Dai Hui, Ren Jing, Wang Chun, Huang Jianfei, Wang Xudong
Medical School, Nantong University, Nantong, 226001, Jiangsu, China.
Department of Clinical Biobank, Affiliated Hospital of Nantong University, Nantong, 226001, Jiangsu, China.
Sci Rep. 2025 Apr 25;15(1):14453. doi: 10.1038/s41598-025-96686-0.
Gastric cancer (GC) remains a leading cause of cancer-related deaths and exhibits considerable heterogeneity among patients. Thus, accurate classifications are essential for predicting prognosis and developing personalized therapeutic strategies. To address this, we retrospectively analyzed multi-omics data from 359 GC samples, incorporating transcriptomic RNA (mRNA), DNA methylation, mutation data, and clinical parameters. Using ten clustering algorithms, we integrated these datasets to classify GC into molecular subtypes. The robustness of our clustering approach was externally validated using an independent cohort generated from different sequencing technologies, and we characterized the heterogeneity of each subtype. Our analysis identified three distinct molecular subtypes of GC, designated CS1, CS2, and CS3. These subtypes exhibited significant differences in survival outcomes, activation of cancer-related pathways, immune microenvironment composition, genomic alterations, and responses to immunotherapy and chemotherapy. Notably, Cathepsin V (CTSV) was significantly downregulated in the immunologically active and highly responsive CS3 subtype, while it was upregulated in the immunologically exhausted CS2 subtype. These findings suggest that CTSV could serve as both a prognostic marker and a molecular classifier. Furthermore, this study provides the first evidence of CTSV's high expression in GC and its potential role in tumor progression. The novel clustering approach, based on ten clustering algorithms and comprehensive analysis of multi-omics data in gastric cancer, can guide prognosis, characterize different clinical and biological features, and elucidate the tumor immune microenvironment, providing insights into the intratumor heterogeneity of GC and potential novel therapeutic strategies. Additionally, CTSV emerges as a prognostic marker linked to tumor immunity and disease progression, which lays the foundation for improved stratification strategies and the development of targeted therapeutic approaches in GC.
胃癌（GC）仍然是癌症相关死亡的主要原因，并且在患者之间表现出相当大的异质性。因此，准确分类对于预测预后和制定个性化治疗策略至关重要。为了解决这个问题，我们回顾性分析了来自359个GC样本的多组学数据，包括转录组RNA（mRNA）、DNA甲基化、突变数据和临床参数。使用十种聚类算法，我们整合这些数据集将GC分类为分子亚型。我们聚类方法的稳健性通过使用由不同测序技术生成的独立队列进行外部验证，并且我们对每个亚型的异质性进行了表征。我们的分析确定了GC的三种不同分子亚型，命名为CS1、CS2和CS3。这些亚型在生存结果、癌症相关通路的激活、免疫微环境组成、基因组改变以及对免疫疗法和化疗的反应方面表现出显著差异。值得注意的是，组织蛋白酶V（CTSV）在免疫活性高且反应性强的CS3亚型中显著下调，而在免疫耗竭的CS2亚型中上调。这些发现表明CTSV可以作为预后标志物和分子分类器。此外，本研究首次证明了CTSV在GC中的高表达及其在肿瘤进展中的潜在作用。基于十种聚类算法和对胃癌多组学数据的综合分析的新型聚类方法，可以指导预后、表征不同的临床和生物学特征以及阐明肿瘤免疫微环境，为GC的肿瘤内异质性和潜在的新型治疗策略提供见解。此外，CTSV作为与肿瘤免疫和疾病进展相关的预后标志物出现，这为改善分层策略和开发GC的靶向治疗方法奠定了基础。
