文章：

无支架功能解析揭示临床相关转移性黑色素瘤细胞外囊泡生物标志物

Scaffold-Free Functional Deconvolution Identifies Clinically Relevant Metastatic Melanoma EV Biomarkers

原文发布日期：30 July 2025

DOI: 10.3390/cancers17152509

类型: Article

开放获取: 是

英文摘要：

Background:Melanoma metastasis, driven by tumor microenvironment (TME)-mediated crosstalk facilitated by extracellular vesicles (EVs), remains a major therapeutic challenge. A critical barrier to clinical translation is the overlap in protein cargo between tumor-derived and healthy cell EVs.Objective:To address this, we developed Scaffold-free Functional Deconvolution (SFD), a novel computational approach that leverages a comprehensive healthy cell EV protein database to deconvolute non-oncogenic background signals.Methods:Beginning with 1915 proteins (identified by MS/MS analysis on an Orbitrap Fusion Lumos Mass Spectrometer using the IonStar workflow) from melanoma EVs isolated using REIUS, SFD applies four sequential filters: exclusion of normal melanocyte EV proteins, prioritization of metastasis-linked entries (HCMDB), refinement via melanocyte-specific databases, and validation against TCGA survival data.Results:This workflow identified 21 high-confidence targets implicated in metabolic-associated acidification, immune modulation, and oncogenesis, and were analyzed for reduced disease-free and overall survival. SFD’s versatility was further demonstrated by surfaceome profiling, confirming enrichment of H7-B3 (CD276), ICAM1, and MIC-1 (GDF-15) in metastatic melanoma EV via Western blot and flow cytometry. Meta-analysis using Vesiclepedia and STRING categorized these targets into metabolic, immune, and oncogenic drivers, revealing a dense interaction network.Conclusions:Our results highlight SFD as a powerful tool for identifying clinically relevant biomarkers and therapeutic targets within melanoma EVs, with potential applications in drug development and personalized medicine.

摘要翻译： 

背景：黑色素瘤转移由肿瘤微环境介导的细胞外囊泡信号交流驱动，是当前治疗的主要挑战。临床转化的关键障碍在于肿瘤来源与健康细胞来源的细胞外囊泡存在蛋白质载物的重叠。 目的：为解决此问题，我们开发了无支架功能解卷积法——一种新型计算方法，通过整合健康细胞外囊泡蛋白质数据库来解析非致癌性背景信号。 方法：基于REIUS技术分离的黑色素瘤细胞外囊泡中鉴定出的1915种蛋白质（通过Orbitrap Fusion Lumos质谱仪采用IonStar流程进行MS/MS分析），SFD方法依次应用四层筛选：排除正常黑色素细胞外囊泡蛋白、优先筛选转移相关数据库条目、通过黑色素细胞特异性数据库精筛、并基于TCGA生存数据进行验证。 结果：该流程最终鉴定出21个高置信度靶标，涉及代谢相关酸化、免疫调节和致癌过程，分析显示这些靶标与无病生存期和总生存期缩短相关。通过表面组学分析进一步验证SFD的普适性，Western blot和流式细胞术证实H7-B3、ICAM1和MIC-1在转移性黑色素瘤细胞外囊泡中显著富集。基于Vesiclepedia和STRING数据库的荟萃分析将这些靶标归类为代谢、免疫和致癌驱动因子，并揭示出密集的相互作用网络。 结论：本研究证明SFD是识别黑色素瘤细胞外囊泡中临床相关生物标志物和治疗靶点的有效工具，在药物开发和个性化医疗领域具有应用潜力。

原文链接：

Scaffold-Free Functional Deconvolution Identifies Clinically Relevant Metastatic Melanoma EV Biomarkers