Background The consensus on how to choose a reference gene for serum or plasma miRNA expression qPCR studies has not been reached and none of the potential candidates have yet been convincingly validated. We proposed a new in silico approach of finding a suitable reference for human, circulating miRNAs and identified a new set of endogenous reference miRNA based on miRNA profiling experiments from Gene Expression Omnibus. We used 3 known normalization algorithms (NormFinder, BestKeeper, GeNorm) to calculate a new normalization score. We searched for a universal set of endogenous miRNAs and validated our findings on 2 new datasets using our approach. Results We discovered and validated a set of 13 miRNAs (miR-222, miR-92a, miR-27a, miR-17, miR-24, miR-320a, miR-25, miR-126, miR-19b, miR-199a-3p, miR-30b, miR-30c, miR-374a) that can be used to create a reliable reference combination of 3 miRNAs. We showed that on average the mean of 3 miRNAs (p = 0.0002) and 2 miRNAs (p = 0.0031) were a better reference than single miRNA. The arithmetic means of 3 miRNAs: miR-24, miR-222 and miR-27a was shown to be the most stable combination of 3 miRNAs in validation sets. Conclusions No single miRNA was suitable as a universal reference in serum miRNA qPCR profiling, but it was possible to designate a set of miRNAs, which consistently contributed to most stable combinations.

Background Multi-gene expression assays are an attractive tool in revealing complex regulatory mechanisms in living organisms. Normalization is an indispensable step of data analysis in all those studies, since it removes unwanted, non-biological variability from data. In targeted qPCR assays it is typically performed with respect to prespecified reference genes, but the lack of robust strategy of their selection is reported in literature, especially in studies concerning circulating microRNAs (miRNA). Unfortunately, this problem impedes translation of scientific discoveries on miRNA biomarkers into widely available laboratory assays. Previous studies concluded that averaged expressions of multi-miRNA combinations are more stable references than single genes. However, due to the number of such combinations the computational load is considerable and may be hindering for objective reference selection in large datasets. Existing implementations of normalization algorithms (geNorm, NormFinder and BestKeeper) have poor performance and may require days to compute stability values for all potential reference as the evaluation is performed sequentially. Results We designed NormiRazor - an integrative tool which implements those methods in a parallel manner on a graphics processing unit (GPU) using CUDA platform. We tested our approach on publicly available miRNA expression datasets. As a result, the times of executions on 8 datasets containing from 50 to 400 miRNAs (subsets of GSE68314) decreased 18.7 ±0.6 (mean ±SD), 104.7 ±4.2 and 76.5 ±2.2 times for geNorm, BestKeeper and NormFinder with respect to previous Python implementation. To allow for easy access to normalization pipeline for biomedical researchers we implemented NormiRazor as an online platform where a user could normalize their datasets based on the automatically selected references. It is available at norm.btm.umed.pl, together with instruction manual and exemplary datasets. Conclusions NormiRazor allows for an easy, informed choice of reference genes for qPCR transcriptomic studies. As such it can improve comparability and repeatability of experiments and in longer perspective help translate newly discovered biomarkers into readily available assays.

Motivation:Multi-gene expression assays are an attractive tool in revealing complex regulatory mechanisms in living organisms. Normalization is an indispensable step of data analysis in all those studies, since it removes unwanted, non-biological variability from data. In targeted qPCR assays the normalization is typically performed with respect to prespecified reference genes, but the lack of robust strategy of their selection is reported in literature, especially in studies concerning circulating microRNAs (miRNA).Results: Previous studies concluded that averaged expressions of multi-miRNA combinations are more stable references than single genes. However, due to the number of such combinations the computational load is considerable and may be hindering for objective reference selection in large datasets. Existing implementations of normalization algorithms (geNorm, NormFinder and BestKeeper) have poor performance as every combination is evaluated sequentially. Thus, we designed an integrative tool which implemented those methods in a parallel manner on a graphics processing unit (GPU) using CUDA platform. We tested our approach on publicly available microRNA expression datasets. As a result the times of executions decreased 19-, 105-and 77-fold respectively for geNorm, BestKeeper and NormFinder.

