Dynamic network biomarker indicates pulmonary metastasis at the tipping point of hepatocellular carcinoma

Biwei Yang,Si Zhang,Luonan Chen,Jinglin Xia,Weixin Liu,Wenqing Tang,Meiyi Li

doi:10.1038/s41467-018-03024-2

Abstract

Developing predictive biomarkers that can detect the tipping point before metastasis of hepatocellular carcinoma (HCC), is critical to prevent further irreversible deterioration. To discover such early-warning signals or biomarkers of pulmonary metastasis in HCC, we analyse time-series gene expression data in spontaneous pulmonary metastasis mice HCCLM3-RFP model with our dynamic network biomarker (DNB) method, and identify CALML3 as a core DNB member. All experimental results of gain-of-function and loss-of-function studies show that CALML3 could indicate metastasis initiation and act as a suppressor of metastasis. We also reveal the biological role of CALML3 in metastasis initiation at a network level, including proximal regulation and cascading influences in dysfunctional pathways. Our further experiments and clinical samples show that DNB with CALML3 reduced pulmonary metastasis in liver cancer. Actually, loss of CALML3 predicts shorter overall and relapse-free survival in postoperative HCC patients, thus providing a prognostic biomarker and therapy target in HCC.

Highlights

Developing predictive biomarkers that can detect the tipping point before metastasis of hepatocellular carcinoma (HCC), is critical to prevent further irreversible deterioration
Some of these biomarkers are effective in identifying HCC patients who are in a metastasis state, it is difficult to pinpoint the critical state or tipping point before metastasis initiation for early diagnosis
To simulate tumour growth and metastasis in livers of HCC patients, we used the spontaneous pulmonary metastasis mouse model, HCCLM3RFP, which involved the orthotopic transplanted human HCCLM3 cell line labelled with a stable fluorescent protein[18]

Summary

Introduction

Developing predictive biomarkers that can detect the tipping point before metastasis of hepatocellular carcinoma (HCC), is critical to prevent further irreversible deterioration. Pathway-based approaches and functional experimental studies have been adopted in identifying the dysfunction of different signalling cascades in HCC metastasis (e.g., insulin-like growth factor (IGF), mitogen-activated protein kinase (MAPK), phosphatidylinositol-3 kinase (PI3K)/AKT/ mammalian target of rapamycin (mTOR), and WNT/β-catenin) 10 and disease-related biomarkers. Some of these biomarkers are effective in identifying HCC patients who are in a metastasis state, it is difficult to pinpoint the critical state or tipping point before metastasis initiation (i.e., to identify HCC patients who are in a metastasis-imminent state) for early diagnosis. In contrast to no statically significant difference, it has been shown that dynamically there is significant difference between nonmetastatic (or normal) and pre-metastatic (or critical) states, which can be explored to develop dynamic biomarkers (rather than the traditional static biomarkers) for predicting the premetastatic (or critical) state

Methods

Results

Conclusion