Using publicly available transcriptomic data to identify mechanistic and diagnostic biomarkers in azoospermia and overall male infertility.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35173218", "kind": "science-record", "revision": 6, "content_hash": "sha256:91ce137c164f42c1a1fbb9aa5306e145ee1c25d37db489cd40be4888035af260", "created_at": "2026-11-01T17:36:55Z", "updated_at": "2026-11-01T22:48:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Using publicly available transcriptomic data to identify mechanistic and diagnostic biomarkers in azoospermia and overall male infertility.", "doi": "10.1038/s41598-022-06476-1", "pmid": "35173218", "authors": ["Temidayo S Omolaoye", "Mahmood Yaseen Hachim", "Stefan S du Plessis"], "journal": "Scientific reports", "year": 2022, "abstract": "Azoospermia, which is the absence of spermatozoa in an ejaculate occurring due to defects in sperm production, or the obstruction of the reproductive tract, affects about 1% of all men and is prevalent in up to 10-15% of infertile males. Conventional semen analysis remains the gold standard for diagnosing and treating male infertility; however, advances in molecular biology and bioinformatics now highlight the insufficiency thereof. Hence, the need to widen the scope of investigating the aetiology of male infertility stands pertinent. The current study aimed to identify common differentially expressed genes (DEGs) that might serve as potential biomarkers for non-obstructive azoospermia (NOA) and overall male infertility. DEGs across different datasets of transcriptomic profiling of testis from human patients with different causes of infertility/ impaired spermatogenesis and/or azoospermia were explored using the gene expression omnibus (GEO) database. Following the search using the GEOquery, 30 datasets were available, with 5 meeting the inclusion criteria. The DEGs for datasets were identified using limma R packages through the GEO2R tool. The annotated genes of the probes in each dataset were intersected with DEGs from all other datasets. Enriched Ontology Clustering for the identified genes was performed using Metascape to explore the possible connection or interaction between the genes. Twenty-five DEGs were shared between most of the datasets, which might indicate their role in the pathogenesis of male infertility. Of the 25 DEGs, eight genes (THEG, SPATA20, ROPN1L, GSTF1, TSSK1B, CABS1, ADAD1, RIMBP3) are either involved in the overall spermatogenic processes or at specific phases of spermatogenesis. We hypothesize that alteration in the expression of these genes leads to impaired spermatogenesis and, ultimately, male infertility. Thus, these genes can be used as potential biomarkers for the early detection of NOA.", "claims": [{"id": "claim-method", "statement": "Identifying DEGs for each dataset was performed as previously described20. In brief, the GEOquery and limma R packages through the GEO2R tool for each dataset was used as follows21: The samples were grouped into diseases and healthy and then analyzed using the following parameters: applying log transformation to the data, applying limma precision weights (vooma), and force normalization. After sorting the genes according to the False Discovery Rate (FDR), the top differentially expressed probes with FDR < 0.05 were selected from each dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35173218", "locator": "sections[4] \"Identification of common DEGs\" paragraph 2", "quote": "Identifying DEGs for each dataset was performed as previously described20. In brief, the GEOquery and limma R packages through the GEO2R tool for each dataset was used as follows21: The samples were grouped into diseases and healthy and then analyzed using the following parameters: applying log transformation to the data, applying limma precision weights (vooma), and force normalization. After sorting the genes according to the False Discovery Rate (FDR), the top differentially expressed probes with FDR < 0.05 were selected from each dataset."}], "state": "complete"}, {"id": "claim-data-access", "statement": "(i)GSE145467 (patients with impaired spermatogenesis (NOA; n = 10) and patients with normal spermatogenesis (OA; n = 10). 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