Discovery proteomics reveals potential protein signature associated with malignant phenotype acquisition in pleomorphic adenoma

MicroRNA copy number alterations in the malignant transformation of pleomorphic adenoma to carcinoma ex pleomorphic adenoma

OBJECTIVE

This study used array comparative genomic hybridization to assess copy number alterations (CNAs) involving miRNA genes in pleomorphic adenoma (PA), recurrent pleomorphic adenoma (RPA), residual PA, and carcinoma ex pleomorphic adenoma (CXPA).

MATERIALS AND METHODS

We analyzed 13 PA, 4 RPA, 29 CXPA, and 14 residual PA using Nexus Copy Number Discovery software. The miRNAs genes affected by CNAs were evaluated based on their expression patterns and subjected to pathway enrichment analysis.

RESULTS

Across the groups, we found 216 CNAs affecting 2261 miRNA genes, with 117 in PA, 59 in RPA, 846 in residual PA, and 2555 in CXPA. The chromosome 8 showed higher involvement in altered miRNAs in PAs and CXPA patients. Six miRNA genes were shared among all groups. Additionally, miR-21, miR-455-3p, miR-140, miR-320a, miR-383, miR-598, and miR-486 were prominent CNAs found and is implicated in carcinogenesis of several malignant tumors. These miRNAs regulate critical signaling pathways such as aerobic glycolysis, fatty acid biosynthesis, and cancer-related pathways.

CONCLUSION

This study was the first to explore CNAs in miRNA-encoding genes in the PA-CXPA sequence. The findings suggest the involvement of numerous miRNA genes in CXPA development and progression by regulating oncogenic signaling pathways.

Clinical applications of mass spectrometry-based proteomics in cancer: where are we?

Tumor tissue processing methodologies in combination with data-independent acquisition mass spectrometry (DIA-MS) have emerged that can comprehensively analyze the proteome of multiple tumor samples accurately and reproducibly. Increasing recognition and adoption of these technologies has resulted in a tranche of studies providing novel insights into cancer classification systems, functional tumor biology, cancer biomarkers, treatment response and drug targets. Despite this, with some limited exceptions, MS-based proteomics has not yet been implemented in routine cancer clinical practice. Here, we summarize the use of DIA-MS in studies that may pave the way for future clinical cancer applications, and highlight the role of alternative MS technologies and multi-omic strategies. We discuss limitations and challenges of studies in this field to date and propose steps for integrating proteomic data into the cancer clinic. This article is protected by copyright. All rights reserved.