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靶向 通过双重调节两个候选预后生物标志物 和 在骨肉瘤中的治疗潜力。

Therapeutic potential of targeting through dual regulation of two candidate prognostic biomarkers and in osteosarcoma.

机构信息

Department of Spinal Surgery, Orthopaedic Medical Center, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, Guangdong Province, China.

Department of Spinal Surgery, The Second Affiliated Hospital, University of South China, Hengyang 421001, Hunan Province, China.

出版信息

Aging (Albany NY). 2020 Dec 3;13(1):1212-1235. doi: 10.18632/aging.202258.

Abstract

Osteosarcoma is the most common primary malignant bone tumor that mostly affects young people's health. The prognosis of patients with unresectable or recurrent osteosarcoma still remains dismal. Based on gene integration analysis from GEO and TARGET databases by R language, the differentially expressed genes of osteosarcoma patients were identified. Biological molecular function analysis indicated that these genes were importantly enriched in the process of cell adhesion molecule binding. Gene significance highly-related to clinical traits of osteosarcoma was found by weighted gene co-expression network analysis. Additionally, receiver operating characteristic curve analysis was conducted to find prognostic markers in LASSO Cox regression model. Two candidate biomarkers, and , for the prognosis of osteosarcoma were detected separately on the basis of WGCNA and LASSO model. Of note, their expression profiles were interrelated with an important therapeutic target . pharmaceutical experiments were performed to explore the biological role and prognostic benefit of candidates. Suppression of effectively upregulated and inhibited , resulting in osteosarcoma cell proliferation arrest and apoptosis. These findings suggest that serves as a core molecule for osteosarcoma therapy due to its bidirectional regulation of candidate prognostic biomarkers and .

摘要

骨肉瘤是最常见的原发性恶性骨肿瘤,主要影响年轻人的健康。不可切除或复发性骨肉瘤患者的预后仍然很差。本研究基于 R 语言对 GEO 和 TARGET 数据库中的基因整合分析,鉴定了骨肉瘤患者的差异表达基因。生物分子功能分析表明,这些基因在细胞黏附分子结合过程中重要富集。通过加权基因共表达网络分析,发现与骨肉瘤临床特征高度相关的基因显著性。此外,通过 LASSO Cox 回归模型进行Receiver Operating Characteristic 曲线分析,寻找预后标志物。基于 WGCNA 和 LASSO 模型,分别检测到两个候选生物标志物 和 ,用于骨肉瘤的预后检测。值得注意的是,它们的表达谱与一个重要的治疗靶点 相关。进行了 药物实验以探索候选物的生物学作用和预后获益。抑制 有效地上调 和抑制 ,导致骨肉瘤细胞增殖停滞和凋亡。这些发现表明, 作为骨肉瘤治疗的核心分子,由于其对候选预后生物标志物 和 的双向调节作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eed7/7835002/82228db8dfe7/aging-13-202258-g001.jpg

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