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A machine learning-defined cellular senescence signature systematically enhances prognostication and guides immunotherapy strategies for the treatment of gliomas

Crossref DOI link: https://doi.org/10.1038/s41698-025-01260-6

Published Online: 2026-01-07

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Xu, Tianbing

Huang, Jing

Liu, Yufei

Lu, Lisen

Lovell, Jonathan F.

Zhu, Mingxin

Jin, Honglin
Funding

Funding for this research was provided by:

Fundamental Research Funds for the Central Universities (Program No. 2662024JC005)

National Natural Science Foundation of China (Grant No. 82473363, 82272851)
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Text and Data Mining valid from 2026-01-07

Version of Record valid from 2026-02-09
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Article History

Received: 29 April 2025

Accepted: 23 December 2025

First Online: 7 January 2026

Competing interests

: The authors declare no competing interests.

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