Ye Zhiyi, Wu Xiaoyang, Wei Zheng, Sun Qiuyan, Wang Yanli, Li Tan, Yuan Yuan, Jing Jingjing
Tumor Etiology and Screening Department of Cancer Institute and General Surgery, the First Hospital of China Medical University, Shenyang, China; Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang, China; Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, the First Hospital of China Medical University, Shenyang, China.
Department of Cardiovascular Ultrasound, the First Hospital of China Medical University, Shenyang, China.
Lab Invest. 2025 Apr;105(4):104101. doi: 10.1016/j.labinv.2025.104101. Epub 2025 Jan 31.
Most patients with gastric cancer (GC) exhibit microsatellite stability, yet comprehensive subtyping for prognostic prediction and clinical treatment decisions for microsatellite-stable GC is lacking. In this work, RNA-sequencing gene expression data and clinical information of patients with microsatellite-stable GC were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. We employed several machine learning methods to develop and validate a signature based on immune-related genes (IRGs) for subtyping patients with microsatellite-stable GC. Moreover, 2 deep learning models based on the Vision Transformer (ViT) architecture were developed to predict GC tumor tiles and identify microsatellite-stable GC subtypes from digital pathology slides. Microsatellite status was evaluated by immunohistochemistry, and prognostic data as well as hematoxylin and eosin whole-slide images were collected from 105 patients with microsatellite-stable GC to serve as an independent validation cohort. A signature comprising 5 IRGs was established and validated, stratifying patients with microsatellite-stable GC into high-risk (microsatellite-stable-HR) and low-risk (microsatellite-stable-LR) groups. This signature demonstrated consistent performance, with areas under the receiver operating characteristic curve (AUC) of 0.65, 0.70, and 0.70 at 1, 3, and 5 years in the TCGA cohort, and 0.70, 0.60, and 0.62 in the GEO cohort, respectively. The microsatellite-stable-HR subtype exhibited higher levels of tumor immune dysfunction and exclusion, suggesting a greater potential for immune escape compared with the microsatellite-stable-LR subtype. Moreover, the microsatellite-stable-HR/LR subtypes showed differential sensitivities to various therapeutic drugs. Leveraging morphologic differences, the tumor recognition segmentation model achieved an impressive AUC of 0.97, whereas the microsatellite-stable-HR/LR identification model effectively classified microsatellite-stable-HR/LR subtypes with an AUC of 0.94. Both models demonstrated promising results in classifying patients with microsatellite-stable GC in the external validation cohort, highlighting the strong ability to accurately differentiate between microsatellite-stable GC subtypes. The IRG-related microsatellite-stable-HR/LR subtypes had the potential to enhance outcome prediction accuracy and guide treatment strategies. This research may optimize precision treatment and improve the prognosis for patients with microsatellite-stable GC.
大多数胃癌（GC）患者表现出微卫星稳定性，但缺乏针对微卫星稳定型GC进行预后预测和临床治疗决策的全面亚型分类。在这项研究中，从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）获取了微卫星稳定型GC患者的RNA测序基因表达数据和临床信息。我们采用了多种机器学习方法来开发和验证基于免疫相关基因（IRG）的特征，用于对微卫星稳定型GC患者进行亚型分类。此外，还开发了2种基于视觉Transformer（ViT）架构的深度学习模型，以预测GC肿瘤切片并从数字病理切片中识别微卫星稳定型GC亚型。通过免疫组织化学评估微卫星状态，并从105例微卫星稳定型GC患者中收集预后数据以及苏木精和伊红全切片图像，作为独立验证队列。建立并验证了一个由5个IRG组成的特征，将微卫星稳定型GC患者分为高风险（微卫星稳定-HR）和低风险（微卫星稳定-LR）组。该特征在TCGA队列中1、3和5年时的受试者操作特征曲线（AUC）下面积分别为0.65、0.70和0.70，在GEO队列中分别为0.70、0.60和0.62，表现出一致的性能。微卫星稳定-HR亚型表现出更高水平的肿瘤免疫功能障碍和排除，表明与微卫星稳定-LR亚型相比，其免疫逃逸潜力更大。此外，微卫星稳定-HR/LR亚型对各种治疗药物表现出不同的敏感性。利用形态学差异，肿瘤识别分割模型的AUC达到了令人印象深刻的0.97，而微卫星稳定-HR/LR识别模型以0.94的AUC有效地对微卫星稳定-HR/LR亚型进行了分类。这两种模型在外部验证队列中对微卫星稳定型GC患者进行分类时都显示出了有前景的结果，突出了准确区分微卫星稳定型GC亚型的强大能力。与IRG相关的微卫星稳定-HR/LR亚型有潜力提高预后预测准确性并指导治疗策略。这项研究可能会优化精准治疗并改善微卫星稳定型GC患者的预后。
