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

Bartha, Áron ; Weltz, Boglárka ; Betancourt, Lazaro Hiram LU ; Gil, Jeovanis LU orcid ; Pinto de Almeida, Natália LU ; Bianchini, Giampaolo ; Szeitz, Beáta ; Szadai, Leticia ; Pla, Indira LU orcid and Kemény, Lajos V. , et al. (2025) In Journal of Proteome Research 24(6). p.3117-3128
Abstract

Using several melanoma proteomics data sets we created a single analysis platform that enables the discovery, knowledge build, and validation of diagnostic, predictive, and prognostic biomarkers at the protein level. Quantitative mass-spectrometry-based proteomic data was obtained from five independent cohorts, including 489 tissue samples from 394 patients with accompanying clinical metadata. We established an interactive R-based web platform that enables the comparison of protein levels across diverse cohorts, and supports correlation analysis between proteins and clinical metadata including survival outcomes. By comparing differential protein levels between metastatic, primary tumor, and nonmalignant samples in two of the cohorts, we... (More)

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

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
mass spectrometry, proteomics, skin cancer, survival, tumor progression
in
Journal of Proteome Research
volume
24
issue
6
pages
12 pages
publisher
The American Chemical Society (ACS)
external identifiers
  • scopus:105004395993
  • pmid:40322912
ISSN
1535-3893
DOI
10.1021/acs.jproteome.4c00749
language
English
LU publication?
yes
id
8404ad9e-e55e-441f-868a-a04cd26f8826
date added to LUP
2025-09-24 11:23:17
date last changed
2025-09-25 03:20:26
@article{8404ad9e-e55e-441f-868a-a04cd26f8826,
  abstract     = {{<p>Using several melanoma proteomics data sets we created a single analysis platform that enables the discovery, knowledge build, and validation of diagnostic, predictive, and prognostic biomarkers at the protein level. Quantitative mass-spectrometry-based proteomic data was obtained from five independent cohorts, including 489 tissue samples from 394 patients with accompanying clinical metadata. We established an interactive R-based web platform that enables the comparison of protein levels across diverse cohorts, and supports correlation analysis between proteins and clinical metadata including survival outcomes. By comparing differential protein levels between metastatic, primary tumor, and nonmalignant samples in two of the cohorts, we identified 274 proteins showing significant differences among the sample types. Further analysis of these 274 proteins in lymph node metastatic samples from a third cohort revealed that 45 proteins exhibited a significant effect on patient survival. The three most significant proteins were HP (HR = 4.67, p = 2.8e-06), LGALS7 (HR = 3.83, p = 2.9e-05), and UBQLN1 (HR = 3.2, p = 4.8e-05). The user-friendly interactive web platform, accessible at https://www.tnmplot.com/melanoma, provides an interactive interface for the analysis of proteomic and clinical data. The MEL-PLOT platform, through its interactive capabilities, streamlines the creation of a comprehensive knowledge base, empowering hypothesis formulation and diligent monitoring of the most recent advancements in the domains of biomedical research and drug development.</p>}},
  author       = {{Bartha, Áron and Weltz, Boglárka and Betancourt, Lazaro Hiram and Gil, Jeovanis and Pinto de Almeida, Natália and Bianchini, Giampaolo and Szeitz, Beáta and Szadai, Leticia and Pla, Indira and Kemény, Lajos V. and Jánosi, Ágnes Judit and Hong, Runyu and Rajeh, Ahmad and Nogueira, Fábio and Doma, Viktória and Woldmar, Nicole and Guedes, Jéssica and Újfaludi, Zsuzsanna and Kim, Yonghyo and Szarvas, Tibor and Pahi, Zoltan and Pankotai, Tibor and Szasz, A. Marcell and Sanchez, Aniel and Baldetorp, Bo and Tímár, József and Németh, István Balázs and Kárpáti, Sarolta and Appelqvist, Roger and Domont, Gilberto Barbosa and Pawlowski, Krzysztof and Wieslander, Elisabet and Malm, Johan and Fenyo, David and Horvatovich, Peter and Marko-Varga, György and Győrffy, Balázs}},
  issn         = {{1535-3893}},
  keywords     = {{mass spectrometry; proteomics; skin cancer; survival; tumor progression}},
  language     = {{eng}},
  number       = {{6}},
  pages        = {{3117--3128}},
  publisher    = {{The American Chemical Society (ACS)}},
  series       = {{Journal of Proteome Research}},
  title        = {{Melanoma Proteomics Unveiled : Harmonizing Diverse Data Sets for Biomarker Discovery and Clinical Insights via MEL-PLOT}},
  url          = {{http://dx.doi.org/10.1021/acs.jproteome.4c00749}},
  doi          = {{10.1021/acs.jproteome.4c00749}},
  volume       = {{24}},
  year         = {{2025}},
}