Optimal consistency in microRNA expression analysis using reference-gene-based normalization

Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma

Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified metabolite signatures, resulting in a lack of robust metabolic biomarkers for the diagnosis and treatment of PA. Herein, we performed a global untargeted plasma metabolomic profiling on PA and identified a highly robust metabolomic signature based on a strategy. Specifically, this strategy is unique in (1) integrating repeated random sampling and a consensus evaluation-based feature selection algorithm and (2) evaluating the consistency of metabolomic signatures among different sample groups. This strategy demonstrated superior robustness and stronger discriminative ability compared with that of other feature selection methods including Student's t-test, partial least-squares-discriminant analysis, support vector machine recursive feature elimination, and random forest recursive feature elimination. More importantly, a highly robust metabolomic signature comprising 45 PA-specific differential metabolites was identified. Moreover, metabolite set enrichment analysis of these potential metabolic biomarkers revealed altered lipid metabolism in PA. In conclusion, our findings contribute to a better understanding of the metabolic changes in PA and may have implications for the development of diagnostic and therapeutic approaches targeting lipid metabolism in PA. We believe that the proposed strategy serves as a valuable tool for screening robust, discriminating metabolic features in the field of metabolomics.

Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review

Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.

Lack of associations of microRNAs with severe NAFLD in people living with HIV: discovery case-control study

Background & objective

Nonalcoholic fatty liver disease (NAFLD) is highly prevalent in people living with HIV (PLWH) and the expression of some microRNAs could be useful as biomarkers for the diagnosis of NAFLD. The aim of this study was to identify patterns of differential expression of microRNAs in PLWH and assess their diagnostic value for NALFD.

Methods

A discovery case-control study with PLWH was carried out. The expression of miRNAs was determined using HTG EdgeSeq technology. Cases were defined as patients with severe NAFLD and controls as patients without NAFLD, characterized using the controlled attenuation parameter (CAP). Cases and controls were matched 1:1 for age, sex, BMI, CD4+ lymphocyte count, active HCV infection, and ART regimen.

Results

Serum 2,083 simultaneous microRNA transcripts were analyzed using HTG technology and compared between cases and controls. Forty-five patients, 23 cases, and 22 controls were included in the study. In the analysis of the expression pattern of the 2,083 microRNAs, no differential expression patterns were found between both groups of patients included in the study.

Conclusion

Analysis of the microRNA transcriptome profile of nonobese PLWH with severe NAFLD did not appear to differ from that of patients without NAFLD. Thus, microRNA might not serve as a proper biomarker for predicting severe NALFD in this population.

Forensic age estimation from human blood using age-related microRNAs and circular RNAs markers

Aging is a complicated process characterized by progressive and extensive changes in physiological homeostasis at the organismal, tissue, and cellular levels. In modern society, age estimation is essential in a large variety of legal rights and duties. Accumulating evidence suggests roles for microRNAs (miRNAs) and circular RNAs (circRNAs) in regulating numerous processes during aging. Here, we performed circRNA sequencing in two age groups and analyzed microarray data of 171 healthy subjects (17-104 years old) downloaded from Gene Expression Omnibus (GEO) and ArrayExpress databases with integrated bioinformatics methods. A total of 1,403 circular RNAs were differentially expressed between young and old groups, and 141 circular RNAs were expressed exclusively in elderly samples while 10 circular RNAs were expressed only in young subjects. Based on their expression pattern in these two groups, the circular RNAs were categorized into three classes: age-related expression between young and old, age-limited expression-young only, and age-limited expression-old only. Top five expressed circular RNAs among three classes and a total of 18 differentially expressed microRNAs screened from online databases were selected to validate using RT-qPCR tests. An independent set of 200 blood samples (20-80 years old) was used to develop age prediction models based on 15 age-related noncoding RNAs (11 microRNAs and 4 circular RNAs). Different machine learning algorithms for age prediction were applied, including regression tree, bagging, support vector regression (SVR), random forest regression (RFR), and XGBoost. Among them, random forest regression model performed best in both training set (mean absolute error = 3.68 years, r = 0.96) and testing set (MAE = 6.840 years, r = 0.77). Models using one single type of predictors, circular RNAs-only or microRNAs-only, result in bigger errors. Smaller prediction errors were shown in males than females when constructing models according to different-sex separately. Putative microRNA targets (430 genes) were enriched in the cellular senescence pathway and cell homeostasis and cell differentiation regulation, indirectly indicating that the microRNAs screened in our study were correlated with development and aging. This study demonstrates that the noncoding RNA aging clock has potential in predicting chronological age and will be an available biological marker in routine forensic investigation to predict the age of biological samples.

ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies

Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.

A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies

Unwanted experimental/biological variation and technical error are frequently encountered in current metabolomics, which requires the employment of normalization methods for removing undesired data fluctuations. To ensure the 'thorough' removal of unwanted variations, the collective consideration of multiple criteria ('intragroup variation', 'marker stability' and 'classification capability') was essential. However, due to the limited number of available normalization methods, it is extremely challenging to discover the appropriate one that can meet all these criteria. Herein, a novel approach was proposed to discover the normalization strategies that are consistently well performing (CWP) under all criteria. Based on various benchmarks, all normalization methods popular in current metabolomics were 'first' discovered to be non-CWP. 'Then', 21 new strategies that combined the 'sample'-based method with the 'metabolite'-based one were found to be CWP. 'Finally', a variety of currently available methods (such as cubic splines, range scaling, level scaling, EigenMS, cyclic loess and mean) were identified to be CWP when combining with other normalization. In conclusion, this study not only discovered several strategies that performed consistently well under all criteria, but also proposed a novel approach that could ensure the identification of CWP strategies for future biological problems.

NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data

Biological processes (like microbial growth & physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N>2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https://idrblab.org/noreva/.

MetaFS: Performance assessment of biomarker discovery in metaproteomics

Metaproteomics suffers from the issues of dimensionality and sparsity. Data reduction methods can maximally identify the relevant subset of significant differential features and reduce data redundancy. Feature selection (FS) methods were applied to obtain the significant differential subset. So far, a variety of feature selection methods have been developed for metaproteomic study. However, due to FS's performance depended heavily on the data characteristics of a given research, the well-suitable feature selection method must be carefully selected to obtain the reproducible differential proteins. Moreover, it is critical to evaluate the performance of each FS method according to comprehensive criteria, because the single criterion is not sufficient to reflect the overall performance of the FS method. Therefore, we developed an online tool named MetaFS, which provided 13 types of FS methods and conducted the comprehensive evaluation on the complex FS methods using four widely accepted and independent criteria. Furthermore, the function and reliability of MetaFS were systematically tested and validated via two case studies. In sum, MetaFS could be a distinguished tool for discovering the overall well-performed FS method for selecting the potential biomarkers in microbiome studies. The online tool is freely available at https://idrblab.org/metafs/.

Identification of Appropriate Reference Genes for Normalizing miRNA Expression in Citrus Infected by Xanthomonas citri subsp. citri

MicroRNAs (miRNAs) are short noncoding RNA molecules that regulate gene expression at the posttranscriptional level. Reverse transcription-quantitative PCR (RT-qPCR) is one of the most common methods used for quantification of miRNA expression, and the levels of expression are normalized by comparing with reference genes. Thus, the selection of reference genes is critically important for accurate quantification. The present study was intended to identify appropriate miRNA reference genes for normalizing the level of miRNA expression in Citrus sinensis L. Osbeck and Citrus reticulata Blanco infected by Xanthomonas citri subsp. citri, which caused citrus canker disease. Five algorithms (Delta Ct, geNorm, NormFinder, BestKeeper and RefFinder) were used for screening reference genes, and two quantification approaches, poly(A) extension RT-qPCR and stem-loop RT-qPCR, were used to determine the most appropriate method for detecting expression patterns of miRNA. An overall comprehensive ranking output derived from the multi-algorithms showed that poly(A)-tailed miR162-3p/miR472 were the best reference gene combination for miRNA RT-qPCR normalization in citrus canker research. Candidate reference gene expression profiles determined by poly(A) RT-qPCR were more consistent in the two citrus species. To the best of our knowledge, this is the first systematic comparison of two miRNA quantification methods for evaluating reference genes. These results highlight the importance of rigorously assessing candidate reference genes and clarify some contradictory results in miRNA research on citrus.

Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

Identification of the gene signature reflecting schizophrenia's etiology by constructing artificial intelligence-based method of enhanced reproducibility

Aims

As one of the most fundamental questions in modern science, “what causes schizophrenia (SZ)” remains a profound mystery due to the absence of objective gene markers. The reproducibility of the gene signatures identified by independent studies is found to be extremely low due to the incapability of available feature selection methods and the lack of measurement on validating signatures’ robustness. These irreproducible results have significantly limited our understanding of the etiology of SZ.

Methods

In this study, a new feature selection strategy was developed, and a comprehensive analysis was then conducted to ensure a reliable signature discovery. Particularly, the new strategy (a) combined multiple randomized sampling with consensus scoring and (b) assessed gene ranking consistency among different datasets, and a comprehensive analysis among nine independent studies was conducted.

Results

Based on a first‐ever evaluation of methods’ reproducibility that was cross‐validated by nine independent studies, the newly developed strategy was found to be superior to the traditional ones. As a result, 33 genes were consistently identified from multiple datasets by the new strategy as differentially expressed, which might facilitate our understanding of the mechanism underlying the etiology of SZ.

Conclusion

A new strategy capable of enhancing the reproducibility of feature selection in current SZ research was successfully constructed and validated. A group of candidate genes identified in this study should be considered as great potential for revealing the etiology of SZ.

Variance component analysis of circulating miR-122 in serum from healthy human volunteers

Micro-RNA (miR)-122 is a promising exploratory biomarker for detecting liver injury in preclinical and clinical studies. Elevations in serum or plasma have been associated with viral and autoimmune hepatitis, non-alcoholic steatohepatitis (NASH), hepatocellular carcinoma, and drug-induced liver injury (DILI). However, these associations were primarily based upon population differences between the disease state and the controls. Thus, little is known about the variability and subsequent variance components of circulating miR-122 in healthy humans, which has implications for the practical use of the biomarker clinically. To address this, we set out to perform variance components analysis of miR-122 in a cohort of 40 healthy volunteers. Employing a quantitative real-time polymerase chain reaction (qRT-PCR) assay to detect miR-122 and other circulating miRNAs in human serum, the relative expression of miR-122 was determined using two different normalization approaches: to the mean expression of a panel of several endogenous miRNAs identified using an adaptive algorithm (miRA-Norm) and to the expression of an exogenous miRNA control (Caenorhabditis elegans miR-39). Results from a longitudinal study in healthy volunteers (N = 40) demonstrated high variability with 117- and 111-fold 95% confidence reference interval, respectively. This high variability of miR-122 in serum appeared to be due in part to ethnicity, as 95% confidence reference intervals were approximately three-fold lower in volunteers that identified as Caucasian relative to those that identified as Non-Caucasian. Variance analysis revealed equivalent contributions of intra- and inter-donor variability to miR-122. Surprisingly, miR-122 exhibited the highest variability compared to other 36 abundant miRNAs in circulation; the next variable miRNA, miR-133a, demonstrated a 45- to 62-fold reference interval depending on normalization approaches. In contrast, alanine aminotransferase (ALT) activity levels in this population exhibited a 5-fold total variance, with 80% of this variance due to inter-donor sources. In conclusion, miR-122 demonstrated higher than expected variability in serum from healthy volunteers, which has implications for its potential utility as a prospective biomarker of liver damage or injury.

Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data

The etiology of schizophrenia (SCZ) is regarded as one of the most fundamental puzzles in current medical research, and its diagnosis is limited by the lack of objective molecular criteria. Although plenty of studies were conducted, SCZ gene signatures identified by these independent studies are found highly inconsistent. As one of the most important factors contributing to this inconsistency, the feature selection methods used currently do not fully consider the reproducibility among the signatures discovered from different datasets. Therefore, it is crucial to develop new bioinformatics tools of novel strategy for ensuring a stable discovery of gene signature for SCZ. In this study, a novel feature selection strategy (1) integrating repeated random sampling with consensus scoring and (2) evaluating the consistency of gene rank among different datasets was constructed. By systematically assessing the identified SCZ signature comprising 135 differentially expressed genes, this newly constructed strategy demonstrated significantly enhanced stability and better differentiating ability compared with the feature selection methods popular in current SCZ research. Based on a first-ever assessment on methods’ reproducibility cross-validated by independent datasets from three representative studies, the new strategy stood out among the popular methods by showing superior stability and differentiating ability. Finally, 2 novel and 17 previously reported transcription factors were identified and showed great potential in revealing the etiology of SCZ. In sum, the SCZ signature identified in this study would provide valuable clues for discovering diagnostic molecules and potential targets for SCZ.

