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A unified model of the hierarchical and stochastic theories of gastric cancer.


ABSTRACT: Gastric cancer (GC) is a life-threatening disease worldwide. Despite remarkable advances in treatments for GC, it is still fatal to many patients due to cancer progression, recurrence and metastasis. Regarding the development of novel therapeutic techniques, many studies have focused on the biological mechanisms that initiate tumours and cause treatment resistance. Tumours have traditionally been considered to result from somatic mutations, either via clonal evolution or through a stochastic model. However, emerging evidence has characterised tumours using a hierarchical organisational structure, with cancer stem cells (CSCs) at the apex. Both stochastic and hierarchical models are reasonable systems that have been hypothesised to describe tumour heterogeneity. Although each model alone inadequately explains tumour diversity, the two models can be integrated to provide a more comprehensive explanation. In this review, we discuss existing evidence supporting a unified model of gastric CSCs, including the regulatory mechanisms of this unified model in addition to the current status of stemness-related targeted therapy in GC patients.

SUBMITTER: Song Y 

PROVIDER: S-EPMC5396111 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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A unified model of the hierarchical and stochastic theories of gastric cancer.

Song Yanjing Y   Wang Yao Y   Tong Chuan C   Xi Hongqing H   Zhao Xudong X   Wang Yi Y   Chen Lin L  

British journal of cancer 20170316 8


Gastric cancer (GC) is a life-threatening disease worldwide. Despite remarkable advances in treatments for GC, it is still fatal to many patients due to cancer progression, recurrence and metastasis. Regarding the development of novel therapeutic techniques, many studies have focused on the biological mechanisms that initiate tumours and cause treatment resistance. Tumours have traditionally been considered to result from somatic mutations, either via clonal evolution or through a stochastic mod  ...[more]

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