ArticleCell communication and signaling : CCS2025
Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer.
Article in Cell communication and signaling : CCS, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Collagen remodeling in breast cancer progression: from molecular mechanisms to diagnostic and therapeutic opportunities.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Identification of prognostic biomarkers and immunotherapy response predictors in lung adenocarcinoma: integrative Mendelian randomization and machine learning analysis.Translational lung cancer research · 2026Article
- High RMI1 expression is associated with cancer cell progression and poor prognosis in prostate cancer.Oncology reports · 2026Article
- Article
- Artificial-intelligence- and multi-omics-guided predictive and drug repurposing framework construction for colorectal cancer: evidence from succinylation-neutrophil signatures.Frontiers in cell and developmental biology · 2026Article
- Review
- Integrating Single-Cell and Bulk RNA Sequencing Reveals the Malignant Phenotype of CBX4 in Prostate Cancer.Journal of Cancer · 2025Article
- To explore the potential of LOXL2 as a biomarker in glioma and construct a genomic integrated clinical prognostic model.Frontiers in oncology · 2025Article
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Authors and funding
16 authors.
Funding
Abstract
backgroundLysyl 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.
methodsWe 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.
resultsThe 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.
conclusionsOur 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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