Construction of immune-related lncRNA signature to predict aggressiveness, immune landscape, and drug resistance of colon cancer

Abstract

Background: Colon cancer remains one of the most common malignancies across the world. Thus far, a biomarker, which can comprehensively predict the survival outcomes, clinical characteristics, and therapeutic sensitivity, is still lacking. Methods: We leveraged transcriptomic data of colon cancer from the existing datasets and constructed immune-related lncRNA (irlncRNA) pairs. After integrating with clinical survival data, we performed differential analysis and identified 11 irlncRNAs signature using Lasso regression analysis. We next plotted the 1-, 5-, and 10-year curve lines of receiver operating characteristics, calculated the areas under the curve, and recognized the optimal cutoff point. Then, we validated the pair-risk model in terms of the survival outcomes of the patients involved. Moreover, we tested the reliability of the model for predicting tumor aggressiveness and therapeutic susceptibility of colon cancer. Additionally, we reemployed the 11 of irlncRNAs involved in the pair-risk model to construct an expression-risk model to predict the prognostic outcomes of the patients involved. Results: We recognized a total of 377 differentially expressed irlncRNAs (DEirlcRNAs), including 28 low-expressed and 349 high-expressed irlncRNAs in colon cancer patients. After performing a univariant Cox analysis, we identified 115 risk irlncRNAs that were significantly correlated with survival outcomes of patients involved. By taking the overlap of the DEirlcRNAs and the risk irlncRNAs, we ultimately recognized 55 irlncRNAs as core irlncRNAs. Then, we established a Cox HR model (pair-risk model) as well as an expression HR model (exp-risk model) based on 11 of the 55 core irlncRNAs. We found that both of the two models significantly outperformed the commonly used clinical characteristics, including age, T, N, and M stages when predicting survival outcomes. Moreover, we validated the pair-risk model as a potential tool for studying the tumor microenvironment of colon cancer and drug susceptibility. Additionally, we noticed that combinational use of the pair-risk model and the exp-risk model yielded a more robust approach for predicting the survival outcomes of patients with colon cancer. Conclusions: We recognized 11 irlncRNAs and created a pair-risk model and an exp-risk model, which have the potential to predict clinical characteristics of colon cancer, either solely or conjointly.