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分析鳞状细胞癌的遗传特征和功能蛋白质组学:分子分类建议。

Genetic Profile and Functional Proteomics of Anal Squamous Cell Carcinoma: Proposal for a Molecular Classification.

机构信息

Biomedica Molecular Medicine SL, C/ Faraday 7, 28049, Madrid, Spain.

Medical Oncology Department, Hospital Universitario La Paz, Paseo de la Castellana 261, 28046, Madrid, Spain.

出版信息

Mol Cell Proteomics. 2020 Apr;19(4):690-700. doi: 10.1074/mcp.RA120.001954. Epub 2020 Feb 27.

Abstract

Anal squamous cell carcinoma is a rare tumor. Chemo-radiotherapy yields a 50% 3-year relapse-free survival rate in advanced anal cancer, so improved predictive markers and therapeutic options are needed. High-throughput proteomics and whole-exome sequencing were performed in 46 paraffin samples from anal squamous cell carcinoma patients. Hierarchical clustering was used to establish groups Then, probabilistic graphical models were used to study the differences between groups of patients at the biological process level. A molecular classification into two groups of patients was established, one group with increased expression of proteins related to adhesion, T lymphocytes and glycolysis; and the other group with increased expression of proteins related to translation and ribosomes. The functional analysis by the probabilistic graphical model showed that these two groups presented differences in metabolism, mitochondria, translation, splicing and adhesion processes. Additionally, these groups showed different frequencies of genetic variants in some genes, such as and Finally, genetic and proteomic characteristics of these groups suggested the use of some possible targeted therapies, such as PARP inhibitors or immunotherapy.

摘要

分析鳞状细胞癌是一种罕见的肿瘤。在晚期肛门癌中,化疗放疗可使 3 年无复发生存率达到 50%,因此需要改进预测标志物和治疗选择。对 46 例肛门鳞状细胞癌患者的石蜡样本进行了高通量蛋白质组学和全外显子测序。采用层次聚类建立组群,然后采用概率图模型研究患者组之间在生物学过程水平上的差异。建立了一种分子分类方法,将一组患者的蛋白表达水平上调,与黏附、T 淋巴细胞和糖酵解有关;另一组患者的蛋白表达水平上调,与翻译和核糖体有关。概率图模型的功能分析表明,这两组在代谢、线粒体、翻译、剪接和黏附过程中存在差异。此外,这些组在一些基因(如 和 )中显示出不同的遗传变异频率。最后,这些组的遗传和蛋白质组学特征表明可能使用一些靶向治疗方法,如 PARP 抑制剂或免疫疗法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bca/7124473/5f4c4d79b6a4/zjw0042061110008.jpg

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