Alteration of miRNA-mRNA interactions in lymphocytes of individuals with schizophrenia

The aetiology of schizophrenia is complex, heterogeneous, and involves interplay of many genetic and environmental influences. While significant progress has been made in the understanding the common heritable component, we are still grappling with the genomic encoding of environmental risk. One class of molecule that has tremendous potential is miRNA. These molecules are regulated by genetic and environmental factors associated with schizophrenia and have a very significant impact on temporospatial patterns of gene expression. To better understand the relationship between miRNA and gene expression in the disorder we analysed these molecules in RNA isolated from peripheral blood mononuclear cells (PBMCs) obtained from an Australian cohort of 36 individuals with schizophrenia and 15 healthy controls using next-generation RNA sequencing. Significant changes in both mRNA and miRNA expression profiles were observed implicating important interaction networks involved in immune activity and development. We also observed sexual dimorphism, particularly in relation to variation in mRNA, with males showing significantly more differentially expressed genes. Interestingly, while we explored expression in lymphocytes, the systems biology of miRNA-mRNA interactions was suggestive of significant pleiotropy with enrichment of networks related to neuronal activity.

Laser microdissection and gene expression profiling in the human postmortem brain

Laser microdissection in combination with gene expression profiling using postmortem human brain tissue provides a powerful approach to interrogating cell type-specific pathologies within neural circuits that are known to be dysfunctional in neuropsychiatric disorders. The success of these experiments critically depends on a number of factors, such as the cellular purity of the sample, the quality of the RNA, the methodologies of data normalization and computational data analysis, and how data are interpreted. Data obtained from these experiments should be validated at the protein level. Furthermore, from the perspective of disease mechanism discovery, it would be ideal to investigate whether manipulation of the expression of genes identified as differentially expressed can rescue or ameliorate the neurobiologic or behavioral phenotypes associated with the specific disease. Thus, the ultimate value of this approach rests upon the fact that the generation of novel disease-related pathophysiologic hypotheses may lead to deeper understanding of disease mechanisms and possible development of effective targeted treatments.

NOREVA: normalization and evaluation of MS-based metabolomics data

Diverse forms of unwanted signal variations in mass spectrometry-based metabolomics data adversely affect the accuracies of metabolic profiling. A variety of normalization methods have been developed for addressing this problem. However, their performances vary greatly and depend heavily on the nature of the studied data. Moreover, given the complexity of the actual data, it is not feasible to assess the performance of methods by single criterion. We therefore developed NOREVA to enable performance evaluation of various normalization methods from multiple perspectives. NOREVA integrated five well-established criteria (each with a distinct underlying theory) to ensure more comprehensive evaluation than any single criterion. It provided the most complete set of the available normalization methods, with unique features of removing overall unwanted variations based on quality control metabolites and allowing quality control samples based correction sequentially followed by data normalization. The originality of NOREVA and the reliability of its algorithms were extensively validated by case studies on five benchmark datasets. In sum, NOREVA is distinguished for its capability of identifying the well performed normalization method by taking multiple criteria into consideration and can be an indispensable complement to other available tools. NOREVA can be freely accessed at http://server.idrb.cqu.edu.cn/noreva/.

