Identifying key biomarkers and therapeutic candidates for post-COVID-19 depression through integrated omics and bioinformatics approaches.

Abstract

Introduction: Depression, the leading cause of disability worldwide, is known to be exacerbated by severe acute respiratory syndrome coronavirus 2 infection, worsening coronavirus disease 2019 (COVID-19) outcomes. However, the mechanisms and treatments for this comorbidity are not well understood.

Methods: This study utilized Gene Expression Omnibus datasets for COVID-19 and depression, combined with protein-protein interaction networks, to identify key genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to understand gene functions. The CIBERSORT algorithm and NetworkAnalyst were used to examine the relationship of immune cell infiltration with gene expression and to predict transcription factors (TFs) and microRNAs (miRNAs) interactions. The Connectivity Map database was used to predict drug interactions with these genes.

Results: TRUB1, PLEKHA7, and FABP6 were identified as key genes enriched in pathways related to immune cell function and signaling. Seven TFs and nineteen miRNAs were found to interact with these genes. Nineteen drugs, including atorvastatin and paroxetine, were predicted to be significantly associated with these genes and potential therapeutic agents for COVID-19 and depression.

Conclusions: This research provides new insights into the molecular mechanisms of post-COVID-19 depression and suggests potential therapeutic strategies, marking a step forward in understanding and treating this complex comorbidity.