scholarly journals Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach

2021 ◽  
Vol 22 (4) ◽  
pp. 1632
Author(s):  
Eskezeia Yihunie Dessie ◽  
Siang-Jyun Tu ◽  
Hui-Shan Chiang ◽  
Jeffrey J.P. Tsai ◽  
Ya-Sian Chang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Gene Signature ◽  
Adaptive Lasso ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
Prognostic Gene ◽  
Prognostic Gene Signature

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide and is often related to late diagnosis and poor survival outcome. More evidence is demonstrating that gene-based prognostic models can be used to predict high-risk HCC patients. Therefore, our study aimed to construct a novel prognostic model for predicting the prognosis of HCC patients. We used multivariate Cox regression model with three hybrid penalties approach including least absolute shrinkage and selection operator (Lasso), adaptive lasso and elastic net algorithms for informative prognostic-related genes selection. Then, the best subset regression was used to identify the best prognostic gene signature. The prognostic gene-based risk score was constructed using the Cox coefficient of the prognostic gene signature. The model was evaluated by Kaplan–Meier (KM) and receiver operating characteristic curve (ROC) analyses. A novel four-gene signature associated with prognosis was identified and the risk score was constructed based on the four-gene signature. The risk score efficiently distinguished the patients into a high-risk group with poor prognosis. The time-dependent ROC analysis revealed that the risk model had a good performance with an area under the curve (AUC) of 0.780, 0.732, 0.733 in 1-, 2- and 3-year prognosis prediction in The Cancer Genome Atlas (TCGA) dataset. Moreover, the risk score revealed a high diagnostic performance to classify HCC from normal samples. The prognosis and diagnosis prediction performances of risk scores were verified in external validation datasets. Functional enrichment analysis of the four-gene signature and its co-expressed genes involved in the metabolic and cell cycle pathways was constructed. Overall, we developed a novel-gene-based prognostic model to predict high-risk HCC patients and we hope that our findings can provide promising insight to explore the role of the four-gene signature in HCC patients and aid risk classification.

scholarly journals A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qian Yan ◽  
Wenjiang Zheng ◽  
Boqing Wang ◽  
Baoqian Ye ◽  
Huiyan Luo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Model ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Performance ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

scholarly journals Identification of a Pyroptosis-Related Gene Signature for Predicting Overall Survival and Response to Immunotherapy in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Susu Zheng ◽  
Xiaoying Xie ◽  
Xinkun Guo ◽  
Yanfang Wu ◽  
Guobin Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Regression Model ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene

Pyroptosis is a novel kind of cellular necrosis and shown to be involved in cancer progression. However, the diverse expression, prognosis and associations with immune status of pyroptosis-related genes in Hepatocellular carcinoma (HCC) have yet to be analyzed. Herein, the expression profiles and corresponding clinical characteristics of HCC samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then a pyroptosis-related gene signature was built by applying the least absolute shrinkage and selection operator (LASSO) Cox regression model from the TCGA cohort, while the GEO datasets were applied for verification. Twenty-four pyroptosis-related genes were found to be differentially expressed between HCC and normal samples. A five pyroptosis-related gene signature (GSDME, CASP8, SCAF11, NOD2, CASP6) was constructed according to LASSO Cox regression model. Patients in the low-risk group had better survival rates than those in the high-risk group. The risk score was proved to be an independent prognostic factor for overall survival (OS). The risk score correlated with immune infiltrations and immunotherapy responses. GSEA indicated that endocytosis, ubiquitin mediated proteolysis and regulation of autophagy were enriched in the high-risk group, while drug metabolism cytochrome P450 and tryptophan metabolism were enriched in the low-risk group. In conclusion, our pyroptosis-related gene signature can be used for survival prediction and may also predict the response of immunotherapy.

scholarly journals The Construction of a Prognostic Model Based on a Peptidyl Prolyl Cis–Trans Isomerase Gene Signature in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Huadi Shi ◽  
Fulan Zhong ◽  
Xiaoqiong Yi ◽  
Zhenyi Shi ◽  
Feiyan Ou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Prognostic Value ◽  
Prognostic Model ◽  
Stepwise Regression ◽  
Gene Signature ◽  
Training Set ◽  
Model Based ◽  
Kaplan Meier ◽  
Validation Set

