Dataset Information

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

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.

SUBMITTER: Fang Z

PROVIDER: S-EPMC7807455 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

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

Fang Zhengyu Z Xu Sumei S Xie Yiwen Y Yan Wenxi W

World journal of surgical oncology 20210113 1

<h4>Background</h4>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.<h4>Methods</h4>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 DE ...[more]

PMID: 33441161

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Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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