State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using imaging biomarkers (radiomics). We gathered CT images and clinical data from 98 patients, who underwent intensity-modulated radiation therapy (IMRT) for head and neck cancers with a planned total dose of 70.0 Gy (33–35 fractions). During the 28-month (median) follow-up 27 patients (28%) developed RIHT. For each patient, we extracted 1316 radiomic features from original and transformed images using manually contoured thyroid masks. Creating models based on clinical, radiomic features or a combination thereof, we considered 3 variants of data preprocessing. Based on their performance metrics (sensitivity, specificity), we picked best models for each variant ((0.8, 0.96), (0.9, 0.93), (0.9, 0.89) variant-wise) and compared them with external NTCP models ((0.82, 0.88), (0.82, 0.88), (0.76, 0.91)). We showed that radiomic-based models did not outperform state-of-art NTCP models (p > 0.05). The potential benefit of radiomic-based approach is that it is dose-independent, and models can be used prior to treatment planning allowing faster selection of susceptible population.

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The aim of this study was to determine the serum profiles of miRNAs in patients with tuberous sclerosis (TSC) upon sirolimus treatment and compare them with those previously treated with everolimus in a similarly designed experiment. Serum microRNA profiling was performed in ten TSC patients before sirolimus therapy and again after 3–6 months using qPCR panels (Exiqon). Of 752 tested miRNAs, 28 showed significant differences in expression between TSC patients before and after sirolimus treatment. Of these, 11 miRNAs were dysregulated in the same directions as in the sirolimus groupcompared with the previously described everolimus group, miR-142-3p, miR-29c-3p, miR-150-5p, miR-425-5p, miR-376a-3p, miR-376a-3p, miR-532-3p, and miR-136-5p were upregulated, while miR-15b-3p, miR-100-5p, and miR-185-5p were downregulated. The most significant changes of expression, with fold changes exceeding 1.25 for both treatments, were noted for miR-136-5p, miR-376a-3p, and miR-150-5p. The results of a pathway analysis of the possible target genes for these miRNAs indicated the involvement of the Ras and MAPK signaling pathway. Upregulation of miR-136, miR-376a-3p, and miR-150-5p was noted in TSC patients treated with mTOR inhibitors, indicating a role in the downregulation of the mTOR pathway. Further studies are needed to determine the relationship between upregulated microRNAs and treatment efficacy.

State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using imaging biomarkers (radiomics). We gathered CT images and clinical data from 98 patients, who underwent intensity-modulated radiation therapy (IMRT) for head and neck cancers with a planned total dose of 70.0 Gy (33-35 fractions). During the 28-month (median) follow-up 27 patients (28%) developed RIHT. For each patient, we extracted 1316 radiomic features from original and transformed images using manually contoured thyroid masks. Creating models based on clinical, radiomic features or a combination thereof, we considered 3 variants of data preprocessing. Based on their performance metrics (sensitivity, specificity), we picked best models for each variant ((0.8, 0.96), (0.9, 0.93), (0.9, 0.89) variant-wise) and compared them with external NTCP models ((0.82, 0.88), (0.82, 0.88), (0.76, 0.91)). Our models reach accuracy comparable with or better than previously presented non-radiomic NTCP models. The benefit of our approach is obtaining the RIHT predictions before treatment planning to adjust IMRT plan to avoid the thyroid region in most susceptible patients.

The aim of this study was to assess the potential implication of microRNA on tuberous sclerosis (TSC) pathogenesis by performing microRNA profiling on cell lines silencing TSC1 or TSC2 genes using qPCR panels, before and after incubation with rapamycin. Significant differences in expression were observed between samples before and after rapamycin treatment in nineteen miRNAs in TSC1, five miRNAs in TSC2 and seven miRNAs in controls. Of miRNAs dysregulated before rapamycin treatment, three normalized after treatment in the TSC1 group (miR-21-3p, miR-433-3p, let-7g-3p) and one normalized in the TSC2 group (miR-1224-3p). Of the miRNAs dysregulated before rapamycin treatment in the TSC1 and TSC2 groups, two did not normalize after treatment (miR-33a-3p, miR-29a-3p). The results of the possible targets indicated that there are four common genes with seed regions susceptible to regulation by those miRNAs: ZBTB20, PHACTR2, PLXNC1 and ATP1B4. Our data show no changes in mRNA expression of these targets after rapamycin treatment. In conclusion, results of our study indicate the involvement of miRNA dysregulation in the pathogenesis of TSC. Some of the miRNA might be used as markers of treatment efficacy and autonomic miRNA as a target for future therapy.