SYSTEMS BIOLOGY SIGNATURE FOR PROGNOSIS OF NON-OSSIFYING FIBROMA

S. Souchelnytskyi
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Abstract

Introduction: Non-ossifying fibroma (NOF) is a frequent fibrotic lesion of bone, observed in up to 40% of children. Extensive NOF lesions and deficient healing may cause a pathological fracture or a malignant transformation. Prediction of complications requires knowledge of the mechanisms controlling NOF, and systemic analysis may provide insight into these mechanisms. Aim: To identify regulators that may predict the risk of complications, e.g., pathologic fracture or malignant transformation. Methods: Data were retrieved from public databases, e.g., PubMed and dedicated databases. We retrieved regulators with confirmed association with NOF, regulators of processes engaged in NOF, and regulators of bone remodelling and giant cell tumors of bone. Systemic analysis was performed using Cytoscape and FunCoup tools. Results: Networks representing NOF mechanisms, bone healing, and malignant transformation were generated. The network analysis identified mechanisms that may predict the efficacy of healing of NOF lesion or the risk of malignant transformation of NOF. Forty-one compounds were identified as potential signature predictor of the efficacy of bone healing. The list contains known and novel regulators of bone. Signalling pathways, hormones, vitamins, minerals, proliferation and differentiation regulators are in the 41 signature. We report here a list of 62 molecules that are engaged in bone tumorigenesis and in NOF, e.g., oncogenes and tumor suppressors, tumorigenesis-associated signalling pathways and hormones Deregulation of these molecules increases the risk of malignant transformation of NOF. Conclusion: The 41 and 62 signatures identify potential markers of the risk of non-efficient healing or malignant transformation of NOF.
非骨化纤维瘤预后的系统生物学特征
简介非骨化纤维瘤(NOF)是一种常见的骨纤维化病变,儿童发病率高达 40%。广泛的非骨化纤维瘤病变和愈合不良可能导致病理性骨折或恶性转化。预测并发症需要了解控制 NOF 的机制,而系统分析可能有助于深入了解这些机制。 目的:确定可预测并发症(如病理性骨折或恶性转化)风险的调节因子。 方法:从公共数据库中检索数据:从公共数据库(如 PubMed 和专用数据库)检索数据。我们检索了已证实与 NOF 相关的调节因子、参与 NOF 过程的调节因子以及骨重塑和骨巨细胞瘤的调节因子。使用 Cytoscape 和 FunCoup 工具进行了系统分析。 分析结果生成了代表 NOF 机制、骨愈合和恶性转化的网络。网络分析确定了可预测 NOF 病变愈合效果或 NOF 恶性转化风险的机制。有 41 种化合物被确定为骨愈合功效的潜在特征预测因子。这些化合物包括已知的和新的骨调节因子。信号通路、激素、维生素、矿物质、增殖和分化调节剂都在这 41 个特征中。我们在此报告了参与骨肿瘤和 NOF 的 62 种分子,如癌基因和肿瘤抑制因子、与肿瘤发生相关的信号通路和激素。 结论41 和 62 信号识别出 NOF 非有效愈合或恶性转化风险的潜在标记。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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