@Article{oncologie.2020.012494,
AUTHOR = {Haoliang Zhang, Xiaowei Xing, Yang Liu, Shuangli Li, Weiyuan Li},
TITLE = {Comprehensive Network Analysis of Different Subtypes of Molecular  Disorders in Lung Cancer},
JOURNAL = {Oncologie},
VOLUME = {22},
YEAR = {2020},
NUMBER = {2},
PAGES = {107--116},
URL = {http://www.techscience.com/oncologie/v22n2/40110},
ISSN = {1765-2839},
ABSTRACT = {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.},
DOI = {10.32604/oncologie.2020.012494}
}