Comprehensive Summary
This study by Hu et al. investigates shared peripheral molecular mechanisms linking migraine and major depressive disorder (MDD), with a focus on identifying common blood-based immune biomarkers. The authors integrated publicly available peripheral blood transcriptomic datasets from migraine and MDD cohorts, identified shared differentially expressed genes, and applied machine learning approaches (LASSO and SVM-RFE) with independent validation cohorts to prioritize diagnostic biomarkers. They found 122 shared differentially expressed genes enriched for innate immune activation alongside relative suppression of adaptive immune pathways, and identified pentraxin 3 (PTX3) and haptoglobin (HP) as key hub genes. PTX3 and HP showed elevated expression across both disorders, correlated with increased neutrophil and monocyte infiltration, and demonstrated moderate-to-strong diagnostic performance alone and in combination, albeit with some cohort-dependent variability. In the discussion, the authors emphasize that these findings support a convergent innate immune dysregulation underlying migraine–depression comorbidity and highlight the value of integrative transcriptomics plus machine learning for uncovering reproducible cross-disorder biomarkers
Outcomes and Implications
This research is important because migraine and MDD frequently co-occur clinically, yet lack objective biomarkers to aid early identification, risk stratification, or mechanistic understanding of their overlap. By identifying PTX3 and HP as accessible blood-based markers reflecting shared inflammatory and oxidative stress pathways, the work provides a biologically grounded framework for immune-informed diagnostics and potentially unified treatment strategies. Clinically, these biomarkers are not yet ready for standalone diagnostic use but could be incorporated into multi-marker panels or used to identify inflammatory subtypes of patients who may benefit from targeted anti-inflammatory or immunomodulatory interventions. The authors suggest that translation to clinical practice will require larger prospective cohorts and experimental validation, placing realistic implementation on a medium-term timeline rather than immediate adoption