Integrated bioinformatics analysis of microarray data from the GEO database to identify the candidate genes linked to poor prognosis in lung adenocarcinoma

Abstract

BACKGROUND:

Lung adenocarcinoma (LUAD) is one of the most common cancers with high morbidity and mortality and remains a crucial factor endangering human health.

OBJECTIVE:

This study aimed to elucidate the potential treatment target and prognostic biomarker in patients with LUAD through a comprehensive bioinformatics analysis.

METHODS:

The three public microarray datasets of GSE118370, GSE116959, and GSE43767 were obtained from the GEO data resource. The DEGs were explored between LUAD and non-malignant samples using GEO2R online tool in GEO data resource. GO along with KEGG analysis of DEGs were examined using WebGestalt tool. The STRING web resource was employed to develop the PPI network of DEGs, whereas Cytoscape software was employed to perform module analysis. Finally, the mRNA, protein expression along with survival analysis of hub genes were explored via GEPIA, HPA along with Kaplan-Meier plotter web resource, respectively.

RESULTS:

Only 82 upregulated and 105 downregulated DEGs were found among the three datasets. Further, GO analysis illustrated that 187 DEGs were primary enriched in extracellular structure organization, tube development along with cell adhesion. The KEGG enrichments showed that these DEGs were primary linked to leukocyte transendothelial migration, vascular smooth muscle contraction along with ECM-receptor interaction. Among the 187 DEGs, the 10 hub genes (P4HB, SPP1, CP, GOLM1, COL1A1, MMP9, COL10A1, APOA1, COL4A6, and TIMP1) were identified. The mRNA along with protein levels of hub genes in LUAD tissues were further verified by Oncomine, UCSC Xena, GEPIA and HPA databases. Additionally, overall survival curves illustrated that LUAD patients with the higher levels of P4HB, SPP1, COL1A1, and MMP9 were dramatically linked to shorter overall survival.

CONCLUSIONS:

The current study identified DEGs candidate genes (P4HB, SPP1, COL1A1, and MMP9) and pathways in LUAD using bioinformatics analysis, which could enhance our understanding of pathogenesis along with underlying molecular events in LUAD, and these hub genes and pathways may help provide candidate treatment targets for LUAD.