Cluster classification and clinical prognostic modeling based on m6A RNA methylation regulators in liver cancer.
10.3760/cma.j.cn501113-20200727-00428
- Author:
Fang Yuan LIU
1
;
Xue Min FENG
2
;
Xiao Lei JI
3
;
Xiu Lan SU
4
Author Information
1. Clinical Medical Research Center, the Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010010, China College of Life Sciences, Inner Mongolia University, Hohhot 010010, China Inner Mongolia Key Laboratory of Medical Cell Biology, Hohhot 010010, China.
2. Clinical Medical Research Center, the Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010010, China.
3. Infectious Disease, the Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010010, China.
4. Clinical Medical Research Center, the Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010010, China Inner Mongolia Key Laboratory of Medical Cell Biology, Hohhot 010010, China.
- Publication Type:Journal Article
- MeSH:
Humans;
Methylation;
Prognosis;
Adenosine/metabolism*;
Liver Neoplasms/genetics*;
RNA/genetics*
- From:
Chinese Journal of Hepatology
2022;30(9):962-969
- CountryChina
- Language:Chinese
-
Abstract:
Objective: Cluster classification based on m6A methylation regulators and construct prognostic evaluation model. Methods: Utilizing consensus cluster to classify the liver cancer samples form TCGA based on the expression of 13 m6A methylation regulators, and verify the function and prognostic significance of the clustered subtypes. Marker genes were further screened to construct a risk prediction model for evaluating the prognosis of liver cancer patients. Results: The two clustered subtypes based on m6A methylation regulators showed significant differences in the prognosis value of liver cancer patients (P=0.048), and 38 prognostic markers related to m6A methylation in liver cancer were screened from the subgroup with poor prognosis. Two m6A regulatory genes, YTHDF1 and YTHDF2, are proved with adverse prognosis by univariate cox analysis (P<0.05, Hazard ratio>1). We used Lasso regression method to build risk assessment model and effectively predicted the prognosis status of liver cancer patients within 4 years (4-year AUC=0.685, 3-year AUC=0.669). Moreover, the assessment model was validated in another dataset of Asia liver cancer patients. Conclusion: The study provided ideas for studying m6A methylation in liver cancer, and the risk prediction model can be used to evaluate the short-term prognosis of liver cancer patients.
