Dataset Information

Transcriptomics Signature from Next-Generation Sequencing Data Reveals New Transcriptomic Biomarkers Related to Prostate Cancer.

ABSTRACT: Prostate cancer is one of the most common types of cancer among Canadian men. Next-generation sequencing using RNA-Seq provides large amounts of data that may reveal novel and informative biomarkers. We introduce a method that uses machine learning techniques to identify transcripts that correlate with prostate cancer development and progression. We have isolated transcripts that have the potential to serve as prognostic indicators and may have tremendous value in guiding treatment decisions. Analysis of normal versus malignant prostate cancer data sets indicates differential expression of the genes HEATR5B, DDC, and GABPB1-AS1 as potential prostate cancer biomarkers. Our study also supports PTGFR, NREP, SCARNA22, DOCK9, FLVCR2, IK2F3, USP13, and CLASP1 as potential biomarkers to predict prostate cancer progression, especially between stage II and subsequent stages of the disease.

SUBMITTER: Alkhateeb A

PROVIDER: S-EPMC6416685 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Transcriptomics Signature from Next-Generation Sequencing Data Reveals New Transcriptomic Biomarkers Related to Prostate Cancer.

Alkhateeb Abedalrhman A Rezaeian Iman I Singireddy Siva S Cavallo-Medved Dora D Porter Lisa A LA Rueda Luis L

Cancer informatics 20190313

Prostate cancer is one of the most common types of cancer among Canadian men. Next-generation sequencing using RNA-Seq provides large amounts of data that may reveal novel and informative biomarkers. We introduce a method that uses machine learning techniques to identify transcripts that correlate with prostate cancer development and progression. We have isolated transcripts that have the potential to serve as prognostic indicators and may have tremendous value in guiding treatment decisions. An ...[more]

PMID: 30890858

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Project description:BACKGROUND: Excretory/secretory proteins (ESPs) play a major role in parasitic infection as they are present at the host-parasite interface and regulate host immune system. In case of parasitic helminths, transcriptomics has been used extensively to understand the molecular basis of parasitism and for developing novel therapeutic strategies against parasitic infections. However, none of transcriptomic studies have extensively covered ES protein prediction for identifying novel therapeutic targets, especially as parasites adopt non-classical secretion pathways. RESULTS: We developed a semi-automated computational approach for prediction and annotation of ES proteins using transcriptomic data from next generation sequencing platforms. For the prediction of non-classically secreted proteins, we have used an improved computational strategy, together with homology matching to a dataset of experimentally determined parasitic helminth ES proteins. We applied this protocol to analyse 454 short reads of parasitic nematode, Strongyloides ratti. From 296231 reads, we derived 28901 contigs, which were translated into 20877 proteins. Based on our improved ES protein prediction pipeline, we identified 2572 ES proteins, of which 407 (1.9%) proteins have classical N-terminal signal peptides, 923 (4.4%) were computationally identified as non-classically secreted while 1516 (7.26%) were identified by homology to experimentally identified parasitic helminth ES proteins. Out of 2572 ES proteins, 2310 (89.8%) ES proteins had homologues in the free-living nematode Caenorhabditis elegans and 2220 (86.3%) in parasitic nematodes. We could functionally annotate 1591 (61.8%) ES proteins with protein families and domains and establish pathway associations for 691 (26.8%) proteins. In addition, we have identified 19 representative ES proteins, which have no homologues in the host organism but homologous to lethal RNAi phenotypes in C. elegans, as potential therapeutic targets. CONCLUSION: We report a comprehensive approach using freely available computational tools for the secretome analysis of NGS data. This approach has been applied to S. ratti 454 transcriptomic data for in silico excretory/secretory proteins prediction and analysis, providing a foundation for developing new therapeutic solutions for parasitic infections.

