ArticleTranslational cancer research2023
A new prognostic model for glioblastoma multiforme based on coagulation-related genes.
Article in Translational cancer research, 2023. 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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Who cites it
7 citing papers in PubMed.
- Integrated Bulk and Single-Cell Transcriptomics Reveals the C3-C3AR1 Axis as a Candidate Mediator of Coagulome-Immune Crosstalk in Osteosarcoma.Biomedicines · 2026Article
- RBM43 promotes glioma aggressiveness and shapes the immune microenvironment through macrophage polarization.Discover oncology · 2026Article
- Identification of immune-related gene signature for predicting prognosis of glioblastoma patients.Translational cancer research · 2026Article
- The Role of Coagulation-Related Genes in Glioblastoma: A Comprehensive Analysis of the Tumor Microenvironment, Prognosis, and Treatment.Biochemical genetics · 2026Article
- PathX-CNN: An Enhanced Explainable Convolutional Neural Network for Survival Prediction and Pathway Analysis in Glioblastoma.bioRxiv : the preprint server for biology · 2025Article
- Construction and validation of a novel prognostic model with palmitoylation-related genes for glioblastoma.Translational cancer research · 2024Article
- Comprehensive machine learning-based integration develops a novel prognostic model for glioblastoma.Molecular therapy. Oncology · 2024Article
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6 authors.
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Abstract
Background: Glioblastoma multiforme (GBM) is the most aggressive, common, and lethal type of primary brain tumor. Multiple cancers have been associated with abnormalities in the coagulation system that facilitate tumor invasion and metastasis. In GBM, the prognostic value and underlying mechanism of coagulation-related genes (CRGs) have not been explored. Methods: RNA sequencing (RNA-seq) and clinical information on GBM were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), respectively. Following the identification of differentially expressed CRGs (DECRGs) between GBM and control samples, the survival-related DECRGs were selected via univariate and multivariate Cox regression analyses to establish a prognostic signature. The prognostic performance and clinical utility of the prognostic signature were assessed by the Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curve analysis, and a nomogram was constructed. The signature genes-related underlying mechanisms were analyzed according to gene set enrichment analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-cell analysis. Finally, the difference in immune cell infiltration, stromal score, immune score, and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) score were compared between different risk groups. Results: A 5-gene prognostic signature ( Conclusions: An analysis of coagulation-related prognostic signatures was conducted in this study, as well as how signature genes may affect GBM progress, providing information that might provide new ideas for the development of GBM-related molecular targeted therapies.
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