Dados do Resumo

Título

PROTEOMICS ANALYSIS IN ADENOIS CYSTIC CARCINOMA SHOWED MOLECULAR SUBTYPES

Introdução

Adenoid cystic carcinoma (AdCC) is characterized by indolent and slow growing, but eventually fatal. AdCC is subdivided into 3 histological groups: cribriform, tubular, and solid patterns. The diagnosis is a challenge due to the high variation in histological subtypes and overlapping features. Local recurrences, metastases, and perineural invasion are eventually expected. Along with a morphologic examination, biomarkers can be used to increase diagnostic accuracy and for prognosis and treatment.

Objetivo

In this study, we conducted a proteomics analysis of AdCC tumors and normal samples to identify and propose a protein profile classification and further identification of possible biomarkers for future validation and clinical applications.

Métodos

Proteomics analyses of 12 tissue samples from AdCC patients and 11 normal samples were performed. The mass spectrometry (MS) analyses were carried out using the nanoElute nanoflow chromatographic system, from Bruker Daltonics, Bremen, Germany, coupled online to a hybrid trapped ion mobility spectrometry-quadrupole time-of-flight mass spectrometer-timsTof Pro. The raw files were processed in the MaxQuant software, version 2.4.0.0. The Perseus analysis software, version 2.0.9.0 (Tyanova and Cox, 2018), was used to filter the proteomic results. We used Metaboanalyst and R software to list the top differentially expressed proteins and for Heatmap analysis. The Fisher Exact test was used to evaluate the significance of an association between the cluster of the heatmap and demographics and clinical data.

Resultados

Proteins were identified and quantified and AdCC and normal samples were compared. The top fifty proteins were plotted on a Heatmap, demonstrating different protein profiles. A gene ontology analysis was performed. To explore the possible biomarkers, we used the volcano plot analysis with adjusted P values <0.0001 and 9 proteins were founded. These proteins are involved in motility, mechanism of invasion, proliferation and differentiation. We, also use the Heatmap to classify only the AdCC samples indicating four distinct groups. Association of categorical clinical, molecular data and overall survival (OS) with AdCC groups was evaluated. Cluster four demonstrated a predominant solid histology, exhibiting worse OS. In addition, we carried out a MYB and NOTCH search in the protein profile and a downregulated expression of Transcriptional activator MYB was found in cluster 4.

Conclusões

Our findings used a very effective and efficient proteomics tools to screen a molecular profile in AdCC. We have detected potential biomarkers between AdCC and normal samples, which play crucial roles in important biological processes. In summary, we screened and verified some proteomics difference, which could provide more clues for the diagnosis of these tumors; however, it is necessary to add a major number of samples for a validation analysis.

Palavras Chave

proteomics; Adenoid Cystic Carcinoma; biomarkers