Immune-Related Genes to Construct a Novel Prognostic Model of Breast Cancer: A Chemosensitivity-Based Study.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34764953", "kind": "science-record", "revision": 4, "content_hash": "sha256:20ac8239d7c02f02f5092f550371aac894edcc8c936d79f109cb648e5b1e7e14", "created_at": "2026-11-01T17:36:27Z", "updated_at": "2026-11-01T22:05:08Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Immune-Related Genes to Construct a Novel Prognostic Model of Breast Cancer: A Chemosensitivity-Based Study.", "doi": "10.3389/fimmu.2021.734745", "pmid": "34764953", "authors": ["Zhi-Min Deng", "Wei Hu", "Fang-Fang Dai", "Meng-Qin Yuan", "Min Hu", "Yan-Xiang Cheng"], "journal": "Frontiers in immunology", "year": 2021, "abstract": "Chemotherapy combined with surgery is effective for patients with breast cancer (BC). However, chemoresistance restricts the effectiveness of BC treatment. Immune microenvironmental changes are of pivotal importance for chemotherapy responses. Thus, we sought to construct and validate an immune prognostic model based on chemosensitivity status in BC. Here, immune-related and chemosensitivity-related genes were obtained from GSE25055. Then, univariate analysis was employed to identify prognostic-related gene pairs from the intersection of the two parts of the genes, and modified least absolute shrinkage and selection operator (LASSO) analysis was performed to build a prognostic model. Furthermore, we investigated the efficiency of this model from various perspectives, and further validation was performed using the Cancer Genome Atlas (TCGA) cohorts. We identified seven immune and chemosensitivity-related gene pairs and incorporated them into the Cox regression model. After multilevel validation, the risk model was found to be closely related to the survival rate, various clinical characteristics, tumor mutation burden (TMB) score, immune checkpoints, and response to chemotherapeutic drugs. In addition, the model was verified to exhibit predictive capacity as an independent factor over other candidate clinical features. Notably, the constructed nomogram was more accurate than any single factor. Altogether, the risk score model and the nomogram have potential predictive value and may have important practical implications.", "claims": [{"id": "claim-method", "statement": "Furthermore, after combining the survival data taken from the GEO database, univariate analysis was computed using Cox regression. Next, least absolute shrinkage and selection operator (LASSO) regression was run 1,000 times with the glmnet package to preliminarily screen the DECIRG pairs, and then Cox proportional hazard regression was used to further reduce the number of pairs to build a risk assessment model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34764953", "locator": "sections[6] \"2.4 Construction of the Risk Assessment Model Based on the DECIRG Pairs\" paragraphs 1-4", "quote": "Furthermore, after combining the survival data taken from the GEO database, univariate analysis was computed using Cox regression. Next, least absolute shrinkage and selection operator (LASSO) regression was run 1,000 times with the glmnet package to preliminarily screen the DECIRG pairs, and then Cox proportional hazard regression was used to further reduce the number of pairs to build a risk assessment model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. The datasets generated and/or analyzed during the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25055) and TCGA datasets (https://portal.gdc.cancer.gov/legacy-archive/search/f).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34764953", "locator": "sections[20] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. The datasets generated and/or analyzed during the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25055) and TCGA datasets (https://portal.gdc.cancer.gov/legacy-archive/search/f)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Seven immune- and chemosensitivity-related gene pairs were identified and incorporated into a Cox regression model, with the resulting model serving as an independent prognostic factor with relatively good accuracy.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34764953", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We identified seven immune and chemosensitivity-related gene pairs and incorporated them into the Cox regression model."}, {"asset": "source:pmid:34764953", "locator": "sections[15] \"3.4 Construction and Valuation of the Risk Score Model for DECIRG Pairs\" paragraph 10", "quote": "In summary, based on the results described above, the risk score model is considered to be an independent prognostic factor and it has a relatively good accuracy."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Seven immune- and chemosensitivity-related gene pairs were identified and incorporated into a Cox regression model, with the resulting model serving as an independent prognostic factor with relatively good accuracy.", "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:34764953"], "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:34764953", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2021.734745", "sha256": "sha256:ce476e20342d8ce53d4f36826604e2a2434475e60edba1d7dd3d13b396c4dc0b", "bytes": 48357, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2021.734745", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8576363", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE25055", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25055", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE25055", "version": null, "note": "Publicly available datasets were analyzed in this study. 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