Conclusion
Our study identified 4 methylated biomarkers in the COAD. Then, we constructed the risk model to provide a theoretical basis and reference value for the research and treatment of COAD.
Methods
COAD transcriptome data, methylation data and clinical information were downloaded from the TCGA database and GEO database. The differentially expressed genes (DEGs) and methylated genes (DMGs) were analyzed and identified in COAD. PCA analysis was applied to divide COAD into subtypes, and the survival and immune cell infiltration of each subtype were evaluated. Cox and LASSO analyses were performed to construct COAD risk model. GSEA was used to evaluate the enrichment pathways. The Kaplan-Meier was used to analyze the difference in survival. ROC curve was plotted to evaluate the accuracy of the model, and GSE17536 was used to verify the accuracy of the risk model. The risk model is combined with the clinicopathological characteristics of COAD patients to perform multivariate Cox regression analysis to obtain independent risk factors and draw nomograms.
Purpose
Colon adenocarcinoma (COAD) is the second leading cause of death in the world, and the new incidence rate ranks third among all cancers. Abnormal DNA methylation is related to the occurrence and development of tumors. In this study, we aimed to identify genes associated with abnormal methylation in COAD.
Results
In total, 4564 DEGs and 1093 DMGs were screened, among which 298 were found to be overlapping genes. For 220 of these overlapping genes, the methylation was significantly negatively correlated to expression levels. An optimal signature from 4 methylated biomarkers was identified to construct the prognostic model.