Bioinformatics role of the WGCNA analysis and co-expression network identifies of prognostic marker in lung cancer

Liang Chengcheng; Sayed Haidar Abbas Raza; Yu Shengchen; Zuhair M Mohammedsaleh; Abdullah F Shater; Fayez M Saleh; Muna O Alamoudi; Bandar H Aloufi; Ahmed Mohajja Alshammari; Nicola M Schreurs; Linsen Zan

doi:10.1016/j.sjbs.2022.02.016

Bioinformatics role of the WGCNA analysis and co-expression network identifies of prognostic marker in lung cancer

Saudi J Biol Sci. 2022 May;29(5):3519-3527. doi: 10.1016/j.sjbs.2022.02.016. Epub 2022 Feb 23.

Affiliations

¹ College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100, PR China.
² Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk 71491, Saudi Arabia.
³ Department of Medical Microbiology, Faculty of Medicine, University of Tabuk, Tabuk 71491, Saudi Arabia.
⁴ Biology Department, Faculty of Science, Hail University, Hail 81411, Saudi Arabia.
⁵ Animal Science, School of Agriculture and Environment, Massey University, Palmerston North, New Zealand.
⁶ National Beef Cattle Improvement Center, Northwest A&F University, Yangling, Shaanxi 712100, PR China.

Abstract

Lung cancer is the most talked about cancer in the world. It is also one of the cancers that currently has a high mortality rate. The aim of our research is to find more effective therapeutic targets and prognostic markers for human lung cancer. First, we download gene expression data from the GEO database. We performed weighted co-expression network analysis on the selected genes, we then constructed scale-free networks and topological overlap matrices, and performed correlation modular analysis with the cancer group. We screened the 200 genes with the highest correlation in the cyan module for functional enrichment analysis and protein interaction network construction, found that most of them focused on cell division, tumor necrosis factor-mediated signaling pathways, cellular redox homeostasis, reactive oxygen species biosynthesis, and other processes, and were related to the cell cycle, apoptosis, HIF-1 signaling pathway, p53 signaling pathway, NF-κB signaling pathway, and several cancer disease pathways are involved. Finally, we used the GEPIA website data to perform survival analysis on some of the genes with GS > 0.6 in the cyan module. CBX3, AHCY, MRPL12, TPGB, TUBG1, KIF11, LRRC59, MRPL17, TMEM106B, ZWINT, TRIP13, and HMMR was identified as an important prognostic factor for lung cancer patients. In summary, we identified 12 mRNAs associated with lung cancer prognosis. Our study contributes to a deeper understanding of the molecular mechanisms of lung cancer and provides new insights into drug use and prognosis.

Keywords: Bioinformatics; Gene Expression Omnibus; Gene Expression Profiling Interactive Analysis (GEPIA); Lung Cancer; Weighted Correlation Network Analysis (WGCNA).