Identification and validation of a novel zinc finger protein-related gene-based prognostic model for breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34721975", "kind": "science-record", "revision": 4, "content_hash": "sha256:3a542d00ec11f15a77f7d2affc3e6eabf5345718cea7e21fa7f3ef3f4490fb61", "created_at": "2026-11-01T17:36:24Z", "updated_at": "2026-11-01T22:05:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of a novel zinc finger protein-related gene-based prognostic model for breast cancer.", "doi": "10.7717/peerj.12276", "pmid": "34721975", "authors": ["Min Ye", "Liang Li", "Donghua Liu", "Qiuming Wang", "Yunuo Zhang", "Jinfeng Zhang"], "journal": "PeerJ", "year": 2021, "abstract": "BACKGROUND: Breast invasive carcinoma (BRCA) is a commonly occurring malignant tumor. Zinc finger proteins (ZNFs) constitute the largest transcription factor family in the human genome and play a mechanistic role in many cancers' development. The prognostic value of ZNFs has yet to be approached systematically for BRCA.\nMETHODS: We analyzed the data of a training set from The Cancer Genome Atlas (TCGA) database and two validation cohort from GSE20685 and METABRIC datasets, composed of 3,231 BRCA patients. After screening the differentially expressed ZNFs, univariate Cox regression, LASSO, and multiple Cox regression analysis were performed to construct a risk-based predictive model. ESTIMATE algorithm, single-sample gene set enrichment analysis (ssGSEA), and gene set enrichment analyses (GSEA) were utilized to assess the potential relations among the tumor immune microenvironment and ZNFs in BRCA.\nRESULTS: In this study, we profiled ZNF expression in TCGA based BRCA cohort and developed a novel prognostic model based on 14 genes with ZNF relations. This model was composed of high and low-score groups for BRCA classification. Based upon Kaplan-Meier survival curves, risk-status-based prognosis illustrated significant differences. We integrated the 14 ZNF-gene signature with patient clinicopathological data for nomogram construction with accurate 1-, 3-, and 5-overall survival predictive capabilities. We then accessed the Genomics of Drug Sensitivity in Cancer database for therapeutic drug response prediction of signature-defined BRCA patient groupings for our selected TCGA population. The signature also predicts sensitivity to chemotherapeutic and molecular-targeted agents in high- and low-risk patients afflicted with BRCA. Functional analysis suggested JAK STAT, VEGF, MAPK, NOTCH TOLL-like receptor, NOD-like receptor signaling pathways, apoptosis, and cancer-based pathways could be key for ZNF-related BRCA development. Interestingly, based on the results of ESTIMATE, ssGSEA, and GSEA analysis, we elucidated that our ZNF-gene signature had pivotal regulatory effects on the tumor immune microenvironment for BRCA.\nCONCLUSION: Our findings shed light on the potential contribution of ZNFs to the pathogenesis of BRCA and may inform clinical practice to guide individualized treatment.", "claims": [{"id": "claim-method", "statement": "After screening the differentially expressed ZNFs, univariate Cox regression, LASSO, and multiple Cox regression analysis were performed to construct a risk-based predictive model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34721975", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "After screening the differentially expressed ZNFs, univariate Cox regression, LASSO, and multiple Cox regression analysis were performed to construct a risk-based predictive model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability: The raw measurements are available in the Supplementary Files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34721975", "locator": "sections[29] \"Data Availability Statement\" paragraphs 1-3", "quote": "The following information was supplied regarding data availability: The raw measurements are available in the Supplementary Files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Similar to that in the previous analysis, the high-risk patient group was associated with earlier death, (Fig. 4B) and reduced overall survival time in comparison to the low-risk patients (Fig. 4D; P < 0.001).", "kind": "quantitative", "target": {"value": 0.001, "unit": "p-value", "tolerance": null, "source": "Similar to that in the previous analysis, the high-risk patient group was associated with earlier death, (Fig. 4B) and reduced overall survival time in comparison to the low-risk patients (Fig. 4D; P < 0.001)."}, "evidence": [{"asset": "source:pmid:34721975", "locator": "sections[11] \"External prognostic signature validation\" paragraph 2", "quote": "Similar to that in the previous analysis, the high-risk patient group was associated with earlier death, (Fig. 4B) and reduced overall survival time in comparison to the low-risk patients (Fig. 4D; P < 0.001)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Similar to that in the previous analysis, the high-risk patient group was associated with earlier death, (Fig. 4B) and reduced overall survival time in comparison to the low-risk patients (Fig. 4D; P < 0.001).", "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:34721975"], "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:GSE20685"], "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:GSE20685"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34721975", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.12276", "sha256": 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