Identification of key genes modules linking brain aging signatures and COVID-19-associated cognitive impairment

Emerging evidence indicates that SARS-CoV-2 infection is associated with aging, particularly in memory-related domains. However, the molecular mechanisms underlying COVID-19-associated aging, often referred to as "brain fog," remain poorly understood. This study aimed to identify key genes implicated in post-COVID cognitive dysfunction.

Transcriptomic data from the frontal cortex, obtained from the GEO dataset GSE188847 and GSE164332, were analyzed using differential expression analysis to identify differentially expressed genes (DEGs) associated with brain fog. Functional enrichment analysis was conducted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Machine learning techniques, including LASSO regression and weighted gene co-expression network analysis (WGCNA), were employed to identify hub genes. Drug-gene interactions were explored using the Comparative Toxicogenomics Database (CTD).

A total of 81 up-regulated and 441 down-regulated DEGs were identified in COVID-19 patients compared to controls. Up-regulated genes were primarily enriched in pathways related to pulmonary fibrosis and immune activation (e.g., IL-17 signaling), while down-regulated genes were associated with synaptic transmission (e.g., GABAergic synapse, adjusted p = 0.003). Integration of LASSO and WGCNA analyses revealed five aging-related hub genes: SST, S100A9, SOCS3, FKBP5, and HBB. Notably, SST expression was significantly reduced, whereas the other four genes showed marked upregulation. CTD analysis identified seven potential therapeutic agents: fulvestrant, bucladesine, S-adenosylmethionine, valproic acid, folic acid, kaempferol, and quercetin.

This study employed transcriptomic analysis and bioinformatics approaches to identify five key aging-related genes associated with COVID-19. These genes may serve as potential therapeutic targets for addressing COVID-19-associated aging.