BackgroundPrognostication of Breast Cancer (BC) relies largely on traditional clinical factors and biomarkers such as hormone or growth factor receptors. Due to their suboptimal specificities, it is challenging to accurately identify the subset of patients who are likely to undergo recurrence and there remains a major need for markers of higher utility to guide therapeutic decisions. MicroRNAs (miRNAs) are small non-coding RNAs that function as post-transcriptional regulators of gene expression and have shown promise as potential prognostic markers in several cancer types including BC.ResultsIn our study, we sequenced miRNAs from 104 BC samples and 11 apparently healthy normal (reduction mammoplasty) breast tissues. We used Case–control (CC) and Case-only (CO) statistical paradigm to identify prognostic markers. Cox-proportional hazards regression model was employed and risk score analysis was performed to identify miRNA signature independent of potential confounders. Representative miRNAs were validated using qRT-PCR. Gene targets for prognostic miRNAs were identified using in silico predictions and in-house BC transcriptome dataset. Gene ontology terms were identified using DAVID bioinformatics v6.7. A total of 1,423 miRNAs were captured. In the CC approach, 126 miRNAs were retained with predetermined criteria for good read counts, from which 80 miRNAs were differentially expressed. Of these, four and two miRNAs were significant for Overall Survival (OS) and Recurrence Free Survival (RFS), respectively. In the CO approach, from 147 miRNAs retained after filtering, 11 and 4 miRNAs were significant for OS and RFS, respectively. In both the approaches, the risk scores were significant after adjusting for potential confounders. The miRNAs associated with OS identified in our cohort were validated using an external dataset from The Cancer Genome Atlas (TCGA) project. Targets for the identified miRNAs were enriched for cell proliferation, invasion and migration.ConclusionsThe study identified twelve non-redundant miRNAs associated with OS and/or RFS. These signatures include those that were reported by others in BC or other cancers. Importantly we report for the first time two new candidate miRNAs (miR-574-3p and miR-660-5p) as promising prognostic markers. Independent validation of signatures (for OS) using an external dataset from TCGA further strengthened the study findings.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1899-0) contains supplementary material, which is available to authorized users.

Piwi-interacting RNAs (piRNAs), whose role in germline maintenance has been established, are now also being classified as post-transcriptional regulators of gene expression in somatic cells. PIWI proteins, central to piRNA biogenesis, have been identified as genetic and epigenetic regulators of gene expression. piRNAs/PIWIs have emerged as potential biomarkers for cancer but their relevance to breast cancer has not been comprehensively studied. piRNAs and mRNAs were profiled from normal and breast tumor tissues using next generation sequencing and Agilent platforms, respectively. Gene targets for differentially expressed piRNAs were identified from mRNA expression dataset. piRNAs and PIWI genes were independently assessed for their prognostic significance (outcomes: Overall Survival, OS and Recurrence Free Survival, RFS). We discovered eight piRNAs as novel independent prognostic markers and their association with OS was confirmed in an external dataset (The Cancer Genome Atlas). Further, PIWIL3 and PIWIL4 genes showed prognostic relevance. 306 gene targets exhibited reciprocal relationship with piRNA expression. Cancer cell pathways such as apoptosis and cell signaling were the key Gene Ontology terms associated with the regulated gene targets. Overall, we have captured the entire cascade of events in a dysregulated piRNA pathway and have identified novel markers for breast cancer prognostication.

One of the most abundant, yet least explored, classes of RNA is the small nucleolar RNAs (snoRNAs), which are well known for their involvement in post-transcriptional modifications of other RNAs. Although snoRNAs were only considered to perform housekeeping functions for a long time, recent studies have highlighted their importance as regulators of gene expression and as diagnostic/prognostic markers. However, the prognostic potential of these RNAs has not been interrogated for breast cancer (BC). The objective of the current study was to identify snoRNAs as prognostic markers for BC. Small RNA sequencing (Illumina Genome Analyzer IIx) was performed for 104 BC cases and 11 normal breast tissues. Partek Genomics Suite was used for analyzing the sequencing files. Two independent and proven approaches were used to identify prognostic markers: case-control (CC) and case-only (CO). For both approaches, snoRNAs significant in the permutation test, following univariate Cox proportional hazards regression model were used for constructing risk scores. Risk scores were subsequently adjusted for potential confounders in a multivariate Cox model. For both approaches, thirteen snoRNAs were associated with overall survival and/or recurrence free survival. Patients belonging to the high-risk group were associated with poor outcomes, and the risk score was significant after adjusting for confounders. Validation of representative snoRNAs (SNORD46 and SNORD89) using qRT-PCR confirmed the observations from sequencing experiments. We also observed 64 snoRNAs harboring piwi-interacting RNAs and/or microRNAs that were predicted to target genes (mRNAs) involved in tumorigenesis. Our results demonstrate the potential of snoRNAs to serve (i) as novel prognostic markers for BC and (ii) as indirect regulators of gene expression.

