Identification of an Immune Signature Predicting Prognosis Risk and Lymphocyte Infiltration in Colon Cancer

• Published in: Frontiers in immunology 2020-09, Vol.11, p.1678-1678
• Main Authors: Li, Xinyu, Wen, Dacheng, Li, Xiaokang, Yao, Chunli, Chong, Wei, Chen, Hao
• Format: Article
• Language: English
• Subjects: Biomarkers, Tumor - genetics; Clinical Decision-Making; colon cancer; Colonic Neoplasms - genetics; Colonic Neoplasms - immunology; Colonic Neoplasms - mortality; Colonic Neoplasms - therapy; Databases, Genetic; DNA Mutational Analysis; Gene Expression Profiling; Humans; immune infiltration; immune signature; Immunology; Lymphocytes, Tumor-Infiltrating - immunology; Mutation; Oligonucleotide Array Sequence Analysis; Predictive Value of Tests; Prognosis; Risk Assessment; Risk Factors; significantly mutated genes; Transcriptome; Tumor Microenvironment; tumor mutation load
• ISSN: 1664-3224; 1664-3224
• DOI: 10.3389/fimmu.2020.01678

Increasing studies have highlighted the effects of the tumor immune micro-environment (TIM) on colon cancer (CC) tumorigenesis, prognosis, and metastasis. However, there is no reliable molecular marker that can effectively estimate the immune infiltration and predict the CC relapse risk. Here, we leveraged the gene expression profile and clinical characteristics from 1430 samples, including four gene expression omnibus database (GEO) databases and the cancer genome atlas (TCGA) database, to construct an immune risk signature that could be used as a predictor of survival outcome and immune activity. A risk model consisting of 10 immune-related genes were screened out in the Lasso-Cox model and were then aggregated to generate the immune risk signature based on the regression coefficients. The signature demonstrated robust prognostic ability in discovery and validation datasets, and this association remained significant in the multivariate analysis after controlling for age, gender, clinical stage, or microsatellite instability status. Leukocyte subpopulation analysis indicated that the low-risk signature was enriched with cytotoxic cells (activated CD4/CD8 T cell and NK cell) and depleted of myeloid-derived suppressor cells (MDSC) and regulatory T cells. Further analysis indicated patients with a low-risk signature harbored higher tumor mutation loads and lower mutational frequencies in significantly mutated genes of and . Together, our constructed signature could predict prognosis and represent the TIM of CC, which promotes individualized treatment and provides a promising novel molecular marker for immunotherapy.