Background
Primary bilateral macronodular adrenal hyperplasia (PBMAH) is a rare disease that is characterised by multiple large benign nodules in the bilateral adrenal cortex, excessive secretion of cortisol, and complex pathogenesis,including somatic and germline mutations. The latest research shows that PBMAH is a genetic disease, and the most reported pathogenic gene is ARMC5.However, there are no target genes for early detection now. Bioinformatics analysis is a powerful method for the identification of biomarkers and possible therapeutic targets of a certain disease from related datasets.
Methods
This study searched and downloaded the transcriptome sequencing data and expression profile dataset GSE171558 related to primary bilateral adrenal macronodular hyperplasia from the gene expression omnibus,GEO, http://www༎ncbi༎nlm༎nih༎gov /geo). We filtered the differentially expressed genes (DEGs) and performed weighted gene coexpression network analysis (WGCNA) on this dataset.Gene Ontology (GO) ,Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment Analysis and Gene Set Enrichment Analysis(GSEA) were performed for the blue module genes. Protein-protein interaction network (PPI) analysis was performed based on the differentially expressed gene.We selected the overlapping genes of the hub gene in the blue gene module and the hub gene in PPI as the final hub gene of PBMAH. And we verified the final hub gene in the GSE25031 dataset.
Results
The blue gene model (677 genes) is mainly enriched in lipid metabolism, with the highest correlation coefficient with PBMAH. Through differential analysis, we screened out 487 DEGs, including 231 up-regulated genes and 256 down-regulated genes. PPI analysis identified 30 hub genes. GPC4 and VCAN were identified as the final hub genes of PBMAH.The raw data of GSE25031 verified the analysis results. The expression of GPC4 was significantly down-regulated in the PBMAH group compared with the normal control group, and VCAN was up-regulated considerably compared with the normal group. Analysis of GSEA data showed that VCAN was connected to PI3K-Akt signalling pathway, Phospholipase D signalling pathway, Rap1 signalling route, Ras signalling pathway, MAPK signalling pathway, etc. GPC4 was associated to cancer-related Pathways, Rap1 signalling pathway, PI3K-Akt signalling pathway, Wnt signalling pathway, etc.
Conclusions
GPC4 and VCAN may participate in the occurrence and development of PBMAH, and these,two hub genes may be potential targets for the intervention of PBMAH.