1 in colon cell growth which showed that TIMP1-KD was associated with significantly impaired colony formation ability compared with that of Si-NC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, E). Based on these findings, it appears that TIMP1 plays a crucial role in colon cell proliferation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTIMP1 knockdown promoted apoptosis\u003c/b\u003e \u003cb\u003ein vitro\u003c/b\u003e\u003c/p\u003e \u003cp\u003eReal-time qPCR was used to detect anti-apoptotic and pro-apoptotic genes expression. The results revealed that the mRNA expression level of anti-apoptotic genes Bcl-2 and Survivin, which play an important role in apoptosis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], were downregulated, while pro-apoptotic genes BAD and Bax were upregulated, respectively in HCT116 and HT29 cells when knockdown of TIMP1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B). Furthermore, the cell apoptosis was also studied using Annexin V-FITC Apoptosis Detection Kit in accordance with the manufacturer's instructions. The apoptosis cells were measured by staining with Annexin V-FITC along with Propidium Iodide. Cells were detected using an inverted fluorescence microscope. The result was consistent with real-time PCR results. Compared with Si-NC cells, there are more apoptotic cells in Si-TIMP1 group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCRC is a leading cause of cancer-related mortality globally. It ranks third in terms of incidence rate among all malignant tumors worldwide \u003csup\u003e1\u003c/sup\u003e. The prognosis of advanced CRC is poor. it is crucial to explore and validate new molecular markers for CRC diagnosis. In this study, we integrated analyzed RNA-seq data and clinical information from GEO and TCGA databases. A total of 5408 DEGs consisting of 2779 upregulated DEGs and 2629 downregulated DEGs were identified between CRC tissues and normal tissues in the TCGA-CRC dataset. Based on WGCNA, we screened the modules related to CRC. The MEbrown module was the most associated with CRC and contained 1639 CRC-associated genes, of which 926 CRC-associated genes were differentially expressed. Subsequently, we conducted a great predictive capability of prognostic risk model. By LASSO and multifactorial Cox analysis, we obtained five characteristic genes of CRC, including \u003cem\u003eTIMP1, PCOLCE2, MEIS2, HDC, and CXCL13\u003c/em\u003e. The Kaplan-Meier analysis showed that patients with high-risk scores had significantly lower OS than those with low-risk scores. The time-dependent ROC analysis for OS obtained an AUC of 0.7 which indicated relatively high specificity and sensitivity of the prognostic signature for CRC. This prognostic risk model still had strong predictive power in both the internal dataset and the GSE39582 external dataset. In addition, the DCA and PCA results demonstrated the strong predictive power of the prognostic risk model in the training set, internal dataset, and external validation set. These five characteristic genes were also differentially expressed in GSE32323 dataset, further demonstrating the superiority of the model.\u003c/p\u003e \u003cp\u003eThe functional enrichment analysis demonstrated that the DEGs were involved in some biological processes, such as cell adhesion, cell communication. In accordance with previous research, these biological processes play crucial role in CRC tumorigenesis and development \u003csup\u003e21\u003c/sup\u003e. KEGG analysis on DE-CRC-related genes revealed that they were also enriched in cGMP-PKG signaling pathway, circadian rhythm, cAMP signaling pathway, and PI3K-Akt signaling pathway related to CRC, the results were consistent with previous knowledge. According to Zhan Ma et al, PHLDA2 regulates epithelial-mesenchymal transition (EMT) and autophagy in CRC via the PI3K/AKT signaling pathway \u003csup\u003e3\u003c/sup\u003e. Si-Yang Li et al has demonstrated that Diosgenin suppresses CRC cells through cAMP/PKA/CREB pathway \u003csup\u003e22\u003c/sup\u003e. Our research revealed that these DEGs may be implicated in the tumorigenesis and development of CRC.\u003c/p\u003e \u003cp\u003eUnivariate, multivariate Cox regression, and LASSO analyses all show that \u003cem\u003eTIMP1, PCOLCE2, MEIS2, HDC, and CXCL13\u003c/em\u003e are significantly related to CRC patient\u0026rsquo;s survival. According the previous studies, PCOLCE2 encodes a functional procollagen c-protease enhancer, and it can promote the enzymatic cleavage of type I procollagen to produce mature structured fibrils \u003csup\u003e23\u003c/sup\u003e. Our findings are consistent with previous research, Chen et al. developed a prognostic gene signature made up by 9 genes, including PCOLCE2 and T1MP1, and they accurately predicted the overall survival in CRC patients \u003csup\u003e24\u003c/sup\u003e. Although the specific mechanism of PCOLCE2 in CRC is less known, according to recent research, PCOLCE2 has been identified as the main gene driving the development