Comprehensive analysis of m6A-related lncRNAs as prognosis factors in the immune microenvironment of colorectal cancer.

BACKGROUND

The prognosis of colorectal cancer (CRC) varies significantly across different immune subtypes. This study aimed to develop a risk prediction model incorporating the tumor immune microenvironment (TIME) to improve prognosis assessment and predict immunotherapy response in CRC patients, given the significant variability in clinical outcomes across different immune subtypes.

METHODS

CRC transcriptome data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses were employed to identify m6A-related long non-coding RNAs (lncRNAs) (mRLs). A risk model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression and further validated through nomogram analysis, time-dependent receiver operating characteristic (ROC) curves, and Kaplan-Meier survival analysis. Differences in immune infiltration scores and clinical characteristics between low-risk group (LRG) and high-risk group (HRG) were also investigated.

RESULTS

An 11-mRL signature model was established based on their expression profiles in CRC and correlation with m6A regulatory factors. This model demonstrated strong predictive performance for OS, as confirmed by Kaplan-Meier analysis, ROC curves, and Cox regression. Notably, the HRG exhibited significantly higher infiltration of specific immune cells and elevated expression of immune checkpoints [programmed cell death protein 1 (PD-1), programmed death-ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA4)] compared to the LRG. Furthermore, the two groups showed distinct responses to immunotherapy, suggesting potential utility in guiding immunosuppressant selection. A nomogram integrating m6A-immune signatures and clinicopathological variables was developed to individualize prognosis prediction.

CONCLUSIONS

This study constructed an mRLs risk model that effectively predicts CRC prognosis and immune profiles, offering a potential tool for personalized therapeutic decision-making.