共表达网络分析确定了一种基因特征作为骨肉瘤能量代谢的预测生物标志物。
Co-expression network analysis identifies a gene signature as a predictive biomarker for energy metabolism in osteosarcoma.
作者信息
Zhu Naiqiang, Hou Jingyi, Ma Guiyun, Guo Shuai, Zhao Chengliang, Chen Bin
机构信息
Department of Minimally Invasive Spinal Surgery, The Affiliated Hospital of Chengde Medical College, Chengde, 067000 China.
Chengde Medical College, Chengde, 067000 China.
出版信息
Cancer Cell Int. 2020 Jun 22;20:259. doi: 10.1186/s12935-020-01352-2. eCollection 2020.
BACKGROUND
Osteosarcoma (OS) is a common malignant bone tumor originating in the interstitial tissues and occurring mostly in adolescents and young adults. Energy metabolism is a prerequisite for cancer cell growth, proliferation, invasion, and metastasis. However, the gene signatures associated with energy metabolism and their underlying molecular mechanisms that drive them are unknown.
METHODS
Energy metabolism-related genes were obtained from the TARGET database. We applied the "NFM" algorithm to classify putative signature gene into subtypes based on energy metabolism. Key genes related to progression were identified by weighted co-expression network analysis (WGCNA). Based on least absolute shrinkage and selection operator (LASSO) Cox proportional regression hazards model analyses, a gene signature for the predication of OS progression and prognosis was established. Robustness and estimation evaluations and comparison against other models were used to evaluate the prognostic performance of our model.
RESULTS
Two subtypes associated with energy metabolism was determined using the "NFM" algorithm, and significant modules related to energy metabolism were identified by WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the genes in the significant modules were enriched in kinase, immune metabolism processes, and metabolism-related pathways. We constructed a seven-gene signature consisting of SLC18B1, RBMXL1, DOK3, HS3ST2, ATP6V0D1, CCAR1, and C1QTNF1 to be used for OS progression and prognosis. Upregulation of CCAR1, and C1QTNF1 was associated with augmented OS risk, whereas, increases in the expression SCL18B1, RBMXL1, DOK3, HS3ST2, and ATP6VOD1 was correlated with a diminished risk of OS. We confirmed that the seven-gene signature was robust, and was superior to the earlier models evaluated; therefore, it may be used for timely OS diagnosis, treatment, and prognosis.
CONCLUSIONS
The seven-gene signature related to OS energy metabolism developed here could be used in the early diagnosis, treatment, and prognosis of OS.
背景
骨肉瘤（OS）是一种常见的恶性骨肿瘤，起源于间质组织，主要发生于青少年和年轻成年人。能量代谢是癌细胞生长、增殖、侵袭和转移的前提条件。然而，与能量代谢相关的基因特征及其潜在的驱动分子机制尚不清楚。
方法
从TARGET数据库中获取能量代谢相关基因。我们应用“NFM”算法根据能量代谢将推定的特征基因分类为亚型。通过加权共表达网络分析（WGCNA）确定与进展相关的关键基因。基于最小绝对收缩和选择算子（LASSO）Cox比例回归风险模型分析，建立了用于预测骨肉瘤进展和预后的基因特征。使用稳健性和估计评估以及与其他模型的比较来评估我们模型的预后性能。
结果
使用“NFM”算法确定了两种与能量代谢相关的亚型，并通过WGCNA鉴定了与能量代谢相关的重要模块。基因本体论（GO）和京都基因与基因组百科全书（KEGG）表明，重要模块中的基因在激酶、免疫代谢过程和代谢相关途径中富集。我们构建了一个由SLC18B1、RBMXL1、DOK3、HS3ST2、ATP6V0D1、CCAR1和C1QTNF1组成的七基因特征，用于骨肉瘤的进展和预后评估。CCAR1和C1QTNF1的上调与骨肉瘤风险增加相关，而SCL18B1、RBMXL1、DOK3、HS3ST2和ATP6VOD1表达的增加与骨肉瘤风险降低相关。我们证实七基因特征是稳健的，并且优于所评估的早期模型；因此，它可用于骨肉瘤及时的诊断、治疗和预后评估。
结论
这里开发的与骨肉瘤能量代谢相关的七基因特征可用于骨肉瘤的早期诊断、治疗和预后评估。
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