生物信息学2025，Vol.23Issue(4)：313-322,10.DOI:10.12113/202409012
基于拓展通路的胶质母细胞瘤预后预测
Prognostic prediction of glioblastoma based on extended pathways
摘要
Abstract
Glioblastoma(GBM)is the most aggressive intracranial primary malignant tumor with the worst prognosis,because the high-dimensional nature of the expression data leads to low accuracy of the results in terms of prognosis prediction.Constructing prognostic prediction models that can solve the robust computational problem for high-dimensional,low-sample-volume data is of positive significance for medical research on GBM,and there is still no better solution in this field.In this study,we proposed an extended pathway-associated deep neural network(EPDNN),which used a graph-theory-based extension of gene pathways by adding tightly regulated genes to the pathways to enable the model to learn more features,and then,data augmentation was carried out by integrating conditional generative adversarial networks,and finally,the performance of the model was evaluated based on the prediction results.After five-fold cross-validation,EPDNN achieved the highest area under the curve(AUC)and F1 scores compared to traditional prognostic prediction classifiers,and the tight genes identified in the extended pathway stage of the model were identified as important genes for GBM in previous biological and medical studies.The EPDNN model outperformed the current state-of-the-art prognostic prediction models,and provided a guided therapy for individualized postoperative treatment of GBM Tools.Meanwhile,the model was able to intuitively represent the hierarchical relationship between genes and pathways and their nonlinear relationship,which maked an exploration on the interpretability study of deep learning.关键词
胶质母细胞瘤/深度神经网络/平衡数据集/拓展通路/预后预测Key words
Glioblastoma/Deep neural network/Balanced dataset/Extended pathway/Prognostic prediction分类
信息技术与安全科学引用本文复制引用
ZHANG Qiaosheng,XU Junjie,WEI Yalong,SUN Zhenyu,ZHANG Heng,ZHONG Zhaoman..基于拓展通路的胶质母细胞瘤预后预测[J].生物信息学,2025,23(4):313-322,10.基金项目
国家自然科学基金项目(No.72174079) （No.72174079）
连云港市科技项目(No.CG2223,No.CG2323) （No.CG2223,No.CG2323）
连云港市博士后基金资助项目(No.LYG20210010). （No.LYG20210010）
