SNP Array in Hematopoietic Neoplasms: A Review.

Jinming Song, Haipeng Shao
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引用次数: 16

Abstract

Cytogenetic analysis is essential for the diagnosis and prognosis of hematopoietic neoplasms in current clinical practice. Many hematopoietic malignancies are characterized by structural chromosomal abnormalities such as specific translocations, inversions, deletions and/or numerical abnormalities that can be identified by karyotype analysis or fluorescence in situ hybridization (FISH) studies. Single nucleotide polymorphism (SNP) arrays offer high-resolution identification of copy number variants (CNVs) and acquired copy-neutral loss of heterozygosity (LOH)/uniparental disomy (UPD) that are usually not identifiable by conventional cytogenetic analysis and FISH studies. As a result, SNP arrays have been increasingly applied to hematopoietic neoplasms to search for clinically-significant genetic abnormalities. A large numbers of CNVs and UPDs have been identified in a variety of hematopoietic neoplasms. CNVs detected by SNP array in some hematopoietic neoplasms are of prognostic significance. A few specific genes in the affected regions have been implicated in the pathogenesis and may be the targets for specific therapeutic agents in the future. In this review, we summarize the current findings of application of SNP arrays in a variety of hematopoietic malignancies with an emphasis on the clinically significant genetic variants.

Abstract Image

造血肿瘤SNP阵列研究进展
细胞遗传学分析在临床上对造血肿瘤的诊断和预后至关重要。许多造血系统恶性肿瘤的特征是染色体结构异常,如特异性易位、倒位、缺失和/或数字异常,这些异常可以通过核型分析或荧光原位杂交(FISH)研究来识别。单核苷酸多态性(SNP)阵列提供了高分辨率的拷贝数变异(CNVs)和获得的拷贝中性杂合性缺失(LOH)/双亲二体(UPD)的鉴定,这些通常是传统细胞遗传学分析和FISH研究无法识别的。因此,SNP阵列已越来越多地应用于造血肿瘤,以寻找具有临床意义的遗传异常。在多种造血肿瘤中发现了大量的CNVs和upd。SNP阵列检测CNVs在某些造血肿瘤中具有预测预后的意义。受影响区域的一些特定基因与发病机制有关,并可能成为未来特定治疗剂的靶点。在这篇综述中,我们总结了SNP阵列在各种造血恶性肿瘤中的应用的最新发现,重点是临床显著的遗传变异。
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来源期刊
自引率
0.00%
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0
审稿时长
11 weeks
期刊介绍: High-Throughput (formerly Microarrays, ISSN 2076-3905) is a multidisciplinary peer-reviewed scientific journal that provides an advanced forum for the publication of studies reporting high-dimensional approaches and developments in Life Sciences, Chemistry and related fields. Our aim is to encourage scientists to publish their experimental and theoretical results based on high-throughput techniques as well as computational and statistical tools for data analysis and interpretation. The full experimental or methodological details must be provided so that the results can be reproduced. There is no restriction on the length of the papers. High-Throughput invites submissions covering several topics, including, but not limited to: Microarrays, DNA Sequencing, RNA Sequencing, Protein Identification and Quantification, Cell-based Approaches, Omics Technologies, Imaging, Bioinformatics, Computational Biology/Chemistry, Statistics, Integrative Omics, Drug Discovery and Development, Microfluidics, Lab-on-a-chip, Data Mining, Databases, Multiplex Assays.
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