Genome-wide miRNAs expression profiles of Schistosoma japonicum schistosomula in response to artesunate

Aim: miRNAs play a significant role in pharmacogenomics and are likely to be important in the molecular mechanism of atesunate (ART) effects on Schistosoma japonicum. Methods: We sequenced the RNAs using an Illumina (Solexa) DNA sequencer and compared the relative expression levels of the miRNAs in 10-day-old schistosomula from ART and the parallel control group. Results: We characterized 95 known miRNAs from S. japonicum schistosomula individuals, including 38 novel miRNA families. Among the detectable 134 miRNAs differentially expressed (>2.0-fold change, p < 0.01) after ART treatment in schistosomula, a total of seven known or novel 3p- or 5p- derived S. japonicum miRNAs were characterized. We propose that sja-miR-125b may regulate the expression of ART metabolizing enzymes, glutathione synthetase or heme-binding protein 2 to help S. japonicum resists or adapts to drug stress and also ART may significantly inhibit sexual maturation of female worms mediated by mir-71b/2 miRNA cluster. Conclusion: This was the first comprehensive miRNAs expression profile analysis of S. japonicum in response to ART, and provides an overview of the complex network of the mechanism of action of ART on S. japonicum.

Evaluating normalization approaches for the better identification of aberrant microRNAs associated with hepatocellular carcinoma

AIM

Dysregulated microRNAs (miRNAs) have been identified in hepatocellular carcinoma (HCC), but only a small proportion have been confirmed. An appropriate normalizer is crucial to determining the accuracy and reliability of data from miRNA studies.

METHODS

Different normalization strategies were used to validate genome-wide miRNA profiles in HCC tumor and non-tumor tissues, and to determine the consistency and discrepancy of data on dysregulated miRNAs.

RESULTS

Two sets of stable miRNAs (miR-30c/miR-30b and miR-30c/miR-126) were identified in HCC tissues by geNorm and NormFinder tools, respectively. The mean of global miRNAs also showed good stability for ranking the top 1-2 miRNAs, but the stabilities of the manufacturer-recommended ncRNAs controls were poor. Four panels of miRNAs were significantly associated with HCC by separately using various normalizers, and 14 miRNAs were consistently identified by three normalization strategies. Although fewer miRNAs (17-26) were dysregulated in HCC using the global mean or the 2 stable miRNAs as normalizers, perfect clustering of tissues was also obtained with only 1 to 2 misclassifications, suggesting the efficiency of the miRNA panels. Using global mean as the normalizer, the authors identified 7 miRNAs, including 2 novel (miR-324-5p and miR-550) significantly upregulated in HCC that were omitted when using 3 endogenous controls as the normalizer.

CONCLUSION

An optimal normalization strategy to identify biologically important miRNAs in HCC tissue studies of miRNA may be the combination of global mean and 2 stable miRNAs. Selection of appropriate normalization strategies to adjust miRNAs levels is particularly important for epidemiological studies dealing with large data sets and covering multiple experimental batches.

microRNA-guided diagnostics in clinical samples

miRNA-guided diagnostics is a powerful molecular approach for evaluating clinical samples through miRNA detection and/or visualization. To date, this approach has been successfully used to diagnose, manage, and/or monitor a wide range of neoplastic and non-neoplastic diseases. Despite the promise of miRNA-guided diagnostics, particularly in the field of minimally invasive biomarkers, several knowledge and practical issues confound or hinder translation into routine clinical practice including: miRNA sequence database errors, suboptimal RNA extraction methods, detection assay variability, a vast array of online resources for bioinformatic analyses, and non-standardized statistical analyses for miRNA clinical testing. In this review, we raise awareness of these issues and recommend research directions to help specialists in endocrinology and metabolism integrate miRNA testing into clinical decision-making.

Data Normalization Strategies for MicroRNA Quantification

BACKGROUND

Different technologies, such as quantitative real-time PCR or microarrays, have been developed to measure microRNA (miRNA) expression levels. Quantification of miRNA transcripts implicates data normalization using endogenous and exogenous reference genes for data correction. However, there is no consensus about an optimal normalization strategy. The choice of a reference gene remains problematic and can have a serious impact on the actual available transcript levels and, consequently, on the biological interpretation of data.

CONTENT

In this review article we discuss the reliability of the use of small RNAs, commonly reported in the literature as miRNA expression normalizers, and compare different strategies used for data normalization.

SUMMARY

A workflow strategy is proposed for normalization of miRNA expression data in an attempt to provide a basis for the establishment of a global standard procedure that will allow comparison across studies.