Identification of Diagnostic Biomarkers and Subtypes of Liver Hepatocellular Carcinoma by Multi-Omics Data Analysis

被引:10
作者
Ouyang, Xiao [1 ]
Fan, Qingju [1 ]
Ling, Guang [1 ]
Shi, Yu [1 ]
Hu, Fuyan [1 ]
机构
[1] Wuhan Univ Technol, Sch Sci, Dept Stat, 122 Luoshi Rd, Wuhan 430070, Peoples R China
基金
中国国家自然科学基金;
关键词
ensemble of decision trees; diagnostic biomarkers; LIHC subtyping; R PACKAGE; CANCER; EXPRESSION; PATHWAY; CLASSIFICATION; METABOLISM; SELECTION; CELLS;
D O I
10.3390/genes11091051
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
As liver hepatocellular carcinoma (LIHC) has high morbidity and mortality rates, improving the clinical diagnosis and treatment of LIHC is an important issue. The advent of the era of precision medicine provides us with new opportunities to cure cancers, including the accumulation of multi-omics data of cancers. Here, we proposed an integration method that involved the Fisher ratio, Spearman correlation coefficient, classified information index, and an ensemble of decision trees (DTs) for biomarker identification based on an unbalanced dataset of LIHC. Then, we obtained 34 differentially expressed genes (DEGs). The ability of the 34 DEGs to discriminate tumor samples from normal samples was evaluated by classification, and a high area under the curve (AUC) was achieved in our studied dataset and in two external validation datasets (AUC = 0.997, 0.973, and 0.949, respectively). Additionally, we also found three subtypes of LIHC, and revealed different biological mechanisms behind the three subtypes. Mutation enrichment analysis showed that subtype 3 had many enriched mutations, including tumor protein p53 (TP53) mutations. Overall, our study suggested that the 34 DEGs could serve as diagnostic biomarkers, and the three subtypes could help with precise treatment for LIHC.
引用
收藏
页码:1 / 18
页数:18
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