Multi-omics data integration for hepatocellular carcinoma subtyping with multi-kernel learning

Jiaying Wang; Yuting Miao; Lingmei Li; Yongqing Wu; Yan Ren; Yuehua Cui; Hongyan Cao

doi:10.3389/fgene.2022.962870

Multi-omics data integration for hepatocellular carcinoma subtyping with multi-kernel learning

Front Genet. 2022 Sep 6:13:962870. doi: 10.3389/fgene.2022.962870. eCollection 2022.

Authors

Jiaying Wang¹, Yuting Miao², Lingmei Li², Yongqing Wu², Yan Ren^{3

4}, Yuehua Cui⁵, Hongyan Cao^{2

6}

Affiliations

¹ Department of Respiratory, Gastroenterology and Oncology (West Branch), The Second Hospital of Shanxi Medical University, Taiyuan, China.
² Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
³ Department of Psychiatry, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.
⁴ Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
⁵ Department of Statistics and Probability, Michigan State University, East Lansing, MI, United States.
⁶ Shanxi Medical University-Yidu Cloud Institute of Medical Data Science, Taiyuan, China.

Abstract

Hepatocellular carcinoma (HCC) is a leading malignant liver tumor with high mortality and morbidity. Patients at the same stage can be defined as different molecular subtypes associated with specific genomic disorders and clinical features. Thus, identifying subtypes is essential to realize efficient treatment and improve survival outcomes of HCC patients. Here, we applied a regularized multiple kernel learning with locality preserving projections method to integrate mRNA, miRNA and DNA methylation data of HCC patients to identify subtypes. We identified two HCC subtypes significantly correlated with the overall survival. The patient 3-years mortality rates in the high-risk and low-risk group was 51.0% and 23.5%, respectively. The high-risk group HCC patients were 3.37 times higher in death risk compared to the low-risk group after adjusting for clinically relevant covariates. A total of 196 differentially expressed mRNAs, 2,151 differentially methylated genes and 58 differentially expressed miRNAs were identified between the two subtypes. Additionally, pathway activity analysis showed that the activities of six pathways between the two subtypes were significantly different. Immune cell infiltration analysis revealed that the abundance of nine immune cells differed significantly between the two subtypes. We further applied the weighted gene co-expression network analysis to identify gene modules that may affect patients prognosis. Among the identified modules, the key module genes significantly associated with prognosis were found to be involved in multiple biological processes and pathways, revealing the mechanism underlying the progression of HCC. Hub gene analysis showed that the expression levels of CDK1, CDCA8, TACC3, and NCAPG were significantly associated with HCC prognosis. Our findings may bring novel insights into the subtypes of HCC and promote the realization of precision medicine.

Keywords: biomarkers; multiple kernel learning; omics data integration; rMKL-LPP; subtype identification.