Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis

Abstract

Background To accurately predict the prognosis of glioma patients. Methods A 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. Results By 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. Conclusion We established a five-lncRNA signature as a potentially reliable tool for survival prediction of glioma patients.

A Novel Immune-Related LncRNA Pair Model to Predict the Prognosis of Triple-Negative Breast Cancer

“…Ten pairs with prognostic values were identi ed by univariate Cox regression analysis. Some lncRNAs of the 10 pairs have been reported to be critically involved in tumorigenesis, such as USP30-AS1 [34][35][36] , HLA-DQB1-AS1 37,38 , and MIR155HG [39][40][41] . Based on the LASSO regression analysis, we constructed a risk assessment model composed of 3 DEirlncRNA pairs (USP30-AS1|SERPINB9P1, AL731567.1|AC004585.1, AC110995.1|AC007991.2).…”

Integrative analysis of expression profile indicates the ECM receptor and LTP dysfunction in the glioma-related epilepsy

“…In vivo, knockdown of OIP5-AS1 inhibited tumor growth, caused tumor regression, and extended survival [ 31 , 32 ]. According to recent studies, LINC00641 inhibits glioma cell growth [ 33 ]. Overexpression of LINC00641 and SNHG14 inhibited cell growth but increased apoptosis in cells [ 34 , 35 ].…”

Role of Long Non-Coding RNA LINC00641 in Cancer

“…In conclusion, LINC00641 competitively binds miR-4262 through the ceRNA mechanism to release NRGN, which plays an anti-tumor role in the progression of glioma, providing a new therapeutic target for glioma patients. Zhang et al ( 70 ) obtained clinical information from the Chinese glioma Genome Atlas (CGGA) and Cancer Genome Atlas (TCGA) databases, identified five lncRNAs through weighted gene coexpression network analysis (WGCNA) to construct Cox regression model, which showed high accuracy in predicting the survival rate of glioma patients.…”