Wang Chunlei, Beylerli Ozal, Gu Yan, Xu Shancai, Ji Zhiyong, Ilyasova Tatiana, Gareev Ilgiz, Chekhonin Vladimir
Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, People's Republic of China.
Institute of Brain Science, Harbin Medical University, Heilongjiang Province, People's Republic of China.
Curr Med Chem. 2024 Sep 6. doi: 10.2174/0109298673316883240829073901.
Glioblastoma is the most common type of brain cancer, with a prognosis that is unfortunately poor. Despite considerable progress in the field, the intricate molecular basis of this cancer remains elusive.
The aim of this study was to identify genetic indicators of glioblastoma and reveal the processes behind its development.
The advent and integration of supercomputing technology have led to a significant advancement in gene expression analysis platforms. Microarray analysis has gained recognition for its pivotal role in oncology, crucial for the molecular categorization of tumors, diagnosis, prognosis, stratification of patients, forecasting tumor responses, and pinpointing new targets for drug discovery. Numerous databases dedicated to cancer research, including the Gene Expression Omnibus (GEO) database, have been established. Identifying differentially expressed genes (DEGs) and key genes deepens our understanding of the initiation of glioblastoma, potentially unveiling novel markers for diagnosis and prognosis, as well as targets for the treatment of glioblastoma.
This research sought to discover genes implicated in the development and progression of glioblastoma by analyzing microarray datasets GSE13276, GSE14805, and GSE109857 from the GEO database. DEGs were identified, and a function enrichment analysis was performed. Additionally, a protein-protein interaction network (PPI) was constructed, followed by module analysis using the tools STRING and Cytoscape.
The analysis yielded 88 DEGs, consisting of 66 upregulated and 22 downregulated genes. These genes' functions and pathways primarily involved microtubule activity, mitotic cytokinesis, cerebral cortex development, localization of proteins to the kinetochore, and the condensation of chromosomes during mitosis. A group of 27 pivotal genes was pinpointed, with biological process analysis indicating significant enrichment in activities, such as division of the nucleus during mitosis, cell division, maintaining cohesion between sister chromatids, segregation of sister chromatids during mitosis, and cytokinesis. The survival analysis indicated that certain genes, including PCNA clamp-associated factor (PCLAF), ribonucleoside- diphosphate reductase subunit M2 (RRM2), nucleolar and spindle-associated protein 1 (NUSAP1), and kinesin family member 23 (KIF23), could be instrumental in the development, invasion, or recurrence of glioblastoma.
The identification of DEGs and key genes in this study advances our comprehension of the molecular pathways that contribute to the oncogenesis and progression of glioblastoma. This research provides valuable insights into potential diagnostic and therapeutic targets for glioblastoma.
胶质母细胞瘤是最常见的脑癌类型，不幸的是其预后很差。尽管该领域取得了相当大的进展，但这种癌症复杂的分子基础仍然难以捉摸。
本研究的目的是确定胶质母细胞瘤的遗传指标，并揭示其发展背后的过程。
超级计算技术的出现和整合推动了基因表达分析平台的显著进步。微阵列分析因其在肿瘤学中的关键作用而获得认可，对肿瘤的分子分类、诊断、预后、患者分层、预测肿瘤反应以及确定药物发现的新靶点至关重要。已经建立了许多致力于癌症研究的数据库，包括基因表达综合数据库（GEO）。识别差异表达基因（DEG）和关键基因有助于加深我们对胶质母细胞瘤发病机制的理解，有可能揭示诊断和预后的新标志物以及胶质母细胞瘤治疗的靶点。
本研究通过分析来自GEO数据库的微阵列数据集GSE13276、GSE14805和GSE109857，试图发现与胶质母细胞瘤发生和发展相关的基因。识别出DEG，并进行功能富集分析。此外，构建了蛋白质 - 蛋白质相互作用网络（PPI），随后使用STRING和Cytoscape工具进行模块分析。
分析产生了88个DEG，包括66个上调基因和22个下调基因。这些基因的功能和途径主要涉及微管活性、有丝分裂胞质分裂、大脑皮层发育、蛋白质向动粒的定位以及有丝分裂期间染色体的凝聚。确定了一组27个关键基因，生物学过程分析表明这些基因在有丝分裂期间细胞核分裂、细胞分裂、维持姐妹染色单体之间的黏连、有丝分裂期间姐妹染色单体的分离以及胞质分裂等活动中显著富集。生存分析表明，某些基因，包括增殖细胞核抗原钳相关因子（PCLAF）、核糖核苷二磷酸还原酶亚基M2（RRM2）、核仁与纺锤体相关蛋白1（NUSAP1）和驱动蛋白家族成员23（KIF23），可能在胶质母细胞瘤的发展、侵袭或复发中起作用。
本研究中DEG和关键基因的识别推进了我们对导致胶质母细胞瘤发生和进展的分子途径的理解。这项研究为胶质母细胞瘤潜在的诊断和治疗靶点提供了有价值的见解。
