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整合蛋白质组学和转录组学分析揭示雌激素受体阳性/孕激素受体阳性与雌激素受体阳性/孕激素受体阴性乳腺癌之间的关键调控基因。

Integrated proteomics and transcriptomics analysis reveals key regulatory genes between ER-positive/PR-positive and ER-positive/PR-negative breast cancer.

作者信息

Lu Zhengjia, Yang Jiao, Feng Yang, Ming Jia

机构信息

Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.

出版信息

BMC Cancer. 2025 Jul 1;25(1):1048. doi: 10.1186/s12885-025-14451-y.


DOI:10.1186/s12885-025-14451-y
PMID:40597935
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12211939/
Abstract

PURPOSE: Hormone receptor-positive breast cancer is characterized by the expression of estrogen receptor (ER) or progesterone receptor (PR), it is generally associated with less aggressive clinical features and more favorable prognostic outcomes, primarily due to the effectiveness of endocrine therapy. However, the loss of PR expression has been correlated with endocrine resistance and poorer prognosis. To date, there is limited research elucidating the underlying mechanisms distinguishing ER-positive/PR-positive from ER-positive/PR-negative breast cancer. This study aims to investigate the molecular mechanisms associated with these two subtypes and to propose recommendations for precision therapy. PATIENTS AND METHODS: Fresh tumor tissues from ER + /PR + patients (n = 5) and ER + /PR- patients (n = 5) were subjected to proteomic analysis to identify differentially expressed proteins. Transcriptomic data were obtained from the TCGA database, encompassing 937 breast cancer patients divided into three subgroups: ER + /PR + (n = 627), ER + /PR- (n = 112), and ER-/PR- (n = 198). Clinical characteristics and prognostic data were collected to analyze disease-specific survival (DSS) and overall survival (OS) across the three subtypes. Differential expression data for both transcripts and proteins were extracted, and Cox regression along with Least Absolute Shrinkage and Selection Operator (LASSO) regression were applied to identify key regulatory genes. A risk scoring formula was employed to classify patients into high-risk and low-risk groups. Kaplan-Meier curves, Gene Set Enrichment Analysis (GSEA), immune cell infiltration analysis, and OncoPredict drug sensitivity predictions were conducted to provide insights into the underlying mechanisms and clinical treatment strategies for this patient cohort. The accuracy of this model was further validated using external GEO datasets (GSE21653, GSE20685, and GSE42568). Additionally, we collected data from 97 hormone receptor-positive breast cancer patients who underwent neoadjuvant chemotherapy at our center between January 2021 and December 2023, assessing their response to chemotherapy using the Miller-Payne score. RESULTS: In the TCGA database, patients with ER + /PR- breast cancer exhibited poorer 5-year DSS and OS compared to those with ER + /PR + status (DSS: P = 0.038; OS: P = 0.052), which was similar to those with ER-/PR- status (DSS: P = 0.47; OS: P = 0.77). 186 differentially expressed proteins (110 up-regulated and 76 down-regulated) were identified based on proteomic analysis. After COX regression and Lasso regression, five key differential genes with prognostic and diagnostic value of ER + /PR + and ER + /PR- patients were finally included, that is HPN, FSCN1, FGD3, LRIG1, and TBC1D7. HPN, FSCN1 and FGD3 can be regarded as a tumor suppressor gene. And LRIG1, and TBC1D7 can be regarded as a risk-associated gene. Patients with high-risk scores had significantly lower survival probabilities compared to those with low-risk scores. Additionally, there were differences in functional pathway enrichment analysis (galactose_metabolism, glycolysis_gluconeogenesis, jak_stat_signaling_pathway, pentose_phosphate_pathway, et al.) and immune cell infiltration (CD8 T cell, Macrophages M1, et al.) between the high-risk and low-risk groups. Drug sensitivity analysis indicated that the low-risk patients may be more sensitive to endocrine drug like fulvestrant, while high-risk patients may be more sensitive to chemotherapy drugs like docetaxel, paclitaxel, and vinorelbine. Of the 97 patients underwent neoadjuvant chemotherapy in our center, the proportion of patients achieving Miller-Payne (MP) score 4 and 5 was higher in ER + /PR- patients (44%) compared to ER + /PR + patients (9.8%). CONCLUSION: We confirmed that ER + /PR- breast cancer patients exhibited worse survival compared with ER + /PR + patients. Five key regulatory genes were identified and potential mechanisms and biological pathways were discovered, our prediction of drug sensitivity offers new insights for developing precise pharmacological treatment strategies for ER + /PR- breast cancer.

