Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer
Our study provides novel insights into the potential value of LOX/LOXL in cancer pathogenesis and treatment, and particularly its prognostic significance in glioma.
Authors
- Nanni Schmitt (ORCID: https://orcid.org/0000-0003-3455-9606)
- Nadine Weimer
- Ling Ma (ORCID: https://orcid.org/0000-0002-2960-1956)
- Vladimir Riabov (ORCID: https://orcid.org/0000-0001-5689-5681)
- Melda Göl
- Alexander Streuer (ORCID: https://orcid.org/0000-0002-3531-534X)
- Verena Nowak (ORCID: https://orcid.org/0000-0003-2523-579X)
- Daniel Nowak (ORCID: https://orcid.org/0000-0001-7130-7921)
- Felicitas Rapp (ORCID: https://orcid.org/0000-0003-0968-1546)
- Iris Palme
- Hong-Hu Zhu
- Eva Altrock (ORCID: https://orcid.org/0000-0002-2652-0958)
- Julia Obländer
- Qingyu Xu (ORCID: https://orcid.org/0000-0003-2281-4454)
- Wolf-Karsten Hofmann
- Alessa Klär
Institutions
- Capital Medical University (CN)
- Heidelberg University (DE)
- Beijing Chao-Yang Hospital, Capital Medical University (CN)
- University Hospital Heidelberg (DE)
- China Academy of Chinese Medical Sciences (CN)
- University Medical Centre Mannheim (DE)
Publication Details
- Journal
- Cell Communication and Signaling
- Published
- 2025-04-05
- DOI
- https://doi.org/10.1186/s12964-025-02176-1
- Citations
- 5
- Primary Topic
- Microbial metabolism and enzyme function
- Type
- article
- Field-Weighted Citation Impact
- 4.03
Funders
- National Natural Science Foundation of China