Research Paper Volume 12, Issue 24 pp 26095—26120

Identification and validation of an immune-related gene signature predictive of overall survival in colon cancer

Xuening Zhang1, *, , Hao Zhao1, *, , Xuezhong Shi1, , Xiaocan Jia2, , Yongli Yang1, ,

  • 1 Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, Henan, China
  • 2 Zhengzhou University Library, Zhengzhou University, Zhengzhou 450001, Henan, China
* Equal contribution

Received: September 21, 2020       Accepted: November 10, 2020       Published: December 19, 2020
How to Cite

Copyright: © 2020 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


The heterogeneity and complexity of tumor-immune microenvironments lead to diverse immunotherapy effects among colon cancer patients. It is crucial to identify immune microenvironment-related biomarkers and construct prognostic risk models. In this study, the immune and stromal scores of 415 cases from TCGA were calculated using the ESTIMATE algorithm. AXIN2, CCL22, CLEC10A, CRIP2, RUNX3, and TRPM5 were screened and established a prognostic immune-related gene (IRG) signature using by univariate, LASSO, and multivariate Cox regression models. The predicted performance of IRG signature was external validated by GSE39582 (n=519). Stratified survival analysis showed IRG signature was an effective predictor of survival in patients with different clinical characteristics. The protein expression level of six genes was validated by immunohistochemistry analysis. Difference analysis indicated the mutation rate, immune cell of resting NK cells and regulatory T cells infiltration and four immune checkpoints of PD-1, PD-L1, LAG3 and VSIR expression levels in the high-risk group were significantly higher than those in the low-risk group. A nomogram incorporating the gene signatures and clinical factors was demonstrated had a good accuracy (1-, 3-, and 5-year AUC= 0.799, 0.791, 0.738). Our study identified a novel IRG signature, which may provide some references for the clinical precision immunotherapy of patients.


PD-1: programmed cell death 1; cytotoxic T-lymphocyte antigen-4: CTLA-4; TME: tumor microenvironment; Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data: ESTIMATE; TMB: tumor mutation burden; Cell type Identification by Estimating Relative Subpopulations of RNA Transcription: CIBERSORT; IRG: immune-related genes; TCGA: The Cancer Genome Atlas; FPKM: Fragments per kilobase million; GDC: Genomic Data Commons; TPM: transcripts per million; GEO: Gene Expression Omnibus; OS: overall survival; FDR: false-discovery rate; DEGs: differentially expressed genes; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; BP: biological process; CC: cellular component; MF: molecular function; LASSO: least absolute shrinkage and selection operator; AUC: area under time-dependent receiver operating characteristic curve; IHC: immunohistochemistry; HPA: Human Protein Atlas; PD-L1: programmed cell death 1 ligand 1; LAG3: lymphocyte-activation gene 3; VSIR: V-Set immunoregulatory receptor.