The Plasma and Serum Metabotyping of Hepatocellular Carcinoma in a Nigerian and Egyptian Cohort using Proton Nuclear Magnetic Resonance Spectroscopy

Optimized Systematic Review Tool: Application to Candidate Biomarkers for the Diagnosis of Hepatocellular Carcinoma

This review aims to develop an appropriate review tool for systematically collating metabolites that are dysregulated in disease and applies the method to identify novel diagnostic biomarkers for hepatocellular carcinoma (HCC). Studies that analyzed metabolites in blood or urine samples where HCC was compared with comparison groups (healthy, precirrhotic liver disease, cirrhosis) were eligible. Tumor tissue was included to help differentiate primary and secondary biomarkers. Searches were conducted on Medline and EMBASE. A bespoke "risk of bias" tool for metabolomic studies was developed adjusting for analytic quality. Discriminant metabolites for each sample type were ranked using a weighted score accounting for the direction and extent of change and the risk of bias of the reporting publication. A total of 84 eligible studies were included in the review (54 blood, 9 urine, and 15 tissue), with six studying multiple sample types. High-ranking metabolites, based on their weighted score, comprised energy metabolites, bile acids, acylcarnitines, and lysophosphocholines. This new review tool addresses an unmet need for incorporating quality of study design and analysis to overcome the gaps in standardization of reporting of metabolomic data. Validation studies, standardized study designs, and publications meeting minimal reporting standards are crucial for advancing the field beyond exploratory studies.

Plasma lipids, tumor parameters and survival in HCC patients with HBV and HCV

INTRODUCTION AND AIMS

Hepatocellular carcinoma (HCC) is a consequence of chronic liver disease, particularly from hepatitis B or C and increasingly from obesity and metabolic syndrome. Since lipids are an important component of cell membranes and are involved in cell signaling and tumor cell growth, we wished to evaluate the relationship between HCC patient plasma lipids and maximum tumor diameter and other indices of HCC human biology.

METHODS

We examined prospectively-collected data from a multi-institutional collaborative Turkish HCC working group, from predominantly HBV-based patients, for plasma lipid profiles, consisting of triglycerides, total cholesterol, LDL-cholesterol (LDL) and HDL-cholesterol (HDL) and compared these with the associated patient maximum tumor diameter (MTD), portal vein thrombosis, alpha-fetoprotein (AFP) and also with patient survival.

RESULTS

We found that both low HDL (p=0.0002) and high LDL (p=0.003) levels were significantly associated with increased MTD, as well as in a final multiple linear regression model on MTD. The combination of low HDL combined with high HDL levels were significant in a regression model on MTD, PVT and an HCC Aggressiveness Index (Odds Ratio 12.91 compared to an Odds Ratio of 1 for the reference). Furthermore, in a Cox regression model on death, the HDL plus LDL combination had a significantly higher Hazard Ratio than the reference category.

CONCLUSIONS

Low plasma HDL, high plasma LDL and especially the combination, were significantly related to more aggressive HCC phenotype and the combination was significantly related to a higher Hazard Ratio for death.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.