Machine learning unveils immune-related signature in multicenter glioma studies

机器学习在多中心胶质瘤研究中揭示免疫相关特征

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作者:Sha Yang, Xiang Wang, Renzheng Huan, Mei Deng, Zhuo Kong, Yunbiao Xiong, Tao Luo, Zheng Jin, Jian Liu, Liangzhao Chu, Guoqiang Han, Jiqin Zhang, Ying Tan

Abstract

In glioma molecular subtyping, existing biomarkers are limited, prompting the development of new ones. We present a multicenter study-derived consensus immune-related and prognostic gene signature (CIPS) using an optimal risk score model and 101 algorithms. CIPS, an independent risk factor, showed stable and powerful predictive performance for overall and progression-free survival, surpassing traditional clinical variables. The risk score correlated significantly with the immune microenvironment, indicating potential sensitivity to immunotherapy. High-risk groups exhibited distinct chemotherapy drug sensitivity. Seven signature genes, including IGFBP2 and TNFRSF12A, were validated by qRT-PCR, with higher expression in tumors and prognostic relevance. TNFRSF12A, upregulated in GBM, demonstrated inhibitory effects on glioma cell proliferation, migration, and invasion. CIPS emerges as a robust tool for enhancing individual glioma patient outcomes, while IGFBP2 and TNFRSF12A pose as promising tumor markers and therapeutic targets.

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