Identification and immuno-infiltration analysis of cuproptosis regulators in human spermatogenic dysfunction.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37065492", "kind": "science-record", "revision": 3, "content_hash": "sha256:ba330ef0b05d32b9dea3c70d48326311eca0d461a88a0bd595dbd6248755b857", "created_at": "2026-11-01T19:01:43Z", "updated_at": "2026-11-01T22:09:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and immuno-infiltration analysis of cuproptosis regulators in human spermatogenic dysfunction.", "doi": "10.3389/fgene.2023.1115669", "pmid": "37065492", "authors": ["Ming Zhao", "Wen-Xiao Yu", "Sheng-Jing Liu", "Ying-Jun Deng", "Zi-Wei Zhao", "Jun Guo", "Qing-He Gao"], "journal": "Frontiers in genetics", "year": 2023, "abstract": "Introduction: Cuproptosis seems to promote the progression of diverse diseases. Hence, we explored the cuproptosis regulators in human spermatogenic dysfunction (SD), analyzed the condition of immune cell infiltration, and constructed a predictive model. Methods: Two microarray datasets (GSE4797 and GSE45885) related to male infertility (MI) patients with SD were downloaded from the Gene Expression Omnibus (GEO) database. We utilized the GSE4797 dataset to obtain differentially expressed cuproptosis-related genes (deCRGs) between SD and normal controls. The correlation between deCRGs and immune cell infiltration status was analyzed. We also explored the molecular clusters of CRGs and the status of immune cell infiltration. Notably, weighted gene co-expression network analysis (WGCNA) was used to identify the cluster-specific differentially expressed genes (DEGs). Moreso, gene set variation analysis (GSVA) was performed to annotate the enriched genes. Subsequently, we selected an optimal machine-learning model from four models. Finally, nomograms, calibration curves, decision curve analysis (DCA), and the GSE45885 dataset were utilized to verify the predictions' accuracy. Results: Among SD and normal controls, we confirmed that there are deCRGs and activated immune responses. Through the GSE4797 dataset, we obtained 11 deCRGs. ATP7A, ATP7B, SLC31A1, FDX1, PDHA1, PDHB, GLS, CDKN2A, DBT, and GCSH were highly expressed in testicular tissues with SD, whereas LIAS was lowly expressed. Additionally, two clusters were identified in SD. Immune-infiltration analysis showed the existing heterogeneity of immunity at these two clusters. Cuproptosis-related molecular Cluster2 was marked by enhanced expressions of ATP7A, SLC31A1, PDHA1, PDHB, CDKN2A, DBT, and higher proportions of resting memory CD4+ T cells. Furthermore, an eXtreme Gradient Boosting (XGB) model based on 5-gene was built, which showed superior performance on the external validation dataset GSE45885 (AUC = 0.812). Therefore, the combined nomogram, calibration curve, and DCA results demonstrated the accuracy of predicting SD. Conclusion: Our study preliminarily illustrates the relationship between SD and cuproptosis. Moreover, a bright predictive model was developed.", "claims": [{"id": "claim-method", "statement": "Spearman correlation analysis was used to determine the correlation coefficient between CRGs and the relative abundance of immune cells to illustrate the relationship between CRGs and SD-related immune cell characteristics.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37065492", "locator": "sections[5] \"Correlation analysis of CRGs and immune cell infiltration\" paragraph 2", "quote": "Spearman correlation analysis was used to determine the correlation coefficient between CRGs and the relative abundance of immune cells to illustrate the relationship between CRGs and SD-related immune cell characteristics."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37065492", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study confirmed differentially expressed cuproptosis-related genes and activated immune responses in spermatogenic dysfunction.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37065492", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Among SD and normal controls, we confirmed that there are deCRGs and activated immune responses."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study confirmed differentially expressed cuproptosis-related genes and activated immune responses in spermatogenic dysfunction.", "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:37065492"], "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. 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