bioRxivCancer Biology
Wiring multiple microenvironment proteomes uncovers the biology in head and neck cancer
A. F. Busso-Lopes, C. Rivera, L. X. Neves, D. C. Granato, F. M. Patroni, T. R. Mazo, A. G. C. Normando, R. R. Domingues, H. Heberle, M. A. Pretti, B. P. Mello, A. N. Rodrigues, P. A. Lacerda, N. A. Galdino, K. J. Gollob, T. S. Medina, N. K. Cervigne, A. C. Prado-Ribeiro, T. B. Brandao, L. L. Villa, M. Uno, M. Boroni, L. P. Kowalski, W. Gonzalez-Arriagada, A. F. Paes Leme
Abstract
The poor prognosis of head and neck cancer (HNC) is associated with the presence of 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 identified a nodal metastasis signature in the blood and saliva. In summary, we present the deepest proteome characterization wiring multiple sampling sites in HNC, thus providing a promising basis for understanding tumoral biology and identifying metastasis-associated signatures.
- organism: human
- assay: proteomics
- research focus: host-pathogen & immune response
- research focus: differential expression
- research focus: cell-cell communication
- approach: machine learning (classical)
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