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

BackgroundMetabolic 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.MethodsExpression 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.ResultsMRLncRI 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.ConclusionMRLncRI can accurately predict the prognosis of CRC patients, is a promising biomarker, and has guiding significance for the clinical treatment of CRC.