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

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32923184", "kind": "science-record", "revision": 8, "content_hash": "sha256:4c657b0540a8981c63f131aaaf56dbb01a5a97bb3c0aeaba3d0b50683ad85a90", "created_at": "2026-11-01T15:41:10Z", "updated_at": "2026-11-01T22:45:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Using bioinformatics and metabolomics to identify altered granulosa cells in patients with diminished ovarian reserve.", "doi": "10.7717/peerj.9812", "pmid": "32923184", "authors": ["Ruifen He", "Zhongying Zhao", "Yongxiu Yang", "Xiaolei Liang"], "journal": "PeerJ", "year": 2020, "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.\nMETHODS: 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.\nRESULTS: 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.\nCONCLUSION: We found that steroidogenesis and inflammation played critical roles in DOR development, and our results provide promising insights for predicting and treating DOR.", "claims": [{"id": "claim-method", "statement": "We used the limma package in R software (Ritchie et al., 2015) to identify DEGs across the DOR and NOR samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32923184", "locator": "sections[4] \"Identifying DEGs\" paragraph 2", "quote": "We used the limma package in R software (Ritchie et al., 2015) to identify DEGs across the DOR and NOR samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:The raw data are available in the Supplemental File.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32923184", "locator": "sections[26] \"Data Availability\" paragraph 2", "quote": "The following information was supplied regarding data availability:The raw data are available in the Supplemental File."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:32923184"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:E-MTAB-391"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:E-MTAB-391"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32923184", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.9812", "sha256": 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