Identification of Early Warning Signals at the Critical Transition Point of Colorectal Cancer Based on Dynamic Network Analysis.

Lei Liu,Hongbo Xie,Denan Zhang,Sibo Ren,Fei Xu,Jingbo Yang,Jiaxuan Lv,Qing Jin,Xiujie Chen,Weifang Ma,Zhuo Shao

doi:10.3389/fbioe.2020.00530

Lei Liu, Hongbo Xie + Show 9 more

Open Access

https://doi.org/10.3389/fbioe.2020.00530

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Abstract

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. Due to the lack of early diagnosis methods and warning signals of CRC and its strong heterogeneity, the determination of accurate treatments for CRC and the identification of specific early warning signals are still urgent problems for researchers. In this study, the expression profiles of cancer tissues and the expression profiles of tumor-adjacent tissues in 28 CRC patients were combined into a human protein–protein interaction (PPI) network to construct a specific network for each patient. A network propagation method was used to obtain a mutant giant cluster (GC) containing more than 90% of the mutation information of one patient. Next, mutation selection rules were applied to the GC to mine the mutation sequence of driver genes in each CRC patient. The mutation sequences from patients with the same type CRC were integrated to obtain the mutation sequences of driver genes of different types of CRC, which provide a reference for the diagnosis of clinical CRC disease progression. Finally, dynamic network analysis was used to mine dynamic network biomarkers (DNBs) in CRC patients. These DNBs were verified by clinical staging data to identify the critical transition point between the pre-disease state and the disease state in tumor progression. Twelve known drug targets were found in the DNBs, and 6 of them have been used as targets for anticancer drugs for clinical treatment. This study provides important information for the prognosis, diagnosis and treatment of CRC, especially for pre-emptive treatments. It is of great significance for reducing the incidence and mortality of CRC.

Highlights

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide (Dienstmann et al, 2017)
Tumor-adjacent tissues are most susceptible to transforming into cancer tissue and will eventually develop into cancer tissue, as the transcriptomes of tumor-adjacent tissue samples often approximate a gene expression signature of invasive cancer, which can be predictive of disease progression in early premalignant lesions (Finak et al, 2008; Graham et al, 2011; Chatterjee et al, 2018)
Using the concept of dynamic network biomarkers (DNBs) to explore the dynamic characteristics of CRC, we can better identify the early warning signals of sudden cancerous changes in the pre-disease state and achieve a true early cancer warning to prevent disease

Summary

INTRODUCTION

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide (Dienstmann et al, 2017). Finding the mutation order of critical genes in CRC patients and blocking the process in time can effectively prevent the development of cancer and even achieve the goal of pre-emptive treatment. It is necessary to develop CRCspecific, personalized biomarkers for different molecular types and tumor stages, taking the heterogeneity of CRC into account In this way, the biomarkers being untargetable or producing a poor or no effect due to low sensitivity and specificity will be resolved. The new concept of dynamic network biomarkers (DNBs) is applicable in this type of scenario It is different from the traditional static method, which was developed on the basis of nonlinear dynamics and complex network theory (Chen et al, 2012; Liu et al, 2012). Using the concept of DNBs to explore the dynamic characteristics of CRC, we can better identify the early warning signals of sudden cancerous changes in the pre-disease state and achieve a true early cancer warning to prevent disease

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DATA AVAILABILITY STATEMENT