Dados do Resumo

Título

A Network-Based Approach to Identify Potential Biomarkers for Salivary Gland Cancer

Introdução

Mucoepidermoid carcinoma (MEC) is the most common malignant salivary gland tumor,
characterized by different cell types (epidermoid, intermediate, and mucous) and three histological
subtypes (low, intermediate, and high grade). Common treatment involves surgery, often
supplemented with radiotherapy or chemotherapy. However, the lack of specific biomarkers for
MEC challenge treatment and highlight the need for a better understanding of its molecular
mechanisms. Bioinformatics analysis can reveal correlations between biological data, such as
proteins and microRNAs, and uncover novel interactions that may be critical for MEC.

Objetivo

To investigate protein expression profiles in salivary gland MEC using immunohistochemistry
(IHC) studies and identify significant protein interactions and signaling pathways through in-silico
analysis on the STRING platform

Métodos

Protein data related to MEC and protein expression were extracted from the PUBMED database,
focusing exclusively on relevant IHC studies. Non-IHC studies, unrelated techniques, review
articles, and studies lacking significant protein expression were excluded. In-silico analysis on the
STRING platform employed the Markov Cluster Algorithm (MCL) with an inflation parameter of 4,
setting a false discovery rate of 0.05 and a minimum interaction score of 0.75. Results were
visualized and analyzed using Cytoscape (v3.10.2) via STRINGApp. Enrichment analysis utilized
REACTOME and KEGG tools with a significance threshold of p < 0.05.

Resultados

175 PUBMED articles were identified, of which 98 (56%) studies met the inclusion criteria,
revealing 115 proteins significantly expressed in IHC studies (only positive findings). STRING/MCL
clustering yielded 15 clusters, with 5 clusters disconnected from the network. Enrichment analysis
highlighted the network's association with pathways related to cancer (18 proteins), adherens
junction (9), proteoglycans in cancer (11), cell cycle (9), PI3K/AKT signaling (11), focal adhesion
(10), ErbB signaling (8), signal transduction (26), apoptosis (6), mitosis (6), and microRNAs in
cancer (8).

Conclusões

Our study identifies connected protein clusters and their roles, particularly demonstrating intricate
interactions in MEC. We highlight the role of bioinformatic tools in integrating complex data.Further experimental validation is essential to deepen our understanding and advance targetedtherapeutic strategies for managing MEC effectively.

Palavras Chave

mucoepidermoid carcinoma; salivary gland tumors; protein expression