Aging plays a significant role in the occurrence and development of idiopathic pulmonary fibrosis (IPF). In this study, we aimed to identify and verify potential aging-associated genes involved in IPF using bioinformatic analysis. The mRNA expression profile dataset GSE150910 available in the Gene Expression Omnibus (GEO) database and R software were used to identify the differentially expressed aging-related genes involved in IPF. Hub gene expression was validated by other GEO datasets. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on differentially expressed aging-related genes. Subsequently, aging-related genes were further screened using three techniques (least absolute shrinkage and selection operator (LASSO) regression, support vector machine, and random forest), and the receiver operating characteristic curves were plotted based on screening results. Finally, real-time quantitative polymerase chain reaction (qRT-PCR) was performed to verify the RNA expression of the six differentially expressed aging-related genes using the blood samples of patients with IPF and healthy individuals. Sixteen differentially expressed aging-related genes were detected, of which the expression of 12 were upregulated and four were downregulated. GO and KEGG enrichment analyses indicated the presence of several enriched terms related to senescence and apoptotic mitochondrial changes. Further screening by LASSO regression, support vector machine, and random forest identified six genes (IGF1, RET, IGFBP2, CDKN2A, JUN, and TFAP2A) that could serve as potential diagnostic biomarkers for IPF. Furthermore, qRT-PCR analysis indicated that among the above-mentioned six aging-related genes, only the expression levels of IGF1, RET, and IGFBP2 in patients with IPF and healthy individuals were consistent with the results of bioinformatic analysis. In conclusion, bioinformatics analysis identified 16 potential aging-related genes associated with IPF, and clinical sample validation suggested that among these, IGF1, RET, and IGFBP2 might play a role in the incidence and prognosis of IPF. Our findings may help understand the pathogenesis of IPF.
Keywords: IPF; aging; bioinformatics analysis; gene; gene expression omnibus dataset.
Copyright © 2022 He and Li.