ArticleCommunications medicine2025
Sex-specific transcriptome similarity networks elucidate comorbidity relationships.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Co-occurrence network characteristics and key comorbidity node identification based on health examination indicators among rural older adults aged 65 and above.BMC geriatrics · 2026Article
- Artificial Intelligence in Transcriptomics: From Human-in-the-Loop to Agentic AI.Journal of personalized medicine · 2026Review
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9 authors.
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Abstract
backgroundBiological differences between women and men lead to variations in the prevalence and progression of many diseases, influencing diagnosis, management, and treatment outcomes. However, the biological mechanisms that contribute to sex differences in disease co-occurrence remain largely unexplored. This study aims to uncover the molecular processes underlying sex-specific patterns of comorbidity.
methodsWe analyze gene expression data from over 100 diseases, considering the biological sex of each sample (8906 samples, 43.06% women). For each sex, we construct disease similarity networks based on differential gene expression profiles and identify enriched biological processes. We then compare these networks with epidemiological data from population-level comorbidity studies to assess their concordance. Finally, we investigate drugs associated with sex-specific comorbidities to identify potential differences in therapeutic response.
resultsWe show that 13-16% of transcriptomically similar disease pairs are sex-specific. These similarities recover 53-60% of known comorbidities that differ between women and men. Diseases can co-occur through the differential alteration of biological processes, with immune and metabolic pathways playing a greater role in women, and extracellular matrix organization and signal transduction pathways in men. We also identify drugs differentially linked to comorbid diseases depending on sex, suggesting possible sex-dependent effects on disease co-occurrence.
conclusionsOur findings demonstrate that transcriptomic data can reveal sex-specific molecular links between diseases and suggest that biological sex should be considered in the design of therapeutic strategies and drug administration.
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