Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer
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 cancer and highlights ca...
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| Hauptverfasser: | , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
05 April 2025
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| In: |
Cell communication and signaling
Year: 2025, Jahrgang: 23, Pages: 1-17 |
| ISSN: | 1478-811X |
| DOI: | 10.1186/s12964-025-02176-1 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s12964-025-02176-1 Verlag, kostenfrei, Volltext: http://biosignaling.biomedcentral.com/articles/10.1186/s12964-025-02176-1 
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| Verfasserangaben: | 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 and Vladimir Riabov |
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| 245 | 1 | 0 | |a Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer |c 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 and Vladimir Riabov |
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| 520 | |a 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 cancer and highlights cancer types where targeting these enzymes and developing LOX/LOXL-based prognostic models could have significant clinical relevance. We assessed the association of LOX/LOXL expression with survival and drug sensitivity via analyzing public datasets (including bulk and single-cell RNA sequencing data of six datasets from Gene Expression Omnibus (GEO), Chinese Glioma Genome Atlas (CGGA) and Cancer Genome Atlas Program (TCGA)). We performed comprehensive machine learning-based bioinformatics analyses, including unsupervised consensus clustering, a total of 10 machine-learning algorithms for prognostic prediction and the Connectivity map tool for drug sensitivity prediction. The clinical significance of the LOX/LOXL family was evaluated across 33 cancer types. Overexpression of LOX/LOXL showed a strong correlation with tumor progression and poor survival, particularly in glioma. Therefore, we developed a novel prognostic model for glioma by integrating LOX/LOXL expression and its co-expressed genes. This model was highly predictive for overall survival in glioma patients, indicating significant clinical utility in prognostic assessment. Furthermore, our analysis uncovered a distinct LOXL2-overexpressing malignant cell population in recurrent glioma, characterized by activation of collagen, laminin, and semaphorin-3 pathways, along with enhanced epithelial-mesenchymal transition. Apart from glioma, our data revealed the role of LOXL3 overexpression in macrophages and in predicting the response to immune checkpoint blockade in bladder and renal cancers. Given the pro-tumor role of LOX/LOXL genes in most analyzed cancers, we identified potential therapeutic compounds, such as the VEGFR inhibitor cediranib, to target pan-LOX/LOXL overexpression 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. | ||
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