Background One of the most widely used evaluation methods in miRNA experiments is qRT-PCR. However, selecting suitable internal controls (IC) is crucial for qRT-PCR experiments. Currently, there is no consensus on the ICs for miRNA qRT-PCR experiments in breast cancer. To this end, we tried to identify the most stable (the least expression alteration) and promising miRNAs in normal and tumor breast tissues by employing TCGA miRNA-Seq data and then experimentally validated them on fresh clinical samples. Methods A multi-component scoring system was used which takes into account multiple expression stability criteria as well as correlation with clinical characteristics. Furthermore, we extended the scoring system for more than two biological sub-groups. TCGA BRCA samples were analyzed based on two grouping criteria: Tumor & Normal samples and Tumor subtypes. The top 10 most stable miRNAs were further investigated by differential expression and survival analysis. Then, we examined the expression level of the top scored miRNA (hsa-miR-361-5p) along with two commonly used ICs hsa-miR-16-5p and U48 on 34 pairs of Primary breast tumor and their adjacent normal tissues using qRT-PCR. Results According to our multi-component scoring system, hsa-miR-361-5p had the highest stability score in both grouping criteria and hsa-miR-16-5p showed significantly lower scores. Based on our qRT-PCR assay, while U48 was the most abundant IC, hsa-miR-361-5p had lower standard deviation and also was the only IC capable of detecting a significant up-regulation of hsa-miR-21-5p in breast tumor tissue. Conclusions miRNA-Seq data is a great source to discover stable ICs. Our results demonstrated that hsa-miR-361-5p is a highly stable miRNA in tumor and non-tumor breast tissue and we recommend it as a suitable reference gene for miRNA expression studies in breast cancer. Additionally, although hsa-miR-16-5p is a commonly used IC, it’s not a suitable one for breast cancer studies.

MIR29B2CHG/C1orf132 is the host gene for generating miR-29b2 and miR-29c. Here, we employed bioinformatics and experimental approaches to decipher expression of C1orf132 in breast cancer cells and tissues. Our data demonstrated a significant downregulation of C1orf132 in triple-negative breast cancer. We also predicted a putative promoter for the longer transcripts of C1orf132. The functionality of the distal promoter was confirmed by transfecting MCF7 cells with a C1orf132 promoter-GFP construct. Knocking-out the promoter by means of CRISPR/Cas9 approach revealed no expression alteration of neighboring genes, CD46 and CD34. However, the expression of miR-29c was reduced by half, suggesting an enhancer effect of the distal promoter on miR-29c generation. Furthermore, the promoter knock-out an elevation of migration ability in MCF12A edited cells. Moreover, the expressions of cell mobility genes e.g., CDH2, FGF2, FGFR1 and the stem cell and EMT-associated transcription factor ZEB1 were significantly elevated in edited cells. RNA sequencing data on the edited and unedited cells revealed many up- and down-regulated genes involved in various cellular pathways, including epithelial to mesenchymal transition and mammary gland development pathways. Altogether, we are reporting the existence of an additional/distal promoter with an enhancer effect on miR-29 generation and an inhibitory effect on cell migration.

BACKGROUND: Identifying the molecular subtypes of breast cancer (BC) plays a crucial role in enhancing the efficacy of therapy. MiRNAs with differential expressions in different subtypes of breast tumors can be considered non-invasive biomarkers for diagnosing BC subtypes. OBJECTIVE: We aimed to investigate the efficacy of miR-190b, miR-584-5p, miR-452-5p, and miR-1306-5p as new potential diagnostic biomarkers in discriminating patients with luminal and non-luminal BCs. METHODS: A group of miRNAs significantly associated with estrogen cell receptors (ER) in breast tumors was identified using feature selection methods analysis on miRNASeq data of TCGA and GSE68085. Among them, four miRNAs were selected as novel potential biomarkers, and their expression levels were assessed within luminal tumors, non-luminal tumors, and adjacent non-tumor tissues by qRT-PCR. Their impact on diagnosis was also evaluated by ROC curve analysis. RESULTS: MiR-190b was remarkably up-regulated, while miRNA-584-5p, miRNA-452-5p, and miRNA-1306-5p were significantly down-regulated in luminal BCs. This group could discriminate luminal and non-luminal BCs at an AUC of 0.977. CONCLUSIONS: According to our findings, these four miRNAs are promising biomarkers in parallel with histologic diagnosis methods for identifying patients who are most likely responding to specific therapies based on ER status.

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Quantification of gene expression is a crucial task in biomedical studies. Although high-throughput methods enable rapid and simultaneous expression quantification of coding or non-coding regions, qPCR is still vastly used due to its high availability, sensitivity, specificity, reproducibility, low cost, and ease of use. A limitation of qPCR has been the need of internal controls or reference genes (RGs) with stable expression in different conditions, to normalize the expression level of the other target genes. So far, several stability criteria and numerous methods for selecting a group of RGs have been proposed, however, important challenges must be addressed. Here we introduce a mathematical basis for precise modeling of qPCR expression normalization and justify widely used stability measures of RGs. We then propose a family of scale-invariant functions, as an alternative to the geometric mean, to optimize aggregated expression of RGs. We provide closed-form optimizations for several scale-invariant aggregation functions. Among them, we show the superiority of weighted geometric mean, whose parameters optimize standard deviation as the stability measure of aggregated RGs expression. We provide experimental support for this finding using real data of solid tumors and liquid biopsies of different sample sizes. The proposed methods can be easily integrated in the existing qPCR expression normalization pipelines of genes and non-coding RNAs. We also provide an implementation of the proposed methods as an R package, with GPU acceleration. Availability and implementation: https://github.com/asalimih/InterOpt