Li Jiang, He Lingli, Zhang Xianrui, Li Xiang, Wang Lishi, Zhu Zhongxu, Song Kai, Wang Xin
Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China.
Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China.
Comput Struct Biotechnol J. 2024 Jan 17;23:752-758. doi: 10.1016/j.csbj.2024.01.010. eCollection 2024 Dec.
Gastric cancer (GC) is one of the most commonly diagnosed malignancies, threatening millions of lives worldwide each year. Importantly, GC is a heterogeneous disease, posing a significant challenge to the selection of patients for more optimized therapy. Over the last decades, extensive community effort has been spent on dissecting the heterogeneity of GC, leading to the identification of distinct molecular subtypes that are clinically relevant. However, so far, no tool is publicly available for GC subtype prediction, hindering the research into GC subtype-specific biological mechanisms, the design of novel targeted agents, and potential clinical applications. To address the unmet need, we developed an R package for predicting GC molecular subtypes based on gene expression profiles. To facilitate the use by non-bioinformaticians, we also provide an interactive, user-friendly web server implementing the major functionalities of . The predictive performance of was demonstrated using case studies on multiple independent datasets.
胃癌(GC)是最常被诊断出的恶性肿瘤之一,每年威胁着全球数百万人的生命。重要的是,GC是一种异质性疾病,这对选择更优化治疗的患者构成了重大挑战。在过去几十年中,各界投入了大量精力来剖析GC的异质性,从而识别出具有临床相关性的不同分子亚型。然而,到目前为止,尚无公开可用的工具用于GC亚型预测,这阻碍了对GC亚型特异性生物学机制的研究、新型靶向药物的设计以及潜在的临床应用。为满足这一未被满足的需求,我们开发了一个基于基因表达谱预测GC分子亚型的R包。为方便非生物信息学专业人员使用,我们还提供了一个交互式、用户友好的网络服务器,实现了该包的主要功能。通过对多个独立数据集的案例研究证明了该包的预测性能。