Radiogenomics reveals significant correlation between quantitative texture radiomic features of biparametric MRI and hypoxia related gene expression in men with localised prostate cancer
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ABSTRACT:
PROVIDER: PRJEB59104 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR10796209.fastq.gz | Fastqsanger.gz | |||
| ERR10796210.fastq.gz | Fastqsanger.gz | |||
| ERR10796211.fastq.gz | Fastqsanger.gz | |||
| ERR10796212.fastq.gz | Fastqsanger.gz | |||
| ERR10796213.fastq.gz | Fastqsanger.gz |
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Project description:The aim of this study was to perform the multiscale correlation between quantitative texture features phenotype of pre-biopsy biparametric MRI (bpMRI) and targeted sequence-based RNA expression for hypoxia-related genes. Images from pre-biopsy 3T bpMRI scans in clinically localised prostate cancer (PCa) patients of various risk categories (n=15) were used to extract textural features. The genomic landscape of hypoxia-related genes expression was obtained using post-radical prostatectomy tissue for targeted RNA expression profiling using the TempO-sequence method. The nonparametric Games Howell test was used to correlate the differential expression of the three important hypoxia-related genes with 28 radiomic texture features. Following this, cBioportal was accessed and a gene-oriented query was conducted to extract Oncoprint genomic output graph of the selected hypoxia-related genes from The Cancer Genome Atlas (TCGA). Correlation analysis using Pearson's coefficients calculated against each selected gene profile; survival analysis using Kaplan-Meier estimators were carried out. We found the quantitative bpMR imaging textural features, including histogram and grey level co-occurrence matrix (GLCM), correlated with hypoxia related genes (ANGPTL4, VEGFA, and P4HA1) seen on RNA sequencing using TempO-Seq method. Further radiogenomic analysis, including data accessed on cBioportal genomic database, confirmed that overexpressed hypoxia-related genes significantly correlated with a poor survival outcome, with a median survival of 81.11: 133.00 months in those with and without alterations of genes respectively. In summary, radiomic texture features of bpMRI in localised PCa correlate with the expression of hypoxia-related genes expression in prostate cancer. The expression data analysis showed that hypoxia-related genes are associated with poor survival.
2023-01-29 | E-MTAB-12593 | biostudies-arrayexpress
Project description:ObjectivesTo perform multiscale correlation analysis between quantitative texture feature phenotypes of pre-biopsy biparametric MRI (bpMRI) and targeted sequence-based RNA expression for hypoxia-related genes.Materials and methodsImages from pre-biopsy 3T bpMRI scans in clinically localised PCa patients of various risk categories (n = 15) were used to extract textural features. The genomic landscape of hypoxia-related gene expression was obtained using post-radical prostatectomy tissue for targeted RNA expression profiling using the TempO-sequence method. The nonparametric Games Howell test was used to correlate the differential expression of the important hypoxia-related genes with 28 radiomic texture features. Then, cBioportal was accessed, and a gene-specific query was executed to extract the Oncoprint genomic output graph of the selected hypoxia-related genes from The Cancer Genome Atlas (TCGA). Based on each selected gene profile, correlation analysis using Pearson's coefficients and survival analysis using Kaplan-Meier estimators were performed.ResultsThe quantitative bpMR imaging textural features, including the histogram and grey level co-occurrence matrix (GLCM), correlated with three hypoxia-related genes (ANGPTL4, VEGFA, and P4HA1) based on RNA sequencing using the TempO-Seq method. Further radiogenomic analysis, including data accessed from the cBioportal genomic database, confirmed that overexpressed hypoxia-related genes significantly correlated with a poor survival outcomes, with a median survival ratio of 81.11:133.00 months in those with and without alterations in genes, respectively.ConclusionThis study found that there is a correlation between the radiomic texture features extracted from bpMRI in localised prostate cancer and the hypoxia-related genes that are differentially expressed. The analysis of expression data based on cBioportal revealed that these hypoxia-related genes, which were the focus of the study, are linked to an unfavourable survival outcomes in prostate cancer patients.
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Project description:Facioscapulohumeral muscular dystrophy (FSHD) is a common, dominantly inherited disease caused by the epigenetic de-repression of the DUX4 gene, a transcription factor normally repressed in somatic cells. As targeted therapies are now possible in FSHD, a better understanding of the relationship between DUX4 activity, muscle pathology and muscle MRI changes are crucial both to understand disease mechanisms and for the design of future clinical trials. Here, we performed MRIs of the lower extremities in 36 individuals with FSHD, followed by needle muscle biopsies in safely accessible muscles. We examined the correlation between MRI characteristics, muscle pathology, and expression of DUX4 target genes. Results show that the presence of elevated MRI STIR signal has substantial predictive value in identifying muscles with active disease and DUX4 target gene expression. In addition, DUX4 target gene expression was detected only in FSHD-affected muscles and not in control muscles, and higher levels of DUX4 target expression was associated with more advanced muscle pathology. These results support the use of MRI to identify FSHD muscles with active disease as measured by histopathology and DUX4 target gene expression and might be useful for the design of studies of disease progression and response to intervention.
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Project description:Background: To analyze the potential of radiomics for disease stratification beyond key molecular, clinical and standard imaging features in patients with glioblastoma. Methods: Quantitative imaging features (n=1043) were extracted from the multiparametric MRI of 181 patients with newly-diagnosed glioblastoma prior to standard-of-care treatment (allocated to a discovery and validation set, 2:1 ratio). A subset of 386/1043 features were identified as reproducible (in an independent MRI-test-retest cohort) and selected for analysis. A penalized Cox-model with 10-fold cross-validation (Coxnet) was fitted on the discovery set to construct a radiomic signature for predicting progression-free and overall survival (PFS, OS). The incremental value of a radiomic signature beyond molecular (MGMT-promoter methylation, DNA-methylation subgroups), clinical (patients age, KPS, extent-of-resection, adjuvant treatment) and standard imaging parameters (tumor volumes) for stratifying PFS and OS was assessed with multivariate Cox-models (performance quantified with prediction error curves). Results: The radiomic signature (constructed from 8/386 features identified through Coxnet) increased the prediction accuracy for PFS and OS (in both discovery and validation set) beyond the assessed molecular, clinical and standard imaging parameters (p≤0.01). Prediction errors decreased by 36% for PFS and 37% for OS when adding the radiomic signature (as compared to 29% and 27% with molecular + clinical features alone). The radiomic signature was - along with MGMT-status - the only parameter with independent significance on multivariate analysis (p≤0.01). Conclusions: Our study stresses the role of integrating radiomics into a multi-layer decision framework with key molecular and clinical features to improve disease stratification and to potentially advance personalized treatment of patients with glioblastoma.
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