PO.CL01.17 · 临床研究

用于乳腺癌精准治疗的干性-端粒生存风险框架（STSRF）的构建与评估：来自多组学方法的洞见

Construction and assessment of the stemness-telomere survival risk framework (STSRF) for precision breast cancer therapy: Insights from multi-omic approaches

编号 5383 展板 21 时间 4/21 09:00–12:00 区域 Section 47 主讲 Zhiyuan Bo, MD;PhD

分会场 Prognostic Biomarkers 3

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作者与单位 Authors & Affiliations

Zhiyuan Bo¹, Jingpei Long¹, Fang Wan¹, Fangfang Chen¹, Jiajun Li², Zhengxiao Zhao³

¹Department of Surgery, Women's Hospital School of Medicine Zhejiang University, Hangzhou, China,²The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China,³Department of Oncology, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China

摘要 Abstract

中文摘要

背景与目的：乳腺癌（BRCA）因其分子异质性和治疗耐药性仍是重大临床挑战。干性和端粒相关基因是肿瘤进展的关键因素，但它们之间的相互作用尚未明确定义。本研究旨在构建一个干性-端粒生存风险框架（STSRF），以改进风险分层并指导精准治疗。方法：我们在留一交叉验证（LOOCV）框架内整合了单细胞和bulk RNA测序数据（n = 1684例BRCA患者）、WGCNA以及101种机器学习模型组合来构建STSRF。使用七种算法和基于组织病理图像的深度学习评估免疫浸润。通过多组学富集分析探索生物学功能。来自DepMap、GDSC、CMap、CTRP和PRISM的药物敏感性数据支持了治疗预测。使用孟德尔随机化（MR）验证因果关系，并通过RT-qPCR和免疫组化（IHC）确认表达模式。结果：STSRF显示出强大的预后能力（最高的1、3、5年AUC分别为0.930、0.807、0.766）。高风险组和低风险组得到有效分层，与免疫浸润和临床特征相关。高风险患者与免疫"冷"表型相关，可能从化疗联合HDAC抑制剂中获益；而低风险患者与免疫"热"表型相关，且化疗药物的IC50值较低，可能对免疫治疗或化疗反应更好。单细胞和MR分析证实了STSRF基因与BRCA风险的生物学相关性。实验验证支持了关键基因的表达模式。结论：STSRF是一个整合了干性和端粒生物学的稳健框架，用于预测BRCA的预后并为个体化治疗提供依据。

查看英文原文 English abstract

Background and Aims: Breast cancer (BRCA) remains a major clinical challenge due to its molecular heterogeneity and therapy resistance. Stemness and telomere-related genes are key contributors to tumor progression, but their interplay is poorly defined. This study aims to construct a Stemness-Telomere Survival Risk Framework (STSRF) to improve risk stratification and guide precision treatment. Methods: We integrated single-cell and bulk RNA sequencing data (n = 1684 BRCA patients) with WGCNA and 101 machine learning model combinations within a LOOCV framework to build the STSRF. Immune infiltration was assessed using seven algorithms and deep learning on histopathological images. Biological functions were explored via multi-omic enrichment analyses. Drug sensitivity data from DepMap, GDSC, CMap, CTRP, and PRISM supported therapeutic predictions. Causal relationships were validated using Mendelian randomization (MR), and expression patterns were confirmed by RT-qPCR and immunohistochemistry (IHC). Results: STSRF showed strong prognostic power ( highest 1-, 3-, 5-year AUCs: 0.930, 0.807, 0.766). High- and low-risk groups were effectively stratified, correlating with immune infiltration and clinical traits. High-risk patients were linked to immune-cold phenotypes and may benefit from chemotherapy combined with HDAC inhibitors, while low-risk patients, associated with immune-hot phenotypes and lower IC50 values for chemotherapy agents, may respond better to immunotherapy or chemotherapy. Single-cell and MR analyses confirmed the biological relevance of STSRF genes to BRCA risk. Experimental validation supported key gene expression patterns. Conclusions: STSRF is a robust framework integrating stemness and telomere biology to predict prognosis and inform personalized therapies in BRCA.

利益披露 Disclosure

Z. Bo, None.. J. Long, None.. F. Wan, None.. F. Chen, None.. J. Li, None.. Z. Zhao, None.

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