Proteomics of Primary Uveal Melanoma: Insights into Metastasis and Protein Biomarkers



Jang, Geeng-Fu, Crabb, Jack S, Hu, Bo, Willard, Belinda, Kalirai, Helen ORCID: 0000-0002-4440-2576, Singh, Arun D, Coupland, Sarah E ORCID: 0000-0002-1464-2069 and Crabb, John W
(2021) Proteomics of Primary Uveal Melanoma: Insights into Metastasis and Protein Biomarkers. CANCERS, 13 (14). 3520-.

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Abstract

Uveal melanoma metastases are lethal and remain incurable. A quantitative proteomic analysis of 53 metastasizing and 47 non-metastasizing primary uveal melanoma (pUM) was pursued for insights into UM metastasis and protein biomarkers. The metastatic status of the pUM specimens was defined based on clinical data, survival histories, prognostic analyses, and liver histopathology. LC MS/MS iTRAQ technology, the Mascot search engine, and the UniProt human database were used to identify and quantify pUM proteins relative to the normal choroid excised from UM donor eyes. The determined proteomes of all 100 tumors were very similar, encompassing a total of 3935 pUM proteins. Proteins differentially expressed (DE) between metastasizing and non-metastasizing pUM (n = 402) were employed in bioinformatic analyses that predicted significant differences in the immune system between metastasizing and non-metastasizing pUM. The immune proteins (n = 778) identified in this study support the immune-suppressive nature and low abundance of immune checkpoint regulators in pUM, and suggest CDH1, HLA-DPA1, and several DE immune kinases and phosphatases as possible candidates for immune therapy checkpoint blockade. Prediction modeling identified 32 proteins capable of predicting metastasizing versus non-metastasizing pUM with 93% discriminatory accuracy, supporting the potential for protein-based prognostic methods for detecting UM metastasis.

Item Type: Article
Uncontrolled Keywords: bioinformatics, immune profiling, iTRAQ, metastasis, prediction modeling, quantitative proteomics, uveal melanoma
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User: Symplectic Admin
Date Deposited: 06 Sep 2021 07:19
Last Modified: 18 Jan 2023 21:30
DOI: 10.3390/cancers13143520
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3135953