Abstract
BackgroundTo accurately predict the prognosis of glioma patients.MethodsA total of 541 samples from the TCGA cohort, 181 observations from the CGGA database and 91 samples from our cohort were included in our study. Long non-coding RNAs (LncRNAs) associated with glioma WHO grade were evaluated by weighted gene co-expression network analysis (WGCNA). Five lncRNA features were selected out to construct prognostic signatures based on the Cox regression model.ResultsBy weighted gene co-expression network analysis (WGCNA), 14 lncRNAs related to glioma grade were identified. Using univariate and multivariate Cox analysis, five lncRNAs (CYTOR, MIR155HG, LINC00641, AC120036.4 and PWAR6) were selected to develop the prognostic signature. The Kaplan-Meier curve depicted that the patients in high risk group had poor prognosis in all cohorts. The areas under the receiver operating characteristic curve of the signature in predicting the survival of glioma patients at 1, 3, and 5 years were 0.84, 0.92, 0.90 in the CGGA cohort; 0.8, 0.85 and 0.77 in the TCGA set and 0.72, 0.90 and 0.86 in our own cohort. Multivariate Cox analysis demonstrated that the five-lncRNA signature was an independent prognostic indicator in the three sets (CGGA set: HR = 2.002, p < 0.001; TCGA set: HR = 1.243, p = 0.007; Our cohort: HR = 4.457, p = 0.008, respectively). A nomogram including the lncRNAs signature and clinical covariates was constructed and demonstrated high predictive accuracy in predicting 1-, 3- and 5-year survival probability of glioma patients.ConclusionWe established a five-lncRNA signature as a potentially reliable tool for survival prediction of glioma patients.
Highlights
To accurately predict the prognosis of glioma patients
Long non-coding RNAs (LncRNAs) with similar expression patterns were divided into the same module by cluster dendrogram trees and eight modules were obtained here
By setting the threshold values as Pearson correlation coefficient > |0.5| and p < 0.01 to select the significant modules, and by inquiring into mean gene significance across all genes in one module, the brown, green and yellow module were considered to be closely related to the WHO grade of glioma (Fig. 1b)
Summary
To accurately predict the prognosis of glioma patients. Glioma is the most prevalent neoplasm in central nervous system (CNS), with the highest malignancy and the worst prognosis compared with other tumors in brain. LncRNA typically has more than 200 nucleotides in length, disabling to encode proteins [6]. This type of RNA has been confirmed to be involved in several biological functions including transcription [7], RNA splicing [6], N6-methyladenosine (m6A) [8] and others. There is mounting evidence that the dysregulated lncRNAs act a pivotal part in many biological processes of malignancy, demonstrating great potentiality as novel diagnostic or prognostic molecular biomarkers [10, 11]. LncRNA-MALAT1 has been considered as a tumor suppressor and down expression of MALAT1 to cause remarkable promotion of invasion and proliferation of the glioma cells [12]. The molecular functions and mechanisms of the great majority of lncRNAs remain ill-defined and the research concerning lncRNAs with diagnosis or prognosis potentiality in glioma is still in the initial stage
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