Lin Quan, Guan Shiwei, Peng Minghui, Zhang Kailun, Zhang Hewei, Mo Taoming, Yu Haibo
Department of Hepatobiliary Surgery, Wenzhou Central Hospital, The Dingli Clinical Institute of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Department of Pathology, Medical School of Nantong University, Nantong, Jiangsu, China.
Front Immunol. 2025 May 1;16:1513589. doi: 10.3389/fimmu.2025.1513589. eCollection 2025.
Pancreatic ductal adenocarcinoma (PDAC) exhibits higher hypoxia level than most solid tumors, and the presence of intratumoral hypoxia is associated with a poor prognosis. However, the identification of hypoxia levels based on pathological images, and the mechanisms regulating ferroptosis resistance, remain to be elucidated. The objective of this study was to construct a deep learning model to evaluate the hypoxia characteristics of PDAC and to explore the role of Sulfide quinone oxidoreductase (SQOR) in hypoxia-mediated ferroptosis resistance.
Multi-omics data were integrated to analyze the correlation between hypoxia score of PDAC, SQOR expression and prognosis, and ferroptosis resistance level. A deep learning model of Whole Slide Images (WSIs) were constructed to predict the hypoxia level of patients. hypoxia cell models, SQOR knockdown experiments and nude mouse xenograft models were used to verify the regulatory function of SQOR on ferroptosis.
PDAC exhibited significantly higher hypoxia levels than normal tissues, correlating with reduced overall survival in patients. In slide level, our deep learning model can effectively identify PDAC hypoxia levels with good performance. SQOR was upregulated in tumor tissues and positively associated with both hypoxia score and ferroptosis resistance. SQOR promotes the malignant progression of PDAC in hypoxic environment by enhancing the resistance of tumor cells to ferroptosis. SQOR knockdown resulted in decreased cell viability, decreased migration ability and increased MDA level under hypoxic Ersatin induced conditions. Furthermore, SQOR inhibitor in combination with ferroptosis inducer has the potential to inhibit tumor growth in a synergistic manner.
This study has established a hypoxia detection model of PDAC based on WSIs, providing a new tool for clinical evaluation. The study revealed a new mechanism of SQOR mediating ferroptosis resistance under hypoxia and provided a basis for targeted therapy.
胰腺导管腺癌(PDAC)的缺氧水平高于大多数实体瘤,肿瘤内缺氧与预后不良相关。然而,基于病理图像识别缺氧水平以及调节铁死亡抗性的机制仍有待阐明。本研究的目的是构建一个深度学习模型来评估PDAC的缺氧特征,并探讨硫化物醌氧化还原酶(SQOR)在缺氧介导的铁死亡抗性中的作用。
整合多组学数据以分析PDAC的缺氧评分、SQOR表达与预后以及铁死亡抗性水平之间的相关性。构建全切片图像(WSIs)的深度学习模型以预测患者的缺氧水平。使用缺氧细胞模型、SQOR敲低实验和裸鼠异种移植模型来验证SQOR对铁死亡的调节功能。
PDAC的缺氧水平显著高于正常组织,与患者总生存期降低相关。在玻片水平上,我们的深度学习模型能够有效识别PDAC的缺氧水平,性能良好。SQOR在肿瘤组织中上调,与缺氧评分和铁死亡抗性均呈正相关。SQOR通过增强肿瘤细胞对铁死亡的抗性促进缺氧环境中PDAC的恶性进展。在缺氧的厄他替尼诱导条件下,敲低SQOR导致细胞活力下降、迁移能力降低和丙二醛水平升高。此外,SQOR抑制剂与铁死亡诱导剂联合使用具有协同抑制肿瘤生长的潜力。
本研究建立了基于WSIs的PDAC缺氧检测模型,为临床评估提供了新工具。该研究揭示了SQOR在缺氧条件下介导铁死亡抗性的新机制,为靶向治疗提供了依据。