Identification of biomarkers and ceRNA network in glioblastoma through bioinformatic analysis and evaluation of potential prognostic values

Fan Hong; Zhenyu Gong; Xu Zhang; Peipei Ma; Yongxiang Yin; Hongxiang Wang

doi:10.21037/atm-21-4925

Identification of biomarkers and ceRNA network in glioblastoma through bioinformatic analysis and evaluation of potential prognostic values

Ann Transl Med. 2021 Oct;9(20):1561. doi: 10.21037/atm-21-4925.

Authors

Fan Hong^#¹, Zhenyu Gong^#^{2

3}, Xu Zhang², Peipei Ma², Yongxiang Yin⁴, Hongxiang Wang⁵

Affiliations

¹ Department of Neurosurgery, Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China.
² Department of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
³ Department of Neurosurgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
⁴ Department of Pathology, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi, China.
⁵ Department of Neurosurgery, Changhai Hospital, Naval Medical University, Shanghai, China.

^# Contributed equally.

Abstract

Background: Glioblastoma (GBM) is one of the most common and malignant primary brain tumors in adults, with high mortality rates and limited treatment. Based on bioinformatic analyses, this study aimed to identify biomarkers and relevant molecular pathways that may serve as potential targets for the treatment of GBM.

Methods: Expression profiles were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database; nine GBM samples and three normal samples were extracted from the GSE104267 dataset. Differentially-expressed messenger RNA (mRNA) and long non-coding RNA (lncRNA) were screened from the preprocessed dataset. The clusterProfiler package in R was used to perform a biological process (BP) analysis of gene ontology (GO), and a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed separately in upregulated and downregulated groups. A competing endogenous RNA (ceRNA) network was constructed using Cytoscape. Based on data downloaded from The Cancer Genome Atlas (TCGA), Kaplan-Meier (K-M) survival curves were established. Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to evaluate IL10RB antisense RNA 1 (IL10RB-AS1) expression in GBM tissue compared with that in normal brain tissue.

Results: A total of 253 differentially-expressed genes (DEGs) were obtained. Based on BP and KEGG enrichment annotation analyses, 11 lncRNA-related pathways were identified through function prediction analysis. A competing endogenous RNA (ceRNA) subnetwork, including 21 nodes and 29 regulatory pairs, was then constructed. Based on the clinical data of GBM in TCGA, one survival-related DEG, IL10RB-AS1, was identified using the log-rank statistical test. K-M survival curves of IL10RB-AS1 and expression levels of IL10RB-AS1 in both GBM and normal brain tissue were obtained.

Conclusions: Through the combination of bioinformatic analyses, one survival-related differentially-expressed lncRNA, IL10RB-AS1, was identified. This, along with several related signaling pathways and ceRNA systems that were elucidated in GBM have potential prognostic value and might offer new possibilities for the treatment of GBM.

Keywords: Glioblastoma (GBM); bioinformatics analysis; differentially-expressed genes; lncRNA; prognosis.