The Identification of Hub Genes Associated with Pure Ground Glass Nodules Using Weighted Gene Co-Expression Network Analysis

Abstract Background Whether there are invasive components in pure ground glass nodules(pGGNs) in the lungs is still a huge challenge to forecast. The objective of our study is to investigate and identify the potential biomarker genes for pure ground glass nodule(pGGN) based on the method of bioinformatics analysis. Methods To investigate differentially expressed genes (DEGs), firstly we used the data obtained from the gene expression omnibus (GEO) database.Next Weighted gene co-expression network analysis (WGCNA) was used to investigate the co-expression network of DEGs and seven key modules were found. We chose the black key module as the key one in correlation with pGGN. SangerBox software was used for analyses of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Then we used STRING to create a protein-protein interaction (PPI) network, and the chosen module genes were analyzed by Cytoscape software. At last the polymerase chain reaction (PCR) was used to evaluate the value of these hub genes in pGGN patients’ tumor tissues compared to controls. Results There are a total of 4475 DEGs were screened out from GSE193725, then 225 DEGs were identified in black key module. And these DEGs were found to be enriched for various functions and pathways, such as extracellular exosome, vesicle, protein processing in endoplasmic reticulum, metabolic pathways, ribosome, and so on. Among these DEGs, 6 overlapped hub genes with high degrees of stress method were selected. These hub genes include RPL4, RPL8, RPLP0, RPS16, RPS2 and CCT3.And relative expression levels of CCT3 and RPL8 mRNA were both regulated in pGGN patients’ tumor tissues compared to controls. To summarize, the determined DEGs, pathways, modules, and overlapped hub genes can throw light on the potential molecular mechanisms of pGGN.