of endometrial cancer, and our findings are supported by this research \u003csup\u003e25\u003c/sup\u003e. MEIS2 is a member of the MEIS protein family that regulates neural crest and limb development \u003csup\u003e26\u003c/sup\u003e, and it has been implicated in the development of human cancer \u003csup\u003e27\u003c/sup\u003e. Recent research showed that in prostate cancer and ovarian cancer, the degree of MEIS2 protein expression was related to the development of clinically metastatic illness and the absence of biochemical recurrence \u003csup\u003e28,29\u003c/sup\u003e. Ziang Wan et al. has firstly demonstrated that the MEIS2 promotes cell migration and invasion in CRC, and acts as a promoter of metastasis in CRC \u003csup\u003e30\u003c/sup\u003e. Histamine dihydrochloride (HDC) is an inhibitor of NOX2-derived ROS \u003csup\u003e31\u003c/sup\u003e, and exerts anti-cancer efficacy in experimental tumor models. Hanna et al. propose that anti-tumor properties of HDC may comprise the targeting of MDSCs \u003csup\u003e32\u003c/sup\u003e. In addition, Chen et al. demonstrated that HDC\u0026thinsp;+\u0026thinsp;granulocytic myeloid cells influence CD8\u0026thinsp;+\u0026thinsp;T cells both directly and indirectly through modulating Tregs, and which hence appear to play crucial roles in suppressing tumoricidal immunity in murine colon cancer \u003csup\u003e33\u003c/sup\u003e. CXCL13, a homeostatic chemokine, is secreted by the stromal cells in the B-cell area of the secondary lymphoid tissues. CXCL13 plays a significant part in the growth of tumor\u003csup\u003e34\u003c/sup\u003e. The previous study demonstrates that CXCL13 can promote prostate cancer cell proliferation through JNK signalling and invasion through activation of ERK\u003csup\u003e35\u003c/sup\u003e. The same findings as our study, Qi XW et al, indicate that CXCR5 and CXCL13 appear to be independent predictors of survival for patients with CRC\u003csup\u003e36\u003c/sup\u003e. Senlin Zhao et al demonstrated that polarized M2 macrophages could induce premetastatic niche formation and promote CRLM by secreting CXCL13, which activated a CXCL13/CXCR5/NFκB/p65/miR-934 positive feedback loop in CRC cells\u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmong these genes, TIMP1 attract our attention. TIMP1 encodes a 931 base-pair mRNA and a 207 amino acid protein. This protein may inhibit the proteolytic action of matrix metalloproteinases (MMPs), which are thought to be crucial for the tumor invasion and development of metastatic disease \u003csup\u003e38,39\u003c/sup\u003e. T1MP1 has been showed that its expression is upregulated in colon cancer \u003csup\u003e19,20\u003c/sup\u003e, and it also plays an important role in the regulation of cell proliferation and anti-apoptotic function \u003csup\u003e40,41\u003c/sup\u003e. A previous study indicated that TIMP1 is a key role in promoting progression and metastasis of human colon cancer, and function as a potential prognostic indicator for colon cancer \u003csup\u003e14\u003c/sup\u003e. Through \u003cem\u003ein vitro\u003c/em\u003e experiments, we verified the inhibitory effect of blocking TIMP1 expression on growth of colorectal cancer cells. Moreover, we investigated the apoptotic effect of TIMP1 on CRC cells, TIMP1 knocked down can promote apoptosis of CRC cells. In summary, we preliminarily investigated the biofunctions of TIMP1 in CRC, which shed lights on CRC treatment.\u003c/p\u003e \u003cp\u003eHowever, it is important to note that there are limitations to this study. Firstly, to confirm the biological functions of TIMP1 in CRC, \u003cem\u003ein vivo\u003c/em\u003e experiments are required. Secondly, it is worth mentioning that the study did not investigate whether a TIMP1 inducer can reverse the stimulative effect of TIMP1 on the growth and apoptosis of colorectal cancer cells.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we developed a new prognostic risk model for CRC, which was validated using both the GSE32323 dataset and the internal dataset, confirming its predictive validity. The genes \u003cem\u003eTIMP1, PCOLCE2, MEIS2, HDC, and CXCL13\u003c/em\u003e were identified as playing crucial roles in CRC patients. Given the significance of TIMP1 in the field of oncology, we conducted \u003cem\u003ein vitro\u003c/em\u003e experiments to investigate its biofunctions in CRC. Our findings have shown that TIMP1 promotes the proliferation of colorectal cancer cells, and silencing TIMP1 inhibited cell proliferation while promoting apoptosis in CRC cell lines. These results provide valuable insights for future research aiming to leverage prognostic genes to combat cancer and evaluate prognosis.