Prediction of relapse and prognosis by expression levels of long noncoding RNA PEG10 in glioma patients

MEDICINE(2019)

Cited 11|Views7
No score
Abstract
Background: Long noncoding RNA paternally expressed 10 (lncRNA PEG10) is highly expressed in a variety of human cancers and related to the clinical prognosis of patients. However, to date there has been no previous study evaluating the prognostic significance of lncRNA PEG10 in gliomas. In the present study, we investigated the expression levels of lncRNA PEG10 to determine the prognostic value of this oncogene in human gliomas. Methods: Expression levels of lncRNA PEG10 were detected by real-time polymerase chain reaction in a hospital-based study cohort of 147 glioma patients and 23 cases of patients with craniocerebral trauma tissues. Associations of lncRNA PEG10 expression with clinicopathological variables and clinical outcome of glioma patients were investigated. Results: The results indicated that expression levels of lncRNA PEG10 were significantly increased in human gliomas compared to normal control brain tissues. In addition, lncRNA PEG10 expression was progressively increased from pathologic grade I to IV (P=.009) and correlated with the Karnofsky performance status (P=.018) in glioma patients. Furthermore, we also found that glioma patients with increased expression of lncRNA PEG10 had a higher risk to relapse and a statistically significant shorter overall survival (OS) than patients with reduced expression of lncRNA PEG10. In multivariate analysis, expression level of lncRNA PEG10 was found to be an independent prognostic factor for both progression-free survival and OS in glioma patients. Conclusions: LncRNA PEG10 served as an oncogene and played crucial roles in the progression of glioma. Molecular therapy targeted on lncRNA PEG10 might bring significant benefits to the clinical outcome of malignant glioma.
More
Translated text
Key words
glioma,long noncoding RNA,paternally expressed 10,prognosis,quantitative real-time PCR
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined