Abstract
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.
Highlights
Cancer is a major public health problem worldwide (Siegel et al, 2020) and the occurrence of cancer is caused by many factors
In the process of univariate Cox regression analysis, we found that a gene appeared to be survival-related in one omics dataset, while it might appear to be unrelated to survival on another omics data even under the same model, selection criteria and set of samples
The recognition of prognostic biomarkers in cancers could predict the prognostic status of each individual patient
Summary
Cancer is a major public health problem worldwide (Siegel et al, 2020) and the occurrence of cancer is caused by many factors. It is controlled by genetics and epigenetics, and influenced by many other regulatory factors, such as miRNAs. A variety of regulatory factors contribute to the heterogeneity of cancer (Marusyk et al, 2012; Swanton, 2012; Burrell et al, 2013), which leads to a low cure rate and poor prognosis. Prognostic biomarkers are used to predict likelihood of recurrence or progression in patients with cancer (Cagney et al, 2018). It is still hard to identify the prognostic biomarkers of cancer accurately
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