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Connecting multiple microenvironment proteomes uncovers the biology in head and neck cancer

Authors :
Ariane F. Busso-Lopes
Leandro X. Neves
Guilherme A. Câmara
Daniela C. Granato
Marco Antônio M. Pretti
Henry Heberle
Fábio M. S. Patroni
Jamile Sá
Sami Yokoo
César Rivera
Romênia R. Domingues
Ana Gabriela C. Normando
Tatiane De Rossi
Barbara P. Mello
Nayane A. L. Galdino
Bianca A. Pauletti
Pammela A. Lacerda
André Afonso N. Rodrigues
André Luis M. Casarim
Reydson A. de Lima-Souza
Ingrid I. Damas
Fernanda V. Mariano
Kenneth J. Gollob
Tiago S. Medina
Nilva K. Cervigne
Ana Carolina Prado-Ribeiro
Thaís Bianca Brandão
Luisa L. Villa
Miyuki Uno
Mariana Boroni
Luiz Paulo Kowalski
Wilfredo Alejandro González-Arriagada
Adriana F. Paes Leme
Source :
Nature Communications. 13
Publication Year :
2022
Publisher :
Springer Science and Business Media LLC, 2022.

Abstract

The poor prognosis of head and neck cancer (HNC) is associated with metastasis within the lymph nodes (LNs). Herein, the proteome of 140 multisite samples from a 59-HNC patient cohort, including primary and matched LN-negative or -positive tissues, saliva, and blood cells, reveals insights into the biology and potential metastasis biomarkers that may assist in clinical decision-making. Protein profiles are strictly associated with immune modulation across datasets, and this provides the basis for investigating immune markers associated with metastasis. The proteome of LN metastatic cells recapitulates the proteome of the primary tumor sites. Conversely, the LN microenvironment proteome highlights the candidate prognostic markers. By integrating prioritized peptide, protein, and transcript levels with machine learning models, we identify nodal metastasis signatures in blood and saliva. We present a proteomic characterization wiring multiple sites in HNC, thus providing a promising basis for understanding tumoral biology and identifying metastasis-associated signatures.

Details

ISSN :
20411723
Volume :
13
Database :
OpenAIRE
Journal :
Nature Communications
Accession number :
edsair.doi.dedup.....5d5fa674cca35e96865bf45e62d30e72
Full Text :
https://doi.org/10.1038/s41467-022-34407-1