Molecular subtypes identified by multiomics analysis based on cuproptosis-related genes precisely predict response to immunotherapy and chemotherapy in colorectal cancer.

Abstract

Cuproptosis is a newly reported type of programmed cell death that is involved in the progression of various diseases. Some studies have reported its potential significance in multiple tumors. Colorectal cancer (CRC) is one of the malignant tumors with high incidence and mortality. The purpose of this study was to further explore the importance of cuproptosis in the CRC development and treatment. We analyzed the expression, alterations, and promoter methylation of cuproptosis-related genes (CRGs) in patients with CRC. Three machine learning methods was used to determine cuproptosis-related feature genes and a diagnostic model was built based on them. Using the unsupervised clustering, patients with CRC were classified into distinct clusters. Then, the LASSO method was used to establish a cuproptosis risk model. We analyzed the association of risk scores with outcomes, immune microenvironment, response to immunotherapy, and sensitivity to chemotherapeutic drugs. The results showed that the expression of CRGs was dysregulated in CRC. The diagnostic model based on cuproptosis-related feature genes showed great clinical value. The patients in two clusters displayed different prognosis and microenvironment. Furthermore, the risk score was correlated with clinical characteristics, immune infiltration and response to immunotherapy and chemotherapy. Above all, the present findings revealed the involvement of cuproptosis in CRC development and provided a diagnostic tool to evaluate CRC occurrence risk. The immune infiltration and drug sensitivity analysis results helped to predict the response of patients in different subtypes of CRC to immunotherapy and chemotherapy.