Identification and Verification of a 17 Immune-Related Gene Pair Prognostic Signature for Colon Cancer.

Abstract

BACKGROUND:

Colon cancer (CC) is a malignant tumor with a high incidence and poor prognosis. Accumulating evidence shows that the immune signature plays an important role in the tumorigenesis, progression, and prognosis of CC. Our study is aimed at establishing a novel robust immune-related gene pair signature for predicting the prognosis of CC.

METHODS:

Gene expression profiles and corresponding clinical information are obtained from two public data sets: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO, GSE39582). We screened out immune-related gene pairs (IRGPs) associated with prognosis in the discovery cohort. Lasso-Cox proportional hazard regression was used to develop the best prognostic signature model. According to this, the patients in the validation cohort were divided into high immune-risk group and low immune-risk group, and the prediction ability of the signature model was verified by survival analysis and independent prognostic analysis.

RESULTS:

A total of 17 IRGPs composed of 26 IRGs were used to construct a prognostic-related risk scoring model. This model accurately predicted the prognosis of CC patients, and the patients in the high immune-risk group indicated poor prognosis in the discovery cohort and validation cohort. Besides, whether in univariate or multivariate analysis, the IRGP signature was an independent prognostic factor. T cell CD4 memory resting in the low-risk group was significantly higher than that in the high-risk group. Functional analysis showed that the biological processes of the low-risk group included "TCA cycle" and "RNA degradation," while the high-risk group was enriched in the "CAMs" and "focal adhesion" pathways.

CONCLUSION:

We have successfully established a signature model composed of 17 IRGPs, which provides a novel idea to predict the prognosis of CC patients.

• Authors: Qianshi Zhang, Zhen Feng, Yongnian Zhang, Shasha Shi, Yu Zhang, Shuangyi Ren
• Journal: BioMed research international (Biomed Res Int) Vol. 2021 Pg. 6057948 ( 2021) ISSN: 2314-6141 [Electronic] United States
• PMID: 34124251 (Publication Type: Journal Article)
• Copyright: Copyright © 2021 Qianshi Zhang et al.

Topics

• Colonic Neoplasms (genetics, immunology, mortality)
• Disease-Free Survival
• Gene Expression Regulation, Neoplastic (immunology)
• Genes, Neoplasm (immunology)
• Humans
• Models, Immunological
• Survival Rate
• Transcriptome