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Chronic obstructive pulmonary disease (COPD) and asthma are common and serious respiratory diseases worldwide. Their clinical overlap and lack of specificity in current biomarkers pose a great diagnostic challenge for early diagnosis. To address this gap, this study aimed to identify common transcriptomic features and potential diagnostic biomarkers for the diseases using an integrated bioinformatics approach. This study analyzed COPD chip data using weighted gene co-expression network analysis, identifying 375 key differential genes. Functional enrichment analysis was performed to assess the biological roles of these genes. Machine learning methods, including least absolute shrinkage and selection operator and random forest, were employed to identify 5 key biomarkers: MYO16, CHML, POLR3B, ZNF101, and ZNF143. The findings revealed that the identified genes were primarily associated with immune response and T cell-related inflammatory pathways. Among the biomarkers, ZNF143 was significantly upregulated in both COPD and asthma, with expression levels notably higher in COPD patients compared to asthma patients. Expression analysis and receiver operating characteristic curve assessment validated ZNF143 as a potential diagnostic biomarker. Additionally, the CIBERSORT algorithm was used to evaluate immune cell infiltration, revealing a positive correlation between ZNF143 and CD8 T cells, M2 macrophages, and γ-δ T cells, and a negative correlation with memory-activated CD4 T cells, plasma cells, and neutrophils. These findings suggest a potential role for ZNF143 in both COPD and asthma, supporting its candidacy as an early diagnostic biomarker. This research offers preliminary insights into the molecular mechanisms underlying these respiratory diseases and may inform future directions for diagnostic and therapeutic exploration.