ArticleFrontiers in bioengineering and biotechnology2020
Identification and Analysis of Glioblastoma Biomarkers Based on Single Cell Sequencing.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- A Systematic Evaluation of Supervised Machine Learning Algorithms for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data.Frontiers in genetics · 2022Pooled it
- Leveraging single-cell and spatial omics for brain tumour insights to improve therapeutic strategies.Molecular brain · 2026Review
- Endothelial cell ferroptosis influences IDH wild-type glioblastoma growth in recurrent glioblastoma multiforme patients.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2024Article
- Article
- Single-cell sequencing: a promising approach for uncovering the mechanisms of tumor metastasis.Journal of hematology & oncology · 2022Review
- Typing characteristics of metabolism-related genes in osteoporosis.Frontiers in pharmacology · 2022Article
- Identification of biomarkers for hepatocellular carcinoma based on single cell sequencing and machine learning algorithms.Frontiers in genetics · 2022Article
- Advances in Immunotherapies for Gliomas.Current neurology and neuroscience reports · 2022Review
- Codependency and mutual exclusivity for gene community detection from sparse single-cell transcriptome data.Nucleic acids research · 2021Article
- A Heterotypic Tridimensional Model to Study the Interaction of Macrophages and Glioblastoma In Vitro.International journal of molecular sciences · 2021Article
- Article
- Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology.International journal of molecular sciences · 2021Review
- Current Achievements and Applications of Transcriptomics in Personalized Cancer Medicine.International journal of molecular sciences · 2021Review
- Visual Genomics Analysis Studio as a Tool to Analyze Multiomic Data.Frontiers in genetics · 2021Article
- PDIA5 is Correlated With Immune Infiltration and Predicts Poor Prognosis in Gliomas.Frontiers in immunology · 2021Article
- The Heterogeneity of Infiltrating Macrophages in Metastatic Osteosarcoma and Its Correlation with Immunotherapy.Journal of oncology · 2021Article
- m5C-Related Signatures for Predicting Prognosis in Cutaneous Melanoma with Machine Learning.Journal of oncology · 2021Article
- Identification of Novel COVID-19 Biomarkers by Multiple Feature Selection Strategies.Computational and mathematical methods in medicine · 2021Article
- TNFSF13 Is a Novel Onco-Inflammatory Marker and Correlates With Immune Infiltration in Gliomas.Frontiers in immunology · 2021Article
- CTLA4 has a profound impact on the landscape of tumor-infiltrating lymphocytes with a high prognosis value in clear cell renal cell carcinoma (ccRCC).Cancer cell international · 2020Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioblastoma (GBM) is one of the most common and aggressive primary adult brain tumors. Tumor heterogeneity poses a great challenge to the treatment of GBM, which is determined by both heterogeneous GBM cells and a complex tumor microenvironment. Single-cell RNA sequencing (scRNA-seq) enables the transcriptomes of great deal of individual cells to be assayed in an unbiased manner and has been applied in head and neck cancer, breast cancer, blood disease, and so on. In this study, based on the scRNA-seq results of infiltrating neoplastic cells in GBM, computational methods were applied to screen core biomarkers that can distinguish the discrepancy between GBM tumor and pericarcinomatous environment. The gene expression profiles of GBM from 2343 tumor cells and 1246 periphery cells were analyzed by maximum relevance minimum redundancy (mRMR). Upon further analysis of the feature lists yielded by the mRMR method, 31 important genes were extracted that may be essential biomarkers for GBM tumor cells. Besides, an optimal classification model using a support vector machine (SVM) algorithm as the classifier was also built. Our results provided insights of GBM mechanisms and may be useful for GBM diagnosis and therapy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.