J Coll Physicians Surg Pak. 2022 Jun;32(6):712-721. doi: 10.29271/jcpsp.2022.06.712.
OBJECTIVE: To screen and identify key genes as potential biomarkers of lung cancer using bioinformatics analysis.
STUDY DESIGN: Observational study.
PLACE AND DURATION OF STUDY: Department of Critical Care Medicine, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China, from August 2018 to April 2021.
METHODOLOGY: Independent microarray datasets (GSE85841 and GSE118370) were downloaded from the Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were screened using GEO2R. Cytohubba was employed to identify the hub genes. Cellular component analysis, hierarchical clustering, and survival analyses of hub genes were performed via BiNGO, UCSC, and cBioPorta. A series of analyses of FGF2 and PIK3R1 were conducted using Oncomine.
RESULTS: A total of 463 DEGs were identified and 11 hub genes were determined. BDNF, FGF2, JAK2, NCAM1, CAV1, TJP1, and PIK3R1 may affect the survival probability and life expectancy of lung cancer patients, but the p-values were not statistically significant. FGF2 and PIK3R1 had the highest node degrees, 40 and 32 respectively. The expression of FGF2 and PIK3R1 were significantly lower in the 4 lung cancer data sets compared with non-lung cancer tissues. And the low expression of FGF2 and PIK3R1 is related to tumor grades, family history of cancer, multiple tumors present, and prior therapy of lung cancer.
CONCLUSION: Evaluation of FGF2 and PIK3R1 as potential biomarkers can contribute to the subsequent theoretical analysis of potential molecular mechanisms and development of lung cancer, so that the diagnosis of lung cancer may be more accurate, and it is possible to provide therapeutic and prognostic medicine targets.
KEY WORDS: Lung neoplasms, Differentially expressed genes, Bioinformatical analysis, Microarray analysis, biomarkers.