乳腺癌是全球女性癌症相关死亡的主要原因，在与乳腺癌的持续斗争中，不可否认迫切需要创新的预后标志物和治疗靶点。这项研究开创了一种先进的方法，通过整合机器学习技术来揭示血管拟态特征，提供对乳腺癌结果的预测性见解。血管拟态是指癌细胞在没有内皮细胞的情况下模仿血管形成的现象，这是一种与肿瘤侵袭性增强和对常规治疗反应减弱相关的特征。该研究的综合分析涵盖了 12 个不同数据集的 6,000 多名乳腺癌患者的数据，包括包括专有临床数据和来自 7 名患者的单细胞数据，总共 43,095 个细胞。该研究采用综合策略，利用 10 种机器学习算法涵盖 108 种独特的组合，仔细检查了 100 种现有的乳腺癌特征。通过免疫组织化学分析寻求实证验证，同时探索潜在的免疫治疗和化疗途径。该研究成功地从多中心队列中鉴定出与血管拟态相关的六个基因，为新型预测模型奠定了基础。该模型在预测复发和死亡风险方面超越了传统临床和分子指标的预后准确性。我们的模型识别出的高风险个体面临着更糟糕的结果。通过 IHC 检测对 30 名患者进行的进一步验证强调了该模型的广泛适用性。值得注意的是，该模型揭示了不同的治疗反应；低风险患者可能会从免疫治疗中获得更大的益处，而高风险患者则对某些化疗（例如 ispinesib）表现出特别的敏感性。该模型标志着在精确评估不同患者群体的乳腺癌预后和治疗反应方面向前迈出了重要一步。它预示着通过量身定制的治疗策略改善患者结果的可能性，凸显了机器学习在彻底改变癌症预后和管理方面的潜力。版权所有 © 2024 Li、Li、Yang、Sun、Wang、Yu 和 Wang。

In the ongoing battle against breast cancer, a leading cause of cancer-related mortality among women globally, the urgent need for innovative prognostic markers and therapeutic targets is undeniable. This study pioneers an advanced methodology by integrating machine learning techniques to unveil a vascular mimicry signature, offering predictive insights into breast cancer outcomes. Vascular mimicry refers to the phenomenon where cancer cells mimic blood vessel formation absent of endothelial cells, a trait associated with heightened tumor aggression and diminished response to conventional treatments.The study's comprehensive analysis spanned data from over 6,000 breast cancer patients across 12 distinct datasets, incorporating both proprietary clinical data and single-cell data from 7 patients, accounting for a total of 43,095 cells. By employing an integrative strategy that utilized 10 machine learning algorithms across 108 unique combinations, the research scrutinized 100 existing breast cancer signatures. Empirical validation was sought through immunohistochemistry assays, alongside explorations into potential immunotherapeutic and chemotherapeutic avenues.The investigation successfully identified six genes related to vascular mimicry from multi-center cohorts, laying the groundwork for a novel predictive model. This model outstripped the prognostic accuracy of traditional clinical and molecular indicators in forecasting recurrence and mortality risks. High-risk individuals identified by our model faced worse outcomes. Further validation through IHC assays in 30 patients underscored the model's extensive applicability. Notably, the model unveiled varying therapeutic responses; low-risk patients might achieve greater benefits from immunotherapy, whereas high-risk patients demonstrated a particular sensitivity to certain chemotherapies, such as ispinesib.This model marks a significant step forward in the precise evaluation of breast cancer prognosis and therapeutic responses across different patient groups. It heralds the possibility of refining patient outcomes through tailored treatment strategies, accentuating the potential of machine learning in revolutionizing cancer prognosis and management.Copyright © 2024 Li, Li, Yang, Sun, Wang, Yu and Wang.