Identifying GAP43, NMU, and TEX29 as Potential Prognostic Biomarkers for COPD Combined With Lung Cancer Patients...

Chronic obstructive pulmonary disease (COPD) and lung cancer, frequently comorbid conditions intricately linked through smoking, represent significant global health challenges. COPD is a common comorbidity in nonsmall cell lung cancer (NSCLC) patients and has been shown to negatively impact prognosis. However, the molecular mechanisms underlying the interplay between COPD and lung cancer remain unclear. This study aims to identify differentially expressed genes (DEGs) associated with COPD-related lung cancer and, using various machine learning (ML) algorithms, uncover potential biomarkers for prognosis. We analyzed RNA sequencing data from 41 lung cancer patients (with and without COPD) and identified 61 DEGs, all of which were upregulated in solitary lung cancer compared to COPD-associated cases. Functional enrichment analysis revealed that these genes are involved in biological processes such as granulocyte chemotaxis and smooth muscle contraction and molecular functions including neuropeptide receptor binding. Three ML methods-support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO), and random forest-were applied to prioritize key biomarkers. Three genes, GAP43, NMU, and TEX29, were consistently selected across all methods. Further analysis demonstrated significant correlations between these genes and immune cell infiltration, with notable differences in immune cell composition observed in COPD-associated lung cancer. High expression levels of GAP43, NMU, and TEX29 were associated with poor survival outcomes in lung cancer patients, as validated through survival analysis of TCGA database data. Our findings suggest that these genes may serve as diagnostic and prognostic biomarkers for COPD-related lung cancer, thereby providing insights into potential therapeutic targets. Further studies with larger cohorts are required to validate these results and elucidate the underlying molecular mechanisms.