Epithelial cell-related prognostic risk model in breast cancer based on single-cell and bulk RNA sequencing.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39286180", "kind": "science-record", "revision": 3, "content_hash": "sha256:399ad60e2b3b818181ed68770155be580f17cca050ed260c96866c843f523221", "created_at": "2026-11-01T20:29:27Z", "updated_at": "2026-11-01T22:12:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Epithelial cell-related prognostic risk model in breast cancer based on single-cell and bulk RNA sequencing.", "doi": "10.1016/j.heliyon.2024.e37048", "pmid": "39286180", "authors": ["Man-Zhi Xia", "Hai-Chao Yan"], "journal": "Heliyon", "year": 2024, "abstract": "Objective: This study aims to construct an epithelial cell-related prognostic risk model for breast cancer (BRCA) and explore its significance.\nMethods: GSE42568, GSE10780, GSE245601, and TCGA-BRCA datasets were sourced from public databases. Epithelial cell-related differentially expressed genes were identified using single-cell data analysis. Venn diagrams determined the intersecting genes between epithelial cell-related and BRCA-related genes. Batch Kaplan-Meier (K-M) survival analysis identified core intersecting genes for BRCA overall survival. Consensus clustering, enrichment, LASSO, and COX regression analyses were performed on the core intersecting genes, and then a prognostic risk model was constructed. The diagnostic and prognostic effectiveness of the risk model was subsequently evaluated and immune infiltration analysis was conducted. Finally, qRT-PCR was used to verify the expression of genes in the risk model.\nResults: There were 374 intersecting genes between epithelial cell-related and BRCA-related genes, among which 51 core intersecting genes were associated with BRCA prognosis. Consensus clustering categorized TCGA-BRCA into C1 and C2, with shared regulation of the estrogen signaling pathway. Three genes (DIRC3, SLC6A2, TUBA3D) were independent predictors of BRCA prognosis, forming the basis for a risk model. Except for exhibiting satisfactory diagnostic efficacy, the risk score elevation correlated with poor prognosis, elevated matrix, immune, and ESTIMATE scores, and negative correlation with microsatellite instability. The in vitro results confirmed the differential expression levels of DIRC3, SLC6A2, and TUBA3D.\nConclusion: The prognostic risk model associated with epithelial cells demonstrates effective diagnostic performance in BRCA, serving as an independent prognostic factor for BRCA patients. Additionally, it exhibits a correlation with immune scores.", "claims": [{"id": "claim-method", "statement": "LASSO regression analysis was applied to the core intersecting genes to prevent overfitting when utilizing the “glmnet” package, followed by undergoing univariate and multivariate COX regression analyses sequentially to identify genes with a P value < 0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39286180", "locator": "sections[7] \"Prognostic modeling\" paragraph 2", "quote": "LASSO regression analysis was applied to the core intersecting genes to prevent overfitting when utilizing the “glmnet” package, followed by undergoing univariate and multivariate COX regression analyses sequentially to identify genes with a P value < 0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data will be made available on request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39286180", "locator": "sections[25] \"Data availability\" paragraph 2", "quote": "Data will be made available on request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ROC analysis showed that the model had a good diagnostic effect on BRCA patients, and the AUCs of the model in the TCGA-BRCA, GSE42568, and GSE10780 datasets were 0.84, 0.88, and 0.66, respectively (Fig. 8D–F).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39286180", "locator": "sections[19] \"Prognostic model evaluation\" paragraph 2", "quote": "ROC analysis showed that the model had a good diagnostic effect on BRCA patients, and the AUCs of the model in the TCGA-BRCA, GSE42568, and GSE10780 datasets were 0.84, 0.88, and 0.66, respectively (Fig. 8D–F)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "ROC analysis showed that the model had a good diagnostic effect on BRCA patients, and the AUCs of the model in the TCGA-BRCA, GSE42568, and GSE10780 datasets were 0.84, 0.88, and 0.66, respectively (Fig. 8D–F).", "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:39286180"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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