Identification of immune-related biomarkers for predicting neoadjuvant chemotherapy sensitivity in HER2 negative breast cancer via bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35800743", "kind": "science-record", "revision": 4, "content_hash": "sha256:a7954f712b973a45cd3b4a7a4fd10504778c9a76c3ba9093baeabedea71cdff5", "created_at": "2026-11-01T18:14:51Z", "updated_at": "2026-11-01T22:06:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of immune-related biomarkers for predicting neoadjuvant chemotherapy sensitivity in HER2 negative breast cancer via bioinformatics analysis.", "doi": "10.21037/gs-22-234", "pmid": "35800743", "authors": ["Dalang Fang", "Yanting Li", "Yanghong Li", "Yongcheng Chen", "Qianfang Huang", "Zhizhai Luo", "Jinghua Chen", "Yingjin Li", "Zaizhi Wu", "Yuanlu Huang", "Yanfei Ma"], "journal": "Gland surgery", "year": 2022, "abstract": "Background: Neoadjuvant chemotherapy (NAC) is an important treatment for breast cancer (BC) patients. However, due to the lack of specific therapeutic targets, only 1/3 of human epidermal growth factor receptor 2 (HER2)-negative patients reach pathological complete response (pCR). Therefore, there is an urgent need to identify novel biomarkers to distinguish and predict NAC sensitive in BC patients.\nMethods: The GSE163882 dataset, containing 159 BC patients treated with NAC, was downloaded from the Gene Expression Omnibus (GEO) database. Patients with pathological complete response (pCR) and those with residual disease (RD) were compared to obtain the differentially expressed genes (DEGs). Functional enrichment analyses were conducted on these DEGs. Then, we intersect the DEGs and immune-related genes to obtain the hub immune biomarkers, and then use the linear fitting model (\"glm\" package) to construct a prediction model composed of 9 immune biomarkers. Finally, the single sample gene set enrichment analysis (ssGSEA) algorithm was used to analyze immune cell invasion in BC patients, and the correlation between immune cell content and immune gene expression levels was analyzed.\nResults: Nine immune-related biomarkers were obtained in the intersection of DEGs and immune-related genes. Compared with RD patients, CXCL9, CXCL10, CXCL11, CXCL13, GZMB, IDO1, and LYZ were highly expressed in pCR patients, while CXCL14 and ESR1 were lowly expressed in pCR patients. After linear fitting of the multi-gene expression model, the area under the curve (AUC) value of the ROC curve diagnosis of pCR patients was 0.844. Immunoinfiltration analysis showed that compared with RD patients, 15 of the 28 immune cell types examined showed high-infiltration in pCR patients, including activated CD8 T cells, effector memory CD8 T cells, and activated CD4 T cells.\nConclusions: This investigation ultimately identified 9 immune-related biomarkers as potential tools for assessing the sensitivity of NAC in HER2-negative BC patients. These biomarkers have great potential for predicting pCR BC patients.", "claims": [{"id": "claim-method", "statement": "Then, we intersect the DEGs and immune-related genes to obtain the hub immune biomarkers, and then use the linear fitting model (“glm” package) to construct a prediction model composed of 9 immune biomarkers.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35800743", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Then, we intersect the DEGs and immune-related genes to obtain the hub immune biomarkers, and then use the linear fitting model (“glm” package) to construct a prediction model composed of 9 immune biomarkers."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GSE163882 dataset, containing 159 BC patients treated with NAC, was downloaded from the Gene Expression Omnibus (GEO) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35800743", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The GSE163882 dataset, containing 159 BC patients treated with NAC, was downloaded from the Gene Expression Omnibus (GEO) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "After linear fitting of the multi-gene expression model, the area under the curve (AUC) value of the ROC curve diagnosis of pCR patients was 0.844.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35800743", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "After linear fitting of the multi-gene expression model, the area under the curve (AUC) value of the ROC curve diagnosis of pCR patients was 0.844."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "After linear fitting of the multi-gene expression model, the area under the curve (AUC) value of the ROC curve diagnosis of pCR patients was 0.844.", "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:35800743"], "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:GSE163882"], "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:GSE163882"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35800743", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/gs-22-234", "sha256": 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