Department of Neurosurgery, Kitasato University School of Medicine, 1-15-1 Kitasato, Minami-ku, Sagamihara 252-0374, Japan.
Cells. 2022 Jul 7;11(14):2142. doi: 10.3390/cells11142142.
Glioblastoma multiforme (GBM) is a lethal tumor that develops in the adult brain. Despite advances in therapeutic strategies related to surgical resection and chemo-radiotherapy, the overall survival of patients with GBM remains unsatisfactory. Genetic research on mutation, amplification, and deletion in GBM cells is important for understanding the biological aggressiveness, diagnosis, and prognosis of GBM. However, the efficacy of drugs targeting the genetic abnormalities in GBM cells is limited. Investigating special microenvironments that induce chemo-radioresistance in GBM cells is critical to improving the survival and quality of life of patients with GBM. GBM cells acquire and maintain stem-cell-like characteristics via their intrinsic potential and extrinsic factors from their special microenvironments. The acquisition of stem-cell-like phenotypes and aggressiveness may be referred to as a reprogramming of GBM cells. In addition to protein synthesis, deregulation of ribosome biogenesis is linked to several diseases including cancer. Ribosomal proteins possess both tumor-promotive and -suppressive functions as extra-ribosomal functions. Incorporation of ribosomes and overexpression of ribosomal protein S6 reprogram and induce stem-cell-like phenotypes in GBM cells. Herein, we review recent literature and our published data on the acquisition of aggressiveness by GBM and discuss therapeutic options through reprogramming.
多形性胶质母细胞瘤(GBM)是一种在成人大脑中发展的致命肿瘤。尽管在与手术切除和化疗放疗相关的治疗策略方面取得了进展,但 GBM 患者的总体生存率仍然不尽如人意。对 GBM 细胞中的突变、扩增和缺失进行遗传研究,对于了解 GBM 的生物学侵袭性、诊断和预后具有重要意义。然而,针对 GBM 细胞中遗传异常的药物疗效有限。研究诱导 GBM 细胞化疗放疗耐药的特殊微环境,对于提高 GBM 患者的生存率和生活质量至关重要。GBM 细胞通过其内在潜能和来自其特殊微环境的外在因素获得并维持干细胞样特征。获得干细胞样表型和侵袭性可能被称为 GBM 细胞的重编程。除了蛋白质合成之外,核糖体生物发生的失调与包括癌症在内的几种疾病有关。核糖体蛋白作为核糖体外的功能,具有促进肿瘤和抑制肿瘤的功能。核糖体的组装和核糖体蛋白 S6 的过表达会对 GBM 细胞进行重编程并诱导其获得干细胞样表型。在此,我们回顾了关于 GBM 获得侵袭性的最新文献和我们已发表的数据,并讨论了通过重编程获得的治疗选择。