ATP2C2 as a novel immune-related marker that defines the tumor microenvironment in triple-negative breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37588742", "kind": "science-record", "revision": 3, "content_hash": "sha256:bf1d43bfb54db5d10fbebf7409d029f603bb79756d58afd0311d5c78d465a14d", "created_at": "2026-11-01T19:02:23Z", "updated_at": "2026-11-01T22:10:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "ATP2C2 as a novel immune-related marker that defines the tumor microenvironment in triple-negative breast cancer.", "doi": "10.21037/tcr-23-83", "pmid": "37588742", "authors": ["Mingyuan Zhao", "Qilong Zhang", "Zichen Song", "Huan Lei", "Jing Li", "Fang Peng", "Shuangyan Lin"], "journal": "Translational cancer research", "year": 2023, "abstract": "Background: Triple-negative breast cancer (TNBC) is an aggressive cancer that affects about 13/100,000 women yearly. Patients with TNBC are often resistant to endocrine and molecular targeted therapy, making clinical treatment challenging. Researches indicate that tumor microenvironment (TME) is related to prognosis in many cancers. Therefore, we aim to identify TME immune-related biomarkers to enhance the prognosis and immunotherapy efficacy in patients with TNBC.\nMethods: The bulk mRNA transcriptome data and clinical information of the (GSE58812) and (GSE25055) datasets were downloaded from the Gene Expression Omnibus (GEO) database, and the ESTIMATE algorithm was used to calculate the ImmuneScore, StromalScore, and ESTIMATEScore. Patients were divided into low and high groups according to the quartiles of ImmuneScore, StromalScore, and the median of ESTIMATEScore to filter differential expression genes (DEGs), respectively. The DEGs were then evaluated using univariate and multivariate Cox regression to identify TME-related genes and its association with survival rate for the construction of a TMErisk model with three biomarkers. Then Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) data were used to compare the gene expression in cancer and normal tissues. xCell analysis calculated the proportion of tumor-infiltrating immune cells in low and high expression of ATPase Secretory Pathway Ca2+ Transporting 2 (ATP2C2). In addition, samples from 20 TNBC patients admitted to our institution were used for immunohistochemical (IHC) examination.\nResults: Three immune-related DEGs were identified, including prolyl 3-hydroxylase 2 (P3H2), sodium voltage-gated channel beta subunit 3 (SCN3B), and ATP2C2 and a TMErisk model was constructed and validated. However, only ATP2C2 was selected for further analysis. ATP2C2 mRNA level of TNBC patients was higher than that of normal breast tissue. Survival analysis showed that patients with high expression of ATP2C2 had a bad prognosis. xCell analysis demonstrated that the expression of ATP2C2 was associated with 16 kinds of tumor-infiltrating immune cells. Protein expression of ATP2C2 in TNBC tissues was higher compared to paired normal tissues in IHC.\nConclusions: This study constructed and validated a TMErisk model that can effectively predict 3- and 5-year survival rate for TNBC patients. TNBC patients with lower expression of ATP2C2 had a good prognosis.", "claims": [{"id": "claim-method", "statement": "The DEGs were then evaluated using univariate and multivariate Cox regression to identify TME-related genes and its association with survival rate for the construction of a TMErisk model with three biomarkers.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37588742", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The DEGs were then evaluated using univariate and multivariate Cox regression to identify TME-related genes and its association with survival rate for the construction of a TMErisk model with three biomarkers."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-83/dss", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37588742", "locator": "sections[26] \"fn-group\" paragraph 1", "quote": "Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-83/dss"}], "state": "complete"}, {"id": "claim-target-result", "statement": "The ROC curve further demonstrated that AUC values of 3- and 5-year had a strong predictive capacity, with AUC values of 0.655 and 0.727, respectively (Figure 3E).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37588742", "locator": "sections[16] \"Screening of TME-related genes and construction of TMErisk score\" paragraph 2", "quote": "The ROC curve further demonstrated that AUC values of 3- and 5-year had a strong predictive capacity, with AUC values of 0.655 and 0.727, respectively (Figure 3E)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The ROC curve further demonstrated that AUC values of 3- and 5-year had a strong predictive capacity, with AUC values of 0.655 and 0.727, respectively (Figure 3E).", "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:37588742"], "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:GSE25055"], "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:GSE25055"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37588742", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-23-83", "sha256": 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