ArticleBiomolecules2022
Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics Methods.
Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Quantitative study of bioinformatics analysis on glioma: a bibliometric analysis.Frontiers in oncology · 2023Pooled it
- KRTCAP2 accelerates malignant progression through modulating tumor cell function and M2 macrophage infiltration in glioma.Frontiers in immunology · 2026Article
- Decoding the immune microenvironment: unveiling CD8 + T cell-related biomarkers and developing a prognostic signature for personalized glioma treatment.Cancer cell international · 2024Article
- NDC80/HEC1 promotes macrophage polarization and predicts glioma prognosis via single-cell RNA-seq and in vitro experiment.CNS neuroscience & therapeutics · 2024Article
- PSMB2 plays an oncogenic role in glioma and correlates to the immune microenvironment.Scientific reports · 2024Article
- Development of a prognostic model related to homologous recombination deficiency in glioma based on multiple machine learning.Frontiers in immunology · 2024Article
- Construction of an anoikis‒related prognostic signature to predict immunotherapeutic response and prognosis in hepatocellular carcinoma.Journal of cancer research and clinical oncology · 2023Article
- Article
- Pro- vs. Anti-Inflammatory Features of Monocyte Subsets in Glioma Patients.International journal of molecular sciences · 2023Article
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Authors and funding
15 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGlioma is the most common primary tumor of the central nervous system with a high lethality rate. This study aims to mine fibroblast-related genes with prognostic value and construct a corresponding prognostic model.
methodsA glioma-related TCGA (The Cancer Genome Atlas) cohort and a CGGA (Chinese Glioma Genome Atlas) cohort were incorporated into this study. Variance expression profiling was executed via the "limma" R package. The "clusterProfiler" R package was applied to perform a GO (Gene Ontology) analysis. The Kaplan-Meier (K-M) curve, LASSO regression analysis, and Cox analyses were implemented to determine the prognostic genes. A fibroblast-related risk model was created and affirmed by independent cohorts. We derived enriched pathways between the fibroblast-related high- and low-risk subgroups using gene set variation analysis (GSEA). The immune infiltration cell and the stromal cell were calculated using the microenvironment cell populations-counter (MCP-counter) method, and the immunotherapy response was assessed with the SubMap algorithm. The chemotherapy sensitivity was estimated using the "pRRophetic" R package.
resultsA total of 93 differentially expressed fibroblast-related genes (DEFRGs) were uncovered in glioma. Seven prognostic genes were filtered out to create a fibroblast-related gene signature in the TCGA-glioma cohort training set. We then affirmed the fibroblast-related risk model via TCGA-glioma cohort and CGGA-glioma cohort testing sets. The Cox regression analysis proved that the fibroblast-related risk score was an independent prognostic predictor in prediction of the overall survival of glioma patients. The fibroblast-related gene signature revealed by the GSEA was applicable to the immune-relevant pathways. The MCP-counter algorithm results pointed to significant distinctions in the tumor microenvironment between fibroblast-related high- and low-risk subgroups. The SubMap analysis proved that the fibroblast-related risk score could predict the clinical sensitivity of immunotherapy. The chemotherapy sensitivity analysis indicated that low-risk patients were more sensitive to multiple chemotherapeutic drugs.
conclusionOur study identified prognostic fibroblast-related genes and generated a novel risk signature that could evaluate the prognosis of glioma and offer a theoretical basis for clinical glioma therapy.
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