开发新型生物信息学算法，系统研究胶质母细胞瘤患者生存时间、关键基因和蛋白质之间的关系。
Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme.
机构信息
College of Computer Science, Sichuan University, Chengdu, 610065, China.
Department of Neurosurgery, Southwest Hospital, Third Military Medical University, Chongqing, P.R. China.
出版信息
BMC Bioinformatics. 2020 Sep 17;21(Suppl 13):383. doi: 10.1186/s12859-020-03674-4.
BACKGROUND
Glioblastoma multiforme (GBM) is one of the most common malignant brain tumors and its average survival time is less than 1 year after diagnosis.
RESULTS
Firstly, this study aims to develop the novel survival analysis algorithms to explore the key genes and proteins related to GBM. Then, we explore the significant correlation between AEBP1 upregulation and increased EGFR expression in primary glioma, and employ a glioma cell line LN229 to identify relevant proteins and molecular pathways through protein network analysis. Finally, we identify that AEBP1 exerts its tumor-promoting effects by mainly activating mTOR pathway in Glioma.
CONCLUSIONS
We summarize the whole process of the experiment and discuss how to expand our experiment in the future.
背景
多形性胶质母细胞瘤（GBM）是最常见的恶性脑肿瘤之一，其确诊后的平均存活时间不足 1 年。
结果
首先，本研究旨在开发新的生存分析算法，以探索与 GBM 相关的关键基因和蛋白质。然后，我们探讨了 AEBP1 上调与原发性神经胶质瘤中 EGFR 表达增加之间的显著相关性，并通过蛋白质网络分析，在 LN229 胶质细胞瘤细胞系中鉴定相关蛋白质和分子途径。最后，我们确定 AEBP1 通过主要激活 mTOR 通路在神经胶质瘤中发挥其促进肿瘤的作用。
结论
我们总结了整个实验过程，并讨论了未来如何扩展我们的实验。
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