A Novel Four-Gene Signature Stratifies Prognosis and Reflects the Immune Landscape in Colorectal Cancer

Background: Colorectal cancer (CRC) exhibits significant molecular heterogeneity, leading to diverse clinical outcomes and highlighting the need for more accurate prognostic biomarkers. This study aimed to identify a novel gene signature to improve risk stratification and to explore its underlying biological relevance, particularly in relation to the tumor immune landscape. Results: A novel four-gene signature comprising SLC16A8, MAGEA1, LINC00634, and PPFIA4 was developed and validated. This signature effectively stratified patients into high- and low-risk groups with markedly different overall survival (p < 0.0001). The model demonstrated strong predictive accuracy for 1-, 3-, and 5-year survival (AUCs = 0.706, 0.735, 0.693, respectively). Importantly, multivariate Cox regression confirmed the signature as a powerful and independent prognostic factor (HR = 3.50, 95% CI = 2.10-5.80, p < 0.001). A clinically practical nomogram integrating the signature was constructed and showed excellent calibration. Furthermore, the risk score was significantly correlated with the infiltration levels of several key immune cells, suggesting that the signature reflects the host's anti-tumor immune status. Conclusions: We have successfully established and validated a novel four-gene signature that serves as an independent and powerful prognostic biomarker for CRC. This signature not only improves personalized risk stratification but also provides a potential link between the tumor's intrinsic molecular features and the surrounding immune landscape. The constructed nomogram offers a valuable tool to aid in clinical decision-making for CRC patients. Methods: Based on an integrated analysis of transcriptome data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project, we identified a pool of candidate prognostic genes. A robust prognostic signature was constructed using LASSO-Cox regression analysis. The signature's performance was comprehensively validated in the TCGA cohort, and its independence from conventional clinicopathological factors was assessed. A nomogram was developed to enhance its clinical utility. The CIBERSORT algorithm was used to investigate the association between the signature and tumor-infiltrating immune cells.