ArticleOncotarget2016
Identification of biomarkers for pseudo and true progression of GBM based on radiogenomics study.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review.International journal of molecular sciences · 2019Pooled it
- Association between the XPG gene rs2094258 polymorphism and risk of gastric cancer.Journal of clinical laboratory analysis · 2018Pooled it
- Significance of the association between hypoxia and pyroptosis-related genes in the prognostic prediction of glioblastoma: insights from model construction and validation.Translational cancer research · 2025Article
- Clinical predictors of pseudoprogression in glioblastoma: a retrospective cohort analysis.Journal of neuro-oncology · 2025Article
- Glioblastoma metabolomics: uncovering biomarkers for diagnosis, prognosis and targeted therapy.Journal of experimental & clinical cancer research : CR · 2025Review
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.The Lancet. Oncology · 2024Review
- Circulating Liquid Biopsy Biomarkers in Glioblastoma: Advances and Challenges.International journal of molecular sciences · 2024Review
- Article
- Assessment and prediction of glioblastoma therapy response: challenges and opportunities.Brain : a journal of neurology · 2023Article
- Evolution and emergence of primate-specific interferon regulatory factor 9.Journal of medical virology · 2023Article
- Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology.Biomedicines · 2022Review
- Liquid Biopsy in Glioblastoma.Cancers · 2022Review
- Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma.NMR in biomedicine · 2022Review
- Liquid Biopsy Strategies to Distinguish Progression from Pseudoprogression and Radiation Necrosis in Glioblastomas.Advanced biosystems · 2020Review
- Circulating biomarkers in patients with glioblastoma.British journal of cancer · 2020Review
- Machine learning and glioma imaging biomarkers.Clinical radiology · 2020Review
- PBK as a Potential Biomarker Associated with Prognosis of Glioblastoma.Journal of molecular neuroscience : MN · 2020Article
- Association ofWorld journal of gastroenterology · 2019Article
- Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients.Neuro-oncology · 2019Article
- Perfusion MRI in treatment evaluation of glioblastomas: Clinical relevance of current and future techniques.Journal of magnetic resonance imaging : JMRI · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
The diagnosis for pseudoprogression (PsP) and true tumor progression (TTP) of GBM is a challenging task in clinical practices. The purpose of this study is to identify potential genetic biomarkers associated with PsP and TTP based on the clinical records, longitudinal imaging features, and genomics data. We are the first to introduce the radiogenomics approach to identify candidate genes for PsP and TTP of GBM. Specifically, a novel longitudinal sparse regression model was developed to construct the relationship between gene expression and imaging features. The imaging features were extracted from tumors along the longitudinal MRI and provided diagnostic information of PsP and TTP. The 33 candidate genes were selected based on their association with the imaging features, reflecting their relation with the development of PsP and TTP. We then conducted biological relevance analysis for 33 candidate genes to identify the potential biomarkers, i.e., Interferon regulatory factor (IRF9) and X-ray repair cross-complementing gene (XRCC1), which were involved in the cancer suppression and prevention, respectively. The IRF9 and XRCC1 were further independently validated in the TCGA data. Our results provided the first substantial evidence that IRF9 and XRCC1 can serve as the potential biomarkers for the development of PsP and TTP.
Indexed as
Identifiers
What OpenQuestion holds
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.