Journal
THERANOSTICS
Volume 9, Issue 24, Pages 7251-7267Publisher
IVYSPRING INT PUBL
DOI: 10.7150/thno.31155
Keywords
DNA methylation-driven genes; hepatocellular carcinoma; diagnosis; prognosis; recurrence
Categories
Funding
- International Science and Technology Cooperation Projects [2016YFE0107100]
- Capital Special Research Project for Health Development [2014-2-4012]
- Beijing Natural Science Foundation [L172055, 7192158]
- National Ten-thousand Talent Program
- Fundamental Research Funds for the Central Universities [3332018032]
- CAMS Innovation Fund for Medical Science (CIFMS) [2017-I2M-4-003, 2018-I2M-3-001]
- Shenzhen Science and Technology Plan [CKCY20180323174659823]
Ask authors/readers for more resources
In this study, we performed a comprehensively analysis of gene expression and DNA methylation data to establish diagnostic, prognostic, and recurrence models for hepatocellular carcinoma (HCC). Methods: We collected gene expression and DNA methylation datasets for over 1,200 clinical samples. Integrated analyses of RNA-sequencing and DNA methylation data were performed to identify DNA methylation-driven genes. These genes were utilized in univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to build a prognostic model. Recurrence and diagnostic models for HCC were also constructed using the same genes. Results: A total of 123 DNA methylation-driven genes were identified. Two of these genes (SPPI and LCAT) were chosen to construct the prognostic model. The high-risk group showed a markedly unfavorable prognosis compared to the low-risk group in both training (HR = 2.81; P < 0.001) and validation (HR = 3.06; P < 0.001) datasets. Multivariate Cox regression analysis indicated the prognostic model to be an independent predictor of prognosis (P < 0.05). Also, the recurrence model successfully distinguished the HCC recurrence rate between the high-risk and low-risk groups in both training (HR = 2.22; P < 0.001) and validation (HR = 2; P < 0.01) datasets. The two diagnostic models provided high accuracy for distinguishing HCC from normal samples and dysplastic nodules in the training and validation datasets, respectively. Conclusions: We identified and validated prognostic, recurrence, and diagnostic models that were constructed using two DNA methylation-driven genes in HCC. The results obtained by integrating multidimensional genomic data offer novel research directions for HCC biomarkers and new possibilities for individualized treatment of patients with HCC.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available