Identification of Genes Related to Endoplasmic Reticulum Stress (ERS) in Chronic Obstructive Pulmonary Disease (COPD) and Clinical Validation

Abstract

Siming Tao,1,* Jing Jing,1,2,* Yide Wang,1,3,* Fengsen Li,1,2 Hongxia Ma1,2 1Department of Respiratory and Critical Care Medicine, Fourth Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China; 2Xinjiang Laboratory of Respiratory Disease Research, Traditional Chinese Medicine Hospital Affiliated to Xinjiang Medical University, Urumqi, People’s Republic of China; 3Department of Respiratory Medicine, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Fengsen Li; Hongxia Ma, Department of Respiratory and Critical Care Medicine, Fourth Affiliated Hospital of Xinjiang Medical University, Urumqi, 830000, People’s Republic of China, Tel [phone], Email [email protected]; [email protected]: Endoplasmic reticulum stress (ERS) is key in chronic obstructive pulmonary disease (COPD) incidence and progression. This study aims to identify potential ERS-related genes in COPD through bioinformatics analysis and clinical experiments.Methods: We first obtained a COPD-related mRNA expression dataset (GSE38974) from the Gene Expression Omnibus (GEO) database. The R software was then used to identify potential differentially expressed genes (DEGs) of COPD-related ERS (COPDERS). Subsequently, the identified DEGs were subjected to protein-protein interaction (PPI), correlation, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Following that, qRT-PCR was used to examine the RNA expression of six ERS-related DEGs in blood samples obtained from the COPD and control groups. The genes were also subjected to microRNA analysis. Finally, a correlation analysis was performed between the DEGs and key clinical indicators.Results: Six ERS-related DEGs (five upregulated and one downregulated) were identified based on samples drawn from 23 COPD patients and nine healthy individuals enrolled in the study. Enrichment analysis revealed multiple ERS-related pathways. The qRT-PCR and mRNA microarray bioinformatics analysis results showed consistent STC2, APAF1, BAX, and PTPN1 expressions in the COPD and control groups. Additionally, hsa-miR-485-5p was identified through microRNA prediction and DEG analysis. A correlation analysis between key genes and clinical indicators in COPD patients demonstrated that STC2 was positively and negatively correlated with eosinophil count (EOS) and lymphocyte count (LYM), respectively. On the other hand, PTPN1 showed a strong correlation with pulmonary function indicators.Conclusion: Four COPDERS-related key genes (STC2, APAF1, BAX, and PTPN1) were identified through bioinformatics analysis and clinical validation, and the expressions of some genes exhibited a significant correlation with the selected clinical indicators. Furthermore, hsa-miR-485-5p was identified as a potential key target in COPDERS, but its precise mechanism remains unclear.Keywords: endoplasmic reticulum stress, chronic obstructive pulmonary disease, bioinformatics analysis, clinical prediction

Keywords

• endoplasmic reticulum stress
• chronic obstructive pulmonary disease
• bioinformatics analysis
• clinical prediction