A Tumor Suppressor Gene-Based Prognostic Classifier Predicts Prognosis, Tumor Immune Infiltration, and Small Molecule Compounds in Breast Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35186006", "kind": "science-record", "revision": 5, "content_hash": "sha256:e40db47474509a0b60264a528583c704447ada1e210f4b25d8683cfcdc2982a7", "created_at": "2026-11-01T17:36:56Z", "updated_at": "2026-11-01T22:05:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Tumor Suppressor Gene-Based Prognostic Classifier Predicts Prognosis, Tumor Immune Infiltration, and Small Molecule Compounds in Breast Cancer.", "doi": "10.3389/fgene.2021.783026", "pmid": "35186006", "authors": ["Suxiao Jiang", "Xiangjing Bu", "Desheng Tang", "Changsheng Yan", "Yan Huang", "Kun Fang"], "journal": "Frontiers in genetics", "year": 2021, "abstract": "Objective: Tumor suppressor genes (TSGs) play critical roles in the cell cycle checkpoints and in modulating genomic stability. Here, we aimed to develop a TSG-based prognostic classifier for breast cancer. Methods: Gene expression profiles and clinical information of breast cancer were curated from TCGA (discovery set) and Gene Expression Omnibus (GEO) repository (GSE12093 and GSE17705 datasets as testing sets). Univariate cox regression analysis and random forest machine learning method were presented for screening characteristic TSGs. After multivariate cox regression analyses, a TSG-based prognostic classifier was constructed. The predictive efficacy was verified by C-index and receiver operating characteristic (ROC) curves. Meanwhile, the predictive independency was assessed through uni- and multivariate cox regression analyses and stratified analyses. Tumor immune infiltration was estimated via ESTIMATE and CIBERSORT algorithms. Small molecule agents were predicted through CMap method. Molecular subtypes were clustered based on the top 100 TSGs with the most variance. Results: A prognostic classifier including nine TSGs was established. High-risk patients were predictive of undesirable prognosis. C-index and ROC curves demonstrated its excellent predictive performance in prognosis. Also, this prognostic classifier was independent of conventional clinicopathological parameters. Low-risk patients exhibited increased infiltration levels of immune cells like T cells CD8. Totally, 48 small molecule compounds were predicted to potentially treat breast cancer. Five TSG-based molecular subtypes were finally constructed, with distinct prognosis and clinicopathological features. Conclusion: Collectively, this study provided a TSG-based prognostic classifier with the potential to predict clinical outcomes and immune infiltration in breast cancer and identified potential small molecule agents against breast cancer.", "claims": [{"id": "claim-method", "statement": "Univariate cox regression models were conducted to evaluate the associations between TSGs and overall survival (OS) across breast cancer patients in discovery set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35186006", "locator": "sections[4] \"Identification of Characteristic Tumor Suppressor Genes\" paragraph 2", "quote": "Univariate cox regression models were conducted to evaluate the associations between TSGs and overall survival (OS) across breast cancer patients in discovery set."}], "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 author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35186006", "locator": "sections[27] \"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 author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study established a nine-tumor-suppressor-gene prognostic classifier that identified high-risk patients with unfavorable breast cancer outcomes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35186006", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A prognostic classifier including nine TSGs was established."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study established a nine-tumor-suppressor-gene prognostic classifier that identified high-risk patients with unfavorable breast cancer outcomes.", "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:35186006"], "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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