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Using TCGA as a training cohort and TCGA/METABRIC/GEO plus an internal cohort for external validation, the study derives a survival-related lipid metabolism-related gene (LMRG) signature from consensus clustering and LASSO. Two LMRG patterns show distinct prognoses and immune infiltration, with pathway differences in immune activity, lipid biosynthesis, and drug metabolism. The signature acts as an independent prognostic factor and informs immunotherapy and chemotherapy response, highlighting ALOX15.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/exploration-of-prognosis-and-immunometabolism-landscapes-in-er-breast-cancer-based-on-a-novel-lipid-metabolism-related-signature-research-article/386064/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/exploration-of-prognosis-and-immunometabolism-landscapes-in-er-breast-cancer-based-on-a-novel-lipid-metabolism-related-signature-research-article/386064.png","ImageObject",300,407,{"name":92,"@type":93},"Sophia Brooks","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What data and cohorts were used to build and validate the lipid metabolism-related signature?","Question",{"text":112,"@type":113},"TCGA samples were used as the training cohort. External validation used METABRIC and GEO datasets as well as an additional internal cohort.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the prognostic patterns and the final signature constructed?",{"text":117,"@type":113},"Two survival-related LMRG molecular patterns were identified using unsupervised consensus clustering, followed by signature derivation with LASSO analysis. A clinicopathologic nomogram was also established.",{"name":119,"@type":110,"acceptedAnswer":120},"Which findings link the signature to immune activity and treatment response?",{"text":121,"@type":113},"Comparing low- versus high-risk groups revealed differences in immune activities, immune checkpoint expression, and immune cell infiltration, alongside pathway differences in lipid biosynthesis and drug metabolism. 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Immunol. 14:1199465 .  \ndoi: 10.3389/fimmu.2023.1199465  \nCOPYRIGHT  \n© 2023 Shen, Huang, Li, Chen, Yao, Hu, Zhou, Huang and Ni. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nExploration of prognosis and immunometabolism landscapes in ER+ breast cancer based on a novel lipid metabolism-related signature  \nLesang Shen 1,2,3†, Huanhuan Huang 1,2,3†, Jiaxin Li 1,2,3†, Wuzhen Chen 1,2,3, Yao Yao 1,2,3, Jianming Hu 1,2,3, Jun Zhou 4, Fengbo Huang 5* and Chao Ni 1,2,3*  \n1 Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University, Hangzhou,  \nZhejiang, China, 2 Key Laboratory of Tumor Microenvironment and Immune Therapy of Zhejiang Province, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China, 3Cancer Center, Zhejiang University, Hangzhou, China, 4 Department of Breast Surgery, Affiliated Hangzhou First People’s Hospital, Zhejiang University, Hangzhou, Zhejiang, China, 5 Department of Pathology, Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China  \nIntroduction: Lipid metabolic reprogramming is gaining attention as a hallmark of cancers. Recent mounting evidence indicates that the malignant behavior of breast cancer (BC) is closely related to lipid metabolism. Here, we focus on the estrogen receptor-positive (ER+) subtype, the most common subgroup of BC, to explore immunometabolism landscapes and prognostic significance according to lipid metabolism-related genes (LMRGs) .  \nMethods: Samples from The Cancer Genome Atlas (TCGA) database were used as training cohort, and samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), Gene Expression Omnibus (GEO) datasets and our cohort were applied for external validation. The survivalrelated LMRG molecular pattern and signature were constructed by unsupervised consensus clustering and least absolute shrinkage and selection operator (LASSO) analysis . A lipid metabolism-related clinicopathologic nomogram was established. Gene enrichment and pathway analysis were performed to explore the underlying mechanism . Immune landscapes, immunotherapy and chemotherapy response were further explored. Moreover, the relationship between gene expression and clinicopathological features was assessed by immunohistochemistry.  \nResults: Two LMRG molecular patterns were identified and associated with distinct prognoses and immune cell infiltration. Next, a prognostic signature based on nine survival-related LMRGs was established and validated. The signature was confirmed to be an independent prognostic factor and an optimal nomogram incorporating age and T stage (AUC of 5-year overall survival: 0 .778) . Pathway enrichment analysis revealed differences in immune activities, lipid biosynthesis a","cbCaiifd4v2qlhRf","https://ap.wps.com/l/cbCaiifd4v2qlhRf","pdf",7688524,16,"English","# Introduction\n## Background: lipid metabolism and ER+ breast cancer\n# Methods\n## Data sources and training/validation design\n## Signature construction and nomogram modeling\n## Enrichment analyses and immune landscape assessment\n# Results\n## LMRG molecular patterns and prognostic differences\n## Nine-gene prognostic signature validation\n## Pathway and immune microenvironment differences\n# Discussion\n## Clinical value for prognosis and treatment guidance","[{\"question\":\"What data and cohorts were used to build and validate the lipid metabolism-related signature?\",\"answer\":\"TCGA samples were used as the training cohort. External validation used METABRIC and GEO datasets as well as an additional internal cohort.\"},{\"question\":\"How were the prognostic patterns and the final signature constructed?\",\"answer\":\"Two survival-related LMRG molecular patterns were identified using unsupervised consensus clustering, followed by signature derivation with LASSO analysis. A clinicopathologic nomogram was also established.\"},{\"question\":\"Which findings link the signature to immune activity and treatment response?\",\"answer\":\"Comparing low- versus high-risk groups revealed differences in immune activities, immune checkpoint expression, and immune cell infiltration, alongside pathway differences in lipid biosynthesis and drug metabolism. The study further assessed sensitivity to immunotherapy and chemotherapy between groups.\"}]","Exploration of prognosis and immunometabolism landscapes in ER+ breast cancer based on a novel lipid metabolism-related signature - Research article | PDF",1790272057]