Objective: The aim of the present study was to construct a prognostic model based on the peptidyl prolyl cis–trans isomerase gene signature and explore the prognostic value of this model in patients with hepatocellular carcinoma.Methods: The transcriptome and clinical data of hepatocellular carcinoma patients were downloaded from The Cancer Genome Atlas and the International Cancer Genome Consortium database as the training set and validation set, respectively. Peptidyl prolyl cis–trans isomerase gene sets were obtained from the Molecular Signatures Database. The differential expression of peptidyl prolyl cis–trans isomerase genes was analyzed by R software. A prognostic model based on the peptidyl prolyl cis–trans isomerase signature was established by Cox, Lasso, and stepwise regression methods. Kaplan–Meier survival analysis was used to evaluate the prognostic value of the model and validate it with an independent external data. Finally, nomogram and calibration curves were developed in combination with clinical staging and risk score.Results: Differential gene expression analysis of hepatocellular carcinoma and adjacent tissues showed that there were 16 upregulated genes. A prognostic model of hepatocellular carcinoma was constructed based on three gene signatures by Cox, Lasso, and stepwise regression analysis. The Kaplan–Meier curve showed that hepatocellular carcinoma patients in high-risk score group had a worse prognosis (p < 0.05). The receiver operating characteristic curve revealed that the area under curve values of predicting the survival rate at 1, 2, 3, 4, and 5 years were 0.725, 0.680, 0.644, 0.630, and 0.639, respectively. In addition, the evaluation results of the model by the validation set were basically consistent with those of the training set. A nomogram incorporating clinical stage and risk score was established, and the calibration curve matched well with the diagonal.Conclusion: A prognostic model based on 3 peptidyl prolyl cis–trans isomerase gene signatures is expected to provide reference for prognostic risk stratification in patients with hepatocellular carcinoma.

scholarly journals Identification of Aging-Related Genes Associated With Clinical and Prognostic Features of Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Xingte Chen ◽  
Lei Wang ◽  
Liang Hong ◽  
Zhixiong Su ◽  
Xiaohong Zhong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Prognostic Features

Background: Aging is a well-studied concept, but no studies have comprehensively analyzed the association between aging-related genes (AGs) and hepatocellular carcinoma (HCC) prognosis.Methods: Gene candidates were selected from differentially expressed genes and prognostic genes in The Cancer Genome Atlas (TCGA) database. A gene risk score for overall survival prediction was established using the least absolute shrinkage and selection operator (LASSO) regression analysis, and this was validated using data from the International Cancer Genome Consortium (ICGC) database. Functional analysis was conducted using gene ontology enrichment, Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis, and immune microenvironment and tumor stemness analyses.Results: Initially, 72 AGs from the TCGA database were screened as differentially expressed between normal and tumor tissues and as genes associated with HCC prognosis. Then, seven AGs (POLA1, CDK1, SOCS2, HDAC1, MAPT, RAE1, and EEF1E1) were identified using the LASSO regression analysis. The seven AGs were used to develop a risk score in the training set, and the risk was validated to have a significant prognostic value in the ICGC set (p < 0.05). Patients with high risk scores had lower tumor differentiation, higher stage, and worse prognosis (all p < 0.05). Multivariate Cox regression analyses also confirmed that the risk score was an independent prognostic factor for HCC in both the TCGA and ICGC sets (all p < 0.05). Further analysis showed that a high risk score was correlated with the downregulation of metabolism and tumor immunity.Conclusion: The risk score predicts HCC prognosis and could thus be used as a biomarker not only for predicting HCC prognosis but also for deciding on treatment.

scholarly journals Comprehensive analysis of lymph nodes metastasis associated genes in cervical cancer and its significance in treatment and prognosis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ping Yang ◽  
Youqin Ruan ◽  
Zhiling Yan ◽  
Yang Gao ◽  
Hongying Yang ◽  
...  
Keyword(s):  
Lymph Node ◽  
High Risk ◽  
Lymph Node Metastasis ◽  
Cervical Carcinoma ◽  
Risk Score ◽  
Risk Groups ◽  
Node Metastasis ◽  
Prognostic Gene ◽  
Prognostic Gene Signature ◽  
Score Model

