Wen Ji, Toomer Kevin H, Chen Zhibin, Cai Xiaodong
Department of Electrical and Computer Engineering, University of Miami, 1251 Memorial Dr, EB406, Coral Gables, FL, 33146, USA.
Breast Cancer Res Treat. 2015 Jun;151(2):295-307. doi: 10.1007/s10549-015-3395-2. Epub 2015 Apr 26.
Transcript variants play a critical role in diversifying gene expression. Alternative splicing is a major mechanism for generating transcript variants. A number of genes have been implicated in breast cancer pathogenesis with their aberrant expression of alternative transcripts. In this study, we performed genome-wide analyses of transcript variant expression in breast cancer. With RNA-Seq data from 105 patients, we characterized the transcriptome of breast tumors, by pairwise comparison of gene expression in the breast tumor versus matched healthy tissue from each patient. We identified 2839 genes, ~10 % of protein-coding genes in the human genome, that had differential expression of transcript variants between tumors and healthy tissues. The validity of the computational analysis was confirmed by quantitative RT-PCR assessment of transcript variant expression from four top candidate genes. The alternative transcript profiling led to classification of breast cancer into two subgroups and yielded a novel molecular signature that could be prognostic of patients' tumor burden and survival. We uncovered nine splicing factors (FOX2, MBNL1, QKI, PTBP1, ELAVL1, HNRNPC, KHDRBS1, SFRS2, and TIAR) that were involved in aberrant splicing in breast cancer. Network analyses for the coordinative patterns of transcript variant expression identified twelve "hub" genes that differentiated the cancerous and normal transcriptomes. Dysregulated expression of alternative transcripts may reveal novel biomarkers for tumor development. It may also suggest new therapeutic targets, such as the "hub" genes identified through the network analyses of transcript variant expression, or splicing factors implicated in the formation of the tumor transcriptome.
转录本变体在使基因表达多样化方面发挥着关键作用。可变剪接是产生转录本变体的主要机制。许多基因因其异常的可变转录本表达而与乳腺癌发病机制相关。在本研究中,我们对乳腺癌中转录本变体的表达进行了全基因组分析。利用来自105名患者的RNA测序数据,通过对每个患者的乳腺肿瘤与匹配的健康组织中的基因表达进行成对比较,我们对乳腺肿瘤的转录组进行了特征分析。我们鉴定出2839个基因,约占人类基因组中蛋白质编码基因的10%,这些基因在肿瘤组织和健康组织之间存在转录本变体的差异表达。通过对四个顶级候选基因的转录本变体表达进行定量逆转录聚合酶链反应评估,证实了计算分析的有效性。可变转录本分析导致将乳腺癌分为两个亚组,并产生了一种新的分子特征,可用于预测患者的肿瘤负荷和生存情况。我们发现了九个参与乳腺癌异常剪接的剪接因子(FOX2、MBNL1、QKI、PTBP1、ELAVL1、HNRNPC、KHDRBS1、SFRS2和TIAR)。对转录本变体表达的协调模式进行网络分析,确定了十二个区分癌转录组和正常转录组的“枢纽”基因。可变转录本的表达失调可能揭示肿瘤发展的新生物标志物。它还可能提示新的治疗靶点,例如通过转录本变体表达的网络分析确定的“枢纽”基因,或与肿瘤转录组形成有关的剪接因子。