Identification of key genes associated with idiopathic pulmonary fibrosis and sarcopenia by bioinformatics analysis - Lanying Shen; Zihan Yi; Jiahao Liu; Yinghua Ying; Yue Hu (PLoS ONE, 2025)
Background Idiopathic pulmonary fibrosis (IPF) and sarcopenia significantly affect patients’ quality of life. The progression and worsening of these conditions are often associated with endoplasmic reticulum (ER) stress, a key cellular stress–response mechanism. This study aimed to investigate the involvement of ER stress in cellular dysfunction in IPF and sarcopenia by identifying ER stress-related crosstalk genes (ERSRCGs). Methods Differential gene expression and weighted gene co-expression network analysis (WGCNA) were used to identify ERSRCGs. Functional enrichment analyses, including the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), Gene Set Variation Analysis (GSVA), and Gene Set Enrichment Analysis (GSEA), were performed to categorize associated pathways. Least absolute shrinkage and selection operator (LASSO) regression was applied to construct diagnostic models for sarcopenia and IPF. The CIBERSORT method was used to examine immune infiltration, and GeneMANIA was used to construct the protein–protein interaction (PPI) network. Results A total of 13 ERSRCGs were substantially associated with sarcopenia and IPF. GO and KEGG analyses revealed enrichment in amino acid metabolism and xenobiotic metabolism pathways. GSEA and GSVA further highlighted the involvement of these genes in multiple biological processes and signaling pathways. LASSO regression identified CTH and IDI1 for IPF, and FOXO1 , CTH, HSD11B1, GSTK1 , and SPTSSA for sarcopenia. Immune infiltration analysis revealed significant correlations between ERSRCGs and immune cell populations in both diseases. Conclusion This study provides novel insights into the interrelated molecular pathways between sarcopenia and IPF, underscoring the potential of ERSRCGs as diagnostic biomarkers and therapeutic targets. The developed diagnostic models highlight key genes that could significantly improve the early detection and risk assessment strategies for these conditions.