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胶质瘤位点特异性分布的相关因素探讨

Factors Associated with Site-specific Distribution of Glioblastoma
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摘要 神经胶质瘤是中枢神经系统肿瘤中最为常见的恶性肿瘤,其治疗极具挑战性。胶质母细胞瘤属于WHOⅣ级的高度恶性脑胶质瘤,常规治疗包括手术、术后化疗联合放疗,然而由于它的高度浸润性、遗传异质性和血脑屏障的存在为胶质母细胞瘤的治疗带来了艰巨的任务,几乎所有胶质母细胞瘤在积极治疗后都会复发,提高胶质瘤总体治疗效率促使我们探索更新的分子治疗策略。大脑的解剖和功能区定义是现代神经外科学的重要理念,是研究脑胶质瘤分布差异的基础。本文从胶质瘤的起源出发,基于大脑连接组学研究的理论,从分子特征、信息通路、肿瘤微环境景观、免疫等方面分析了胶质瘤空间特异分布的可能原因,试图寻找新的生物分子靶标治疗路径,为胶质瘤更有效的治疗提供一些新的科学思路。 The treatment of glioblastoma,the most prevalent malignant tumor in the central nervous system,poses considerable challenges.Glioblastoma multiforme,classified as a grade IV highly malignant brain glioma by the World Health Organization,is typically managed through a combination of surgery,postoperative chemotherapy,and radiotherapy.The treatment of glioblastoma is complicated by its infiltrative nature,genetic heterogeneity,and presence of the blood-brain barrier.Almost all cases of glioblastoma experience recurrence despite aggressive therapy,exploring the development of updated molecular treatment strategies that can improve overall efficacy.A crucial aspect in modern neurosurgery is the precise delineation of brain regions in terms of their anatomy and function.It serves as the fundamental basis for investigating variations in the distribution of brain gliomas.Hence,this review will elucidate the origin of glioblastomas and analyze the potential factors contributing to the spatially specific distribution of gliomas on the basis of a theoretical framework of brain connectomics research.Molecular characteristics,information pathways,tumor microenvironment landscape,and immunology will inform the analysis.We aim to identify novel biomolecular targets and therapeutic pathways to gain scientific insights for effective glioblastoma treatment.
作者 李雯婷 暴洪博 梁鹏 LI Wenting;BAO Hongbo;LIANG Peng(Department of Neurosurgery,Harbin Medical University Cancer Hospital,Harbin 150081,China)
出处 《肿瘤防治研究》 CAS 2024年第3期210-215,共6页 Cancer Research on Prevention and Treatment
关键词 胶质瘤 解剖 空间分布 分子特征 Glioma Anatomy Spatial distribution Molecular feature
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