Pathway-Based Integrative Analysis of Metabolome and Microbiome Data from Hepatocellular Carcinoma and Liver Cirrhosis Patients.

Jung-Hwan Yoon,Taesung Park,Eun Ju Cho,Sung Won Cho,Soon Sun Kim,Jae Youn Cheong,Boram Kim

doi:10.3390/cancers12092705

Abstract

Simple SummaryHepatocellular carcinoma (HCC) is one of the diseases associated with human microbiome. The human microbiome is known to affect human disease through the metabolites. The aim of this study was to identify the pathways associated with HCC by integrating microbiome and metabolomic data via a novel pathway-based integrative method named HisCoM-MnM representing Hierarchical structural Component Model for pathway analysis of Microbiome and Metabolome. Application of HisCoM-MnM to datasets from HCC and liver cirrhosis (LC) patients successfully identified HCC-related pathways related to cancer metabolic reprogramming along with the significant metabolome and metagenome that make up those pathways.Aberrations of the human microbiome are associated with diverse liver diseases, including hepatocellular carcinoma (HCC). Even if we can associate specific microbes with particular diseases, it is difficult to know mechanistically how the microbe contributes to the pathophysiology. Here, we sought to reveal the functional potential of the HCC-associated microbiome with the human metabolome which is known to play a role in connecting host phenotype to microbiome function. To utilize both microbiome and metabolomic data sets, we propose an innovative, pathway-based analysis, Hierarchical structural Component Model for pathway analysis of Microbiome and Metabolome (HisCoM-MnM), for integrating microbiome and metabolomic data. In particular, we used pathway information to integrate these two omics data sets, thus providing insight into biological interactions between different biological layers, with regard to the host’s phenotype. The application of HisCoM-MnM to data sets from 103 and 97 patients with HCC and liver cirrhosis (LC), respectively, showed that this approach could identify HCC-related pathways related to cancer metabolic reprogramming, in addition to the significant metabolome and metagenome that make up those pathways.

Highlights

Recent advances in throughput and improvement in the accuracy of metagenomic sequencing have enabled active research regarding specific microbiomes associated with diseases
Serum α-fetoprotein (AFP) levels were higher in the hepatocellular carcinoma (HCC) than liver cirrhosis (LC) group
We considered data sets consisting of 103 HCC patients and 97 LC patients who had both microbiome and metabolomic data

Summary

Introduction

Recent advances in throughput and improvement in the accuracy of metagenomic sequencing have enabled active research regarding specific microbiomes associated with diseases. The use of other types of biological data can enhance the understanding of biological processes, and functions of the microbiome, in specific disease phenotypes. Among the numerous types of omics data, metabolome have improved our ability to understand the structure and function of the microbiome, in numerous disease states [14,15]. Noecker et al [16] used a community-based metabolite potential score to estimate relative metabolic creation or depletion capacity of the microbiome, and attempted to identify key microbial species. This group compared predicted and measured metabolites, to identify interactions between humans and the microbiome

Objectives

Methods

Results

Discussion

Conclusion