Chronic Obstructive Pulmonary Disease (COPD) is a frequently underdiagnosed (by over 70%) disease due to its heterogeneity and the current diagnostic criteria, which make it challenging to diagnose in the early stages. This study aims to identify transcriptomic biomarkers in blood samples to facilitate early diagnosis and improve our understanding of the biological pathways involved, paying particular attention to sex differences.
Whole blood RNA samples from 37 patients and 35 matched controls (14 and 18 females, respectively) were analysed. Differential expression analyses were performed for COPD patients compared to controls, both globally and stratified by sex. An empirical Bayes approach was used to adjust for sex and age. Predictive models were built using Support Vector Machine and Random Forest algorithms with K-best AI feature selection.
A total of 511, 569, and 378 genes were differentially expressed in the global, female, and male analyses, respectively (p < 0.05), with 11 genes being common to all comparisons. Diagnostic models demonstrated high performance, particularly in sex-specific models based on AI selection (accuracy ≥ 95%; MCC ≥ 0.87), which outperformed models based on shared genes. The selected transcripts were related to immune response, oxidative stress, energy metabolism and ciliary function, some of which were modulated in a sex-specific manner.
Our results suggest that the pathogenic mechanisms of COPD may differ between men and women. This highlights the importance of considering sex as a relevant biological
factor in COPD research. They provide new insights into the early pathobiology of COPD and contribute to the move towards personalised medicine.
