Identification and Validation of Potential Biomarkers and Therapeutic Targets of COVID-19-related Depression

Background: The prevalence of depression in COVID-19 patients is notably high, disrupting daily life routines and compounding the burden of other chronic health conditions. In addition, to elucidate the connection between COVID-19 and depression, we conducted an analysis of commonly differentially expressed genes [co-DEGs], uncovering potential biomarkers and therapeutic avenues specific to COVID-19-related depression.

Methods: We obtained gene expression profiles from the Gene Expression Omnibus [GEO] database with strategic keyword searches ["COVID-19", "depression," and "SARS"]. We used functional enrichment analysis of the co-DEGs to decipher their likely biological roles. Then, we utilized protein-protein interaction [PPI] network analysis to identify hub genes among the co- DEGs. These findings were validated via an independent third-party dataset.

Results: Our analysis of blood samples from COVID-19 patients revealed 10,716 upregulated genes and 10,319 downregulated genes. In addition, by applying the same approach to depression samples, we identified 571 upregulated and 847 downregulated genes. Furthermore, by intersecting these datasets, we extracted 121 upregulated and 175 downregulated co-DEGs. Through PPI network construction and hub gene selection, we identified MPO, ARG1, CD163, FCGR1A, ELANE, LCN2, and CR1 as co-upregulated hub genes and MRPL13, RPS23, and MRPL1 as co-downregulated hub genes. The incorporation of third-party datasets revealed that these hub genes are specific targets of SARS-CoV-2, not generic viral response mechanisms.

Conclusion: The identification of potential biomarkers represents a groundbreaking strategy for assessing and treating depression in the context of COVID-19, with the potential to reduce its prevalence among these patients. However, to fully harness this potential, additional clinical research is paramount.