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

Abstract

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.

Keywords: REIUS isolation; biomarker discovery; disease-free survival (DFS); extracellular vesicles (EV); melanoma; metastasis; overall survival (OS); proteomics; tumor microenvironment (TME).

Figure 1

Comprehensive characterization of extracellular vesicles (EVs) isolated from normal melanocytes cells using the REIUS method. (A) Nanoparticle tracking analysis (NTA) profiles of EVs from four melanocyte exotypes (MX1–MX4), showing particle size distribution and concentration (mean diameter: 92.5 ± 54.7 nm; mean concentration: 9 × 10⁸ particles/mL). (B) Nano-Tracking Analysis (NTA) profile of Mel888 melanoma-isolated REIUS exosomes, analyzed at the same time with melanocytes as biological reference. (C) Western blot analysis of melanocyte-derived fractions for EV marker CD63 and loading control β-actin, comparing lysate, REIUS EV, REIUS FT 5 kD conc. (FT = flowthrough, i.e., REIUS isolation concentrated using 5 kD MWCO filtration spin columns to check for isolation failure) ultracentrifugation (UC) pellet, and UC Sup. 5 kD conc (UC supernatant concentrated using 5 kD MWCO filtration spin columns to check for isolation failure). (D) Transmission electron microscopy (TEM) image of melanocyte REIUS EVs, confirming vesicular morphology and size (scale bar: 100 nm).

Figure 2

Functional annotation of proteins isolated from REIUS-derived extracellular vesicles. (A) Gene Ontology (GO) annotation of identified proteins categorized into biological process, cellular component, and molecular function. Enriched categories include protein binding, exosomal localization, and nucleic acid-associated functions. Statistical significance was determined using DAVID’s modified Fisher’s exact test with Benjamini–Hochberg correction for multiple testing (FDR < 0.05). (B) KEGG pathway analysis of identified proteins, highlighting predominant involvement in metabolic pathways, ribosome function, and biosynthesis of antibiotics, with further subcategorization of metabolic processes. Pathways with a false discovery rate (FDR) < 0.05, adjusted by the Benjamini–Hochberg method, were considered statistically significant. (C) Word cloud representation highlights the most frequent molecular functions and protein classes among the dataset, emphasizing binding, receptor, kinase, and ribosomal proteins. Word colors are for visual clarity only and do not represent any specific meaning. (D) The DFS workflow for identifying function-specific extracellular vesicle (EV) proteins. EV protein candidates (x) are filtered by subtracting proteins found in a “Healthy EV” reference database to yield non-scaffold proteins (y). These are then cross-referenced with a function-specific database to identify function-specific EV proteins (z). The process can be iteratively repeated for sub-functions.

Figure 3

Systematic identification and survival analysis of metastatic proteins from REIUS-derived extracellular vesicles. (A) Filtering workflow for protein identification, beginning with 1915 REIUS protein hits and culminating in 21 metastatic proteins available for TCGA analysis through sequential database filtering. (B,C) Kaplan–Meier survival analysis showing hazard ratios and 95% confidence intervals for disease-free survival (DFS) and overall survival (OS), respectively. (D) Functional classification of the 21 proteins into metabolic/acidification, immune modulation, oncogenic driver, and unknown metastatic categories. (E) Western blot of proteins with commercially available antibodies confirms biochemically the presence of some of the 21 proteins identified through SFD.

Figure 4

Boolean pipeline-based identification and validation of immunosuppressive biomarkers on melanoma-derived extracellular vesicles. (A) Schematic of the stepwise filtering process integrating REIUS proteomics, cRFP subtraction, melanoma specificity, cell surface localization (CSPA), and immunosuppressive function (HisgAtlas), resulting in 18 candidate proteins. (B,C) Immunoblot analysis of metastasis-associated and predicted EV surface proteins in cell lysates and exosomes from three melanoma cell lines. (D) Flow cytometry analysis of CSPG4 surface expression on exosomes, demonstrating robust detection across all lines. (E) Flow cytometry analysis of CD276 expression on exosomes, confirming the presence of this immunosuppressive marker on the surface of exosomes.

Figure 5

Systematic identification and network categorization of metastatic melanoma extracellular vesicle (EV) proteins. (A) Compilation of a comprehensive Healthy EV Database from 24 normal human sample types to define baseline EV protein content. (B) Stepwise filtering pipeline: melanoma EV proteomics data are filtered to remove healthy “scaffold” proteins and cross-referenced with the Human Cancer Metastasis Database (HCMDB), resulting in 183 metastatic melanoma EV proteins. (C) STRING network analysis of these 183 proteins, functionally grouped into metabolic/acidification, oncogenic drivers, immune modulation, and metastasis-associated protein categories, reveals distinct interaction clusters underlying metastatic processes in melanoma. Edges in the STRING network are color-coded to indicate the type of supporting evidence: green lines represent gene neighborhood, red lines indicate gene fusions, blue lines show gene co-occurrence, purple lines denote experimental evidence, yellow lines indicate text mining, light blue (sky blue) lines represent database annotation, and black lines indicate co-expression.