Comprehensive Network Analysis of Different Subtypes of Molecular Disorders in Lung Cancer

Lung cancer is the leading cause of death in cancer patients. Based on a modular and comprehensive analysis method, it is intended to identify their common pathogenesis. We downloaded data and analyzed differences in lung adenocarcinoma samples, lung squamous cell carcinoma samples, and normal samples. Co-expression analysis, enrichment analysis, and hypergeometric testing were used to predict transcription factors, ncRNAs, and retrospective target genes. We get 4596 differentially expressed genes in common differences in high multiples and clustered into 14 modules dysfunction. The 14 genes (including DOK2, COL5A1, and TSPAN8) have the highest connectivity in the dysfunction module and are identified as the core genes of the module. Module genes are also substantially involved in biological processes, including extracellular matrix, carbohydrate-binding and renal system development, and involved signal transduction including PPAR signal transduction, cGMP-PKG signal transduction, PI3K-Akt signal transduction, and Apelin signal transduction. We identified ncRNA pivot (miR-335-5p, ANCR, TUG1) and Transcription Factors pivot (RELA, SP1) to regulate dysfunction module genes primarily. The analysis showed that comprehensive co-expression analysis helped us to understand the transcription factor ncRNA. Moreover, it helps us understand the molecular pathogenesis of co-expression of modular genes that regulate lung adenocarcinoma and squamous cell carcinoma. It provides a precious resource and theoretical basis for further experiments by biologists.