Type 1 diabetes (T1D) is characterized by the immune-mediated destruction of insulin-producing islet β cells. Biomarkers capable of identifying T1D risk and dissecting disease-related heterogeneity represent an unmet clinical need. Toward the goal of informing T1D biomarker strategies, we profiled coding and noncoding RNAs in human islet-derived exosomes and identified RNAs that were differentially expressed under proinflammatory cytokine stress conditions. Human pancreatic islets were obtained from cadaveric donors and treated with/without IL-1β and IFN-γ. Total RNA and small RNA sequencing were performed from islet-derived exosomes to identify mRNAs, long noncoding RNAs, and small noncoding RNAs. RNAs with a fold change ≥1.3 and a p-value <0.05 were considered as differentially expressed. mRNAs and miRNAs represented the most abundant long and small RNA species, respectively. Each of the RNA species showed altered expression patterns with cytokine treatment, and differentially expressed RNAs were predicted to be involved in insulin secretion, calcium signaling, necrosis, and apoptosis. Taken together, our data identify RNAs that are dysregulated under cytokine stress in human islet-derived exosomes, providing a comprehensive catalog of protein coding and noncoding RNAs that may serve as potential circulating biomarkers in T1D.

Transfer RNAs (tRNAs, key molecules in protein synthesis) have not been investigated as potential prognostic markers in breast cancer (BC), despite early findings of their dysregulation and diagnostic potential. We aim to comprehensively profile tRNAs from breast tissues and to evaluate their role as prognostic markers (Overall Survival, OS and Recurrence Free Survival, RFS). tRNAs were profiled from 11 normal breast and 104 breast tumor tissues using next generation sequencing. We adopted a Case-control (CC) and Case-Only (CO) association study designs. Risk scores constructed from tRNAs were subjected to univariate and multivariate Cox-proportional hazards regression to investigate their prognostic value. Of the 571 tRNAs profiled, 76 were differentially expressed (DE) and three were significant for OS in the CC approach. We identified an additional 11 tRNAs associated with OS and 14 tRNAs as significant for RFS in the CO approach, indicating that CC alone may not capture all discriminatory tRNAs in prognoses. In both the approaches, the risk scores were significant in the multivariate analysis as independent prognostic factors, and patients belonging to high-risk group were associated with poor prognosis. Our results confirmed global up-regulation of tRNAs in BC and identified tRNAs as potential novel prognostic markers for BC.

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Our hypothesis is that diabetes leads to loss of diurnal oscillatory rhythms in gut microbiota altering circulating metabolites. We performed an observational study where we compared diurnal changes of the gut microbiota with temporal changes of plasma metabolites. Metadata analysis from bacterial DNA from fecal pellets collected from 10-month old control (db/m) and type 2 diabetic (db/db) mice every 4 h for a 24-h period was used for prediction analysis. Blood plasma was collected at a day and night time points and was used for untargeted global metabolomic analysis. Feeding and activity behaviors were recorded. Our results show that while diabetic mice exhibited feeding and activity behavior similar to control mice, they exhibited a loss of diurnal oscillations in bacteria of the genus Akkermansia, Bifidobacterium, Allobaculum, Oscillospira and a phase shift in the oscillations of g.Prevotella, proteobacteria, and actinobacteria. Analysis of the circulating metabolites showed alterations in the diurnal pattern of metabolic pathways where bacteria have been implicated, such as the histidine, betaine, and methionine/cysteine pathway, mitochondrial function and the urea cycle. Functional analysis of the differential microbes revealed that during the day, when mice are asleep, the microbes of diabetic mice were enriched in processing carbon and pyruvate metabolic pathways instead of xenobiotic degradation as was observed for control mice. Altogether, our study suggests that diabetes led to loss of rhythmic oscillations of many gut microbiota with possible implications for temporal regulation of host metabolic pathways.

Discoveries on nonprotein-coding RNAs have induced a paradigm shift in our overall understanding of gene expression and regulation. We now understand that coding and noncoding RNA machinery work in concert to maintain overall homeostasis. Based on their length, noncoding RNAs are broadly classified into two groups—long (>200 nt) and small noncoding RNAs (<200 nt). These RNAs perform diverse functions—gene regulation, splicing, translation, and posttranscriptional modifications. MicroRNAs (miRNAs) and PIWI-interacting RNAs (piRNAs) are two classes of small noncoding RNAs that are now classified as master regulators of gene expression. They have also demonstrated clinical significance as potential biomarkers and therapeutic targets for several diseases, including cancer. Despite these similarities, both these RNAs are generated through contrasting mechanisms, and one of the aims of this review is to cover the distance travelled since their discovery and compare and contrast the various facets of these RNAs. Although these RNAs show tremendous promise as biomarkers, translating the findings from bench to bedside is often met with roadblocks. The second aim of this review therefore is to highlight some of the challenges that hinder application of miRNA and piRNA as in guiding treatment decisions.