Abstract
AbstractBackgroundIdiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disease characterized by excessive scarring of lung tissue. Recent studies have indicated a potential link between IPF and type 2 diabetes (T2D), suggesting that T2D may contribute to the pathogenesis of IPF or vice versa. In this study, we aim to investigate the underlying molecular mechanisms and pathways involved in the development of IPF complicated with T2D using microarray data analysis.MethodsThe datasets for Type 2 Diabetes (T2D) (GSE25724) and Idiopathic Pulmonary Fibrosis (IPF) (GSE110147) were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis was conducted using the ‘limma’ package in R to identify genes that were significantly differentially expressed between T2D and IPF samples. Functional enrichment analysis of these differentially expressed genes was performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG).To explore protein-protein interactions, protein-protein interaction networks were constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape. CytoHubba, a plugin in Cytoscape, was utilized to identify hub genes in the network. The identified hub genes were further validated in independent datasets: GSE166467 for T2D and GSE24206 for IPF.To assess the predictive value of the hub genes, receiver operating characteristic (ROC) curves were generated. Additionally, gene set enrichment analysis was performed to uncover potential biological pathways associated with the hub genes. Finally, an analysis of immune infiltration within the hub gene network was conducted.ResultsA total of 255 differentially expressed genes (DEGs) were found to be commonly dysregulated in both Type 2 Diabetes (T2D) and Idiopathic Pulmonary Fibrosis (IPF). Pathways related to metabolic processes were significantly enriched in the analysis of both T2D and IPF. Through the validation process, RPL30 was identified as a hub gene, exhibiting an area under the curve (AUC) value greater than 0.65 for both T2D and IPF. Additionally, we identified 92 transcription factors (TFs) and 54 microRNAs (miRNAs) that may potentially regulate the expression of RPL30.ConclusionsThis study provides novel insights by highlighting the role of RPL30 in the shared pathogenesis of pulmonary fibrosis and Type 2 diabetes (T2D). The findings suggest that RPL30 has the potential to serve as a biomarker and therapeutic target for these conditions.
Publisher
Cold Spring Harbor Laboratory