Breast cancer has a high incidence and mortality. More than 1500 patients are diagnosed with breast cancer every year in Slovenia. In recent years, many studies have been carried out on the altered expression of cannabinoid receptors in breast cancer samples, with conflicting results on the impact of cannabinoids on treatment outcome. The conflicting research may be due to the use of different breast cancer biological samples (e.g. tissues, cell lines). The aim of my thesis was to show that databases are an appropriate tool to identify biological samples that can be used as an internal control of expression levels in downstream analyses of selected genes; i.e. a positive control with a high level of expression and a negative control with a low level/no expression. In the first part of the thesis, I identified cell lines representing positive and negative expression controls for the cannabinoid receptor genes CNR1 and CNR2 based on the analysis of different databases (CCLE, GTEx, GeneCards, Human Protein Atlas, etc.). The analysis showed that both genes are expressed in almost all tissues, but that the expression level of CNR1 is always higher than that of CNR2. The expression level of the CNR1 gene is also higher than that of CNR2 in the cell lines. As a control for CNR1 expression, I selected the U-251-MG cell line with an expression level of 8.32 log2(TPM+1). As a negative control for CNR1 and CNR2 expression, I selected the SK-BR-3 cell line with an expression level of 0.0 log2(TPM+1). In the second, laboratory part of the thesis, I experimentally determined the theoretically defined expression level in the selected cell lines. I isolated total RNA from the cell lines, reverse transcribed it into cDNA and quantitatively analysed the expression of the CNR1 and CNR2 genes by quantitative PCR. I used the housekeeping genes RPLP0 and HPRT1 for normalisation. The results of the experimental analysis showed that the theoretically selected control cell lines have the expected (high and low) expression levels of CNR1 and CNR2 genes. This confirmed my hypothesis that the selection of cell lines based on the database analysis is plausible and reliable.
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