Exploration of the immune microenvironment of breast cancer in large population cohorts.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36046784", "kind": "science-record", "revision": 3, "content_hash": "sha256:5b446d8b26c6d586343103687f67ce77dde471f333d95dd4c45cda77f55ed1d9", "created_at": "2026-11-01T18:15:13Z", "updated_at": "2026-11-01T21:28:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploration of the immune microenvironment of breast cancer in large population cohorts.", "doi": "10.3389/fendo.2022.955630", "pmid": "36046784", "authors": ["Youyuan Deng", "Jianguo Wang", "Zhiya Hu", "Yurong Cai", "Yiping Xu", "Ke Xu"], "journal": "Frontiers in endocrinology", "year": 2022, "abstract": "Tumor immune microenvironment is associated with tumor progression. However, previous studies have not fully explored the breast cancer (BC) immune microenvironment. All the data analyzed in this study were obtained from the open-access database, including The Cancer Genome Atlas, Gene Expression Omnibus (TCGA), and cBioPortal databases. R software v4.0 and SPSS 13.0 were used to perform all the statistical analysis. Firstly, the clinical and expression profile information of TCGA, GSE20685, GSE20711, GSE48390, GSE58812, and METABRIC cohorts was collected. Then, 53 immune terms were quantified using the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. A prognosis model based on HER2_Immune_PCA, IL12_score, IL13_score, IL4_score, and IR7_score was established, which showed great prognosis prediction efficiency in both training group and validation group. A nomogram was then established for a better clinical application. Clinical correlation showed that elderly BC patients might have a higher riskscore. Pathway enrichment analysis showed that the pathway of oxidative phosphorylation, E2F targets, hedgehog signaling, adipogenesis, DNA repair, glycolysis, heme metabolism, and mTORC1 signaling was activated in the high-risk group. Moreover, Tumor Immune Dysfunction and Exclusion and Genomics of Drug Sensitivity in Cancer analysis showed that low-risk patients might be more sensitive to PD-1 therapy, cisplatin, gemcitabine, paclitaxel, and sunitinib. Finally, four genes, XCL1, XCL2, TNFRSF17, and IRF4, were identified for risk group classification. In summary, our signature is a useful tool for the prognosis and prediction of the drug sensitivity of BC.", "claims": [{"id": "claim-method", "statement": "Firstly, univariate Cox regression and LASSO analysis were performed to screen the prognosis-related immune terms based on the TCGA database ( Figure 2A ). The terms meeting the criteria of univariate Cox regression are shown in Table S7 . Then, multivariate Cox regression analysis identified five immune terms used for model construction, namely, HER2_Immune_PCA, IL12_score, IL13_score, IL4_score, and IR7_score ( Figure 2B ).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36046784", "locator": "sections[13] \"Prognosis model construction\" paragraph 2", "quote": "Firstly, univariate Cox regression and LASSO analysis were performed to screen the prognosis-related immune terms based on the TCGA database ( Figure 2A ). The terms meeting the criteria of univariate Cox regression are shown in Table S7 . Then, multivariate Cox regression analysis identified five immune terms used for model construction, namely, HER2_Immune_PCA, IL12_score, IL13_score, IL4_score, and IR7_score ( Figure 2B )."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://portal.gdc.cancer.gov/; https://www.ncbi.nlm.nih.gov/gds/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36046784", "locator": "sections[20] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://portal.gdc.cancer.gov/; https://www.ncbi.nlm.nih.gov/gds/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In summary, our signature is a useful tool for the prognosis and prediction of the drug sensitivity of BC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36046784", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In summary, our signature is a useful tool for the prognosis and prediction of the drug sensitivity of BC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In summary, our signature is a useful tool for the prognosis and prediction of the drug sensitivity of BC.", "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:36046784"], "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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Then, multivariate Cox regression analysis identified five immune terms used for model construction, namely, HER2_Immune_PCA, IL12_score, IL13_score, IL4_score, and IR7_score ( Figure 2B )."}}, {"id": "method-2", "family": "correlation_analysis", "specific": "spearman", "label": "Correlation analysis", "dsl_operations": ["spearman_rho"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Spearman correlation"}}, {"id": "method-3", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "Pathway enrichment"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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