Commonalities and differences in gene expression patterns in major depressive disorder and chronic spontaneous urticaria: implications for comorbidity - Jiang, Yibo; Li, Wen; Zheng, Lihao; Ba, Nan (Frontiers in Genetics, 2025)
Chronic Spontaneous Urticaria (CSU) disrupts patients’ physical wellbeing through recurrent wheals that last for up to 24 h and pruritus, and may also contribute to the development and progression of mental health conditions such as depression due to poor control of CSU symptoms. Clinical evidence has shown significant comorbidity between CSU and major depressive disorder (MDD), but our understanding of the underlying molecular mechanisms and how they differ by sex remains limited. In this work, five gene expression datasets were obtained from the Gene Expression Omnibus (GEO), encompassing three for MDD (GSE52790, GSE38206, GSE76826) and two exclusively whole-blood datasets for CSU (GSE167882, GSE72541). Following data preprocessing and batch effect correction, differentially expressed genes (DEGs) in CSU and MDD were identified in male, female, and total populations. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to characterize putative pathophysiological mechanisms. Subsequently, machine learning algorithms were implemented via 10-fold cross-validation to build transcriptomic classifiers for MDD in a sex-stratified manner. Results indicated shared molecular patterns between CSU and MDD, with 26 key genes in the total population, 6 in males and 7 in females. Further feature selection yielded 6 core transcriptomic features in the total population (BCL11A, BEX2, C5AR1, DDX60L, LCE3D, NAMPT), 2 in males (GNAQ, RNF19B), and 4 in females (GNG7, LCE3D, PYGL, UPP1). Validation analyses showed that these transcriptomic classifiers conferred high classification accuracy, with the model for females surpassing that for males. Potential drug candidates targeting shared molecular mechanisms were identified. Overall, these findings reveal sex-specific molecular signatures that may underlie CSU-MDD comorbidity and offer insight into future precision treatment paradigms.