生物信息学分析基于胶质母细胞瘤的全基因组表达数据构建了生存相关变量的最佳预后指数（OPISV）。

Bioinformatic analysis constructs an optimal prognostic index for survival-related variables (OPISV) based on whole-genome expression data in Glioblastoma.

作者信息

Pan Junjia, Yan Dejun, Liang Yaoe, Yang Lin, Hu Chun, Chen Meilan

机构信息

Guangdong Second Provincial General Hospital, Guangzhou 510317, China; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China; Department of Anesthesiology, the Affiliated Panyu Central Hospital, Guangzhou Medical University, Guangzhou, China.

出版信息

Int J Biol Macromol. 2024 Dec;282(Pt 5):137184. doi: 10.1016/j.ijbiomac.2024.137184. Epub 2024 Nov 4.

DOI:10.1016/j.ijbiomac.2024.137184

PMID:39505178

Abstract

PURPOSE

Using clinical information and transcriptomic sequencing data from glioblastoma (GBM) patients in the TCGA database to perform gene-by-gene analysis that is aligned with individual patient characteristics and develop an optimal prognostic index of survival-related variables (OPISV) through iterative machine learning techniques to predict the prognosis of GBM patients.

STUDY DESIGN

The TCGA dataset was utilized as the training dataset, while two GEO datasets served as independent validation cohorts. Initially, survival analysis (p < 0.001***), differential gene expression analysis (p < 0.05*), and univariate Cox regression analysis (p < 0.05*) were employed to identify genes that are highly correlated with patient prognosis and exhibit significant differences in survival status. Subsequently, incorporating the non-excludable variable of age, a multivariate Cox regression analysis was performed in a stepwise manner to construct the OPISV. Finally, logistic and LASSO regressions were used to validate the association between the identified genes and patient survival. The OPISV performance is evaluated and its potential mechanisms are explored.

RESULTS

Age, CTSD, PTPRN, PTPRN2, NSUN5, DNAJC30 and SOX21 emerged as the optimal variables through multivariate Cox regression iterations. Further analysis characterized Age, PTPRN and DNAJC30 as independent prognostic risk factors for constructing OPISV, which is validated with external GEO datasets and GEPIA database. In OPISV_high populations, significantly upregulated GABAergic synapse function was exposed. Differential genes identified from gene clustering of the GABAergic synapse pathway and gene module highly correlated with GABAergic synapse in the WGCNA analysis are pointing unequivocally to the glioma progress.

CONCLUSION

OPISV is feasible for predicting patient survival, as it may serve as a potential mechanism underlying the involvement of GABAergic synapses in the progression of GBM.

摘要

目的

利用TCGA数据库中胶质母细胞瘤（GBM）患者的临床信息和转录组测序数据，进行与个体患者特征相匹配的逐基因分析，并通过迭代机器学习技术开发生存相关变量的最佳预后指数（OPISV），以预测GBM患者的预后。

研究设计

将TCGA数据集用作训练数据集，而两个GEO数据集用作独立验证队列。最初，采用生存分析（p<0.001***）、差异基因表达分析（p<0.05*）和单变量Cox回归分析（p<0.05*）来识别与患者预后高度相关且在生存状态上表现出显著差异的基因。随后，纳入年龄这一不可排除变量，逐步进行多变量Cox回归分析以构建OPISV。最后，使用逻辑回归和LASSO回归来验证所识别基因与患者生存之间的关联。评估OPISV的性能并探索其潜在机制。

结果

通过多变量Cox回归迭代，年龄、CTSD、PTPRN、PTPRN2、NSUN5、DNAJC30和SOX21成为最佳变量。进一步分析将年龄、PTPRN和DNAJC30表征为构建OPISV的独立预后危险因素，这在外部GEO数据集和GEPIA数据库中得到验证。在OPISV高的人群中，GABA能突触功能显著上调。从GABA能突触途径的基因聚类和WGCNA分析中与GABA能突触高度相关的基因模块中鉴定出的差异基因明确指向胶质瘤进展。