A Four-Pseudogene Classifier Identified by Machine Learning Serves as a Novel Prognostic Marker for Survival of Osteosarcoma
A Four-Pseudogene Classifier Identified by Machine Learning Serves as a Novel Prognostic Marker for Survival of Osteosarcoma
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DOI：
10.3390/genes10060414
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发表时间：
2019-06-01
期刊：
影响因子：
3.5
通讯作者：
Zhang, Xiaoqi
中科院分区：
文献类型：
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作者：
Liu, Feng;Xing, Lu;Zhang, Xiaoqi
骨肉瘤是一种常见的恶性肿瘤，由于缺乏预测标志物，死亡率高，预后差。越来越多的证据表明，假基因（一种非编码基因）在肿瘤发生中发挥着重要作用。本研究的目的是通过机器学习识别骨肉瘤的预后假基因特征。94例骨肉瘤患者的RNA-Seq数据与临床随访信息的样本参与了研究。筛选出与生存相关的假基因，并通过cox回归分析（单变量、lasso和多变量）构建相关特征模型。在不同的亚组中进一步验证了签名的预测价值。通过共表达分析确定推定的生物学功能。总共鉴定了125个与生存相关的假基因，（RPL11-551L14.1，HR：0.65（95% CI：0.44-0.95）; RPL7AP28，HR：0.32（95% CI：0.14-0.76）; RP 4 - 706 A16.3，HR：1.89（95% CI：1.35-2.65）; RP 11 - 326 A19.5，HR：0.52（95%CI：0.37-0.74））特征能有效区分高危和低危患者，并具有较高的敏感性和特异性，可预测预后（AUC：0.878）。此外，签名适用于不同性别、年龄和转移状态的患者。共表达分析显示，这四个假基因参与调节恶性表型、免疫和DNA/RNA编辑。这四个假基因标志不仅是一个有前途的预后和生存的预测，但也是一个潜在的标志物，用于监测治疗方案。因此，我们的研究结果可能具有潜在的临床意义。
Osteosarcoma is a common malignancy with high mortality and poor prognosis due to lack of predictive markers. Increasing evidence has demonstrated that pseudogenes, a type of non-coding gene, play an important role in tumorigenesis. The aim of this study was to identify a prognostic pseudogene signature of osteosarcoma by machine learning. A sample of 94 osteosarcoma patients' RNA-Seq data with clinical follow-up information was involved in the study. The survival-related pseudogenes were screened and related signature model was constructed by cox-regression analysis (univariate, lasso, and multivariate). The predictive value of the signature was further validated in different subgroups. The putative biological functions were determined by co-expression analysis. In total, 125 survival-related pseudogenes were identified and a four-pseudogene (RPL11-551L14.1, HR: 0.65 (95% CI: 0.44-0.95); RPL7AP28, HR: 0.32 (95% CI: 0.14-0.76); RP4-706A16.3, HR: 1.89 (95% CI: 1.35-2.65); RP11-326A19.5, HR: 0.52(95% CI: 0.37-0.74)) signature effectively distinguished the high- and low-risk patients, and predicted prognosis with high sensitivity and specificity (AUC: 0.878). Furthermore, the signature was applicable to patients of different genders, ages, and metastatic status. Co-expression analysis revealed the four pseudogenes are involved in regulating malignant phenotype, immune, and DNA/RNA editing. This four-pseudogene signature is not only a promising predictor of prognosis and survival, but also a potential marker for monitoring therapeutic schedule. Therefore, our findings may have potential clinical significance.