Abstract
BackgroundThe prognosis of the glioblastoma (GBM) is dismal. This study aims to select an optimal RNA signature for prognostic prediction of GBM patients.MethodsFor the training set, the long non-coding RNA (lncRNA) and mRNA expression profiles of 151 patients were downloaded from the TCGA. Differentially expressed mRNAs (DEGs) and lncRNAs (DE-lncRNAs) were identified between good prognosis and bad prognosis patients. Optimal prognostic mRNAs and lncRNAs were selected respectively, by using univariate Cox proportional-hazards (PH) regression model and LASSO Cox-PH model. Subsequently, four prognostic scoring models were built based on expression levels or expression status of the selected prognostic lncRNAs or mRNAs, separately. Each prognostic model was applied to the training set and an independent validation set. Function analysis was used to uncover the biological roles of these prognostic DEGs between different risk groups classified by the mRNA-based signature.ResultsWe obtained 261 DEGs and 33 DE-lncRNAs between good prognosis and bad prognosis patients. A panel of eight mRNAs and a combination of ten lncRNAs were determined as predictive RNAs by LASSO Cox-PH model. Among the four prognostic scoring models using the eight-mRNA signature or the ten-lncRNA signature, the one based on the expression levels of the eight mRNAs showed the greatest predictive power. The DEGs between different risk groups using the eight prognostic mRNAs were functionally involved in calcium signaling pathway, neuroactive ligand-receptor interaction pathway, and Wnt signaling pathway.ConclusionThe eight-mRNA signature has greater prognostic value than the ten-lncRNA-based signature for GBM patients based on bioinformatics analysis.
Highlights
The prognosis of the glioblastoma (GBM) is dismal
Expressed mRNAs and Long non-coding RNAs (lncRNAs) Briefly, according to RefSeq ID information provided by the training set and the validation set, we annotated mRNAs and lncRNAs of the two sets based on HUGO Gene Nomenclature Committee (HGNC) [15], the database which records information of 4055 lncRNAs and 19,198 protein-coding genes
Identification of DEMs and DELs Following data annotation, we obtained 17,299 mRNAs and 770 lncRNAs overlapped by the The Cancer Genome Atlas (TCGA) set and the validation set
Summary
The prognosis of the glioblastoma (GBM) is dismal. This study aims to select an optimal RNA signature for prognostic prediction of GBM patients. Glioblastoma (GBM) is the most aggressive primary brain neoplasm [1]. The prognosis of GBM patients is dismal that they have a survival of only 12–15 months. Considerable efforts have been made to identify prognostic gene signatures for GBM. A threegene signature of prognostic value for patients with MGMT promoter-methylated GBM is reported by performing bioinformatics analysis [4]. Silencing of the signal transducer and activator of transcription-3 (STAT3), an important mediator for the subtype of highly aggressive mesenchymal GBM, by a novel aptamer-siRNA chimera (Gint.T) inhibits tumor growth and angiogenesis in a mouse model. The phosphorylation of STAT3 on Serine727 is identified as a potential prognostic marker for GBM patients [7]
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