BackgroundReal-time quantitative PCR (qPCR) is a broadly used technique in the biomedical research. Currently, few different analysis models are used to determine the quality of data and to quantify the mRNA level across the experimental conditions.MethodsWe developed an R package to implement methods for quality assessment, analysis and testing qPCR data for statistical significance. Double Delta CT and standard curve models were implemented to quantify the relative expression of target genes from CT in standard qPCR control-group experiments. In addition, calculation of amplification efficiency and curves from serial dilution qPCR experiments are used to assess the quality of the data. Finally, two-group testing and linear models were used to test for significance of the difference in expression control groups and conditions of interest.ResultsUsing two datasets from qPCR experiments, we applied different quality assessment, analysis and statistical testing in the pcr package and compared the results to the original published articles. The final relative expression values from the different models, as well as the intermediary outputs, were checked against the expected results in the original papers and were found to be accurate and reliable.ConclusionThe pcr package provides an intuitive and unified interface for its main functions to allow biologist to perform all necessary steps of qPCR analysis and produce graphs in a uniform way.

Autophagy, an intracellular degradation process, is essential for maintaining cell homeostasis by removing damaged organelles and proteins under various conditions of stress. In cancer, autophagy has conflicting functions. It plays a key role in protecting against cancerous transformation by maintaining genomic stability against genotoxic components, leading to cancerous transformation. It can also promote cancer cell survival by supplying minimal amounts of nutrients during cancer progression. However, the molecular mechanisms underlying how autophagy regulates the epithelial-to-mesenchymal transition (EMT) and cancer metastasis are unknown. Here, we show that starvation-induced autophagy promotes Snail (SNAI1) degradation and inhibits EMT and metastasis in cancer cells. Interestingly, SNAI1 proteins were physically associated and colocalized with LC3 and SQSTM1 in cancer cells. We also found a significant decrease in the levels of EMT and metastatic proteins under starvation conditions. Furthermore, ATG7 knockdown inhibited autophagy-induced SNAI1 degradation in the cytoplasm, which was associated with a decrease in SNAI1 nuclear translocation. Moreover, cancer cell invasion and migration were significantly inhibited by starvation-induced autophagy. These findings suggest that autophagy-dependent SNAI1 degradation could specifically regulate EMT and cancer metastasis during tumorigenesis.

Raf kinase inhibitor protein (RKIP) plays a critical role in many signaling pathways as a multi-functional adapter protein. In particular, the loss of RKIP’s function in certain types of cancer cells results in epithelial to mesenchymal transition (EMT) and the promotion of cancer metastasis. In addition, RKIP inhibits autophagy by modulating LC3-lipidation and mTORC1. How the RKIP-dependent inhibition of autophagy is linked to EMT and cancer progression is still under investigation. In this study, we investigated the ways by which RKIP interacts with key gene products in EMT and autophagy during the progression of prostate cancer. We first identified the gene products of interest using the corresponding gene ontology terms. The weighted-gene co-expression network analysis (WGCNA) was applied on a gene expression dataset from three groups of prostate tissues; benign prostate hyperplasia, primary and metastatic cancer. We found two modules of highly co-expressed genes, which were preserved in other independent datasets of prostate cancer tissues. RKIP showed potentially novel interactions with one EMT and seven autophagy gene products (TGFBR1; PIK3C3, PIK3CB, TBC1D25, TBC1D5, TOLLIP, WDR45 and WIPI1). In addition, we identified several upstream transcription modulators that could regulate the expression of these gene products. Finally, we verified some RKIP novel interactions by co-localization using the confocal microscopy analysis in a prostate cancer cell line. To summarize, RKIP interacts with EMT and autophagy as part of the same functional unit in developing prostate cancer.

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ObjectivesmicroRNAs regulate expression of target genes by specifically binding to their transcripts, subsequently leading to translational inhibition or mRNA degradation. Gene regulation by microRNAs has been implicated in a wide range of physiological and pathological conditions. Here, we leverage the use of public-access data and the available genomic annotations to pre-calculate the correlation of the expression of a large number of microRNAs with gene at the mRNA and protein level in the context of cancers.ResultsExpression data of miRNAs, mRNAs and proteins in cancer patients from The Cancer Genome Atlas along with TargetScan miRNAs-target annotations were used to calculate the expression correlations between miRNAs and features (mRNAs/proteins) in a number of cancer studies. We then packed the output of this analysis into a database and made it available through an interactive web application. The miRCancerdb is an easy-to-use database to investigate the microRNAs-dependent regulation of target genes involved in development of cancer.