An Immune Infiltration-Based Risk Scoring System for Prognostic Stratification in Colorectal Adenocarcinoma

TL;DR

This study develops and validates a gene-based risk scoring system using immune cell-associated genes to improve prognostic stratification in colorectal adenocarcinoma, outperforming traditional methods.

Contribution

Introduces a novel immune infiltration-based risk score combining CCL8 and TYR genes, validated across multiple cohorts for better prognostic accuracy in CRC.

Findings

Risk score effectively stratifies patients into prognostic groups.

High AUC values indicate good predictive performance.

Independent prognostic value confirmed in multivariate analysis.

Abstract

Background: Colorectal adenocarcinoma (CRC) remains a leading cause of cancer-related mortality worldwide, with variable patient outcomes despite treatment advances. Traditional prognostic methods based on clinicopathological variables alone do not fully capture the biological complexity of the disease. This study aims to develop a risk scoring system based on genes associated with tumor-infiltrating immune cells (TIIC-associated genes) to improve prognostic assessment in CRC. Methods: RNA-seq gene expression and clinicopathological data from TCGA-CRC (647 tumor samples, 51 normal tissues) were analyzed to identify differentially expressed TIIC-associated genes through comparison with the CIBERSORTx database. Univariate and multivariate Cox analyses were performed to screen for prognostic markers. A Gaussian mixture model was applied to cluster prognostic models and select the model with the most robust gene combination. The resulting risk scoring system was validated in an external cohort (GSE39582) and integrated with clinicopathological variables to develop a prognostic nomogram. Results: From 128 TIIC-associated genes, an optimal prognostic model comprising CCL8 and TYR was identified. The risk score was calculated as 0.152*Exp(CCL8) - 0.516*Exp(TYR). Kaplan-Meier analysis confirmed significant survival differences between high-risk and low-risk groups in both TCGA-CRC (p < 0.05) and GSE39582 (p < 0.05). Time-dependent ROC analysis showed AUC values ranging from 0.605 to 0.696 for 1-year, 3-year, and 5-year survival in TCGA-CRC and GSE39582. Multivariate Cox analysis identified TNM_T, TNM_N, and risk score as independent prognostic factors. Conclusion: Our risk scoring system based on CCL8 and TYR effectively stratifies CRC patients into distinct prognostic groups and could guide treatment decisions, particularly when integrated with TNM staging in a nomogram.