Data_Sheet_1_Identification of Pan-Cancer Prognostic Biomarkers Through Integration of Multi-Omics Data.ZIP (4.09 kB)

Data_Sheet_1_Identification of Pan-Cancer Prognostic Biomarkers Through Integration of Multi-Omics Data.ZIP

Download (4.09 kB)
dataset
posted on 02.04.2020 by Ning Zhao, Maozu Guo, Kuanquan Wang, Chunlong Zhang, Xiaoyan Liu

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.

History

References

Licence

Exports