摘要

目的:激素受体阳性乳腺癌的特征是雌激素受体(ER)或孕激素受体(PR)表达,通常与侵袭性较低的临床特征和更有利的预后结果相关,主要归因于内分泌治疗的有效性。然而,PR表达缺失与内分泌抵抗和较差的预后相关。迄今为止,阐明ER阳性/PR阳性与ER阳性/PR阴性乳腺癌潜在机制差异的研究有限。本研究旨在探究与这两种亚型相关的分子机制,并为精准治疗提出建议。 患者与方法:对来自ER+/PR+患者(n = 5)和ER+/PR-患者(n = 5)的新鲜肿瘤组织进行蛋白质组学分析,以鉴定差异表达蛋白。转录组数据来自TCGA数据库,涵盖937例乳腺癌患者,分为三个亚组:ER+/PR+(n = 627)、ER+/PR-(n = 112)和ER-/PR-(n = 198)。收集临床特征和预后数据,分析三种亚型的疾病特异性生存(DSS)和总生存(OS)情况。提取转录本和蛋白质的差异表达数据,并应用Cox回归以及最小绝对收缩和选择算子(LASSO)回归来鉴定关键调控基因。采用风险评分公式将患者分为高风险和低风险组。进行Kaplan-Meier曲线分析、基因集富集分析(GSEA)、免疫细胞浸润分析和OncoPredict药物敏感性预测,以深入了解该患者队列的潜在机制和临床治疗策略。使用外部GEO数据集(GSE21653、GSE20685和GSE42568)进一步验证该模型的准确性。此外,我们收集了2021年1月至2023年12月期间在本中心接受新辅助化疗的97例激素受体阳性乳腺癌患者的数据,使用Miller-Payne评分评估他们对化疗的反应。 结果:在TCGA数据库中,与ER+/PR+状态的患者相比,ER+/PR-乳腺癌患者的5年DSS和OS较差(DSS:P = 0.038;OS:P = 0.052),这与ER-/PR-状态的患者相似(DSS:P = 0.47;OS:P = 0.77)。基于蛋白质组学分析鉴定出186种差异表达蛋白(110种上调和76种下调)。经过COX回归和Lasso回归后,最终纳入了5个对ER+/PR+和ER+/PR-患者具有预后和诊断价值的关键差异基因,即HPN、FSCN1、FGD3、LRIG1和TBC1D7。HPN、FSCN1和FGD3可被视为肿瘤抑制基因。而LRIG1和TBC1D7可被视为风险相关基因。高风险评分的患者与低风险评分的患者相比,生存概率显著更低。此外,高风险组和低风险组在功能通路富集分析(半乳糖代谢、糖酵解/糖异生、jak_stat信号通路、磷酸戊糖途径等)和免疫细胞浸润(CD8 T细胞、M1巨噬细胞等)方面存在差异。药物敏感性分析表明,低风险患者可能对氟维司群等内分泌药物更敏感,而高风险患者可能对多西他赛、紫杉醇和长春瑞滨等化疗药物更敏感。在本中心接受新辅助化疗的97例患者中,ER+/PR-患者达到Miller-Payne(MP)评分4和5的比例(44%)高于ER+/PR+患者(9.8%)。 结论:我们证实,与ER+/PR+患者相比,ER+/PR-乳腺癌患者的生存情况更差。鉴定出5个关键调控基因,发现了潜在机制和生物学途径,我们对药物敏感性的预测为开发针对ER+/PR-乳腺癌的精准药物治疗策略提供了新见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6124/12211939/2cc0e5635212/12885_2025_14451_Fig5_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6124/12211939/2cc0e5635212/12885_2025_14451_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6124/12211939/9b628db965cf/12885_2025_14451_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6124/12211939/478c962f3afd/12885_2025_14451_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6124/12211939/806fe8de44dd/12885_2025_14451_Fig3_HTML.jpg
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