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAcquirement of the data of the CRC patients\u003c/h2\u003e \u003cp\u003eThe TCGA-CRC dataset (training cohort) including RNA-seq data and clinical information of 51 normal tissue samples and 606 CRC tissue samples with survival information, was downloaded 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). Two independent cohorts (GSE39582 and GSE32323) were acquired from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GSE39582 dataset, including a total of 556 CRC samples containing survival information, were used as the validation set. GSE32323 dataset, which consisted of 17 CRC tissue samples and 17 normal tissue samples, was utilized to analyze the expression of the characteristic genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of differentially expressed CRC-related genes (DE-CRC-related genes)\u003c/h2\u003e \u003cp\u003eThe \"Deseq2\" package \u003csup\u003e42\u003c/sup\u003e was used for differential expression analysis to obtain the differentially expressed genes (DEGs) between the normal and CRC tissue samples. The \u0026ldquo;ggplot2\u0026rdquo; and \u0026ldquo;pheatmap\u0026rdquo; packages were adopted to plot the volcano map and heatmap to visualize DEGs, respectively. Gene modules with comparable expression patterns have been identified using weighted gene co-expression network analysis (WGCNA) \u003csup\u003e43\u003c/sup\u003e, and the relationship between modules and particular traits has been examined. In this study, we used the R package \"WGCNA\" to create a gene co-expression network for CRC in the TCGA-CRC dataset. For the network construction to be reliable, abnormal samples were first eliminated. Then the appropriate threshold for network construction was selected, and a minimum number of genes per module of 30. The link between clinical traits (CRC and normal tissues samples) and module attributes was examined using the Pearson correlation analysis (MES), and the module with the highest correlation was identified as the module most closely related to CRC. The \"VennDiagram\" package \u003csup\u003e44\u003c/sup\u003e was applied to visualize the DE-CRC-related genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFunctional analysis and protein-protein interactions (PPI) network of DE-CRC-related genes\u003c/h2\u003e \u003cp\u003eBased on DE-CRC-related genes, the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org\u003c/span\u003e\u003cspan address=\"http://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was applied to establish the PPI network's structure. Then the DAVID database was adopted to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on these DE-CRC-related genes and visualize the results by the R package \"ggplot2\".\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of the prognostic risk model\u003c/h2\u003e \u003cp\u003eBy using univariate Cox analysis of DE-CRC-related genes in the training set, the prognosis-related genes were acquired (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.03). Subsequently, the most predictive characteristic genes were identified by the least absolute shrinkage and selection operator (LASSO) \u003csup\u003e45\u003c/sup\u003e analysis and multivariate Cox analysis then sequentially. The R package \"biomaRt\" \u003csup\u003e46\u003c/sup\u003e was applied to visualize the localization of the characteristic genes on the chromosomes. Subsequently, risk score of each CRC patient was calculated based on the formula:\u003c/p\u003e \u003cp\u003eRisk score = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{1}^{n}coef\\left({gene}_{i}\\right)*expr\\left({gene}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe training set of CRC patients was divided into two groups based on the median risk score. The difference in overall survival (OS) between the two groups was then displayed using Kaplan-Meier (K-M) curves. The \"timeROC\" package \u003csup\u003e47\u003c/sup\u003e was applied to display the receiver operating characteristic (ROC) curves to perform an assessment of the prognostic capability of the prognostic model. Meanwhile, the stability of this prognostic risk model was investigated in the external GSE71014 dataset and the internal validation set. In addition, decision curve analysis (DCA) run by the \"DecisionCurve\" package and principal component analysis (PCA) were used to further assess the predictive power of the prognostic risk model. Finally, whether these characteristic genes were differentially expressed in GSE32323 was investigated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of the nomogram model\u003c/h2\u003e \u003cp\u003eTo determine if clinicopathological characteristics (age, mismatch repair protein expression deficiency, lymphovascular infiltration, M staging, N staging, T staging, and tumor stage) and risk scores were independent predictive factors for CRC patients, univariate and multifactorial Cox analyses were performed. The \"rms\" package was adopted to construct the nomogram to predict survival probability based on independent prognostic criteria. Both the calibration and the ROC curves were adopted to validate whether the nomogram can be used as an optimal model for clinical decision-making.