OncoTargets and Therapy (Sep 2021)
Elevated RGMA Expression Predicts Poor Prognosis in Patients with Glioblastoma
Abstract
Thi Le Phan,1,* Hyun-Jin Kim,1,* Suk Jun Lee,2 Moon-Chang Choi,3 Sung-Hak Kim1 1Department of Animal Science, College of Agriculture and Life Sciences, Chonnam National University, Gwangju, 61186, Republic of Korea; 2Department of Biomedical Laboratory Science, College of Health & Medical Sciences, Cheongju University, Chungbuk, 28503, Republic of Korea; 3Department of Biomedical Science, Chosun University, Gwangju, 61452, Republic of Korea*These authors contributed equally to this workCorrespondence: Moon-Chang ChoiDepartment of Biomedical Science, Chosun University, 309 Pilmundaero, Gwangju, 61452, Republic of KoreaTel +82 62 230 6758Email [email protected] KimDepartment of Animal Science, College of Agriculture and Life Sciences, Chonnam National University, 77 Yongbongro, Gwangju, 61186, Republic of KoreaTel +82 62 530 2115Email [email protected]: Glioblastoma (GBM) is the most aggressive type of human brain tumor with a poor prognosis and a low survival rate. Secreted proteins from tumors are recently considered as important modulators to promote tumorigenesis by communicating with microenvironments. Repulsive guidance molecule A (RGMA) was initially characterized as an axon guidance molecule after secretion in the brain during embryogenesis but has not been studied in GBM. In this study, we investigated secreted gene expression patterns and the correlation between RGMA expression and prognosis in GBM using in silico analysis.Methods: RGMA mRNA levels in normal human astrocyte (NHA), human glioma cells, and GBM patient-derived glioma stem cells (GSCs) were assessed by qRT‐PCR. Patient survival analysis was performed with the Kaplan–Meier curve and univariate and multivariate analyses using publicly available datasets. The predictive roles of RGMA in progressive malignancy were evaluated using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA).Results: RGMA mRNA expression was elevated in glioma cells and GSCs compared with NHA and correlated with unfavorable prognosis in glioma patients. Thus, RGMA could serve as an independent predictive factor for GBM. Furthermore, the increased levels of RGMA expression and its putative receptor, neogenin (NEO1), were associated with poor patient survival rates in GBM.Conclusion: We identified RGMA as an independent prognostic biomarker for progressive malignancy in glioblastoma and address the possibilities to develop novel therapeutic strategies against glioblastoma.Keywords: bioinformatics, glioblastoma, glioma, glioma stem cell, RGMA
