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Copy number variations (CNVs) in cell-free DNA (cfDNA) are emerging as noninvasive biomarkers for various cancers. However, multiple-level analysis of cfDNA CNVs for hepatocellular carcinoma (HCC) patients with radical treatments remains uninvestigated. Here, CNVs at genome-wide, chromosomal-arm, and bin levels were analyzed in cfDNA from 117 HCC patients receiving radical treatments. Then, the relationship between cfDNA CNVs and clinical outcomes was explored. Our results showed that a concordant profile of CNVs was observed between cfDNA and tumor tissue DNA. Three genome-wide CNV indicators including tumor fraction (TFx), prediction score (P-score), and stability score (S-score) were calculated and demonstrated to exhibit significant correlation with poorer overall survival (OS) and recurrence-free survival (RFS). Furthermore, the high-frequency cfDNA CNVs at chromosomal-arm level including the loss of 4q, 17p, and 19p and the gain of 8q and 1q clearly predicted HCC prognosis. Finally, a bin-level risk score was constructed to improve the ability of CNVs in predicting prognosis. Altogether, our study indicates that the multiple-level cfDNA CNVs are significantly associated with OS and RFS in HCC patients with radical treatments, suggesting that cfDNA CNVs detected by low-coverage whole-genome sequencing (WGS) may be used as potential prognostic biomarkers of HCC patients.
Pubmed ID: 34490703
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Java toolset for working with next generation sequencing data in the BAM format.
View all literature mentionsWeb tool where one component is front end Xena Browser and another component is back end Xena Hubs. Web based Xena Browser empowers biologists to explore data across multiple Xena Hubs with variety of visualizations and analyses. Xena Hubs host genomics data from laptops, public servers, behind firewall, or in cloud, and can be public or private. Xena Browser receives data simultaneously from multiple Xena Hubs and integrates them into single coherent visualization within browser. Allows users to explore functional genomic data sets for correlations between genomic and/or phenotypic variables.
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