Using bioinformatics and metabolomics to identify altered granulosa cells in patients with diminished ovarian reserve

Abstract

Background: During fertility treatment, diminished ovarian reserve (DOR) is a challenge that can seriously affect a patient's reproductive potential. However, the pathogenesis of DOR is still unclear and its treatment options are limited. This study aimed to explore DOR's molecular mechanisms.

Methods: We used R software to analyze the mRNA microarray dataset E-MTAB-391 downloaded from ArrayExpress, screen for differentially expressed genes (DEGs), and perform functional enrichment analyses. We also constructed the protein-protein interaction (PPI) and miRNA-mRNA networks. Ovarian granulosa cells (GCs) from women with DOR and the control group were collected to perform untargeted metabolomics analyses. Additionally, small molecule drugs were identified using the Connectivity Map database.

Results: We ultimately identified 138 DEGs. Our gene ontology (GO) analysis indicated that DEGs were mainly enriched in cytokine and steroid biosynthetic processes. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG), the DEGs were mainly enriched in the AGE (advanced glycation end-product)-RAGE (receptor for AGE) signaling pathway in diabetic complications and steroid biosynthesis. In the PPI network, we determined that JUN, EGR1, HMGCR, ATF3, and SQLE were hub genes that may be involved in steroid biosynthesis and inflammation. miRNAs also played a role in DOR development by regulating target genes. We validated the differences in steroid metabolism across GCs using liquid chromatography-tandem mass spectrometry (LC-MS/MS). We selected 31 small molecules with potentially positive or negative influences on DOR development.

Conclusion: We found that steroidogenesis and inflammation played critical roles in DOR development, and our results provide promising insights for predicting and treating DOR.

Keywords: Bioinformatics analysis; Diminished ovarian reserve; Inflammation; Metabolomics; Steroid.

Figure 1. Volcano plot of all DEGs.

The orangered and blue dots represent significantly upregulated and downregulated DEGs, respectively. DEGs, differentially expressed genes; FC, fold change.

Figure 2. The GO enrichment analysis of the DEGs.

(A) Top 20 enriched BP terms of downregulated genes. (B) Top 20 enriched BP terms of upregulated genes. The length of bars represents the number of genes, the color of bars represents corresponding adjusted P-value. GO, Gene Ontology; DEGs, differentially expressed genes; BP, biological process.

Figure 3. Pathway of steroid biosynthesis from KEGG.

The genes with blue are downregulated DEGs. KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.

Figure 4. The PPI network and modular analysis.

(A) The PPI network of DEGs. (B) Module 1. (C) Module 2.Node represents gene, node size represents node degree. The orangered and blue nodes represent upregulated and downregulated genes, respectively. The depth of color represents the level of differential expression and the width of lines represents the combined score between two proteins. PPI, protein-protein interaction; DEGs, differentially expressed genes.

Figure 5. The DEMs-DEGs regulatory network.

(A) The upregulated DEMs and targeted DEGs. (B) The downregulated DEMs and targeted DEGs. Circle nodes represent DEGs and triangle nodes represent DEMs. Orangered represent upregulation and blue represent downregulation. DEGs, differentially expressed genes; DEMs, differentially expressed miRNAs.

Figure 6. Result of CMap analysis.

The orangered dots represent the synergistic small molecule drugs. The blue dots represent antagonistic small molecule drugs. CMap, Connectivity Map.