Prediction of colon cancer prognosis and treatment based on immune-related lncRNAs and tumor microenvironment

Background Increasing evidence suggests that long non-coding RNAs (lncRNAs) are closely related to the development of tumors, and no exception is made in colon cancer, which plays a crucial role in the development, metastasis, and prognosis of colon cancer. In this study, we analyzed the relationship between immune-related genes and lncRNAs and the prognosis of colon cancer based on the tumor microenvironment, and further explored possible small molecule inhibitors in order to provide more directions for clinical medication. Method We used transcriptomic and clinical data from colon cancer patients from The Cancer Genome Atlas (TCGA). Using weighted gene co-expression network analysis(WGCNA) correlation analysis, the relationship between relevant clinical traits and prognosis was analyzed, and risk-related genes and lncRNAs were identified. Subsequently, we constructed a prognostic correlation model using LASSO regression and validated the model using univariate and multivariate Cox regression analysis. In addition, we performed correlation analyses of risk scores and characteristics for survival and prognosis. Results We found 147 immune genes and lncRNAs associated with prognosis by WGCNA analysis, and then used LASSO regression analysis and cross-validation to find 37 immune genes and lncRNAs corresponding to the points with the smallest errors for the construction of prognosis-related models, risk scoring was performed for each sample, and patients were divided into two groups according to the median value: low-risk group and high-risk group. Then differential analysis, survival analysis, prognostic analysis and bioinformatics analysis of tumor microenvironment were performed for genes and lncRNAs in the high and low risk groups to obtain differences in expression levels as well as risk scores in the high and low risk groups, which had a good predictive ability for the prognosis of colon cancer patients compared with other clinical biomarkers. By enrichment analysis, we found that these prognostically relevant genes were associated with multiple biological functions and multiple signaling pathways. In addition, small molecule drugs that can inhibit high-risk genes were found by analyzing genes in the CMAP database in high and low risk groups. Conclusion Our study systematically assessed the role of immune-related lncRNAs in the tumor microenvironment and colon cancer prognosis. Risk scores based on genes and lncRNAs can reflect the survival and prognosis of patients with colon cancer. Model validation also shows that the corresponding genes and lncRNAs can be used as reliable biomarkers for predicting the prognosis and treatment response of patients with colon cancer.