Identification of Pan-Cancer Prognostic Biomarkers Through Integration of Multi-Omics Data
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.
CITE THIS COLLECTION
REFERENCES
- https://doi.org//10.1002/sim.4780080803
- https://doi.org//10.1093/nar/gku770
- https://doi.org//10.1016/s0272-6386(12)80284-8
- https://doi.org//10.1016/j.devcel.2010.10.005
- https://doi.org//10.1038/onc.2016.302
- https://doi.org//10.1038/nature12625
- https://doi.org//10.1093/neuonc/nox242
- https://doi.org//10.1038/nature10166
- https://doi.org//10.1038/nature11404
- https://doi.org//10.1038/nature12222
- https://doi.org//10.1056/NEJMoa1505917
- https://doi.org//10.1158/1078-0432.CCR-17-0853
- https://doi.org//10.1056/NEJMoa1506597
- https://doi.org//10.1038/ng.3173
SHARE
Usage metrics
Read the peer-reviewed publication
AUTHORS (5)
CATEGORIES
- Bioprocessing, Bioproduction and Bioproducts
- Industrial Biotechnology Diagnostics (incl. Biosensors)
- Industrial Microbiology (incl. Biofeedstocks)
- Industrial Molecular Engineering of Nucleic Acids and Proteins
- Industrial Biotechnology not elsewhere classified
- Medical Biotechnology Diagnostics (incl. Biosensors)
- Biological Engineering
- Regenerative Medicine (incl. Stem Cells and Tissue Engineering)
- Medical Biotechnology not elsewhere classified
- Agricultural Marine Biotechnology
- Biomaterials
- Biomechanical Engineering
- Biotechnology
- Biomarkers
- Biomedical Engineering not elsewhere classified
- Genetic Engineering
- Synthetic Biology
- Bioremediation
- Medical Molecular Engineering of Nucleic Acids and Proteins