Abstract Background Cervical carcinoma is one of the most common malignant tumors of the female reproductive system. Lymph nodes metastasis, the most common metastasis, which can be detected even in small-size tumor patients, results in worse prognosis. Therefore, it is of great significance to explore novel lymph nodes metastasis associated biomarkers, which can predict the prognosis and provide a good reference for clinical decision making in cervical carcinoma patients. However, systematic and comprehensive studies related to the key molecules in lymph node metastasis in cervical carcinoma patients are still absent. Methods Transcriptome and clinical data of 307 cervical carcinoma patients were obtained from The Cancer Genome Atlas (TCGA). Then, survival of patients with and without lymph node metastasis was analyzed by Kaplan-Meier (K-M) curves. Differential expressed genes (DEGs) were detected between tumor and control samples using limma package and defined as lymph node metastasis related genes. Univariate and multivariate Cox regression analyses were carried out to screen robust prognostic gene signature. The risk score model and nomogram for predicting survival were constructed based on prognostic gene signature. The performance of the risk score model was evaluated by operating characteristic (ROC) curves. Based on risk score, patients were divided into low- and high- risk groups. DEGs, functional enrichment analysis and tumor microenvironment (immune infiltration and expressions of immune checkpoints) were detected in low- and high-risk groups. Results A total of 103 lymph node metastasis-associated genes were identified. Univariate and multivariate Cox regression analyses identified TEKT2, LPIN2, FABP4 and CXCL2 as prognostic gene signature. The risk score model was constructed and validated in cervical carcinoma patients. 345 DEGs identified between high- and low-risk groups were significantly enriched into immune-related biological processes. Furthermore, we found that the immune infiltration and expressions of immune checkpoints were significantly different between low- and high-risk groups. Conclusion Our study revealed that lymph node metastasis played an important role in the prognosis of cervical carcinoma patients. Furthermore, we established a risk score model based on lymph node metastasis related genes, which could accurately predict the survival of cervical carcinoma patients. Besides, our findings in tumor microenvironments of low- and high-risk groups improved our understanding of the relationship between lymph node metastasis related genes and cervical carcinoma.

scholarly journals Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma

2020 ◽  
Author(s):  
Cankun Zhou ◽  
Chaomei Li ◽  
Fangli Yan ◽  
Yuhua Zheng
Keyword(s):  
Endometrial Carcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Gene Signature ◽  
Cancer Genome ◽  
Functional Enrichment ◽  
Immune Gene ◽  
Uterine Corpus ◽  
Immune Genes

Abstract Background: Uterine corpus endometrial carcinoma (UCEC) is a frequent gynecological malignancy with a poor prognosis especially when at an advanced stage. In the present study, we explored the potential of an immune-related gene signature to predict overall survival in UCEC patients.Methods: We analyzed expression data of 616 UCEC patients from The Cancer Genome Atlas database and the International Cancer Genome Consortium as well as immune genes from the ImmPort database and identified the signature. We constructed a transcription factor regulatory network based on Cistrome databases and performed functional enrichment and pathway analyses for the differentially expressed immune genes. Moreover, the prognostic value of 410 immune genes was determined using Cox regression analysis then constructed a prognostic model. Finally, we performed immune infiltration analysis using TIMER-generating immune cell content.Results: Results indicated that the immune cell microenvironment as well as the PI3K-Akt, and MARK signaling pathways were involved in UCEC development. The established prognostic model revealed a ten-gene prognosis signature , comprising PDIA3, LTA, PSMC4, TNF, SBDS, HDGF, HTR3E, NR3C1, PGR, and CBLC . This can be used as an independent tool to predict the prognosis of UCEC owing to the observed risk-score. In addition, levels of B cells and neutrophils were significantly correlated with the patient's risk score, and the expression of ten genes is associated with immune cell infiltrates.Conclusions: In summary, we present a 10-gene signature with the potential to predict the prognosis of UCEC. This is expected to guide future development of individualized treatment approaches.

