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基于机器学习的计算机模拟分析确定了赖氨酰氧化酶的特征,用于癌症预后和治疗反应预测。

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

作者信息

Xu Qingyu, Ma Ling, Streuer Alexander, Altrock Eva, Schmitt Nanni, Rapp Felicitas, Klär Alessa, Nowak Verena, Obländer Julia, Weimer Nadine, Palme Iris, Göl Melda, Zhu Hong-Hu, Hofmann Wolf-Karsten, Nowak Daniel, Riabov Vladimir

机构信息

Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68169, Germany.

Department of Hematology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.

出版信息

Cell Commun Signal. 2025 Apr 5;23(1):169. doi: 10.1186/s12964-025-02176-1.

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.

摘要

背景

赖氨酰氧化酶(LOX/LOXL1 - 4)对癌症进展至关重要,但其转录调控、潜在治疗靶点、预后价值及在免疫调节中的作用仍知之甚少。本研究全面评估了LOX/LOXL在癌症中的表达,并突出了靶向这些酶以及开发基于LOX/LOXL的预后模型可能具有显著临床相关性的癌症类型。

方法

我们通过分析公共数据集(包括来自基因表达综合数据库(GEO)、中国胶质瘤基因组图谱(CGGA)和癌症基因组图谱计划(TCGA)的六个数据集的批量和单细胞RNA测序数据)评估了LOX/LOXL表达与生存及药物敏感性的关联。我们进行了基于机器学习的全面生物信息学分析,包括无监督一致性聚类、用于预后预测的总共10种机器学习算法以及用于药物敏感性预测的连接图谱工具。

结果

在33种癌症类型中评估了LOX/LOXL家族的临床意义。LOX/LOXL的过表达与肿瘤进展和不良生存密切相关,尤其是在胶质瘤中。因此,我们通过整合LOX/LOXL表达及其共表达基因开发了一种新的胶质瘤预后模型。该模型对胶质瘤患者的总生存具有高度预测性,表明在预后评估中具有显著的临床实用性。此外,我们的分析在复发性胶质瘤中发现了一个独特的LOXL2过表达恶性细胞群体,其特征是胶原蛋白、层粘连蛋白和信号素-3途径的激活以及上皮-间质转化增强。除了胶质瘤,我们的数据还揭示了LOXL3过表达在巨噬细胞中的作用以及在预测膀胱癌和肾癌对免疫检查点阻断的反应中的作用。鉴于LOX/LOXL基因在大多数分析的癌症中具有促肿瘤作用,我们确定了潜在的治疗化合物,如VEGFR抑制剂西地尼布,以靶向癌症中泛LOX/LOXL的过表达。

结论

我们的研究为LOX/LOXL在癌症发病机制和治疗中的潜在价值,特别是其在胶质瘤中的预后意义提供了新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dc4/11971788/18d7892e3665/12964_2025_2176_Fig1_HTML.jpg

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