- Titel
- Screen Study of Potential Prostate Cancer Associated Genes via Single Nucleotide Variants Detection
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-169047
- Datum der Einreichung
- 12.12.2016
- Datum der Verteidigung
- 18.10.2017
- Abstract (EN)
- Prostate Cancer (PCa) is the second most diagnosed cancer in men across the world; it is considered the fifth leading cause of cancer related death according to cancer statistics 2012. Being a member of the internal parts in males reproductive system, testing any abnormality with the prostate gland remains both troublesome and inconvenient, and foremost inaccurate. The diagnostic practice starts with prostate-specific antigen (PSA) level testing, which in return is highly indecisive, provoking an over diagnosis and treatment. Genomic alteration and Single Nucleotide Variants (SNV s) are assumed to play a role during PCa progression. On behalf of the RIBOLUTION project, a project with the aim of finding diagnostic biomarkers from RNA sequences, SNV s in RNA sequences were analysed to pinpoint potential candidate genes in PCa. The fact that the cohort provides whole-transcriptome data of pro- static tissue promotes the possibility to obtain comprehensive knowledge of the cancerous changes. The advantage of detecting SNV s in RNA sequences relies in focusing on only those, which could be relevant to the gene’s func- tion. However, methods for detecting and analysing SNV s solely in RNA sequences are currently not yet established. This study aimed to (1) establish fitting and applicable assays to identify, inspect and conclude the potential role of SNV s in RNA sequences, (2) use the obtained knowledge to single out the genes that are potentially relevant for PCa. SNV s in the RIBOLUTION cohort were investigated. Prostate tissue was obtained from 40 PCa patients, and then RNA was sequenced using Next Generation Sequencing. In 16 patients, a pairwise prostatic tissue was taken, one a confirmed tumor tissue and the second a tumor-free tissue. As a control, samples from 8 men with benign prostatic hyperplasia were likewise sequenced. Different computational pipelines were established and successfully fulfilled the aim. The CVR Module (Calling Variants in RNA-Seq) is a computer- based pipeline intended to identify SNV s and discriminate between false positive and true positive calls. Validating the SNV s reported by the accomplished Module has shown high sensitivity (> 80% validated SNV s). Much as novel SNV s that had ∼ 101% higher median calling quality in comparison to SNV s found in dbSNP, the Single Nucleotide Polymorphism Database. In agreement with current knowledge, novel SNV s was observed in tumor samples with slight but significant increase vs. tfree tissue (P < 0.05, testing on proportion). On top of that, positive correlation between non-silent effect and novel SNV in tumor samples was also observed (P < 0.05, r = 0.33, Pearson’s correlation). Moreover, more than 40% of the candidate genes were found in COSMIC, the Catalog Of Somatic Mutations In Cancer; some of them are confirmed somatic mutation (cancer associated). About 11% were also reported in studies to be disease associated or observed in other diseases, mostly heredity related. Potential PCa associated genes were identified via combination of three different systematic methods: mutational clustering, mutational functional bias, and covariates of the mutated genes. The first method (mutational clustering), however, did not reveal any significant insight. The top candidate genes were then selected in accordance with the latter methods. The list of top candidate genes includes > 50% genes with direct association with PCa; > 80% genes previously reported in other cancer types, while ∼ 35% that are in- volved in PCa associated complexes. Besides well known and validated PCa biomarker (alpha-methylacyl-CoA racemase (AMACR)), we identify for the first time, from mutational prospective, 22% of the genes to be potentially associated with PCa. Among those, one of the most promising candidate genes is NWD1 (NACHT and WD repeat domain containing 1). This gene was mentioned in a previous study to be a potential player in PCa prognosis. We add to this our novel observation, NWD1 was found significantly mutated in the entire tumor samples. These significant findings were proven to be tumor-specific when they were compared to the available control and tumor-free (P < 0.05, non-parametric ranking). We conclude that analyzing SNV s from RNA is as useful and informative as DNA-based ones, and accomplish further benefits that could be gained once the suggested methods are adapted.
- Freie Schlagwörter (EN)
- Prostate cancer, mutations, single nucleotide variants, biomarker, computational analysis, driver genes, statistics
- Klassifikation (DDC)
- 610
- Den akademischen Grad verleihende / prüfende Institution
- Universität Leipzig, Leipzig
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:15-qucosa2-169047
- Veröffentlichungsdatum Qucosa
- 19.12.2017
- Dokumenttyp
- Dissertation
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0