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Open AccessArticle

A Four-Pseudogene Classifier Identified by Machine Learning Serves as a Novel Prognostic Marker for Survival of Osteosarcoma

by Feng Liu 1,†, Lu Xing 1,†, Xiaoqian Zhang 2 and Xiaoqi Zhang 1,*
1
Shandong Provincial Key Laboratory of Oral Tissue Regeneration, School of Stomatology, Shandong University, Jinan 250014, Shandong, China
2
Department of Stomatology, Haiyuan College of Kunming Medical University, Kunming 650000, Yunnan, China
*
Author to whom correspondence should be addressed.
These authors contribute equally to this work.
Genes 2019, 10(6), 414; https://doi.org/10.3390/genes10060414
Received: 23 April 2019 / Revised: 22 May 2019 / Accepted: 24 May 2019 / Published: 29 May 2019
(This article belongs to the Special Issue Associations Between Non-Coding RNA and Diseases)
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. View Full-Text
Keywords: noncoding RNA; pseudogene; biomarker; prognosis; survival; machine learning; osteosarcoma noncoding RNA; pseudogene; biomarker; prognosis; survival; machine learning; osteosarcoma
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Liu, F.; Xing, L.; Zhang, X.; Zhang, X. A Four-Pseudogene Classifier Identified by Machine Learning Serves as a Novel Prognostic Marker for Survival of Osteosarcoma. Genes 2019, 10, 414.

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