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.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:16904 |
| Date | 19 December 2017 |
| Creators | Al-Hasani, Hoor |
| Contributors | Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0081 seconds