| 摘要: |
| ［摘要］ 目的 为解析不同血流剪切应力模式诱导的内皮细胞力学信号时序转录特征，构建低流体剪切应力（LFSS）分子特征，并评估其在泛癌预后中的临床价值，同时探索人工智能模型在力学信号动态预测中的应用潜力。方法 整合基因表达综合数据库（GEO）中三个独立的人源内皮细胞剪切应力转录组数据集，采用Stouffer加权Z法进行跨队列荟萃分析，筛选稳健差异表达基因（DEGs）。结合Theil-Sen回归与k-means聚类解析静止剪切（ST）、振荡剪切（OS）和脉冲剪切（PS）条件下基因表达的时间动态模式。基于LFSS特异性上调基因构建LFSS评分，并在癌症基因组图谱（TCGA）泛癌队列（n=11 160）中通过Kaplan-Meier法和多因素Cox比例风险回归模型评估其与总体生存的关联。进一步构建长短期记忆网络（LSTM）模型，对剪切应力诱导的基因时序变化进行预测，并与随机森林（RF）和支持向量回归（SVR）模型进行性能比较。结果 荟萃分析共鉴定出811个DEGs。时序分析显示，OS通过持续激活YAP/TAZ信号轴诱导细胞周期及DNA复制异常，而PS主要通过KLF2/KLF4通路触发生理性保护反应。LFSS评分在13种癌症中与总体生存显著相关［假发现率（FDR)<0.05］，并与肿瘤微环境中的缺氧及异常血管生成通路呈正相关。LSTM模型在基因表达时间序列预测中的表现优于RF和SVR模型，其决定系数R2为0.842，平均绝对误差（MAE）为0.068，具有更好的泛化能力。结论 LFSS可诱导特异性的内皮细胞转录重塑，并在多种癌症中具有重要的预后指示意义。基于LSTM的人工智能模型能够高效捕捉剪切应力相关的时序动态特征，为肿瘤血管力学异常的分子解析及精准治疗评估提供了新的医工融合研究范式。 |
| 关键词: 人工智能 流体剪切应力 内皮细胞 长短期记忆网络 泛癌预后 医工融合 |
| DOI：10.3969/j.issn.1674-3806.2026.01.04 |
| 分类号:R 73；TP 18 |
| 基金项目:深圳市科技计划项目（编号：JCYJ20220530153612028）；中国医学科学院肿瘤医院深圳医院科研课题（编号：E010222007） |
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| AI-driven temporal decoding of endothelial mechanotransduction and its medico-engineering applications in assessing pan-cancer prognosis |
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NIU Niu1, WU Bin2, LIU Hu3, WANG Ning4, ZHANG Ke4, HUANG Sizhao4
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1.Department of Medical Oncology, Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen, National Cancer Center, National Clinical Research Center for Cancer, Shenzhen 518116, China; 2.Department of Respiratory and Critical Care Medicine, South China Hospital of Shenzhen University, Shenzhen 518110, China; 3.Department of Medical Oncology, the Second Affiliated Hospital of Southern University of Science and Technology(the Third People′s Hospital of Shenzhen), Shenzhen 518112, China; 4.Chengdu Tianji-Suan Technology Co. Ltd, Chengdu 610041, China
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| Abstract: |
| ［Abstract］ Objective To decode the temporal transcriptional responses of endothelial cells to distinct shear stress patterns, and to construct a low fluid shear stress(LFSS) molecular features, and to assess its clinical value in pan-cancer prognosis, and to explore the application potential of artificial intelligence(AI) for dynamic mechanobiological prediction. Methods Three independent human endothelial transcriptome datasets related to shear stress were integrated by using the Gene Expression Omnibus(GEO). Robust differentially expressed genes(DEGs) were identified using cross-cohort meta-analysis by weighted Stouffer′s Z method. Temporal expression patterns under steady shear stress(ST), oscillatory shear stress(OS), and pulsatile shear stress(PS) were decoded by using Theil-Sen regression and k-means clustering. LFSS scoring was constructed based on LFSS-specific upregulated genes and was evaluated in The Cancer Genome Atlas(TCGA) pan-cancer atlas cohort(n=11 160) by using Kaplan-Meier survival analysis and multivariable Cox proportional hazards regression models. A long short-term memory(LSTM) network model was further constructed to predict shear stress-induced gene expression dynamics and compared with random forest(RF) and support vector regression(SVR) models. Results A total of 811 robust DEGs were identified across the datasets. Temporal analysis revealed that OS induced aberrant cell cycle and DNA replication programs via the sustained activation of the YAP/TAZ signaling axis, whereas PS predominantly triggered a physiological protective response via the KLF2/KLF4 pathway. The LFSS scores were significantly correlated with overall survival in 13 types of cancers［false discovery rate(FDR)<0.05］ and were positively correlated with hypoxia and pathological angiogenesis pathways in the tumor microenvironment. The LSTM model outperformed RF and SVR models, with a coefficient of determination(R2) of 0.842 and a mean absolute error(MAE) of 0.068, demonstrating that the LSTM model had superior generalization performance. Conclusion LFSS can induce specific endothelial transcriptional remodeling and has significant prognostic implications in a variety of cancers. LSTM-based AI models can effectively capture the shear stress-related temporal dynamics, providing a novel medico-engineering framework for decoding vascular mechanical abnormalities and supporting the assessment of precise treatment for tumors. |
| Key words: Artificial intelligence(AI) Fluid shear stress Endothelial cells Long short-term memory Pan-cancer prognosis Medico-engineering integration |
