ArticleBioscience reports2020
In silico identification of the prognostic biomarkers and therapeutic targets associated with cancer stem cell characteristics of glioma.
Article in Bioscience reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 22 citations in OpenAlex.
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- A novel DNA methylation-driver gene signature for long-term survival prediction of hepatitis-positive hepatocellular carcinoma patients.Cancer medicine · 2022Article
- Astragalus mongholicus Bunge and Curcuma aromatica Salisb. inhibits liver metastasis of colon cancer by regulating EMT via the CXCL8/CXCR2 axis and PI3K/AKT/mTOR signaling pathway.Chinese medicine · 2022Article
- Identification of hub genes related to CD4Frontiers in genetics · 2022Article
- Lamivudine improves cognitive decline in SAMP8 mice: Integrating in vivo pharmacological evaluation and network pharmacology.Journal of cellular and molecular medicine · 2021Article
- Molecular Subtypes Based on the Stemness Index Predict Prognosis in Glioma Patients.Frontiers in genetics · 2021Article
- Prognostic Model of Colorectal Cancer Constructed by Eight Immune-Related Genes.Frontiers in molecular biosciences · 2020Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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