Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer

Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer

Abstract Background Lysyl oxidases (LOX/LOXL1-4) are crucial for cancer progression, yet their transcriptional regulation, potential therapeutic targeting, prognostic value and involvement in immune regulation remain poorly understood. This study comprehensively evaluates LOX/LOXL expression in canc...

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Main Authors: Qingyu Xu, Ling Ma, Alexander Streuer, Eva Altrock, Nanni Schmitt, Felicitas Rapp, Alessa Klär, Verena Nowak, Julia Obländer, Nadine Weimer, Iris Palme, Melda Göl, Hong-hu Zhu, Wolf-Karsten Hofmann, Daniel Nowak, Vladimir Riabov
Format: Article
Language:English
Published: BMC 2025-04-01
Series:Cell Communication and Signaling
Subjects:
Machine learning
Pan cancer
Lysyl oxidases
Response prediction
Prognostic model
Online Access:https://doi.org/10.1186/s12964-025-02176-1
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https://doi.org/10.1186/s12964-025-02176-1

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