Background

Esophageal cancer is the most common cause of cancer-related death worldwide with a diverse geographical distribution, poor prognosis, and diagnosis in advanced stages of the disease. Identification of the mechanisms involved in esophageal cancer development is evaluative to improve outcomes for patients. Genetically engineered mouse models (GEMMs) of cancer provide the physiologic, molecular, and histologic features of the human tumors to determine the pathogenesis and treatments for cancer, hence exhibiting a source of tremendous potential for oncology research. The advancement of cancer modeling in mice has improved to the extent that researchers can observe and manipulate the disease process in a specific manner. Despite the significant differences between mice and humans, mice can be great models for human oncology researches due to similarities between them at the molecular and physiological levels. Due to most of the existing esophageal cancer GEMMs do not propose an ideal system for pathogenesis of the disease, genetic risks, and microenvironment exposure, so identification of challenges in GEM modeling and well-developed technologies are required to obtain the most value for patients. In this review, we describe the biology of human and mouse, followed by the exciting esophageal cancer mouse models with a discussion of applicability and challenges of these models for generating new GEMMs in future studies.

中文翻译：

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食管癌基因工程小鼠模型

背景

食管癌是世界范围内癌症相关死亡的最常见原因，其地理分布多样，预后不良，且诊断为疾病晚期。确定参与食管癌发展的机制对于改善患者的预后具有重要意义。癌症的基因工程小鼠模型 (GEMM) 提供了人类肿瘤的生理、分子和组织学特征，以确定癌症的发病机制和治疗方法，因此显示出肿瘤学研究的巨大潜力。小鼠癌症建模的进步已经提高到研究人员可以以特定方式观察和操纵疾病过程的程度。尽管小鼠和人类之间存在显着差异，由于小鼠在分子和生理水平上的相似性，它们可以成为人类肿瘤学研究的绝佳模型。由于大多数现有的食管癌 GEMM 并未提出疾病发病机制、遗传风险和微环境暴露的理想系统，因此需要识别 GEM 建模中的挑战和成熟的技术，以获得对患者的最大价值。在这篇综述中，我们描述了人类和小鼠的生物学，然后是令人兴奋的食管癌小鼠模型，并讨论了这些模型在未来研究中生成新 GEMM 的适用性和挑战。和微环境暴露，因此需要识别 GEM 建模中的挑战和成熟的技术，以获得对患者的最大价值。在这篇综述中，我们描述了人类和小鼠的生物学，然后是令人兴奋的食管癌小鼠模型，并讨论了这些模型在未来研究中生成新 GEMM 的适用性和挑战。和微环境暴露，因此需要识别 GEM 建模中的挑战和成熟的技术，以获得对患者的最大价值。在这篇综述中，我们描述了人类和小鼠的生物学，然后是令人兴奋的食管癌小鼠模型，并讨论了这些模型在未来研究中生成新 GEMM 的适用性和挑战。