Brain research · 2025
Hypoxia-related gene expression in major depressive disorder: identifying diagnostic biomarkers and implications for immune cell infiltration.
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Pan Ruyue, Yang Yan, Kong Qinghuo, Yu Jie, Chen Jiaxu
Аннотация
BACKGROUND: Major depressive disorder (MDD) is a debilitating mental illness influenced by genetic and environmental factors. This study aimed to investigate the hypoxia-related differentially expressed genes (HRDEGs) associated with MDD, which remain unknown, construct a diagnostic model, and analyze immune infiltration.
METHODS: GEOquery, SVA, and limma were used to standardize gene expression datasets. Principal component analysis was used to confirm batch correction effectiveness. ClusterProfiler was used for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Single-sample gene set enrichment analysis (GSEA) was used to assess immune cell infiltration among risk groups. A diagnostic model and regulatory networks of mRNA-microRNA and mRNAtranscription factors were also constructed.
RESULTS: We identified 474 DEGs (231 upregulated and 243 downregulated). Among these, 22 HRDEGs, including NPM1, SLC1A5, and NOS1, were identified. GO and KEGG analyses revealed that HRDEGs were involved in biological processes related to hypoxic responses. The SVM model, incorporating five key genes, showed a high diagnostic accuracy for MDD. Risk stratification showed remarkable upregulation of SLC1A5 in the high-risk MDD samples. Immune infiltration analysis indicated a positive correlation between HP1BP3 expression and natural killer T cells and a negative correlation with activated CD8+ T cells, suggesting that MDD may be associated with immune system dysfunction.
CONCLUSION: HRDEGs are significantly associated with MDD and could serve as potential diagnostic biomarkers. Thesefindings provide new insights into the immunological aspects of MDD pathogenesis, laying the foundation for further studies on hypoxia pathways and immunomodulatory treatment strategies for severe depression.
METHODS: GEOquery, SVA, and limma were used to standardize gene expression datasets. Principal component analysis was used to confirm batch correction effectiveness. ClusterProfiler was used for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Single-sample gene set enrichment analysis (GSEA) was used to assess immune cell infiltration among risk groups. A diagnostic model and regulatory networks of mRNA-microRNA and mRNAtranscription factors were also constructed.
RESULTS: We identified 474 DEGs (231 upregulated and 243 downregulated). Among these, 22 HRDEGs, including NPM1, SLC1A5, and NOS1, were identified. GO and KEGG analyses revealed that HRDEGs were involved in biological processes related to hypoxic responses. The SVM model, incorporating five key genes, showed a high diagnostic accuracy for MDD. Risk stratification showed remarkable upregulation of SLC1A5 in the high-risk MDD samples. Immune infiltration analysis indicated a positive correlation between HP1BP3 expression and natural killer T cells and a negative correlation with activated CD8+ T cells, suggesting that MDD may be associated with immune system dysfunction.
CONCLUSION: HRDEGs are significantly associated with MDD and could serve as potential diagnostic biomarkers. Thesefindings provide new insights into the immunological aspects of MDD pathogenesis, laying the foundation for further studies on hypoxia pathways and immunomodulatory treatment strategies for severe depression.