Establishment and verification of a prognostic tumor microenvironment-based and immune-related gene signature in colon cancer

ABSTRACT:

Background

Gastrointestinal malignant cancers affect many sites in the intestinal tract, including the colon. In this study, we purposed to improve prognostic predictions for colon cancer (CC) patients by establishing a novel biosignature of immune-related genes (IRGs) based on the tumor microenvironment (TME).

Methods

Using the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm, we calculated the stromal and immune scores of every CC patient extracted from The Cancer Genome Atlas (TCGA). We then identified 4 immune-related messenger RNA (mRNA) biosignatures through a Cox and least absolute shrinkage and selection operator (LASSO) univariate analysis, and a Cox multivariate analysis. Relationships between tumor immune infiltration and the risk score were evaluated through the CIBERSORT algorithm and Tumor Immune Estimation Resource (TIMER) database.

Results

Our studies showed that individuals who had a high immune score (P=0.017) and low stromal score (P=0.041) had a favorable overall survival (OS) rate. By comparing high/low scores cohort, 220 differentially expressed genes (DEGs) were determined. Then an immune-related four-mRNA biosignature, including PDIA2, NAFTC1, VEGFC, and CD1B was identified. Kaplan-Meier, calibration, and receiver operating characteristic (ROC) curves verified the model’s performance. By using univariate and multivariate Cox analyses, we found each biosignature was an independent risk factor for assessing a CC patient’s survival. Three external GEO cohorts validated its good efficiency in estimating OS among individuals with CC. Moreover, the signature was also related to infiltration of several cells of the immune system in the tumor microenvironment.

Conclusions

The resultant model in our study included 4 IRGs associated with the TME. These IRGs can be utilized as an auxiliary variable to estimate and help improve the prognosis of individuals with CC.

SUBMITTER: Guo T 

PROVIDER: S-EPMC8576251 | biostudies-literature |

REPOSITORIES: biostudies-literature