ArticleJournal of oncology2022
A Novel Risk Score Model Based on Eleven Extracellular Matrix-Related Genes for Predicting Overall Survival of Glioma Patients.
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 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
5 citing papers in PubMed, 5 citations in OpenAlex.
- Mining and experimental validation of machine learning-based immune-related diagnostic biomarkers for hepatocellular carcinoma.Journal of gastrointestinal oncology · 2026Article
- Identification and validation of an ECM organization-related gene signature as a prognostic biomarker and therapeutic target for glioma patients.Genes & genomics · 2023Article
- Identification and characterization of putative biomarkers and therapeutic axis in Glioblastoma multiforme microenvironment.Frontiers in cell and developmental biology · 2023Article
- Construction and validation of an angiogenesis-related gene expression signature associated with clinical outcome and tumor immune microenvironment in glioma.Frontiers in genetics · 2022Article
- The role of costimulatory molecules in glioma biology and immune microenvironment.Frontiers in genetics · 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
Gliomas are the most common lethal primary brain tumors with variable survival outcomes for patients. The extracellular matrix (ECM) is linked with clinical prognosis of glioma patients, but it is not commonly used as a clinical indicator. Herein, we investigated changes in ECM-related genes (ECMRGs) via analyzing the transcriptional data of 938 gliomas from TCGA and CGGA datasets. Based on least absolute shrinkage and selection operator (LASSO) Cox regression analysis, a 11-ECMRG signature that is strongly linked with overall survival (OS) in glioma patients was identified. This signature was characterized by high-risk and low-risk score patterns. We found that the patients in the high-risk group are significantly linked with malignant molecular features and worse outcomes. Univariate and multivariate Cox regression analyses suggested that the signature is an independent indicator for glioma prognosis. The prediction accuracy of the signature was verified through time-dependent receiver operating characteristic (ROC) curves and calibration plots. Further bioinformatics analyses implied that the ECMRG signature is strongly associated with the activation of multiple oncogenic and metabolic pathways and immunosuppressive tumor microenvironment in gliomas. In addition, we confirmed that the high-risk score is an indicator for a therapy-resistant phenotype. In addition to bioinformatics analyses, we functionally verified the oncogenic role of bone morphogenetic protein 1 (BMP1) in gliomas
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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.