Abstract
Glioma is the common histological subtype of malignancy in central nervous system, with a high morbidity and mortality. Cancer stem cells (CSCs) play an important role in regulating the tumorigenesis and progression of glioma; however, the prognostic biomarkers and therapeutic targets associated with CSC characteristics have not been fully acknowledged in glioma. In order to identify the prognostic stemness-related genes (SRGs) of glioma in silico, the RNA sequencing data of patients with glioma were retrieved from The Cancer Genome Atlas (TCGA) databases. The mRNA expression-based stemness index (mRNAsi) was significantly associated with the glioma histologic grade, isocitrate dehydrogenase 1 (IDH1) mutation and overall survival of glioma patients by the nonparametric test and Kaplan–Meier survival analysis. A total of 340 SRGs were identified as the overlapped stemness-related differential expressed genes (DEGs) of different histologic grade screened by the univariate Cox analysis. Based on 11 prognostic SRGs, the predict nomogram was constructed with the AUC of 0.832. Moreover, the risk score of the nomogram was an independent prognostic factor, indicating its significant applicability. Besides other eight reported biomarkers of glioma, we found that F2RL2, CLCNKA and LOXL4 were first identified as prognostic biomarkers for glioma. In conclusion, this bioinformatics study demonstrates the mRNAsi as a reliable index for the IDH1 mutation, histologic grade and OS of glioma patients and provides a well-applied model for predicting the OS for patients with glioma based on prognostic SRGs. Additionally, this in silico study also identifies three novel prognostic biomarkers (F2RL2, CLCNKA and LOXL4) for glioma patients.
Highlights
Glioma is the most common histological subtype of primary tumor of central nervous system (CNS), accounting for approximately 80% of brain malignancies, with 15,000–17,000 new cases annually in the United States [1,2]
The 340 stemness-related gene (SRG) in different histologic grade gliomas were integrated into the univariate Cox analysis to identified key SRGs and we found that CCL7, CXCL6, SELE, C2CD4A, GPR141, TNFSF14, CCR2, GPR171, PTGER2 and VGLL3 were associated with histologic grade (Figure 5E)
We developed a prediction model based on the prognostic SRGs that was well-applied in predicting the prognosis of glioma patients
Summary
Glioma is the most common histological subtype of primary tumor of central nervous system (CNS), accounting for approximately 80% of brain malignancies, with 15,000–17,000 new cases annually in the United States [1,2]. Identification the difference between LGGs and GBM, along with neoplasm histologic grade, may assist oncologists in finding the prognostic biomarkers and potential targets for the treatment of glioma. CD44, Prominin-1, L1CAM and NPM1 [6] They can work with immune niche factors and cellular microenvironment to regulate the tumorigenesis and progression of glioma [7]. Identification of CSC-associated biomarkers may predict the tumor progression of glioma and provide CSC-based diagnostic and therapeutic strategies. It has been reported that mRNAsi is a reliable index in bladder cancer and is associated with tumorigenesis and tumor stage; its roles in glioma is still unclear. Based on the prognostic SRGs, the predict model was constructed This bioinformatics analysis provides potential prognostic biomarkers that may assist oncologists in clinical diagnosis and treatment of glioma
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