ArticleJournal of oncology2022
Identification of Prognostic Biomarkers of Glioblastoma Based on Multidatabase Integration and Its Correlation with Immune-Infiltration Cells.
Article in Journal of oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 5 citations in OpenAlex.
- A manganese metabolism-related gene signature for prognosis prediction and immune microenvironment description of glioblastoma.Discover oncology · 2026Article
- Biomarkers for predicting immunotherapy response and resistance in glioblastoma.Frontiers in immunology · 2026Review
- RNA networks of lysosomal-related biomarkers in Parkinson's disease and their correlations with freezing of gait-associated genes.Frontiers in genetics · 2026Article
- Advancing glioblastoma therapy with surface-modified nanoparticles.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Review
- Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms.Journal of molecular neuroscience : MN · 2024Article
- Identification and validation of an ECM organization-related gene signature as a prognostic biomarker and therapeutic target for glioma patients.Genes & genomics · 2023Article
- Dihydrotanshinone I Inhibits the Lung Metastasis of Breast Cancer by Suppressing Neutrophil Extracellular Traps Formation.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Glioblastoma (GBM) is the most malignant of all known intracranial tumors; meanwhile, most patients have a poor prognosis. In order to improve the poor prognosis of GBM patients as much as possible, it is specifically significant to identify biomarkers related to the gene diagnosis and gene therapy. Methods: In this study, a total of 343 GBM specimens and 259 nontumor specimens were collected from four Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) database; then, we analyzed the differentially expressed genes (DEGs) from the above data. Through Venn diagram analysis, 54 common upregulated DEGs and 22 common downregulated DEGs were triumphantly recognized. Results: On the basis of the degree of formation communication in protein-protein interaction network (PPIN), the 10 upregulated central genes were ranked, incorporating LOX, IGFBP3, CD44, TIMP1, FN1, VEGFA, POSTN, COL1A1, COL1A2, and COL3A1. By combining the expression levels and the clinical features of GBM, we found that four hub genes (TIMP1, FN1, POSTN, and LOX) were significantly upregulated and related to poor prognosis of GBM. Meanwhile, univariate and multivariate Cox regression analysis suggested that TIMP1 could be one of the independent prognostic factors for GBM patients. Furthermore, TIMP1 was particularly correlated with the immune marker of macrophage M1, macrophage M2, neutrophils, tumor-associated macrophage, and Tregs. We then analyzed the role of TIMP1 in GBM cancer cell lines by relevant experiments, which indicated that TIMP1 knockdown resulted in the decreased cell proliferation, migration, and invasion. Conclusions: Taken together, these findings demonstrated that TIMP1 might be a new biomarker to determine prognosis and immune infiltration of GBM patients.
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Registered trials
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