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eThe analysis of characteristic genes\u003c/h2\u003e \u003cp\u003eThe mutation frequencies of all genes in the TCGA-CRC dataset were analyzed using the maftools package. The expression of characteristic genes in CRC/normal tissues was assessed by the Human Protein Atlas (HPA) database. The copy number variation (CNV) status of characteristic genes was also analyzed through the gene set cancer analysis (GSCA) database. Explore the biological functions enriched by genes associated with characteristic genes through gene set enrichment analysis (GSEA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCell culture\u003c/h2\u003e \u003cp\u003eHCT116 and HT29 human colorectal cancer cell lines were obtained from American Type Culture Collection (ATCC, Manassas, VA, USA) and were cultured in Dulbecco\u0026rsquo;s Modified Eagle\u0026rsquo;s Medium (DMEM; Gibco, Thermo Fisher Scientific, USA) with 10% fetal bovine serum, and 1% penicillin and streptomycin. All cells were incubated in a humidified atmosphere of 5% CO\u003csub\u003e2\u003c/sub\u003e at 37 ℃.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Cell transfection\u003c/h2\u003e \u003cp\u003eThe control siRNA, and TIMP1 siRNA were all constructed by Servicebio Technology Company. Approximately 0.8 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells were inoculated into 96-well culture plates. After cell adhesion, the culture medium was replaced with 100 \u0026micro;l serum-free medium. Subsequently, in serum-free medium, siRNA-TIMP1 or siRNA-NC were mixed with 3 \u0026micro;l Lipofectamine transfection reagent (13778030, Thermo Fisher Scientific, USA) to form transfection complexes. The mixture was dropped into the corresponding wells and incubated for 6 h. Each well was supplemented with 400 \u0026micro;l of FBS containing 10% medium and incubated for 24 h. The silencing effect of TIMP1 was analyzed by RT-qPCR to detect the mRNA expression level of TIMP1 in both cells.\u003c/p\u003e \u003cp\u003eSi-RNA-TIMP1 target sequence:\u003c/p\u003e \u003cp\u003eSense: 5\u0026prime;⁃CCAGCGUUAUGAGAUCAAGAUTT⁃3\u0026prime;;\u003c/p\u003e \u003cp\u003eAntisense: 5\u0026prime;⁃AUCUUGAUCUCAUAACGCUGGTT⁃3\u0026prime;;\u003c/p\u003e \u003cp\u003esiRNA-Control Target:\u003c/p\u003e \u003cp\u003eSense: 5\u0026prime;⁃CCGGTTCTCCGAACGTGTCAC⁃3\u0026rsquo;;\u003c/p\u003e \u003cp\u003eAntisense: 5\u0026prime;⁃AATTCAAAAATTCTCCGAACGT⁃3\u0026prime;;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMTT assay\u003c/h2\u003e \u003cp\u003eHCT116 and HT29 Cells were digested using trypsin, resuspended with fresh complete medium, and counted 5 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells per well were inoculated in 96-well cell plates for 24 h. At each detection point, 10 \u0026micro;l MTT reaction solution (298-93-1, Solarbio, China) was added to each well. Then, the plates were incubated in the incubator for 2 h. Subsequently, 100 \u0026micro;l DMSO was added in each well after discarding the supernatant. The absorbance at 570 nm was measured by a microplate reader (BMG Spectrostar, BMG Labtech, Germany). The results were analyzed by Prism 9.0 (GraphPad, La Jolla, CA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eColony forming assay\u003c/h2\u003e \u003cp\u003eApproximately 500 cells were seeded in six-well plates and cultured at 37\u0026deg;C under a humidified atmosphere containing 5% CO\u003csub\u003e2\u003c/sub\u003e, and the medium was changed every 72 h. After 12 days, the cells were fixed with 4% paraformaldehyde for 15 minutes and stained with 0.1% crystalline violet staining solution (G1062, Solarbio, China) for 15 minutes. Colonies were then photographed. All experiments were performed in triplicate. ImageJ software was utilized to count the number of stained colonies \u003csup\u003e48\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eQuantitative Real-time PCR (RT-qPCR)\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from each group of cells using FastPure Cell/Tissue Total RNA Isolation Kit V2 (V2, Vazyme, China) according to the manufacturer\u0026rsquo;s instructions. Reverse transcription was performed using a reverse transcription kit (K1622, Thermo Fisher Scientific, USA). Gene expression was measured using a Taqman Probe (USP26, Applied Biosystems, Thermo Fisher Scientific, USA) by a QuantStudio\u0026trade; 5 (Thermo Fisher Scientific, USA) fluorescence PCR instrument. Relative mRNA values were calculated by the ΔΔCt method. GAPDH was used as an internal standardized control.