Bioinformatics-driven discovery of shared biomarkers linking depression and cognitive impairment

Major depressive disorder (MDD) is frequently comorbid with mild cognitive impairment (MCI), yet its molecular basis remains unclear.

This study aimed to identify shared differentially expressed genes (DEGs) and biological pathways that may underlie the comorbidity between MDD and MCI. Using integrative bioinformatics approaches applied to transcriptomic datasets, we sought to uncover molecular biomarkers that could inform early diagnosis and provide novel targets for mechanism-based therapeutic strategies.

Transcriptomic datasets from MDD (GSE58430) and MCI (GSE140831) patients were analyzed to identify DEGs. Functional enrichment analyses were performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Protein-protein interaction (PPI) networks were constructed to identify core genes.

A total of 301 DEGs were shared between MDD and MCI. Gene Ontology and KEGG enrichment analyses revealed key biological processes involved in neuroinflammation, oxidative stress, synaptic dysfunction, and apoptotic signaling. The PPI network analysis identified nine hub genes with high connectivity: HSP90AB1, CDC42, NFKB1, CD8A, CALM3, PARP1, CD44, H2BC21, and MYH9.

These findings reveal shared molecular biomarkers and pathways linking MDD and MCI, providing insights into their comorbidity. The identified core genes, particularly PARP1 and CDC42, may serve as novel targets for early diagnosis and mechanism-based therapeutic strategies in psychiatry and neurodegenerative disorders.