Identification of miRNA's as specific biomarkers in prostate cancer diagnostics : a combined in silico and molecular approach
There are over 100 different types of cancer, and each of these cancers are classified by the type of cell that it initially affects. For the purpose of this research we will be focussing on prostate cancer (PC). Prostate cancer is the second most common form of cancer in men around the world and annually approximately 4500 men in South Africa are diagnosed making PC a global epidemic. Prostate cancer is a type of cancer which starts in the prostate it is normally a walnut-sized gland found right below the bladder. PC follows a natural course, starting as a tiny group of cancer cells that can grow into a tumour. In some men if PC is not treated it may spread to surrounding tissue by a process called direct invasion/ spread and could lead to death. Current diagnostic tests for prostate cancer have low specificity and poor sensitivity. Although many PC's are slow growing there is currently no test to distinguish between these and cancers that will become aggressive and life threatening. Therefore the need for a less invasive early detection method with the ability to overcome the lack of specificity and sensitivity of current available diagnostic test is required. Biomarkers have recently been identified as a viable option for early detection of disease for example biological indicators ie. DNA, RNA, proteins and microRNAs (miRNAs). Since first described in the 1990s, circulating miRNAs have provided an active and rapidly evolving area of research that has the potential to transform cancer diagnostics and prognostics. In particular, miRNAs could provide potentially new biomarkers for PC as diagnostic molecules. Circulating miRNAs are highly stable and are both detectable and quantifiable in a range of accessible bio-fluids, having the potential to be useful as diagnostic, prognostic and predictive biomarkers. In this study we aimed to identify miRNAs as potential biomarkers to detect and distinguish between various types of PC in its earliest stage. The major objectives of the study were to identify miRNAs and their gene targets that play a critical role in disease onset and progression to further understand their mechanism of action in PC using several in silico methods, and to validate the potential diagnostic miRNAs using qRT-PCR in several cell lines. The identification of specific miRNAs and their targets was done using an "in-house" designed pipeline. Bioinformatic analyses was done using a number of databases including STRING, DAVID, DIANA and mFold database, and these combined with programming and statistical analyses was used for the identification of potential miRNAs specific to PC. Our study identified 40 miRNAs associated with PC using our "in-house" parameters in comparison to the 20-30 miRNAs known to be involved in PC found in public databases e.g. miRBase. A comparison between our parameters and those used in public databases showed a higher degree of specificity for the identification PC-associated miRNAs. These selected miRNAs were analysed using different bioinformatics tools, and were confirmed to be novel miRNAs associated with PC. The identified miRNAs were experimentally validated using qRT-PCR to generate expression profiles for PC as well as various other cancers. Prostate lines utilised in this study included PNT2C2 (normal) which was compared to BPH1 (Benign) and LNCaP (Metastatic). In the study the expression profiles of eight potential miRNA biomarkers for the detection of PC was determined using qRT-PCR, and to distinguish PC from other cancers. QRT-PCR data showed that miRNA-3 and -5 were up-regulated in the BPH1 and LNCaP when compared to PNT2C2. In addition miRNA-8 was also shown to be up-regulated in LNCaP. Based on these results it was shown that a miRNA profile could be established to distinguish between BPH1 and the LNCaP prostate cell lines. The results suggest that one miRNA as a diagnostic marker may be sufficient to differentiate between different cancer cell lines. Furthermore by creating a unique profile for each cancer cell line by using a combination of miRNAs could be a suitable approach as well. Finally, it was shown that through the use of a single or combination of all eight miRNAs a unique profile for all the cancer cell lines tested in this study can be created. This is an important finding which could have potential diagnostic or prognostic implications in clinical practice.