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Wang, J.* ; Zhou, X.* ; Yu, P.* ; Yao, J.* ; Guo, P.* ; Xu, Q.* ; Zhao, Y.* ; Wang, G.* ; Li, Q.* ; Zhu, X.* ; Wei, G.W.* ; Wang, W. ; Ni, T.*

A transcriptome-based human universal senescence index (hUSI) robustly predicts cellular senescence under various conditions.

Nature Aging, DOI: 10.1038/s43587-025-00886-2 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Despite the manifestation and contribution of cellular senescence to aging and various diseases, accurate identification of heterogeneous senescent cells remains challenging. Current senescence evaluation methods rely mainly on limited markers or homogeneous samples, which might fail to capture universal senescence features, limiting their generalizability. Here we developed the human universal senescence index (hUSI), an accurate and robust senescence evaluation method for diverse cells and samples. Based on features learned from the most comprehensive cellular senescence-associated transcriptome data so far, hUSI demonstrated its convincing connections with senescence phenotypes and superior robustness in predicting senescence state. Using hUSI, we discovered potential senescence regulators and mapped senescent cell accumulation across cell types in coronavirus disease 2019 (COVID-19). The method also facilitates decoding heterogeneous senescence states in melanoma tumors, identifying prognosis-associated signaling pathways. Overall, hUSI demonstrates its utility in characterizing cellular senescence across biological contexts, with broad applications in aging research and clinical practice.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Gene-expression; Cells; Cancer; Program; P53; Signatures; Melanoma; Surveillance; Resistance; Mechanism
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 2662-8465
e-ISSN 2662-8465
Zeitschrift Nature Aging
Verlag Springer
Verlagsort Campus, 4 Crinan St, London, N1 9xw, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
Förderungen Shanghai Municipal Science and Technology Major Project
National Natural Science Foundation of China
National Key Research and Development Program of China
National Natural Science Foundation of China (National Science Foundation of China)
DOI 10.1038/s43587-025-00886-2
WOS ID 001498492600001
Scopus ID 105007001428
PubMed ID 40442321
Erfassungsdatum 2025-06-02
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