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This study aimed to investigate the molecular signature that predict the prognosis and immune response of breast cancer (BRCA) based on TAN-related gene (TANRG) expression data. The RNA-seq data of BRCA were gathered from The Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) datasets. Univariate Cox regression analysis and the least absolute shrinkage and selection operator for selecting prognostic genes. A neo-TAN-related risk signature was constructed by multivariate Cox regression analysis. Time-dependent receiver operating characteristic (ROC) curve analyses and Kaplan\u2013Meier analyses were performed to validate the signature in GEO cohorts and the triple-negative breast cancer (TNBC) subtype. We constructed an independent prognostic factor model with 11 TANRGs. The areas under the ROC curve (AUCs) of the TCGA training cohorts for 3-, 5-, and 7-year overall survival were 0.72, 0.73, and 0.73, respectively. The AUCs of the GEO test cohorts for 3-, 5-, and 7-year overall survival were 0.83, 0.89, and 0.94 (GSE25066) and 0.67, 0.69, and 0.73 (GSE58812), respectively. The proportion of immune subtypes differed among the different risk groups. The IC50 values differed significantly between risk groups and can be used as a guide for systemic therapy. The prognostic model developed by TANRGs has excellent predictive performance in BRCA patients. In addition, this feature is closely related to the prediction of survival, immune activity and treatment response in BRCA patients.<\/jats:p>","DOI":"10.1038\/s41598-024-55513-8","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T16:02:14Z","timestamp":1709568134000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A novel tumor-associated neutrophil gene signature for predicting prognosis, tumor immune microenvironment, and therapeutic response in breast cancer"],"prefix":"10.1038","volume":"14","author":[{"given":"Jianyou","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinbo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhonglai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuyi","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,4]]},"reference":[{"issue":"6","key":"55513_CR1","doi-asserted-by":"publisher","first-page":"524","DOI":"10.3322\/caac.21754","volume":"72","author":"AN Giaquinto","year":"2022","unstructured":"Giaquinto, A. 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