Fonseca Felipe Paiva, Macedo Carolina Carneiro Soares, Dos Santos Costa Sara Ferreira, Leme Adriana Franco Paes, Rodrigues Romênia Ramos, Pontes Hélder Antônio Rebelo, Altemani Albina, van Heerden Willie F P, Martins Manoela Domingues, de Almeida Oslei Paes, Santos-Silva Alan Roger, Lopes Márcio Ajudarte, Vargas Pablo Agustin
Department of Oral Diagnosis, Oral Pathology Division, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, Brazil; Department of Surgery and Pathology, School of Dentistry, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Department of Oral Diagnosis, Oral Pathology Division, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, Brazil.
Oral Surg Oral Med Oral Pathol Oral Radiol. 2019 Dec;128(6):639-650. doi: 10.1016/j.oooo.2019.07.016. Epub 2019 Aug 1.
The aim of this study was to determine the proteome of adenoid cystic carcinoma (AdCC) and polymorphous adenocarcinoma (PAc) and to identify a protein signature useful in distinguishing these two neoplasms.
Ten cases of AdCC and 10 cases of PAc were microdissected for enrichment of neoplastic tissue. The samples were submitted to liquid chromatography-tandem mass spectrometry (LC-MS/MS), and the proteomics data were analyzed by using the MaxQuant software. LC-MS/MS spectra were searched against the Human UniProt database, and statistical analyses were performed with Perseus software. Bioinformatic analyses were performed by using discovery-based proteomic data on both tumors.
LC-MS/MS analysis identified 1957 proteins. The tumors shared 1590 proteins, and 261 were exclusively identified in AdCC and 106 in PAc. Clustering analysis of the statistically significant proteins clearly separated AdCC from PAc. Protein expression 10 times higher in one group than in the other led to a signature of 16 proteins-6 upregulated in AdCC and 10 in PAc. A new clustering analysis showed reverse regulation and also differentiated both tumors.
Global proteomics may be useful in discriminating these two malignant salivary neoplasms that frequently show clinical and microscopic overlaps, but additional validation studies are still necessary to determine the diagnostic potential of the protein signature obtained.
本研究旨在确定腺样囊性癌(AdCC)和多形性腺癌(PAc)的蛋白质组,并识别有助于区分这两种肿瘤的蛋白质特征。
对10例AdCC和10例PAc进行显微切割以富集肿瘤组织。将样本进行液相色谱-串联质谱分析(LC-MS/MS),并使用MaxQuant软件分析蛋白质组学数据。针对人类UniProt数据库搜索LC-MS/MS谱图,并使用Perseus软件进行统计分析。利用基于发现的两种肿瘤蛋白质组学数据进行生物信息学分析。
LC-MS/MS分析鉴定出1957种蛋白质。两种肿瘤共有1590种蛋白质,261种仅在AdCC中鉴定到,106种仅在PAc中鉴定到。对具有统计学意义的蛋白质进行聚类分析,可明显将AdCC与PAc区分开。一组蛋白质表达比另一组高10倍,从而形成了一个由16种蛋白质组成的特征——AdCC中有6种上调,PAc中有10种上调。一项新的聚类分析显示出反向调节,也区分了这两种肿瘤。
整体蛋白质组学可能有助于区分这两种经常在临床和显微镜下表现出重叠的恶性唾液腺肿瘤,但仍需要额外的验证研究来确定所获得的蛋白质特征的诊断潜力。