Identifying biomarkers of endoplasmic reticulum stress and analyzing immune cell infiltration in chronic obstructive pulmonary disease using machine learning - Zhang, Shuaiyang; Duan, Hangyu; Yan, Jun (Frontiers in Medicine, 2024)
Background Endoplasmic reticulum stress (ERS) is a crucial factor in the progression of chronic obstructive pulmonary disease (COPD). However, the key genes associated with COPD and immune cell infiltration remain to be elucidated. Therefore, this study aimed to identify biomarkers pertinent to the diagnosis of ERS in COPD and delve deeper into the association between pivotal genes and their possible interactions with immune cells. Methods We selected the genetic data of 189 samples from the Gene Expression Omnibus database, including 91 control and 98 COPD samples. First, we identified the differentially expressed genes between patients with COPD and controls and then screened the ERS genes associated with COPD. Second, 22 core ERS genes associated with COPD were screened using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model and Support Vector Machine Recursive Feature Elimination (SVM-RFE), and the predictive effects of the screened core genes in COPD were evaluated. Third, we explored immune cell infiltration associated with COPD and conducted an in-depth analysis to explore the possible connections between the identified key genes and their related immune cells. Results A total of 66 differentially expressed endoplasmic reticulum stress–related genes (DE-ERGs) were identified in this study, among which 12 were upregulated and 54 were downregulated. The 22 key genes screened were as follows: AGR3, BCHE, CBY1, CHRM3, CYP1B1, DCSTAMP, DDHD1, DMPK, EDEM3, EDN1, FKBP10, HSPA2, KPNA2, LGALS3, MAOB, MMP9, MPO, MTTP, PIK3CA, PTGIS, PURA, and TMCC1. Their expression was significantly different between COPD and healthy samples, and the difference between the groups was significant. Receiver operating characteristic curve analysis revealed that CBY1 (area under the curve [AUC] = 0.800), BCHE (AUC = 0.773), EDEM3 (AUC = 0.768), FKBP10 (AUC = 0.760), MAOB (AUC = 0.736), and MMP9 (AUC = 0.729) showed a strong ability to distinguish COPD samples from normal samples. Immune cell infiltration results associated with the three key genes were also obtained. Conclusion The insights of our study have the potential to present new evidence for exploring emerging diagnostic signs of COPD while also contributing to a better understanding of its developmental mechanisms.