Comprehensive analyses of correlation and survival reveal informative lncRNA prognostic signatures in colon cancer.

BACKGROUND:Colon cancer is a commonly worldwide cancer with high morbidity and mortality. Long non-coding RNAs (lncRNAs) are involved in many biological processes and are closely related to the occurrence of colon cancer. Identification of the prognostic signatures of lncRNAs in colon cancer has great significance for its treatment. METHODS:We first identified the colon cancer-related mRNAs and lncRNAs according to the differential analysis methods using the expression data in TCGA. Then, we performed correlation analysis between the identified mRNAs and lncRNAs by integrating their expression values and secondary structure information to estimate the co-regulatory relationships between the cancer-related mRNAs and lncRNAs. Besides, the competing endogenous RNA regulation network based on co-regulatory relationships was constructed to reveal cancer-related regulatory patterns. Meanwhile, we used traditional regression analysis (univariate Cox analysis, random survival forest analysis, and lasso regression analysis) to screen the cancer-related lncRNAs. Finally, by combining the identified colon cancer-related lncRNAs according to the above analyses, we constructed a risk prognosis model for colon cancer through multivariate Cox analysis and also validated the model in the colon cancer dataset in TCGA cohorts. RESULTS:Six lncRNAs were found highly correlated with the overall survival of colon cancer patients, and a risk prognosis model based on them was constructed to predict the overall survival of colon cancer patients. In particular, EVX1-AS, ZNF667-AS1, CTC-428G20.6, and CTC-297N7.9 were first reported to be related to colon cancer by using our model, among which EVX1-AS and ZNF667-AS1 have been predicted to be related to colon cancer in LncRNADisease database. CONCLUSIONS:This study identified the potential regulatory relationships between lncRNAs and mRNAs by integrating their expression values and secondary structure information and presented a significant 6-lncRNA risk prognosis model to predict the overall survival of colon cancer patients.