RT-qPCR is the gold standard and the most commonly used method for measuring gene expression. Selection of appropriate reference gene(s) for normalization is a crucial part of RT-qPCR experimental design, which allows accurate quantification and reliability of the results. Because there is no universal reference gene and even commonly used housekeeping genes’ expression can vary under certain conditions, careful selection of an appropriate internal control must be performed for each cell type or tissue and experimental design. The aim of this study was to identify the most stable reference genes during osteogenic differentiation of the human osteosarcoma cell lines MG-63, HOS, and SaOS-2 using the geNorm, NormFinder, and BestKeeper statistical algorithms. Our results show that TBP, PPIA, YWHAZ, and EF1A1 are the most stably expressed genes, while ACTB, and 18S rRNA expressions are most variable. These data provide a basis for future RT-qPCR normalizations when studying gene expression during osteogenic differentiation, for example, in studies of osteoporosis and other bone diseases.

Osteoporosis is a metabolic bone disease that mostly affects the elderly. A lot of drugs are available, mostly with an antiresorptive effect but just a few with an osteoanabolic effect, meaning they promote bone building. PTH (1-34) or teriparatide is an osteoanabolic drug, but its efficacy varies between individuals. We performed a literature review and extracted a dataset of 62 microRNAs (miRNAs) from 10 different studies; predicted miRNA target interactions (MTIs) were obtained with the help of four software tools: DIANA, miRWalk, miRDB and TargetScan. With the construction of an interactome of PTH-regulated miRNAs and their predicted target genes, we elucidated miR-146a-5p, miR-551b-5p, miR-205-3p, miR-33a-3p, miR-338-5p as miRNAs with the most interactions and miR-410-3p as the miRNA targeting bone-related pathways with the highest significance. These miRNAs could help in further understanding the mechanism of action of PTH on bone metabolism and osteoporosis. They also have the potential for novel network-based biomarkers for osteoporosis treatment efficacy and safety and as new therapeutic targets.