Identification of Key Genes and Signaling Pathways in Male Androgenetic Alopecia by Bioinformatics Analysis

Objective: The primary aim of our research was to investigate the molecular pathogenesis and predict potential therapeutic targets of male androgenetic alopecia (MAGA) by using bioinformatics to collate and analyze gene expression profiles. Methods: The data of MAGA gene chip (GSE90594) was downloaded from GEO database, 14 pairs of normal males and MAGA scalp tissues were selected as study subjects, which were divided into alopecia group and healthy group. The online tool GEO2R was used to analyze the differentially expressed genes(DEGs) between the two groups. Gene ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) network map construction and key node analysis were also performed in the present study. Results: The results of GEO2R analysis showed that 214 differentially expressed genes (DEGs) were identified, including 50 up-regulated genes and 164 down-regulated genes. GO enrichment analysis showed that DEGs were mainly enriched in immune response, intermediate filaments, hair growth cycle, epidermis development and structural constituent, aging and other terms. KEGG pathway analysis results showed that DEGs was mainly enriched in cytokine-cytokine receptor interaction, cell adhesion molecules, chemokine, synthesis and degradation of ketone bodies, IL-17 signaling pathway as well as other. PPI analysis results suggested that a total of 128 genes and 1235 interactions participated in the construction of PPI network map, including eight key DEGs and two important gene clusters. Conclusion: The pathogenesis of MAGA was closely related to multiple genes and pathways in the body. Among them, KRT82, KRTAP24-1, KRT39, KRTAP3-1, KRTAP12-1, KRTAP26-1, KRTAP3-2 and KRTAP17-1 might play key roles. Further related study will be carried out for experimental and clinical verification.

• Publication year

  2020

• Venue

  International Conferences on Biological Information and Biomedical Engineering

• Publication date

  2020-07-03

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1145/3403782.3403795
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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