Dafne Alejandra Reyes, Victor Manuel Saure Sarría, Marcela Salazar-Viedma, Vívian D'Afonseca
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引用次数: 5
Abstract
Gastric cancer (GC) is one of the most frequent tumors in the world. Stomach adenocarcinoma is a heterogeneous tumor, turning the prognosis prediction and patients' clinical management difficult. Some diagnosis tests for GC are been development using knowledge based in polymorphisms, somatic copy number alteration (SCNA) and aberrant histone methylation. This last event, a posttranslational modification that occurs at the chromatin level, is an important epigenetic alteration seen in several tumors including stomach adenocarcinoma. Histone methyltransferases (HMT) are the proteins responsible for the methylation in specific amino acids residues of histones tails. Here, were presented several HMTs that could be relating to GC process. We use public data from 440 patients with stomach adenocarcinoma. We evaluated the alterations as SCNAs, mutations, and genes expression level of HMTs in these aforementioned samples. As results, it was identified the 10 HMTs most altered (up to 30%) in stomach adenocarcinoma samples, which are the PRDM14, PRDM9, SUV39H2, NSD2, SMYD5, SETDB1, PRDM12, SUV39H1, NSD3, and EHMT2 genes. The PRDM9 gene is among most mutated and amplified HMTs within the data set studied. PRDM14 is downregulated in 79% of the samples and the SUV39H2 gene is down expressed in patients with recurred/progressed disease. Several HMTs are altered in many cancers. It is important to generate a genetic atlas of alterations of cancer-related genes to improve the understanding of tumorigenesis events and to propose novel tools of diagnosis and prognosis for the cancer control.
期刊介绍:
The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.