Melanoma Proteomics Unveiled: Harmonizing Diverse Data Sets for Biomarker Discovery and Clinical Insights via MEL-PLOT

• DOI: 10.1021/acs.jproteome.4c00749
• Publication Date: 2025-05-05T10:50:46Z
• Authors: Áron Bartha; Boglárka Weltz; Lazaro Hiram Beta...; Jeovanis Gil; Natália Pinto de ...; Giampaolo Bianchini; Beáta Szeitz; Leticia Szadai; Indira Pla; Lajos V. Kemény; Ágnes Judit Jánosi; Runyu Hong; Ahmad Rajeh; Fábio Nogueira; Viktória Doma; Nicole Woldmar; Jéssica Guedes; Zsuzsanna Újfaludi; Yonghyo Kim; Tibor Szarvas; Zoltan Pahi; Tibor Pankotai; A. Marcell Szasz; Aniel Sanchez; Bo Baldetorp; József Tímár; István Balázs Németh; Sarolta Kárpáti; Roger Appelqvist; Gilberto Barbosa ...; Krzysztof Pawlowski; Elisabet Wieslander; Johan Malm; David Fenyo; Peter Horvatovich; György Marko-Varga; Balázs Győrffy
• Keywords: Biomarker Discovery; False Discovery Rate; Plot (graphics)

Using several melanoma proteomics data sets we created a single analysis platform that enables the discovery, knowledge build, and validation of diagnostic, predictive, prognostic biomarkers at protein level. Quantitative mass-spectrometry-based proteomic was obtained from five independent cohorts, including 489 tissue samples 394 patients with accompanying clinical metadata. We established an interactive R-based web comparison levels across diverse supports correlation between proteins metadata survival outcomes. By comparing differential metastatic, primary tumor, nonmalignant in two identified 274 showing significant differences among sample types. Further these lymph node metastatic third cohort revealed 45 exhibited effect on patient survival. The three most were HP (HR = 4.67, p 2.8e-06), LGALS7 3.83, 2.9e-05), UBQLN1 3.2, 4.8e-05). user-friendly platform, accessible https://www.tnmplot.com/melanoma, provides interface for data. MEL-PLOT through its capabilities, streamlines creation comprehensive base, empowering hypothesis formulation diligent monitoring recent advancements domains biomedical research drug development.