PO.CH02.02 · 化学
生态型指导的多组学分析鉴定胃癌中潜在的细胞表面治疗靶点
Ecotype-guided multi-omics profiling identifies potential cell-surface therapeutic targets in gastric cancer
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
胃癌（GC）表现出显著的异质性、复杂的分子改变以及有限的治疗选择。为全面界定其生物学特性和易感性，我们对159例胃腺癌和30例匹配的癌旁正常组织进行了15层多组学分析，涵盖基因组学、表观基因组学、转录组学、蛋白质组学、翻译后修饰、蛋白-蛋白相互作用、代谢组学和微生物组分析，产生了超过385,000个特征。通过整合细胞状态解卷积，我们基于细胞状态定义了胃肿瘤生态型，为多组学整合提供了新框架。这些生态型捕获了不同的肿瘤生态系统和基质-免疫组成，并提供了比传统基因组或组织学分类更深入的机制见解。利用机器学习和大规模AI模型，我们鉴定了生态型特异性分子特征，并将其与临床结局相关联。为优先确定治疗机会，我们对蛋白和糖蛋白应用了高离群值分析。多个细胞表面相关靶点显示出强烈的高离群值表达，包括若干已知或新兴的治疗候选靶点。细胞外基质（ECM）成分在高离群值蛋白中显著富集，凸显了它们在肿瘤生长、侵袭和潜在治疗靶向中的核心作用。我们进一步表征了高离群值糖蛋白中改变的细胞表面糖基化模式，揭示了免疫调节和ECM结合方面的变化。磷酸化位点分辨分析鉴定了与侵袭性肿瘤行为相关的关键信号特征。重要的是，将这些高离群值事件嵌入生态型和基因组亚型框架中，揭示了单从基因组分类无法察觉的、不同的生态型特异性模式。单细胞分析将许多靶点定位于特定基质区室，特别是富含成纤维细胞的生态系统，提示靶向成纤维细胞驱动的微环境可能为侵袭性GC亚群提供新的治疗策略。总之，本研究建立了一种生态型指导的蛋白基因组学方法，将细胞表面蛋白、糖蛋白和信号节点提名为胃癌中的精准治疗候选靶点，并提供了一个广泛适用的模型，用于剖析其他复杂恶性肿瘤中的异质性。
查看英文原文 English abstract
Gastric cancer (GC) exhibits marked heterogeneity, complex molecular alterations, and limited therapeutic options. To comprehensively define its biology and vulnerabilities, we performed 15-layer multi-omics profiling of 159 gastric adenocarcinomas and 30 matched normal adjacent tissues, encompassing genomics, epigenomics, transcriptomics, proteomics, post-translational modifications, protein-protein interactions, metabolomics, and microbiome analyses, yielding more than 385,000 features. By integrating cell-state deconvolution, we defined gastric tumor ecotypes based on cellular states, providing a new framework for multi-omics integration. These ecotypes captured distinct tumor ecosystems and stromal-immune compositions and offered deeper mechanistic insight than conventional genomic or histologic classifications.Leveraging machine learning and large-scale AI models, we identified ecotype-specific molecular features and linked them to clinical outcome. To prioritize therapeutic opportunities, we applied high-outlier analysis to proteins and glycoproteins. Multiple cell-surface-associated targets showed strong high-outlier expression, including several known or emerging therapeutic candidates. Extracellular matrix (ECM) components were significantly enriched among high-outlier proteins, underscoring their central role in tumor growth, invasion, and potential therapeutic targeting. We further characterized altered cell-surface glycosylation patterns in high-outlier glycoproteins, revealing changes in immune regulation and ECM engagement. Phosphosite-resolved analysis identified key signaling signatures associated with aggressive tumor behavior.Importantly, embedding these high-outlier events within ecotype and genomic subtype frameworks revealed distinct, ecotype-specific patterns that were not apparent from genomic classification alone. Single-cell analyses localized many targets to specific stromal compartments, particularly fibroblast-rich ecosystems, suggesting that targeting fibroblast-driven niches may provide new therapeutic strategies for aggressive GC subsets. Overall, this study establishes an ecotype-guided proteogenomic approach to nominate cell-surface proteins, glycoproteins, and signaling nodes as precision therapy candidates in gastric cancer, and offers a broadly applicable model for dissecting heterogeneity in other complex malignancies.
利益披露 Disclosure
Y. Wang, None..
H. Zhang, None.