Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction

doi:10.1038/s41598-025-98565-0

	Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction
	Fan, Xuan 1,2,3; He, Zihao 4; Guo, Jing 6; Bu, Dechao 8; Han, Dongchen 2,3; Qu, Xinchi 2,3; Li, Qihang 5; Cheng, Sen 7; Han, Aiqing 1,3; Guo, Jincheng 2,3
	2025-04-24
发表期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
卷号	15 期号:1 页码:17
摘要	Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data from glioblastoma (GBM) and low-grade glioma (LGG) samples, we identified 55 distinct cell states via the EcoTyper framework, validated for stability and prognostic impact in an independent cohort. We constructed multi-omics datasets of 620 samples, integrating transcriptomic, copy number variation (CNV), somatic mutation (MUT), Microbe (MIC), EcoTyper result data. A scRNA-seq enhanced Self-Normalizing Network-based glioma prognosis model achieved a C-index of 0.822 (training) and 0.817 (test), with AUC values of 0.867, 0.876, and 0.844 at 1, 3, and 5 years in the training set, and 0.820, 0.947, and 0.936 in the test set. Gradient attribution analysis enhanced the interpretability of the model and identified key molecular markers. The classification into high- and low-risk groups was validated as an independent prognostic factor. HDAC inhibitors are proposed as potential treatments. This study demonstrates the potential of integrating scRNA-seq and multi-omics data for robust glioma prognosis and clinical decision-making support.
DOI	10.1038/s41598-025-98565-0
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2022YFF1203303] ; Ningbo Science and Technology Innovation Yongjiang 2035 Project[2024Z229]
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:001475735000048
出版者	NATURE PORTFOLIO
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42390
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Cheng, Sen; Han, Aiqing; Guo, Jincheng
作者单位	1.Beijing Univ Chinese Med, Sch Management, Ningbo, Peoples R China 2.Beijing Univ Chinese Med, Sch Tradit Chinese Med, Ningbo, Peoples R China 3.Beijing Univ Chinese Med, Beijing 100029, Peoples R China 4.Ningbo 2 Hosp, Ningbo 315010, Peoples R China 5.Henan Univ, Kaifeng 475004, Peoples R China 6.Peking Univ, Peking Univ Hosp 3, Dept Neurosurg, Beijing, Peoples R China 7.Capital Med Univ, Dept Neurosurg, Affiliated Beijing Shijitan Hosp, Beijing 100038, Peoples R China 8.Chinese Acad Sci, Inst Comp Technol, Res Ctr Ubiquitous Comp Syst, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Fan, Xuan,He, Zihao,Guo, Jing,et al. Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction[J]. SCIENTIFIC REPORTS,2025,15(1):17.
APA	Fan, Xuan.,He, Zihao.,Guo, Jing.,Bu, Dechao.,Han, Dongchen.,...&Guo, Jincheng.(2025).Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction.SCIENTIFIC REPORTS,15(1),17.
MLA	Fan, Xuan,et al."Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction".SCIENTIFIC REPORTS 15.1(2025):17.