Biomarker Identification for Liver Hepatocellular Carcinoma and Cholangiocarcinoma Based on Gene Regulatory Network Analysis

REGLIV: Molecular regulation data of diverse living systems facilitating current multiomics research

Multiomics is a powerful technique in molecular biology that facilitates the identification of new associations among different molecules (genes, proteins & metabolites). It has attracted tremendous research interest from the scientists worldwide and has led to an explosive number of published studies. Most of these studies are based on the regulation data provided in available databases. Therefore, it is essential to have molecular regulation data that are strictly validated in the living systems of various cell lines and in vivo models. However, no database has been developed yet to provide comprehensive molecular regulation information validated by living systems. Herein, a new database, Molecular Regulation Data of Living System Facilitating Multiomics Study (REGLIV) is introduced to describe various types of molecular regulation tested by the living systems. (1) A total of 2996 regulations describe the changes in 1109 metabolites triggered by alterations in 284 genes or proteins, and (2) 1179 regulations describe the variations in 926 proteins induced by 125 endogenous metabolites. Overall, REGLIV is unique in (a) providing the molecular regulation of a clearly defined regulatory direction other than simple correlation, (b) focusing on molecular regulations that are validated in a living system not simply in an in vitro test, and (c) describing the disease/tissue/species specific property underlying each regulation. Therefore, REGLIV has important implications for the future practice of not only multiomics, but also other fields relevant to molecular regulation. REGLIV is freely accessible at: https://idrblab.org/regliv/.

Novel Prognostic Signatures of Hepatocellular Carcinoma Based on Metabolic Pathway Phenotypes

Hepatocellular carcinoma is a disastrous cancer with an aberrant metabolism. In this study, we aimed to assess the role of metabolism in the prognosis of hepatocellular carcinoma. Ten metabolism-related pathways were identified to classify the hepatocellular carcinoma into two clusters: Metabolism_H and Metabolism_L. Compared with Metabolism_L, patients in Metabolism_H had lower survival rates with more mutated TP53 genes and more immune infiltration. Moreover, risk scores for predicting overall survival based on eleven differentially expressed metabolic genes were developed by the least absolute shrinkage and selection operator (LASSO)-Cox regression model in The Cancer Genome Atlas (TCGA) dataset, which was validated in the International Cancer Genome Consortium (ICGC) dataset. The immunohistochemistry staining of liver cancer patient specimens also identified that the 11 genes were associated with the prognosis of liver cancer patients. Multivariate Cox regression analyses indicated that the differentially expressed metabolic gene-based risk score was also an independent prognostic factor for overall survival. Furthermore, the risk score (AUC = 0.767) outperformed other clinical variables in predicting overall survival. Therefore, the metabolism-related survival-predictor model may predict overall survival excellently for HCC patients.