A risk scoring system based on tumor microenvironment cells to predict prognosis and immune activity in triple-negative breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35061208", "kind": "science-record", "revision": 4, "content_hash": "sha256:28cc39a8fe7e2c2656a1129bfb80dd7f1ba5148fbab5e9c3ce9b35a779966e7b", "created_at": "2026-11-01T17:36:47Z", "updated_at": "2026-11-01T22:05:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A risk scoring system based on tumor microenvironment cells to predict prognosis and immune activity in triple-negative breast cancer.", "doi": "10.1007/s12282-021-01326-w", "pmid": "35061208", "authors": ["Anli Yang", "Minqing Wu", "Mengqian Ni", "Lijuan Zhang", "Mingyue Li", "Peijun Wei", "Yonggang Yang", "Weikai Xiao", "Xin An"], "journal": "Breast cancer (Tokyo, Japan)", "year": 2022, "abstract": "The tumor microenvironment (TME) interacting with the malignant cells plays a vital role in cancer development. Herein, we aim to establish and verify a scoring system based on the characteristics of TME cells for prognosis prediction and personalized treatment guidance in patients with triple-negative breast cancer (TNBC). 158 TNBC samples from The Cancer Genome Atlas (TCGA) were included as the training cohort, and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (N = 297), as well as GSE58812 (N = 107), were included as the validation cohort. The enrichment scores of 64 immune and stromal cells were estimated by the xCell algorithm. In the training cohort, cells with prognostic significance were found out using univariate Cox regression analysis and further applied to the random survival forest (RSF) model. Based on the scores of M2 macrophages, CD8+ T cells, and CD4+ memory T cells, a risk scoring system was constructed, which divided TNBC patients into 4 phenotypes (M2low, M2highCD8+ThighCD4+Thigh, M2highCD8+ThighCD4+Tlow, and M2highCD8+Tlow). Furthermore, types 1 and 2 patients were merged into the low-risk group, while types 3 and 4 patients were in the high-risk group. The low-risk group had superior survival outcomes than the high-risk one, which was further confirmed in the validation cohort. Moreover, in the low-risk group, immune-related pathways were significantly enriched, and a higher level of antitumoral immune cells and immune checkpoint molecules, including PD-L1, PD-1, and CTLA-4, could be observed. Additionally, consistent results were achieved in the SYSUCC cohort when the scoring system was applied. In summary, this novel scoring system might predict the survival and immune activity of patients and might serve as a potential index for immunotherapy.", "claims": [{"id": "claim-method", "statement": "We found 6 cells with prognostic significance by the univariate Cox regression analysis and enrolled them into the random survival forest (RSF) model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35061208", "locator": "sections[6] \"Prognosis-related cell selection and model construction\" paragraph 2", "quote": "We found 6 cells with prognostic significance by the univariate Cox regression analysis and enrolled them into the random survival forest (RSF) model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated for this study are available on request to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35061208", "locator": "sections[20] \"Data availability\" paragraph 2", "quote": "The datasets generated for this study are available on request to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We found that the AUC was 0.706 in the 1st year, 0.607 in the 3rd year, and 0.678 in the 5th year, indicating the moderate ability of our system in prognosis prediction.", "kind": "quantitative", "target": {"value": 0.706, "unit": "auc", "tolerance": null, "source": "We found that the AUC was 0.706 in the 1st year, 0.607 in the 3rd year, and 0.678 in the 5th year, indicating the moderate ability of our system in prognosis prediction."}, "evidence": [{"asset": "source:pmid:35061208", "locator": "sections[12] \"Prognostic and diagnostic value of the classifier\" paragraph 2", "quote": "We found that the AUC was 0.706 in the 1st year, 0.607 in the 3rd year, and 0.678 in the 5th year, indicating the moderate ability of our system in prognosis prediction."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "We found that the AUC was 0.706 in the 1st year, 0.607 in the 3rd year, and 0.678 in the 5th year, indicating the moderate ability of our system in prognosis prediction.", "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:35061208"], "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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