Predictive model for prognosis, immune microenvironment and drug sensitivity of colon carcinoma based on...

Colon cancer is a major cause of morbidity and mortality worldwide. Copper-induced cell death, known as cuproptosis, is a form of apoptosis that has been extensively studied in human diseases and is widely associated with tumor progression, prognosis, and immune response. However, the role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of colon cancer remains unclear.

This study aims to explore the role of cuproptosis-related long non-coding RNAs (lncRNAs) in predicting the prognosis of colon cancer and to establish a risk prediction model based on these lncRNAs to guide clinical decisions and improve patient outcomes.

A total of 19 cuproptosis-related genes were collected, and 1330 lncRNAs associated with cuproptosis were identified. Seven cuproptosis-related lncRNAs with prognostic value were selected from The Cancer Genome Atlas (TCGA) database. Using R software (version 4.1.0), the expression levels of the 19 genes were extracted, and the subjects were divided into high- and low-risk subgroups. A risk score model was developed based on cuproptosis-related genes and the seven co-expressed lncRNAs. The dataset was randomly split into a training set and a validation set. Analysis of clinicopathologic features, TME infiltration, and mutations was conducted, and nomogram predictions were validated using calibration plots to assess the predictive accuracy of the model.

The high-risk group had significantly shorter overall survival compared to the low-risk group (P<0.001), and the risk score was an independent prognostic factor (P<0.001). In the training set, the AUC values at 1, 3, and 5 years were 0.666, 0.621, and 0.669, respectively. Furthermore, low-risk patients had a higher survival rate. The genetic markers also correlated with tumor immune cell infiltration, clinical features, and prognosis.

This study established a novel method based on cuproptosis-related lncRNAs to predict the prognosis of colon cancer. The model has potential clinical applications in identifying patients sensitive to immunotherapy and antitumor treatments, thereby enhancing precision treatment strategies for colon cancer.