[Multi-omics integration deciphers immune-metabolic heterogeneity in colorectal cancer: a prognostic model and therapeutic strategies targeting ANGPTL4/FABP4/RBP7].

OBJECTIVES

To explore the heterogeneity of tumor immune microenvironment (TIME) in colorectal cancer (CRC) and the resultant treatment resistance and develop a prognostic model for predicting the treatment outcomes of CRC.

METHODS

The multi-omics data including transcriptomic, somatic mutation and single-cell RNA sequencing data of a total of 1136 CRC samples from a TCGA cohort (n=568) and a GEO cohort (n=568) were integrated. The TCGA cohort was divided into Immunity_High and Immunity_Low subtypes by single-sample gene set enrichment analysis (ssGSEA) and t-SNE algorithm. A prognostic model was constructed using univariate Cox regression combined with LASSO-Cox regression, and the functions of the core genes including ANGPTL4, FABP4 and RBP7 were verified by single cell RNA-seq, molecular docking, kinetic simulations and qPCR/IHC experiments.

RESULTS

The Immunity_High subtype of CRC was enriched with CD8+ T cells and had significantly longer patient survival, while the Immunity_Low subtype was characterized by M0 macrophage infiltration. The prognostic model constructed based on 12 genes including ANGPTL4, FABP4 and RBP7 showed good predictive performance, with the AUC of 3-year survival rate reaching 0.765, an overall AUC of 0.76 in the TCGA cohort, and an overall AUC of 0.70 in the GEO cohort. Mechanistically, ANGPTL4 regulated macrophage polarization, FABP4 mediated fibroblast reprogramming, and RBP7 up-regulated PD-L1 expression. Molecular docking and kinetic simulations confirmed stable binding of retinoic acid and rosiglitazone to the core targets with the minimum binding energy of -8.3 kcal/mol.

CONCLUSIONS

The constructed prognostic model and the identified potential therapeutic targets provide new strategies to address immune therapy resistance for CRC.