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Background Treatment options for metastatic castrate-resistant prostate cancer (mCRPC) are limited and typically are centered on docetaxel-based chemotherapy. We previously reported that elevated miR-375 levels were significantly associated with poor overall survival of mCRPC patients. In this study, we evaluated if miR-375 induced chemo-resistance to docetaxel through regulating target genes associated with drug resistance. Methods We first compared miR-375 expression level between prostate cancer tissues and normal prostate tissues using data from The Cancer Genome Atlas (TCGA). To examine the role of miR-375 in docetaxel resistance, we transfected miR-375 using a pre-miRNA lentiviral vector and examined the effects of exogenously overexpressed miR-375 on cell growth in two prostate cancer cell lines, DU145 and PC-3. To determine the effect of overexpressed miR-375 on tumor growth and chemo-resistance in vivo, we injected prostate cancer cells overexpressing miR-375 into nude mice subcutaneously and evaluated tumor growth rate during docetaxel treatment. Lastly, we utilized qRT-PCR and Western blot assay to examine two miR-375 target genes, SEC23A and YAP1, for their expression changes after miR-375 transfection. Results By examining 495 tumor tissues and 52 normal tissues from TCGA data, we found that compared to normal prostate, miR-375 was significantly overexpressed in prostate cancer tissues (8.45-fold increase, p value = 1.98E-23). Docetaxel treatment induced higher expression of miR-375 with 5.83- and 3.02-fold increases in DU145 and PC-3 cells, respectively. Interestingly, miR-375 appeared to play a dual role in prostate cancer proliferation. While miR-375 overexpression caused cell growth inhibition and cell apoptosis, elevated miR-375 also significantly reduced cell sensitivity to docetaxel treatment in vitro, as evidenced by decreased apoptotic cells. In vivo xenograft mouse study showed that tumors with increased miR-375 expression were more tolerant to docetaxel treatment, demonstrated by greater tumor weight and less apoptotic cells in miR-375 transfected group when compared to empty vector control group. In addition, we examined expression levels of the two miR-375 target genes (SEC23A and YAP1) and observed significant reduction in the expression at both protein and mRNA levels in miR-375 transfected prostate cancer cell lines. TCGA dataset analysis further confirmed the negative correlations between miR-375 and the two target genes (r = −0.62 and −0.56 for SEC23A and YAP1, respectively; p

Background Chromosome conformation capture (3C) is a powerful and widely used technique for detecting the physical interactions between chromatin regions in vivo. The principle of 3C is to convert physical chromatin interactions into specific DNA ligation products, which are then detected by quantitative polymerase chain reaction (qPCR). However, 3C-qPCR assays are often complicated by the necessity of normalization controls to correct for amplification biases. In addition, qPCR is often limited to a certain cycle number, making it difficult to detect fragment ligations with low frequency. Recently, digital PCR (dPCR) technology has become available, which allows for highly sensitive nucleic acid quantification. Main advantage of dPCR is its high precision of absolute nucleic acid quantification without requirement of normalization controls. Results To demonstrate the utility of dPCR in quantifying chromatin interactions, we examined two prostate cancer risk loci at 8q24 and 2p11.2 for their interaction target genes MYC and CAPG in LNCaP cell line. We designed anchor and testing primers at known regulatory element fragments and target gene regions, respectively. dPCR results showed that interaction frequency between the regulatory element and MYC gene promoter was 0.7 (95% CI 0.40–1.10) copies per 1000 genome copies while other regions showed relatively low ligation frequencies. The dPCR results also showed that the ligation frequencies between the regulatory element and two EcoRI fragments containing CAPG gene promoter were 1.9 copies (95% CI 1.41–2.47) and 1.3 copies per 1000 genome copies (95% CI 0.76–1.92), respectively, while the interaction signals were reduced on either side of the promoter region of CAPG gene. Additionally, we observed comparable results from 3C-dPCR and 3C-qPCR at 2p11.2 in another cell line (DU145). Conclusions Compared to traditional 3C-qPCR, our results show that 3C-dPCR is much simpler and more sensitive to detect weak chromatin interactions. It may eliminate multiple and complex normalization controls and provide accurate calculation of proximity-based fragment ligation frequency. Therefore, we recommend 3C-dPCR as a preferred method for sensitive detection of low frequency chromatin interactions. Electronic supplementary material The online version of this article (doi:10.1186/s12867-016-0076-6) contains supplementary material, which is available to authorized users.

RNA sequencing (RNA-seq) is emerging as a critical approach in biological research. However, its high-throughput advantage is significantly limited by the capacity of bioinformatics tools. The research community urgently needs user-friendly tools to efficiently analyze the complicated data generated by high throughput sequencers. We developed a standalone tool with graphic user interface (GUI)-based analytic modules, known as eRNA. The capacity of performing parallel processing and sample management facilitates large data analyses by maximizing hardware usage and freeing users from tediously handling sequencing data. The module miRNA identification” includes GUIs for raw data reading, adapter removal, sequence alignment, and read counting. The module “mRNA identification” includes GUIs for reference sequences, genome mapping, transcript assembling, and differential expression. The module “Target screening” provides expression profiling analyses and graphic visualization. The module “Self-testing” offers the directory setups, sample management, and a check for third-party package dependency. Integration of other GUIs including Bowtie, miRDeep2, and miRspring extend the program’s functionality. eRNA focuses on the common tools required for the mapping and quantification analysis of miRNA-seq and mRNA-seq data. The software package provides an additional choice for scientists who require a user-friendly computing environment and high-throughput capacity for large data analysis. eRNA is available for free download at https://sourceforge.net/projects/erna/?source=directory .