Project description:BackgroundPsychiatric disorders such as schizophrenia (SZ) and bipolar disorder (BP) are projected to lead the global disease burden within the next decade. Several lines of evidence suggest that epigenetic- or genetic-mediated dysfunction is frequently present in these disorders. To date, the inheritance patterns have been complicated by the problem of integrating epigenomic and transcriptomic factors that have yet to be elucidated. Therefore, there is a need to build a comprehensive database for storing epigenomic and transcriptomic data relating to psychiatric disorders.DescriptionWe have developed the PD_NGSAtlas, which focuses on the efficient storage of epigenomic and transcriptomic data based on next-generation sequencing and on the quantitative analyses of epigenetic and transcriptional alterations involved in psychiatric disorders. The current release of the PD_NGSAtlas contains 43 DNA methylation profiles and 37 transcription profiles detected by MeDIP-Seq and RNA-Seq, respectively, in two distinct brain regions and peripheral blood of SZ, BP and non-psychiatric controls. In addition to these data that were generated in-house, we have included, and will continue to include, published DNA methylation and gene expression data from other research groups, with a focus on psychiatric disorders. A flexible query engine has been developed for the acquisition of methylation profiles and transcription profiles for special genes or genomic regions of interest of the selected samples. Furthermore, the PD_NGSAtlas offers online tools for identifying aberrantly methylated and expressed events involved in psychiatric disorders. A genome browser has been developed to provide integrative and detailed views of multidimensional data in a given genomic context, which can help researchers understand molecular mechanisms from epigenetic and transcriptional perspectives. Moreover, users can download the methylation and transcription data for further analyses.ConclusionsThe PD_NGSAtlas aims to provide storage of epigenomic and transcriptomic data as well as quantitative analyses of epigenetic and transcriptional alterations involved in psychiatric disorders. The PD_NGSAtlas will be a valuable data resource and will enable researchers to investigate the pathophysiology and aetiology of disease in detail. The database is available at http://bioinfo.hrbmu.edu.cn/pd_ngsatlas/.

Project description:BackgroundMolecular characterization of nonmuscle invasive bladder cancer (NMIBC) may provide a biologic rationale for treatment response and novel therapeutic strategies.ObjectiveTo identify genetic alterations with potential clinical implications in NMIBC.Design, setting, and participantsPretreatment index tumors and matched germline DNA from 105 patients with NMIBC on a prospective Institutional Review Board-approved protocol underwent targeted exon sequencing analysis in a Clinical Laboratory Improvement Amendments-certified clinical laboratory.Outcome measurements and statistical analysisComutation patterns and copy number alterations were compared across stage and grade. Associations between genomic alterations and recurrence after intravesical bacillus Calmette-Guérin (BCG) were estimated using Kaplan-Meier and Cox regression analyses.Results and limitationsTERT promoter mutations (73%) and chromatin-modifying gene alterations (69%) were highly prevalent across grade and stage, suggesting these events occur early in tumorigenesis. ERBB2 or FGFR3 alterations were present in 57% of high-grade NMIBC tumors in a mutually exclusive pattern. DNA damage repair (DDR) gene alterations were seen in 30% (25/82) of high-grade NMIBC tumors, a rate similar to MIBC, and were associated with a higher mutational burden compared with tumors with intact DDR genes (p<0.001). ARID1A mutations were associated with an increased risk of recurrence after BCG (hazard ratio=3.14, 95% confidence interval: 1.51-6.51, p=0.002).ConclusionsNext-generation sequencing of treatment-naive index NMIBC tumors demonstrated that the majority of NMIBC tumors had at least one potentially actionable alteration that could serve as a target in rationally designed trials of intravesical or systemic therapy. DDR gene alterations were frequent in high-grade NMIBC and were associated with increased mutational load, which may have therapeutic implications for BCG immunotherapy and ongoing trials of systemic checkpoint inhibitors. ARID1A mutations were associated with an increased risk of recurrence after BCG therapy. Whether ARID1A mutations represent a predictive biomarker of BCG response or are prognostic in NMIBC patients warrants further investigation.Patient summaryAnalysis of frequently mutated genes in superficial bladder cancer suggests potential targets for personalized treatment and predictors of treatment response, and also may help develop noninvasive tumor detection tests.

Dataset Information

Transcriptomics Signature from Next-Generation Sequencing Data Reveals New Transcriptomic Biomarkers Related to Prostate Cancer.

Publications

Transcriptomics Signature from Next-Generation Sequencing Data Reveals New Transcriptomic Biomarkers Related to Prostate Cancer.

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