Risk assessment model constructed by differentially expressed lncRNAs for the prognosis of glioma

Abstract

A risk assessment model was constructed using differentially expressed long non-coding (lnc)RNAs for the prognosis of glioma. Transcriptome sequencing of the lncRNAs and mRNAs from glioma samples were obtained from the TCGA database. The samples were divided into bad and good prognosis groups based on survival time, then differently expressed lncRNAs between these two groups were screened using DEseq and edgeR packages. Multivariate Cox regression analysis was performed to establish a risk assessment system according to the weighted regression coefficient of lncRNA expression. Survival analysis and receiver operating characteristic curve were conducted for the risk assessment model. Furthermore, the co-expression network of the screened lncRNAs was constructed, followed by the functional enrichment analysis for associated genes. A total of 117 lncRNAs were screened using edgeR and DEseq packages. Among all differently expressed lncRNAs, five lncRNAs (RP3-503A6, LINC00940, RP11-453M23, AC009411 and CDRT7) were identified to establish the risk assessment model. The risk assessment model demonstrated a good prognostic function with high area under the curve values in the training, validation and entire sets. The risk score was certified as an independent prognostic factor for gliomas. Multiple genes were screened to be co-expressed with these five lncRNAs. Functional enrichment analysis demonstrated that they were involved in cytoskeleton, adhesion and Janus kinase/signal transducer and activator of transcription signaling pathway-associated processes. The present study established a risk assessment model integrating five significantly different expressed lncRNAs, which may help to assess the prognosis of patients with glioma with increased accuracy.

Promoting Prognostic Model Application: A Review Based on Gliomas

“…All three high-quality models exhibit high accuracy [ 71 – 73 ]. Except for the slightly decreased AUC values (0.722) when submitted to external validation based on Chen's study [ 73 ], the other two models show similar high accuracy in training, internal validation, and the entire set with AUC from 0.84 to 0.91 [ 71 , 72 ].…”

Long noncoding RNA HNF1A‐AS1 regulates proliferation and apoptosis of glioma through activation of the JNK signaling pathway via miR‐363‐3p/MAP2K4

“…S. Wu et al, 2017). Some lncRNAs have been known to be abnormally expressed in glioma tissues and take part in the progress of glioma (Q. N. Chen et al, 2017;C. Hu, Zhou, Liu, & Kang, 2018;D.…”

“…In recent years, lncRNAs have attracted increasing attention owing to their regulatory roles in the progress of cancers (Song, Zhang, Huang, & Hai, 2018; X. S. Wu et al, 2017). Some lncRNAs have been known to be abnormally expressed in glioma tissues and take part in the progress of glioma (Q. N. Chen et al, 2017; C. Hu, Zhou, Liu, & Kang, 2018; D. M. Wu et al, 2018). In this study, we concentrated on the function of HNF1A‐AS1, and found that HNF1A‐AS1 was upregulated in glioma tissues and cell lines.…”

Vorasidenib in IDH1/2-mutant low-grade glioma: the grey zone of patient’s selection

“…Perhaps the time has come to abandon the old binary risk stratification (“low-risk” versus “high-risk”), which still contains arbitrary elements (like the age cut-off), proving impractical in real-world clinical decision-making, and to adopt a new one, also taking into account many emerging prognostic biomarkers (BRAF V600E mutation, methylome, transcriptome sequencing) ( 17 ).…”