Dysfunction of PLA2G6 and CYP2C44-associated network signals imminent carcinogenesis from chronic inflammation to hepatocellular carcinoma

Abstract

Little is known about how chronic inflammation contributes to the progression of hepatocellular carcinoma (HCC), especially the initiation of cancer. To uncover the critical transition from chronic inflammation to HCC and the molecular mechanisms at a network level, we analyzed the time-series proteomic data of woodchuck hepatitis virus/c-myc mice and age-matched wt-C57BL/6 mice using our dynamical network biomarker (DNB) model. DNB analysis indicated that the 5th month after birth of transgenic mice was the critical period of cancer initiation, just before the critical transition, which is consistent with clinical symptoms. Meanwhile, the DNB-associated network showed a drastic inversion of protein expression and coexpression levels before and after the critical transition. Two members of DNB, PLA2G6 and CYP2C44, along with their associated differentially expressed proteins, were found to induce dysfunction of arachidonic acid metabolism, further activate inflammatory responses through inflammatory mediator regulation of transient receptor potential channels, and finally lead to impairments of liver detoxification and malignant transition to cancer. As a c-Myc target, PLA2G6 positively correlated with c-Myc in expression, showing a trend from decreasing to increasing during carcinogenesis, with the minimal point at the critical transition or tipping point. Such trend of homologous PLA2G6 and c-Myc was also observed during human hepatocarcinogenesis, with the minimal point at high-grade dysplastic nodules (a stage just before the carcinogenesis). Our study implies that PLA2G6 might function as an oncogene like famous c-Myc during hepatocarcinogenesis, while downregulation of PLA2G6 and c-Myc could be a warning signal indicating imminent carcinogenesis.

Individual-specific edge-network analysis for disease prediction

“…Recently, DNB model with its criterion (i.e. CI: composite index) based on multiple samples ( 11 , 31 – 34 ) has been adopted to successfully identify the tipping points of cell fate decision ( 35 , 36 ), to study immune checkpoint blockade ( 37 , 38 ), and also to quantify edge-biomarkers ( 10 ): \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{equation*}{\rm{CI}} = \frac{{\overline {{\rm PC{C}_{{\rm in}}}} }}{{\overline {{\rm PC{C_{out}}}} }} \times \overline {{\rm S{D_{in}}}} .\end{equation*}\end{document} In this work, DNB criterion is further re-defined by the above second-order moment measurements on the basis of single-sample, i.e. single-sample composite index (sCI) is defined as: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{equation*}{\rm sCI }= \overline {\frac{{\mathop \sum \nolimits_{x,y \in {\rm Marker}} |{\rm sPCC}\left( {x,y} \right)|}}{{\overline {\mathop \sum \nolimits_{x \in {\rm Marker},y \notin {\rm Marker}{\rm }} \left| {{\rm sPCC}\left( {x,y} \right)} \right|} }}} \times \overline {\mathop \sum \nolimits_{x \in {\rm Marker}} \left| {x - {u_x}} \right|} \end{equation*}\end{document} where, the numerator is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\overline {{\rm PC{C_{in}}}}$\end{document} , which is the average sPCC of the expressions of genes in the dominant group or DNB (e.g.…”

Low-Grade Dysplastic Nodules Revealed as the Tipping Point during Multistep Hepatocarcinogenesis by Dynamic Network Biomarkers

“…In conclusion, DNB during hepatocarcinogenesis can be used as early-warning signals of HCC, and this work also opens a new way to understand the underlying mechanisms responsible for HCC initiation and provides a new method to facilitate the identification of molecular targets. This method can also be applied to the analysis of other diseases [ 42 , 43 , 44 ] in a similar manner.…”

Emodin alleviates alternatively activated macrophage and asthmatic airway inflammation in a murine asthma model

“…Animals ad libitum access to water and food and were allowed to acclimate for at least 2 dition of the experiment [18] -…”