ArticleBrainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)2019
Multi-stage Association Analysis of Glioblastoma Gene Expressions with Texture and Spatial Patterns.
Article in Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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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.
- System analysis based on the migration- and invasion-related gene sets identifies the infiltration-related genes of glioma.Frontiers in oncology · 2023Article
- LRRC46 Accumulates at the Midpiece of Sperm Flagella and Is Essential for Spermiogenesis and Male Fertility in Mouse.International journal of molecular sciences · 2022Article
- The oncogenic role of tubulin alpha-1c chain in human tumours.BMC cancer · 2022Article
- Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration.Applied sciences (Basel, Switzerland) · 2021Article
- A pilot radiogenomic study of DIPG reveals distinct subgroups with unique clinical trajectories and therapeutic targets.Acta neuropathologica communications · 2021Article
- Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology.Annual review of biomedical engineering · 2020Review
- Advanced Imaging and Computational Techniques for the Diagnostic and Prognostic Assessment of Malignant Gliomas.Cancer journal (Sudbury, Mass.)Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Glioblastoma is the most aggressive malignant primary brain tumor with a poor prognosis. Glioblastoma heterogeneous neuroimaging, pathologic, and molecular features provide opportunities for subclassification, prognostication, and the development of targeted therapies. Magnetic resonance imaging has the capability of quantifying specific phenotypic imaging features of these tumors. Additional insight into disease mechanism can be gained by exploring genetics foundations. Here, we use the gene expressions to evaluate the associations with various quantitative imaging phenomic features extracted from magnetic resonance imaging. We highlight a novel correlation by carrying out multi-stage genomewide association tests at the gene-level through a non-parametric correlation framework that allows testing multiple hypotheses about the integrated relationship of imaging phenotype-genotype more efficiently and less expensive computationally. Our result showed several novel genes previously associated with glioblastoma and other types of cancers, as the LRRC46 (chromosome 17), EPGN (chromosome 4) and TUBA1C (chromosome 12), all associated with our radiographic tumor features.
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Registered trials
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