Construction and validation of a metabolic-associated lncRNA risk index for predicting colorectal cancer prognosis

BACKGROUND: Metabolic reprogramming is one of the most important events in the development of tumors. Similarly, long non-coding RNAs are closely related to the occurrence and development of colorectal cancer (CRC). However, there is still a lack of systematic research on metabolism-related lncRNA in CRC. METHODS: Expression data of metabolism-related genes and lncRNA were obtained from The Cancer Genome Atlas (TCGA). Hub metabolism-related genes (HMRG) were screened out by differential analysis and univariate Cox analysis; a metabolism-related lncRNA risk index (MRLncRI) was constructed by co-expression analysis, univariate Cox regression analysis, LASSO, and multivariate Cox regression analysis. Survival curves were drawn by the Kaplan-Meier method. The ssGSEA method assessed the tumor microenvironment of the sample, and the IPS assessed the patient’s response to immunotherapy. “Oncopredict” assessed patient sensitivity to six common drugs. RESULTS: MRLncRI has excellent predictive ability for CRC prognosis. Based on this, we also constructed a nomogram that is more suitable for clinical applications. Most immune cells and immune-related terms were higher in the high-risk group. IPS scores were higher in the high-risk group. In addition, the high-risk and low-risk groups were sensitive to different drugs. CONCLUSION: MRLncRI can accurately predict the prognosis of CRC patients, is a promising biomarker, and has guiding significance for the clinical treatment of CRC.