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The occurence of prostate cancer (PCa) has been consistently rising since three decades and remains the third leading cause of cancer-related deaths after lung and bowel cancer in Germany. Despite of new methods of early detection, such as prostate-specific antigen (PSA) testing, it persists to be the most common cancer in german men with over 63,400 new diagnoses in Germany every year and exhibits high prevalence in other countries of Northern andWestern Europe as well [64]. Men over the age of 70 are most commonly affected by the lethal disease, whereas an indisposition before 50 is rare. The malignant prostate tumor can be healed through operation or irradiation while the cancer hasn’t reached the stage of metastasis in which other therapeutic methods have to be employed [14] [15]. In the metastatic phase, the patient usually exhibits symptoms when the tumors size affects the urethra or the cancer spreads to other tissue, often the bones [16].
The high prevalence of this disease marks the importance of further research into prognosis and diagnosis methods, whereby identification of further biomarkers in PCa poses a major topic of scientific analysis. For this task, the effectiveness of high-throughput RNA sequencing of the transcriptome (RNA molecules of an organism or specific cell type) is frequently exploited [66]. RNA sequencing or RNA-Seq in short, offers the possibility of transcriptome assessment, enabling the identification of transcriptional aberrations in diseases as well as uncharacterized RNA species such as non-coding RNAs (ncRNAs) which remain undetected by conventional methods [49]. To alleviate interpretation of the sequenced reads they are assembled to reconstruct the transcriptome as close to the original state as possible, thus enabling rapid detection of relevant biomolecules in the data [49]. Transcriptomic studies often require highly accurate and complete gene annotations on the reference genome of the examined organism. However, most gene annotations and reference genomes are far from complete, containing a multitude of unidentified protein-coding and non-coding genes and transcripts. Therefore, refinement of reference genomes and annotations by inclusion of novel sequences, discovered in high quality transcriptome assemblies, is necessary [24].
There are multiple ways to gain information about an individual and its health status, but an increasingly popular field in medicine has become the analysis of human breath, which carries a lot of information about metabolic processes within the individuals body. The information in exhaled breath consists of volatile (organic) compounds (VOCs). These VOCs are products of metabolic processes within the individuals body, thus might be an indicator for diseases disturbing those processes. The compounds are to be detected by mass-spectrometric (MS) or ion-mobility spectrometric (IMS) techniques, making the analysis of these compounds not only bounded to exhaled breath. The resulting data is spectral data, capturing concentrations of the VOCs indirectly through intensities. However, a number of about 3000 VOCs [1] could already be determined in human exhaled breath. The number of research paper about VOC-analysis and detection had risen nearly constantly over the last decade 1. Furthermore, the technique to identify VOCs could also be used to capture biomarker from alien species within the individuals body. Extracting VOCs from an individual can be done by non- or minimal invasive techniques. However, the manual identification of VOCs and biomarkers related to a certain disease or infection is not feasible due to the complexity of the sample and often unknown metabolic products, thus automized techniques are needed. [1–4] To establish breath analysis as a diagnosis tool, machine learning methodes could be used. Machine learning has become a popular and common technique when dealing with medical data, due to the rapid analysis. Taking this advantage, breath analysis using machine learning could become the model of choice for diagnosis, keeping in mind that conventional methodes are laboratory based and thus when trying detect bacterial infection need sometimes several days to identify the organism. [5]
The study “Proteomic and systems biological database analysis of changed proteins from rat brain tissue after diving “ is about system biological testing of proteomic data obtained by rat brain after experimental diving in a pressure chamber. Basically, brain tissue from animal decompression sickness (DCS) was analyzed by mass spectrometry and has given two larger sets of modified proteins. Thereupon, the resulting up- and down-regulated proteins wereidentified and later compared by means of systems of biological databases, in this case GeneGo MetaCoreTM, in order to find similar or various affected cell biological signaling pathways when two different mass-spectrometry methods were compared.