{"title":"Unveiling novel transcriptomic prognostic biomarkers for specific breast cancer subtypes and treatment regimens.","authors":"Krittin Pitinanon, Chotiphuk Kusalasaiyanon, Wathunya Hawhan, Yu-Yu Liu, Pasrawin Taechawattananant, Varalee Yodsurang","doi":"10.1007/s43440-026-00895-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Breast cancer (BRCA) is the most common cancer in women worldwide, yet current gene expression panels offer limited insight into treatment responses across different subtypes and therapies. This study aimed to identify reliable biomarkers for predicting treatment outcomes in specific BRCA subtypes and treatment regimens.</p><p><strong>Methods: </strong>This study analyzed transcriptomic data from The Cancer Genome Atlas to identify differentially expressed genes (DEGs) in patient groups treated with different combinations of hormone therapy (H), chemotherapy (C), radiotherapy (R), and targeted therapy (T). Non-negative matrix factorization clustering was performed to stratify patients into clusters representing different BRCA subtypes. Functional enrichment analysis was performed, and survival assessments were conducted using the METABRIC dataset.</p><p><strong>Results: </strong>A total of 1,148 DEGs were identified across treatment regimens, with 75 common DEGs shared across multiple regimens. Among these, 12 candidate biomarkers were associated with luminal subtypes treated with H, including LRP1B, of which high expression predicted cancer recurrence. In triple-negative breast cancer (TNBC) treated with C, 76 candidate biomarkers were identified, including TTYH1 for recurrence and ANXA8L1 and MPZ for non-recurrence. Functional analyses identified intermediate filament organization and keratinization as pathways associated with specific candidate biomarkers of TNBC following C. Survival analysis using METABRIC strengthened the prognostic ability of LRP1B and TTYH1 to predict worse survival and ANXA8L1 and MPZ to predict prolonged survival, with four additional prognostic biomarkers.</p><p><strong>Conclusion: </strong>This study identified gene expression prognostic biomarkers for luminal and TNBC subtypes, thereby supporting personalized therapies. Further experimental validation is required to confirm these findings for clinical application.</p><p><strong>Clinical trial registry: </strong>No.</p>","PeriodicalId":19947,"journal":{"name":"Pharmacological Reports","volume":" ","pages":""},"PeriodicalIF":4.5000,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pharmacological Reports","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s43440-026-00895-x","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Breast cancer (BRCA) is the most common cancer in women worldwide, yet current gene expression panels offer limited insight into treatment responses across different subtypes and therapies. This study aimed to identify reliable biomarkers for predicting treatment outcomes in specific BRCA subtypes and treatment regimens.
Methods: This study analyzed transcriptomic data from The Cancer Genome Atlas to identify differentially expressed genes (DEGs) in patient groups treated with different combinations of hormone therapy (H), chemotherapy (C), radiotherapy (R), and targeted therapy (T). Non-negative matrix factorization clustering was performed to stratify patients into clusters representing different BRCA subtypes. Functional enrichment analysis was performed, and survival assessments were conducted using the METABRIC dataset.
Results: A total of 1,148 DEGs were identified across treatment regimens, with 75 common DEGs shared across multiple regimens. Among these, 12 candidate biomarkers were associated with luminal subtypes treated with H, including LRP1B, of which high expression predicted cancer recurrence. In triple-negative breast cancer (TNBC) treated with C, 76 candidate biomarkers were identified, including TTYH1 for recurrence and ANXA8L1 and MPZ for non-recurrence. Functional analyses identified intermediate filament organization and keratinization as pathways associated with specific candidate biomarkers of TNBC following C. Survival analysis using METABRIC strengthened the prognostic ability of LRP1B and TTYH1 to predict worse survival and ANXA8L1 and MPZ to predict prolonged survival, with four additional prognostic biomarkers.
Conclusion: This study identified gene expression prognostic biomarkers for luminal and TNBC subtypes, thereby supporting personalized therapies. Further experimental validation is required to confirm these findings for clinical application.
背景：乳腺癌（BRCA）是全世界女性中最常见的癌症，然而目前的基因表达小组对不同亚型和治疗方法的治疗反应提供的见解有限。本研究旨在确定可靠的生物标志物，以预测特定BRCA亚型和治疗方案的治疗结果。方法：本研究分析来自The Cancer Genome Atlas的转录组学数据，以鉴定接受不同激素治疗(H)、化疗(C)、放疗(R)和靶向治疗(T)组合治疗的患者组中的差异表达基因（DEGs）。采用非负矩阵因子聚类将患者分为代表不同BRCA亚型的聚类。进行功能富集分析，并使用METABRIC数据集进行生存评估。结果：在治疗方案中共鉴定出1148个deg，多个方案中共有75个共同的deg。其中，12个候选生物标志物与H治疗的腔内亚型相关，包括LRP1B，其高表达可预测癌症复发。在C治疗的三阴性乳腺癌（TNBC）中，确定了76个候选生物标志物，包括TTYH1复发和ANXA8L1和MPZ非复发。功能分析发现，中间纤维组织和角化是与TNBC c后特异性候选生物标志物相关的途径。使用METABRIC的生存分析增强了LRP1B和TTYH1预测更差生存的能力，增强了ANXA8L1和MPZ预测延长生存的能力，并增加了四个预后生物标志物。结论：本研究确定了luminal和TNBC亚型的基因表达预后生物标志物，从而支持个性化治疗。需要进一步的实验验证来证实这些发现的临床应用。临床试验注册：No。
期刊介绍：
Pharmacological Reports publishes articles concerning all aspects of pharmacology, dealing with the action of drugs at a cellular and molecular level, and papers on the relationship between molecular structure and biological activity as well as reports on compounds with well-defined chemical structures.
Pharmacological Reports is an open forum to disseminate recent developments in: pharmacology, behavioural brain research, evidence-based complementary biochemical pharmacology, medicinal chemistry and biochemistry, drug discovery, neuro-psychopharmacology and biological psychiatry, neuroscience and neuropharmacology, cellular and molecular neuroscience, molecular biology, cell biology, toxicology.
Studies of plant extracts are not suitable for Pharmacological Reports.
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