Bioinformatic analysis of differentially expressed genes and pathways in idiopathic pulmonary fibrosis

Background: Using bioinformatic methods to explore the differentially expressed genes (DEGs) of human idiopathic pulmonary fibrosis (IPF) and to elucidate the pathogenesis of IPF from the genetic level. Methods: The GSE110147 gene expression profile was downloaded from the GEO database. The data of lung adenocarcinoma (LUAD) samples, lung squamous cell carcinoma (LUSC) samples and normal samples were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. DEGs between IPF patients and healthy donors were analyzed using the GEO2R tool. Use the clusterprofiler package in R software to perform gene ontology (GO) and KEGG pathway enrichment analysis, and then perform function annotation and protein-protein interaction (PPI) network construction in the STRING online tool. The Genome Browser tool of the university of california santa cruz (UCSC) online website was used to predict transcription factors (TFs) of genes. In the final, the results were analyzed synthetically. Results: A total of 9,183 DEGs were identified, of which 4,545 genes were down-regulated, and 4638 were up-regulated. MMP1, SPP1, and BPIFB1 were the top three DEGs with the highest significant upregulation. These DEGs played an important role in the occurrence of IPF through the MAPK (mitogen-activated protein kinase) signaling pathway. Furthermore, 50 DEGs were enriched in the expression of PD-L1 and the PD-1 checkpoint pathway in cancer, of which 11 genes were re-enriched in the pathway of non-small cell lung cancer. The expression of the 11 genes were extensively regulated by CTCFL, SP2 and ZNF341. Most of them were differentially expressed between lung cancers and normal lung tissues. The overall survival (OS) curve of LUAD were significantly stratified by AKT2, KRAS, PIK3R1, meanwhile the OS curve of LUAC was significantly stratified by MAPK3. Conclusions: Bioinformatics analysis revealed that DEGs including MPP1 might be potential targets and biomarkers of IPF, and the MAPK signaling pathway is related to the occurrence and development of IPF. The development of IPF lung cancer complications may be related to the activation of genes enriched in PD-L1 expression and PD-1 checkpoint pathway, which provides clues to the pathogenesis of IPF combined with lung cancer.

Automated summary

This study used bioinformatic methods to analyze DEGs in human IPF, revealing potential biomarkers and targets such as MPP1. The MAPK signaling pathway was found to be related to the occurrence and development of IPF. Additionally, the development of IPF lung cancer complications may be associated with the activation of genes enriched in PD-L1 expression and PD-1 checkpoint pathway, providing insights into the pathogenesis of IPF combined with lung cancer.