骨肉瘤是最常见的恶性骨肿瘤之一，具有高化疗耐药性和不良预后，表现出异常的基因调控和表观遗传事件。甲氨蝶呤 (MTX) 通常用作骨肉瘤新辅助化疗的主要药物；但甲氨蝶呤的高剂量和强耐药性限制了其治疗效果和应用前景。研究表明，一些编码或非编码RNA（例如DNA甲基化和microRNA）的异常表达和功能障碍会影响骨肉瘤进展的关键特征，例如增殖、迁移、侵袭和耐药。全面的多组学分析对于了解其耐药和致病机制至关重要。目前，基于网络分析的非负矩阵分解（netNMF）方法被广泛用于多组学数据融合分析。然而，数据噪声的影响和正则化参数设置不灵活影响了其性能，同时整合和处理不同类型的遗传数据也是一个挑战。在本研究中，我们引入了一种新颖的自适应全变分netNMF（ATV-netNMF）方法，通过整合甲基化和基因表达数据来识别特征模块和特征基因，该方法可以自适应地选择各向异性平滑方案来基于通过在netNMF中引入自适应全变差约束来获取数据的梯度信息。通过与其他类似方法的比较，结果表明该方法能够更有效地提取多组学融合特征。此外，通过将甲氨蝶呤（MTX）耐药性的mRNA和miRNA数据与提取的特征基因相结合，四个基因，羧肽酶E（CPE）、LIM、SH3蛋白1（LASP1）、丙酮酸脱氢酶激酶1（PDK1）和丝氨酸β-最终鉴定出类内酰胺酶蛋白（LACTB）。结果表明，基因特征可以可靠地预测骨肉瘤患者的预后状态和免疫状态。版权所有©2023 Jiang，Han，Min，Kong，Wang，Gao。

Osteosarcoma is one of the most common malignant bone tumors with high chemoresistance and poor prognosis, exhibiting abnormal gene regulation and epigenetic events. Methotrexate (MTX) is often used as a primary agent in neoadjuvant chemotherapy for osteosarcoma; However, the high dosage of methotrexate and strong drug resistance limit its therapeutic efficacy and application prospects. Studies have shown that abnormal expression and dysfunction of some coding or non-coding RNAs (e.g., DNA methylation and microRNA) affect key features of osteosarcoma progression, such as proliferation, migration, invasion, and drug resistance. Comprehensive multi-omics analysis is critical to understand its chemoresistant and pathogenic mechanisms. Currently, the network analysis-based non-negative matrix factorization (netNMF) method is widely used for multi-omics data fusion analysis. However, the effects of data noise and inflexible settings of regularization parameters affect its performance, while integrating and processing different types of genetic data is also a challenge. In this study, we introduced a novel adaptive total variation netNMF (ATV-netNMF) method to identify feature modules and characteristic genes by integrating methylation and gene expression data, which can adaptively choose an anisotropic smoothing scheme to denoise or preserve feature details based on the gradient information of the data by introducing an adaptive total variation constraint in netNMF. By comparing with other similar methods, the results showed that the proposed method could extract multi-omics fusion features more effectively. Furthermore, by combining the mRNA and miRNA data of methotrexate (MTX) resistance with the extracted feature genes, four genes, Carboxypeptidase E (CPE), LIM, SH3 protein 1 (LASP1), Pyruvate Dehydrogenase Kinase 1 (PDK1) and Serine beta-lactamase-like protein (LACTB) were finally identified. The results showed that the gene signature could reliably predict the prognostic status and immune status of osteosarcoma patients.Copyright © 2023 Jiang, Han, Min, Kong, Wang and Gao.