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...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
|
| Series: | Cell Communication and Signaling |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12964-025-02176-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850153520501620736 |
|---|---|
| author | 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 |
| author_facet | 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 |
| author_sort | Qingyu Xu |
| collection | DOAJ |
| description | 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 cancer and highlights cancer types where targeting these enzymes and developing LOX/LOXL-based prognostic models could have significant clinical relevance. Methods 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. Results 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. Conclusions Our study provides novel insights into the potential value of LOX/LOXL in cancer pathogenesis and treatment, and particularly its prognostic significance in glioma. |
| format | Article |
| id | doaj-art-bcf1865cec9c47dbbfc6848f1ad40a2b |
| institution | OA Journals |
| issn | 1478-811X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Cell Communication and Signaling |
| spelling | doaj-art-bcf1865cec9c47dbbfc6848f1ad40a2b2025-08-20T02:25:41ZengBMCCell Communication and Signaling1478-811X2025-04-0123111710.1186/s12964-025-02176-1Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancerQingyu Xu0Ling Ma1Alexander Streuer2Eva Altrock3Nanni Schmitt4Felicitas Rapp5Alessa Klär6Verena Nowak7Julia Obländer8Nadine Weimer9Iris Palme10Melda Göl11Hong-hu Zhu12Wolf-Karsten Hofmann13Daniel Nowak14Vladimir Riabov15Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Hematology, Beijing Chao-Yang Hospital, Capital Medical UniversityDepartment of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg UniversityDepartment of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg UniversityAbstract 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 cancer and highlights cancer types where targeting these enzymes and developing LOX/LOXL-based prognostic models could have significant clinical relevance. Methods 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. Results 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. Conclusions Our study provides novel insights into the potential value of LOX/LOXL in cancer pathogenesis and treatment, and particularly its prognostic significance in glioma.https://doi.org/10.1186/s12964-025-02176-1Machine learningPan cancerLysyl oxidasesResponse predictionPrognostic model |
| spellingShingle | 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 Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer Cell Communication and Signaling Machine learning Pan cancer Lysyl oxidases Response prediction Prognostic model |
| title | Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer |
| title_full | Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer |
| title_fullStr | Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer |
| title_full_unstemmed | Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer |
| title_short | Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer |
| title_sort | machine learning based in silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer |
| topic | Machine learning Pan cancer Lysyl oxidases Response prediction Prognostic model |
| url | https://doi.org/10.1186/s12964-025-02176-1 |
| work_keys_str_mv | AT qingyuxu machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer AT lingma machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer AT alexanderstreuer machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer AT evaaltrock machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer AT nannischmitt machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer AT felicitasrapp machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer AT alessaklar machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer AT verenanowak machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer AT juliaoblander machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer AT nadineweimer machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer AT irispalme machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer AT meldagol machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer AT honghuzhu machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer AT wolfkarstenhofmann machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer AT danielnowak machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer AT vladimirriabov machinelearningbasedinsilicoanalysisidentifiessignaturesoflysyloxidasesforprognosticandtherapeuticresponsepredictionincancer |