Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Department of Neurosurgery, The Second People's Hospital of Hunan Province, The Hospital of Hunan University of Chinese Medicine, Changsha, China.
Biomed Pharmacother. 2019 Sep;117:109116. doi: 10.1016/j.biopha.2019.109116. Epub 2019 Jun 24.
A pseudogene is a gene copy that has lost its original coding ability. Pseudogenes participate in numerous biological processes including oncogenesis.
We screened for prognostic pseudogenes for lower-grade glioma (LGG) and explored the potential molecular mechanisms.
LGG data downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases were used as training and validation dataset, respectively. Univariate Cox proportional hazard regression was performed to identify pseudogenes with significant prognostic value. Robust likelihood-based survival model and LASSO regression were performed to screen for the most survival-relevant pseudogenes. A risk score model was constructed based on the prognostic pseudogenes to predict the prognosis of LGG patients.
Five pseudogenes (PKMP3, AC027612.4, HILS1, RP5-1132H15.3 and HSPB1P1) were identified as prognostic gene-signatures. Using the risk score model established based on the five pseudogenes, LGG patients were stratified into distinct prognosis groups in both TCGA and CGGA datasets (P < 0.0001). Univariate and multivariate Cox regression analyses confirmed that the risk score generated from the model was an independent prognostic factor in LGG patients (p < 0.05). Furthermore, functional analysis revealed the potential biological mechanisms mediated by the five prognostic pseudogenes.
Five novel pseudogenes capable of predicting survival in LGG patients were identified. Our findings provide novel insights into the biological role of pseudogenes in LGG.
假基因是失去原始编码能力的基因副本。假基因参与包括致癌作用在内的许多生物过程。
我们筛选了低级神经胶质瘤（LGG）的预后假基因，并探讨了潜在的分子机制。
使用从癌症基因组图谱（TCGA）和中国神经胶质瘤基因组图谱（CGGA）数据库下载的 LGG 数据作为训练和验证数据集。进行单变量 Cox 比例风险回归以鉴定具有显著预后价值的假基因。进行稳健似然生存模型和 LASSO 回归以筛选与生存最相关的假基因。基于预后假基因构建风险评分模型，以预测 LGG 患者的预后。
鉴定出五个假基因（PKMP3、AC027612.4、HILS1、RP5-1132H15.3 和 HSPB1P1）作为预后基因特征。使用基于这五个假基因建立的风险评分模型，LGG 患者在 TCGA 和 CGGA 数据集均被分为明显不同的预后组（P<0.0001）。单因素和多因素 Cox 回归分析证实，该模型生成的风险评分是 LGG 患者的独立预后因素（p<0.05）。此外，功能分析揭示了五个预后假基因介导的潜在生物学机制。
鉴定出五个可预测 LGG 患者生存的新型假基因。我们的研究结果为假基因在 LGG 中的生物学作用提供了新的见解。
