Integrative analyses of biomarkers and pathways for adipose tissue after bariatric surgery

Adipocyte. 2020 Dec;9(1):384-400. doi: 10.1080/21623945.2020.1795434.

Abstract

We explored potential biomarkers and molecular mechanisms regarding multiple benefits after bariatric surgery. Differentially expressed genes (DEGs) for subcutaneous adipose tissue (AT) after bariatric surgery were identified by analyzing two expression profiles from the GEO. Subsequently, enrichment analysis, GSEA, PPI network, and gene-microRNAs and gene-TFs networks were interrogated to identify hub genes and associated pathways. Co-expressed DEGs included one that was up-regulated and 22 that were down-regulated genes. The enrichment analyses indicated that down-regulated DEGs were significantly involved in inflammatory responses. GSEA provided comprehensive evidence that most genes enriched in pro-inflammation pathways, while gene-sets after surgery enriched in metabolism. We identified nine hub genes in the PPI network, most of which were validated as highly expressed and hypomethylated in obesity by Attie Lab Diabetes and DiseaseMeth databases, respectively. DGIdb was also applied to predict potential therapeutic agents that might reverse abnormally high hub gene expression. Bariatric surgery induces a significant shift from an obese pro-inflammatory state to an anti-inflammatory state, with improvement in adipocyte metabolic function - representing key mechanisms whereby AT function improves after bariatric surgery. Our study deepens a mechanistic understanding of the benefits of bariatric surgery and provides potential biomarkers or treatment targets for further research.

Keywords: Bariatric surgery; DEGs; PPI network; adipose tissue; bioinformatics; differentially expressed genes; enrichment analyses; hub genes; obesity; potential therapeutic agents.

MeSH terms

  • Adipose Tissue / metabolism*
  • Bariatric Surgery
  • Biomarkers*
  • Computational Biology* / methods
  • DNA Methylation
  • Databases, Genetic
  • Epigenesis, Genetic
  • Gene Expression Profiling
  • Gene Expression Regulation*
  • Gene Ontology
  • Gene Regulatory Networks*
  • Humans
  • Protein Interaction Mapping
  • Protein Interaction Maps
  • Signal Transduction*

Substances

  • Biomarkers

Grants and funding

This work was supported by the Natural Science Foundation of Guangdong Province under Grant No. 2016A030313521 and Cultivating Plan Program for National Natural Science Foundation of Shenzhen Hospital, Southern Medical University under Grant No. CNGZRJJPY202001.