Identification of Biomarker Genes Based on Multi-Omics Analysis in Non- Small Cell Lung Cancer

Background: Non-small cell lung cancer (NSCLC) is a complex disease with a high mortality rate and a poor prognosis, but its molecular mechanisms and effective biomarkers are still unclear. Comprehensive analysis of multiple histological data can effectively exclude random events and is helpful in improving the reliability of the findings. In this study, we used three types of omics data, RNA-seq, microRNA-seq, and DNA methylation data, from public databases to explore the potential biomarker genes oftwo major subtypes of NSCLC. Results: : Through the combined differential analysis of multi-omics, we found 873 and 1378 potential high-risk genes in LUAD and LUSC, respectively. Then, we used WGCNA and PPI analyses to identify hub-genes and LASSO regressionto construct prognostic models, and we obtained 15 prognostic genes. We also used survival analysis, univariate COX analysis, and GEO datasets to validate prognostic genes. Finally, we found ten genes associated with NSCLC, and eight of them have been reported in previous research. Conclusions: : In this study, we have provided a reliable analysis method for predicting biomarker genes of complex diseases. Two novel biomarker genes were identified: NES and ESAM . The two genes were both gene expression down-regulation and DNA methylation up-regulation, and regulated by miR-122 and miR-154 . Moreover, the NES gene can contribute to the clinical diagnosis and prognosis of NSCLC.