Identification of Pan-Cancer Prognostic Biomarkers Through Integration of Multi-Omics Data
Identification of Pan-Cancer Prognostic Biomarkers Through Integration of Multi-Omics Data
复制标题
通过整合多组学数据鉴定泛癌症预后生物标志物
DOI：
10.3389/fbioe.2020.00268
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发表时间：
2020
影响因子：
5.7
通讯作者：
Xiaoyan Liu
中科院分区：
文献类型：
--
作者：
Ning Zhao;Maozu Guo;Kuanquan Wang;Chunlong Zhang;Xiaoyan Liu
致力于治疗癌症的预后生物标志物很难识别。虽然高通量测序技术使我们能够通过分析组学数据来更深入地挖掘预后生物标志物，但缺乏有效的方法来综合利用多组学数据。在这项工作中，我们整合了多个组学数据[DNA甲基化(DM)、基因表达(GE)、体细胞拷贝数变化和微RNA表达(ME)]，并提出了一种通过期望“分数”来对基因进行排序的方法。应用该方法，获得了13种癌症的肿瘤特异性预后生物标志物。用C指数(0.76~0.96)进一步评估生物标记物的预后能力。此外，通过比较13个与生存相关的基因列表，发现7个基因(SLK、API5、BTBD2、PTAR1、VPS37A、EIF2B1和ZRANB1)与多种癌症的预后相关。特别是，SLK更有可能与癌症相关，因为它的错义突变率很高，并与细胞黏附有关。此外，网络分析表明，EPRS、hnRNPA2B1、BPTF、LRRK1和PUM1与癌症有广泛的相关性。综上所述，我们的方法更好地整合了多组学数据，可以扩展到其他疾病的研究。与以往的方法相比，预后生物标志物具有更好的预后能力。我们的研究结果可以为转化医学研究人员和临床医生提供参考。
Prognostic biomarkers dedicating to treat cancer are very difficult to identify. Although high-throughput sequencing technology allows us to mine prognostic biomarkers much deeper by analyzing omics data, there is lack of effective methods to comprehensively utilize multi-omics data. In this work, we integrated multi-omics data [DNA methylation (DM), gene expression (GE), somatic copy number alternation, and microRNA expression (ME)] and proposed a method to rank genes by desiring a “Score.” Applying the method, cancer-specific prognostic biomarkers for 13 cancers were obtained. The prognostic powers of the biomarkers were further assessed by C-indexes (ranged from 0.76 to 0.96). Moreover, by comparing the 13 survival-related gene lists, seven genes (SLK, API5, BTBD2, PTAR1, VPS37A, EIF2B1, and ZRANB1) were found to be associated with prognosis in a variety of cancers. In particular, SLK was more likely to be cancer-related due to its high missense mutation rate and associated with cell adhesion. Furthermore, after network analysis, EPRS, HNRNPA2B1, BPTF, LRRK1, and PUM1 were demonstrated to have a broad correlation with cancers. In summary, our method has a better integration of multi-omics data that can be extended to the researches of other diseases. And the prognostic biomarkers had a better prognostic power than previous methods. Our results could provide a reference for translational medicine researchers and clinicians.