Exploring Inflammatory-Related Hub Genes as Therapeutic Targets in Major Depressive Disorder: Implications for Immunological Pathways
Abstract
This study explored the mechanisms of action of inflammation related central genes in severe depression (MDD) and analyzes their potential as therapeutic targets. By identifying key genes and establishing the link between immune regulatory mechanisms and depression, we provide a theoretical basis for developing more accurate diagnostic and treatment methods.
Gene expression datasets related to MDD were obtained from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) associated with inflammatory processes were identified and analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Protein-protein interaction (PPI) networks were constructed to identify hub genes. Additionally, we explored regulatory networks of miRNAs, transcription factors, and potential drug interactions were explored. Immune infiltration analysis was performed to examine immune cell profiles.
Seven key genes—HMGB1, HSP90AB1, MAPK1, MMP9, MYD88, S100A12, and TLR2—were identified as central players in the inflammatory pathways underlying MDD. These genes demonstrated moderate diagnostic accuracy with AUC values ranging from 0.5 to 0.7. Enrichment analyses revealed significant associations with immune signaling pathways, including IL-17 and Toll-like receptor signaling. Immune infiltration analysis highlighted altered abundances of regulatory T cells, neutrophils, and dendritic cells in MDD samples.
Inflammatory-related hub genes play crucial roles in linking immune dysregulation to the pathophysiology MDD pathophysiology. These findings offer insights into the immunological underpinnings of MDD and present potential therapeutic targets for intervention through immune-modulatory approaches.
2. Pearson G, Robinson F, Beers Gibson T, Xu BE, Karandikar M, Berman K, et al. Mitogen-activated protein (MAP) kinase pathways: regulation and physiological functions. Endocr Rev. 2001;22(2):153-83.
3.Davis S, Meltzer PS. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics. 2007;23(14):1846-7.
4.Spijker S, Van Zanten JS, De Jong S, Penninx BW, van Dyck R, Zitman FG, et al. Stimulated gene expression profiles as a blood marker of major depressive disorder. Biol Psychiat. 2010;68(2):179-86.
5. Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinformatics. 2016;54:1-30.
6. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882-3.
7. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.
8. Mi H, Muruganujan A, Ebert D, Huang X, Thomas PD. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 2019;47(D1):D419-26.
9. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27-30.
10. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16(5):284-7.
11. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. P Natl Acad Sci Usa. 2005;102(43):15545-50.
12. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739-40.
13. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607-13.
14. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498-504.
15. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8 Suppl 4(Suppl 4):S11.
16. Yang X, Li Y, Lv R, Qian H, Chen X, Yang CF. Study on the Multitarget Mechanism and Key Active Ingredients of Herba Siegesbeckiae and Volatile Oil against Rheumatoid Arthritis Based on Network Pharmacology. Evid-Based Compl Alt. 2019;2019:8957245.
17. Vlachos IS, Paraskevopoulou MD, Karagkouni D, Georgakilas G, Vergoulis T, Kanellos I, et al. DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Res. 2015;43(Database issue):D153-9.
18. Zhang Q, Liu W, Zhang HM, Xie GY, Miao YR, Xia M, et al. hTFtarget: A Comprehensive Database for Regulations of Human Transcription Factors and Their Targets. Genom Proteom Bioinf. 2020;18(2):120-8.
19. Zhou KR, Liu S, Sun WJ, Zheng LL, Zhou H, Yang JH, et al. ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data. Nucleic Acids Res. 2017;45(D1):D43-50.
20. Grondin CJ, Davis AP, Wiegers JA, Wiegers TC, Sciaky D, Johnson RJ, et al. Predicting molecular mechanisms, pathways, and health outcomes induced by Juul e-cigarette aerosol chemicals using the Comparative Toxicogenomics Database. Curr Res Toxicol. 2021;2:272-81.
21. Xiao B, Liu L, Li A, Xiang C, Wang P, Li H, et al. Identification and Verification of Immune-Related Gene Prognostic Signature Based on ssGSEA for Osteosarcoma. Front Oncol. 2020;10:607622.
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Issue | Articles in Press | |
Section | Original Article(s) | |
Keywords | ||
Diagnostic biomarkers Immune system Inflammation Major depressive disorder Terapeutic targets |
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