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Genome-scale DNA methylome and transcriptome profiles of early prostate cancer recurrence after prostatectomy [RNA-seq]

ABSTRACT: Prostate cancer (PCa) is a major health burden world-wide and despite early treatment, a very large proportion of patients present with biochemical recurrence (BCR) post-treatment, reflected by a rise in PSA over a clinical threshold. Novel transcriptomic and epigenomic biomarkers can provide a powerful tool for clinical management of PCa. Here we provide matched RNA-Sequencing and array-based genome-wide DNA methylome data of PCa patients (n = 17) with, or without, evidence of BCR following early radical prostatectomy. We have utilized Formalin-Fixed Paraffin-Embedded tissues to generate this data and included technical replicates to provide further validity of the data. We describe the sample features, experimental design, methods and bioinformatic pipeline for processing these multi-omic data. Importantly, we provide comprehensive clinical, histopathological, and follow-up data for each patient to enable correlating transcriptome and methylome features with clinical features. Our data will contribute towards the efforts of developing epigenomic and transcriptomic markers for BCR and also facilitate a deeper understanding of the molecular basis of PCa recurrence.

ORGANISM(S): Homo sapiens

PROVIDER: GSE278135 | GEO | 2025/03/23

REPOSITORIES: GEO

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Project description:Accurate estimation of recurrence risk is needed for optimal treatment of clinically localized prostate cancer (PCa) patients. We combined classical clinicopathological predictors of biochemical recurrence (BCR) and molecular markers into a new risk score for predicting BCR after radical prostatectomy (RP). A retrospective study which included 122 PCa patients who attended our department between 2000 and 2007 was conducted. Total RNA was isolated from formalin-fixed paraffin embedded PCa tissue. Gene expression patterns were analyzed in 21 selected samples from 7 localized, 6 locally advanced, and 8 metastatic PCa patients using Illumina microarrays. Expression levels of 41 genes were validated by quantitative PCR in an independent retrospective series of 101 patients with ≥ 10 years follow-up who underwent RP for clinically localized PCa. Univariate and multivariate logistic regression analysis were used to identify individual predictors of BCR. A risk score (RS) for predicting BCR including clinicopathological and gene expression data was developed and Kaplan-Meier curves were generated. Interaction networks between the genes of the model were built by GeneMANIA Cytoscape plugin. In a median follow-up of 120 months (range 3-190), 37 patients developed BCR (36.6%). Expression levels of 7,930 transcripts differed between clinically localized and locally advanced-metastatic PCa groups (FDR<0.1). A set of 41 genes were validated by quantitative PCR. Regression analysis showed that expression of ASF1B and MCL1 as well as Gleason score, extracapsular extension, seminal vesicle invasion and positive margins were independent prognostic factors of BCR. A RS generated using these gene expression and clinicopathological variables was able to discriminate between two groups of patients with a significantly different probability of BCR (HR 6.24; CI 3.23-12.4, p<0.01), improving the individual discriminative performance of each of these variables on their own. Direct interactions between the two genes of the model were not found. In conclusion, identification of new gene expression patterns associated with a high probability of BCR in clinically localized PCa patients treated with RP, and their combination with clinicopathological variables in a robust, easy-to-use and reliable classifier may contribute to improve PCa risk stratification and, consequently, to tailor treatment and surveillance strategies in these patients.

Project description:DNA methylation plays a vital role in genome dynamics and, in the human genome, occurs predominantly at cytosine guanine dinucleotide (CpG) sites. The diploid haploid human genome analysed here contains around 2060 million CpG sites (methylome) where DNA methylation can vary, affecting many biological processes in health and disease. Using whole-genome bisulfite sequencing, we report the essentially complete (92.6282%) methylome of human peripheral blood mononuclear cells (PBMC) which constitute an important source for clinical blood tests world-wide. We find the majority of CpG sites (68.4% at false positive rate of 0.46%) and only <0.2% of non-CpG sites to be methylated, demonstrating that non-CpG cytosine methylation is negligible in human PBMC. Analysis of the PBMC methylome revealed a rich landscape of epigenomic data for 20 distinct features including regulatory, protein-coding, RNA gene coding, non-coding and repeat sequences. Alu element mobility, for instance, was found to negatively correlate with their methylation levels, emphasizing the critical role of DNA methylation in genome stability. Integration of our methylome data with the previously determined genome sequence of the same Asian individual analysed here, enabled a first assessment of allele-specific methylation (ASM) differences between the two haploid methylomes of any individual. Using a conservative cut-off (p <0.001), we identified 599 haploid differentially methylated regions (hDMRs) covering 287 genes. Of these, 76 genes had hDMRs within 2kb of their transcriptional start sites of which >80% displayed allele-specific expression (ASE) after random testing using TA clone sequencing of the same PBMC sample. These data show, that ASM is a recurrent phenomenon and highly correlated with ASE, suggesting that imprinting may be more common than previously thought. Our study not only provides a comprehensive resource for future epigenomic research but also demonstrates a paradigm of large-scale epigenomics studies through new sequencing technology. We report the essentially complete (92.6282%) methylome of human peripheral blood mononuclear cells (PBMC) which constitute an important source for clinical blood tests world-wide.

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Genome-scale DNA methylome and transcriptome profiles of early prostate cancer recurrence after prostatectomy [RNA-seq]

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