| Literature DB >> 28107175 |
Lei Wang1, Zhengtao Yu1, Shaiqi Sun1, Jun Peng1, Rongjun Xiao1, Shengpan Chen1, Xiaokun Zuo1, Quan Cheng1, Ying Xia1.
Abstract
The current grade classification system of gliomas is based on the histopathological features of these tumors and has great significance in defining groups of patients for clinical assessment. However, this classification system is also associated with a number of limitations, and as such, additional clinical assessment criteria are required. Long non-coding RNAs (lncRNAs) play a critical role in cellular functions and are currently regarded as potential biomarkers for glioma diagnosis and prognosis. Therefore, the molecular classification of glioma based on lncRNA expression may provide additional information to assist in the systematic identification of glioma. In the present paper, we review the emerging evidence indicating that specific lncRNAs may have the potential for use as key novel biomarkers and thus provide a powerful tool for the systematic diagnosis of glioma.Entities:
Keywords: biomarker; diagnosis; glioma; long non-coding RNA; molecular classification; prognosis
Mesh:
Substances:
Year: 2017 PMID: 28107175 DOI: 10.1515/revneuro-2016-0066
Source DB: PubMed Journal: Rev Neurosci ISSN: 0334-1763 Impact factor: 4.353