scholarly journals Identification of the Pyroptosis-Related Gene Signature for Overall Survival Prediction in Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Shuang Liu ◽  
Ruonan Shao ◽  
Xiaoyun Bu ◽  
Yujie Xu ◽  
Ming Shi
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Multivariable Analysis ◽  
Low Risk ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Incidence And Mortality ◽  
Risk Patients

Hepatocellular carcinoma (HCC) is the second most lethal malignant tumor worldwide, with an increasing incidence and mortality. Due to general resistance to antitumor drugs, only limited therapies are currently available for advanced HCC patients, leading to a poor prognosis with a 5-year survival rate less than 20%. Pyroptosis is a type of inflammation-related programmed cell death and may become a new potential target for cancer therapy. However, the function and prognostic value of pyroptosis-related genes (PRGs) in HCC remain unknown. Here, we identified a total of 58 PRGs reported before and conducted a six-PRG signature via the LASSO regression method in the GEO training cohort, and model efficacy was further validated in an external dataset. The HCC patients can be classified into two subgroups based on the median risk score. High-risk patients have significantly shorter overall survival (OS) than low-risk patients in both training and validation cohorts. Multivariable analysis indicated that the risk score was an independent prognostic factor for OS of HCC patients. Functional enrichment analysis and immune infiltration evaluation suggested that immune status was more activated in the low-risk group. In summary, PRGs can be a prediction factor for prognosis of HCC patients and targeting pyroptosis is a potential therapeutic alternative in HCC.

scholarly journals Development of a Prognostic Gene Signature For Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Cuiyun Wu ◽  
Yaosheng Luo ◽  
Yinghui Chen ◽  
Hongling Qu ◽  
Lin Zheng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Differentially Expressed Genes ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Risk Scores ◽  
Prognostic Gene ◽  
Prognostic Gene Signature

Abstract Background: Accurate prediction of overall survival is important for prognosis and the assignment of appropriate personalized clinical treatment in hepatocellular carcinoma (HCC) patients. The aim of the present study was to establish an optimal gene model for the independent prediction of prognosis associated with common clinical patterns.Methods: Gene expression profiles and the corresponding clinical information of the LIHC cohort were obtained from The Cancer Genome Atlas. Differentially expressed genes were found using the R package “limma”. Subsequently, a prognostic gene signature was developed using the LASSO Cox regression model. Kaplan–Meier, log-rank, and receiver operating characteristic (ROC) analyses were performed to verify the predictive accuracy of the prognostic model. Finally, a nomogram and calibration plot were created using the “rms” package.Results: Differentially expressed genes were screened with threshold criteria (FDR < 0.01 and |log FC|>3) and 563 differentially expressed genes were obtained, including 448 downregulated and 115 upregulated genes. Using the LASSO Cox regression model, a prognostic gene signature was developed based on nine genes,IQGAP3, BIRC5, PTTG1, STC2, CDKN3, PBK, EXO1, NEIL3, and HOXD9, the expression levels of which were quantitated using RT-qPCR. According to the risk scores, patients were separated into high-risk and low-risk groups. Patients with lower risk scores generally had a better survival rate than those with higher risk scores. The mortality rate in the high-risk group was 42.02%, while that in the low-risk group was 12.50%. Results of the log-rank test showed significant differences in mortality between the two groups (HR: 4.86; 95% CI: 2.72–8.69; P = 1.01E-08). Subsequently, we assessed the prognostic accuracy of the gene signature using an ROC curve and the results show good sensitivity and specificity, with an average area under the curve (AUC) of 0.81 at 5 years (P < 0.01). Following multivariate adjustment for conventional clinical patterns, the prognostic gene signature remained a powerful and independent factor (HR: 4.70; 95% CI: 2.61–8.38; P = 2.06E-07), confirming its robust predictive ability of overall survival in HCC patients. Finally, a nomogram was established based on the gene signature and four clinicopathological features, which demonstrated an advantageous discriminating ability with the potential to facilitate clinical decision-making in HCC.Conclusion: Our prognostic gene signature can be used as a combined biomarker for the independent prediction of overall survival in HCC patients. Moreover, we created a nomogram that can be used to infer prognosis and aid individualized decisions regarding treatment and surveillance.

Nine glycolysis-related gene signature predicting the survival of patients with endometrial adenocarcinoma

2020 ◽  
Author(s):  
liu jinhui ◽  
Li siyue ◽  
Gao feng ◽  
meng huangyang ◽  
Nie sipei ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Cellular Respiration ◽  
Immunohistochemistry Staining ◽  
Glycolysis Pathway

Abstract Background: Endometrial cancer is the fourth most common cancer in women. The death rate for endometrial cancer has increased. Glycolysis of cellular respiration is a complex reaction and is the first step in most carbohydrate catabolism, which was proved to participate in tumors. Methods: We analyzed the sample data of over 500 patients from TCGA database. The bioinformatic analysis included GSEA, cox and lasso regression analysis to select prognostic genes, as well as construction of a prognostic model and a nomogram for OS evaluation. The immunohistochemistry staining, survival analysis and expression level validation were also performed. Maftools package was for mutation analysis. GSEA identified Glycolysis was the most related pathway to EC. Results: According to the prognostic model using the train set, 9 glycolysis-related genes including B3GALT6, PAM, LCT, GMPPB, GLCE, DCN, CAPN5, GYS2 and FBP2 were identified as prognosis-related genes. Based on nine gene signature, the EC patients could be classified into high and low risk subgroups, and patients with high risk score showed shorter survival time. Time-dependent ROC analysis and Cox regression suggested that the risk score predicted EC prognosis accurately and independently. Analysis of test and train sets yielded consistent results A nomogram which incorporated the 9‐mRNA signature and clinical features was also built for prognostic prediction. Immunohistochemistry staining and TCGA validation showed that expression levels of these genes do differ between EC and normal tissue samples. GSEA revealed that the samples of the low-risk group were mainly concentrated on Bile Acid Metabolism. Patients in the low-risk group displayed obvious mutation signatures compared with those in the high-risk group. Conclusion: This study found that the Glycolysis pathway is associated with EC and screened for hub genes on the Glycolysis pathway, which may serve as new target for the treatment of EC.

scholarly journals Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Zhengyu Fang ◽  
Sumei Xu ◽  
Yiwen Xie ◽  
Wenxi Yan
Keyword(s):  
Colon Cancer ◽  
Prognostic Model ◽  
Cox Regression ◽  
Predictive Ability ◽  
Gene Signature ◽  
Training Dataset ◽  
Normal Tissues ◽  
Prognostic Gene ◽  
Prognostic Gene Signature ◽  
T Classification

Abstract Background Colon cancer is a worldwide leading cause of cancer-related mortality, and the prognosis of colon cancer is still needed to be improved. This study aimed to construct a prognostic model for predicting the prognosis of colon cancer. Methods The gene expression profile data of colon cancer were obtained from the TCGA, GSE44861, and GSE44076 datasets. The WGCNA module genes and common differentially expressed genes (DEGs) were used to screen out the prognosis-associated DEGs, which were used to construct a prognostic model. The performance of the prognostic model was assessed and validated in the TCGA training and microarray validation sets (GSE38832 and GSE17538). At last, the model and prognosis-associated clinical factors were used for the construction of the nomogram. Results Five colon cancer-related WGCNA modules (including 1160 genes) and 1153 DEGs between tumor and normal tissues were identified, inclusive of 556 overlapping DEGs. Stepwise Cox regression analyses identified there were 14 prognosis-associated DEGs, of which 12 DEGs were included in the optimized prognostic gene signature. This prognostic model presented a high forecast ability for the prognosis of colon cancer both in the TCGA training dataset and the validation datasets (GSE38832 and GSE17538; AUC > 0.8). In addition, patients’ age, T classification, recurrence status, and prognostic risk score were associated with the prognosis of TCGA patients with colon cancer. The nomogram was constructed using the above factors, and the predictive 3- and 5-year survival probabilities had high compliance with the actual survival proportions. Conclusions The 12-gene signature prognostic model had a high predictive ability for the prognosis of colon cancer.

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