| Objective: This study aims to construct a colorectal cancer (CRC) prognosis prediction model based on disulfide death associated long-chain non coding ribonucleic acids (DRLncRNAs), and to explore its potential application in predicting prognosis and guiding personalized treatment. Method: The data for this study was sourced from the Cancer Genome Atlas (TCGA) database, which includes RNA sequencing data and detailed clinical information of CRC patients. Using co expression network analysis to screen DRLncRNAs. Preliminary screening of DRLncRNAs related to prognosis was conducted through univariate Cox regression analysis, followed by variable screening and dimensionality reduction using LASSO Cox regression analysis. Finally, key DRLncRNAs for constructing a prognostic risk model were identified through multivariate Cox regression analysis. Evaluate the predictive ability of prognostic risk models for survival in CRC patients through Kaplan Meier survival curve analysis. The accuracy of the model was analyzed using receiver operating characteristic (ROC) curves, and the ability of different gene expression profiles to distinguish risk stratification was evaluated using principal component analysis (PCA). Gene ontology (GO) and KEGG enrichment analysis were used to evaluate the enrichment of high and low expression gene sets in different risk groups. Result: Five key DRLncRNAs (AC068580.3, AL161729.4, ZEB1-AS1, AC073896.3, and SNHG16) were successfully screened. In the overall concentration, the area under the curve (AUC) of the model''s 1-year, 3-year, and 5-year survival predictions are 0.674, 0.746, and 0.727, respectively; In the training set, the predicted AUC for 1-year, 3-year, and 5-year survival were 0.669, 0.762, and 0.756, respectively; In the test set, the predicted AUC for 1-year, 3-year, and 5-year survival were 0.666, 0.725, and 0.697, respectively. SNHG16 and AC073896.3 are protective factors, and high expression was associated with better prognosis; ZEB1-AS1, AC068580.3, and AL161729.4 were risk factors, and high expression indicates poor prognosis. The Kaplan Meier survival analysis results showed that there was a significant difference in survival between the high-risk and low-risk groups (P<0.05), and the overall survival rate of the high-risk group was significantly lower (P<0.05). The enrichment analysis results showed that the phosphatidylinositol mediated signal transduction, mitochondrial gene expression, and other biological processes related to 5 DRLncRNAs may be involved in the occurrence, development, and poor prognosis of CRC. The results of this study showed that a prognostic risk model based on five DRLncRNAs (AC068580.3, AL161729.4, ZEB1-AS1, AC073896.3, and SNHG16) was successfully established, which showed independent correlation with the overall survival status of CRC patients. In addition, the prognostic risk model constructed based on these 5 DRLncRNAs can effectively predict the prognosis of CRC patients. Conclusion: This study identified five DRLncRNAs, including AC068580.3, AL161729.4, ZEB1-AS1, AC073896.3, and SNHG16, that are associated with the prognosis of CRC patients. The prognostic risk model constructed based on these five DRLncRNAs has a high evaluation efficiency for the prognosis of CRC patients. |