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eAnnexin V/PI Assay for the Detection of Cell Apoptosis\u003c/h2\u003e \u003cp\u003eHCT116and HT29 cells were cultured in confocal dishes (2 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells/dish). After 24h, the cells were attached to the dishes. The intervention was performed for 24h, then cells were washed with PBS two times. The Annexin V-FITC Apoptosis Detection Kit (KTA0002-100T, Abbkine, USA) was used to perform apoptosis analysis according to the instructions. Annexin V-FITC and Propidium iodide staining was used to detect apoptotic cells. After incubation for 15 min, the stained cells were detected under confocal microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were carried out with GraphPad Prism 9.0 (GraphPad, La Jolla, USA). Numerical data are displayed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). All experiments and analyses were performed in triplicate to ensure accuracy and reliability. The statistical significance of the differences was analyzed by Student\u0026rsquo;s t test, unpaired t test, and one-way ANOVA according to the test of homogeneity of variances. Statistical significance was set at *, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; and ***, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMS, HF conceived and designed the study. HF, WGZ, ZYL,FJY performed the literature search and manuscript writing; HF, SA wrote the original draft; SA, MJ, CTN, PNO,KV and MAA provided input as content experts; MS, HF, MAA and BMM reviewed and proofread the writing. All authors have contributed, read, and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll the data are available within the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDekker, E., Tanis, P. J., Vleugels, J. L. A., Kasi, P. M. \u0026amp; Wallace, M. B. Colorectal cancer. Lancet 394, 1467\u0026ndash;1480, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(19)32319-0\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(19)32319-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchreuders, E. H. \u003cem\u003eet al.\u003c/em\u003e Colorectal cancer screening: a global overview of existing programmes. 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Nat Methods 9, 671\u0026ndash;675, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nmeth.2089\u003c/span\u003e\u003cspan address=\"10.1038/nmeth.2089\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer, Prognostic biomarkers, WGCNA, Bioinformatics, TIMP1","lastPublishedDoi":"10.21203/rs.3.rs-4242994/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4242994/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eColorectal cancer (CRC) is a prevalent malignancy with rising incidence and mortality rates. It is essential to identify potential prognostic gene biomarkers for CRC. We analyzed public datasets, revealing 5408 differentially expressed genes (DEGs) between CRC and adjacent normal tissues. Utilizing the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases, we identified 2779 up-regulated and 2629 down-regulated genes. Weighted gene co-expression network analysis (WGCNA) yielded the MEbrown module, containing 1639 genes highly correlated with CRC. A total of 926 differentially expressed CRC-related genes were screened for subsequent analysis. Then, a prognostic risk model with five characteristic genes (\u003cem\u003eTIMP1, PCOLCE2, MEIS2, HDC and CXCL13\u003c/em\u003e) was constructed. This model demonstrated strong predictive ability in the GSE32323 dataset and an internal test set. All five characteristic genes harbored predominantly missense mutations, with TIMP1 exhibiting the highest variant allele frequency. Functional enrichment analysis, including gene set enrichment analysis (GSEA) and histological expression analysis in the HPA database, highlighted the biological significance of TIMP1 in CRC. TIMP1 is upregulated in the tumor tissue and enriched in CRC-related pathways such as type I interferon receptor binding, oxidative phosphorylation, and Notch signaling pathways. Additionally, using siRNA technology, the impact of TIMP1 on cellular proliferation and apoptosis in CRC cell lines (HCT116 and HT29) was investigated, showing that TIMP1 knockdown significantly inhibited proliferation and promoted apoptosis. This study presents a novel prognostic risk model comprising five characteristic genes (\u003cem\u003eTIMP1, PCOLCE2, MEIS2, HDC and CXCL13\u003c/em\u003e) for CRC, which are strongly associated with overall survival in CRC patients with TIMP1 identified as having cancer-promoting properties in CRC. Our study suggests that TIMP1 holds promise as both a biomarker and a therapeutic target for CRC.\u003c/p\u003e","manuscriptTitle":"Bioinformatics mining and experimental validation of prognostic biomarkers in colorectal cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-29 12:54:11","doi":"10.21203/rs.3.rs-4242994/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":"d8390ada-a995-437b-a583-efbce7cf6ad3","owner":[],"postedDate":"April 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30980028,"name":"Biological sciences/Cancer"},{"id":30980029,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":30980030,"name":"Health sciences/Biomarkers"}],"tags":[],"updatedAt":"2025-02-25T07:38:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-29 12:54:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4242994","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4242994","identity":"rs-4242994","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}