Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types.

Isoforms of human miRNAs (isomiRs) are constitutively expressed with tissue- and disease-subtype-dependencies. We studied 10 271 tumor datasets from The Cancer Genome Atlas (TCGA) to evaluate whether isomiRs can distinguish amongst 32 TCGA cancers. Unlike previous approaches, we built a classifier that relied solely on \u27binarized\u27 isomiR profiles: each isomiR is simply labeled as \u27present\u27 or \u27absent\u27. The resulting classifier successfully labeled tumor datasets with an average sensitivity of 90% and a false discovery rate (FDR) of 3%, surpassing the performance of expression-based classification. The classifier maintained its power even after a 15× reduction in the number of isomiRs that were used for training. Notably, the classifier could correctly predict the cancer type in non-TCGA datasets from diverse platforms. Our analysis revealed that the most discriminatory isomiRs happen to also be differentially expressed between normal tissue and cancer. Even so, we find that these highly discriminating isomiRs have not been attracting the most research attention in the literature. Given their ability to successfully classify datasets from 32 cancers, isomiRs and our resulting \u27Pan-cancer Atlas\u27 of isomiR expression could serve as a suitable framework to explore novel cancer biomarkers

Characterizing microRNA regulators of lung disease

Lung diseases are one of the leading causes of mortality and morbidity worldwide. Understanding these diseases at a molecular level remains a critical component to developing effective therapeutics. Previous work has shown that gene expression alterations play an important role in disease initiation, maintenance, and progression as well as serve as diagnostic tools in disease. However, much remains to be uncovered regarding the role that microRNAs play in both healthy and diseased lung tissue. This thesis seeks to utilize methods of bioinformatics, cell biology, and molecular biology to examine the effect of miR-4423 on lung epithelial cell differentiation (Aim 1), miR-424 on never smoker derived lung adenocarcinoma (Aim 2), and miR-34c isomiRs in interstitial lung disease (ILD) (Aim 3). First, we examined the role of miR-4423 in lung mucociliary epithelium by employing the use of an air-liquid interface culture system, finding miR-4423 has an effect in ciliated cell differentiation and that a loss of miR-4423 is associated with cancer progression. These findings suggest that miR-4423’s actions in airway epithelium differentiation may potentially provide a therapeutic role in lung cancer. Next, we validated transcriptomic differences between lung tumor tissues resected from never and ever smokers. Specifically, miR-424, a predicted regulator of a large number of gene expression changes in never smoker lung adenocarcinoma, was found to regulate cell migration, potentially identifying a novel target and/or pathway for therapeutic action. Lastly, the function of microRNA isomiRs is relatively unknown. We validated miR-34c as upregulated in ILD and modulated both miR-34c and a miR-34c 5’ isomiR in lung relevant cell lines to explore their differing biological roles. We found that they are capable of targeting differing mRNA, indicating an independent role for isomiRs in disease. The studies contained in this dissertation offer valuable insight into the biology of microRNAs in the lung and how they might be employed as therapeutic targets for a number of common lung diseases. In addition, biological insights into the complexity of microRNAs in the lung highlight the need to better understand diseases influenced by microRNA expression and microRNA variants in regards to actionable therapeutics.2017-12-01T00:00:00

Omics analysis of educated platelets in cancer and benign disease of the pancreas

Pancreatic ductal adenocarcinoma (PDAC) is traditionally associated with thrombocy-tosis/hypercoagulation and novel insights on platelet-PDAC “dangerous liaisons” are warranted. Here we performed an integrative omics study investigating the biological processes of mRNAs and expressed miRNAs, as well as proteins in PDAC blood platelets, using benign disease as a refer-ence for inflammatory noise. Gene ontology mining revealed enrichment of RNA splicing, mRNA processing and translation initiation in miRNAs and proteins but depletion in RNA transcripts. Remarkably, correlation analyses revealed a negative regulation on SPARC transcription by isomiRs involved in cancer signaling, suggesting a specific ”education” in PDAC platelets. Platelets of benign patients were enriched for non-templated additions of G nucleotides (#ntaG) miRNAs, while PDAC presented length variation on 3′ (lv3p) as the most frequent modification on miRNAs. Additionally, we provided an actionable repertoire of PDAC and benign platelet-ome to be exploited for future studies. In conclusion, our data show that platelets change their biological repertoire in patients with PDAC, through dysregulation of miRNAs and splicing factors, supporting the presence of de novo protein machinery that can “educate” the platelet. These novel findings could be further exploited for innovative liquid biopsies platforms as well as possible therapeutic targets

An isomiR expression panel based novel breast cancer classification approach using improved mutual information

© 2018 The Author(s). Background: Gene expression-based profiling has been used to identify biomarkers for different breast cancer subtypes. However, this technique has many limitations. IsomiRs are isoforms of miRNAs that have critical roles in many biological processes and have been successfully used to distinguish various cancer types. Biomarker isomiRs for identifying different breast cancer subtypes has not been investigated. For the first time, we aim to show that isomiRs are better performing biomarkers and use them to explain molecular differences between breast cancer subtypes. Results: In this study, a novel method is proposed to identify specific isomiRs that faithfully classify breast cancer subtypes. First, as a null hypothesis method we removed the lowly expressed isomiRs from small sequencing data generated from diverse breast cancers types. Second, we developed an improved mutual information-based feature selection method to calculate the weight of each isomiR expression. The weight of isomiR measures the importance of a given isomiR in classifying breast cancer subtypes. The improved mutual information enables to apply the dataset in which the feature is continuous data and label is discrete data; whereby, the traditional mutual information cannot be applied in this dataset. Finally, the support vector machine (SVM) classifier is applied to find isomiR biomarkers for subtyping. Conclusions: Here we demonstrate that isomiRs can be used as biomarkers in the identification of different breast cancer subtypes, and in addition, they may provide new insights into the diverse molecular mechanisms of breast cancers. We have also shown that the classification of different subtypes of breast cancer based on isomiRs expression is more effective than using published gene expression profiling. The proposed method provides a better performance outcome than Fisher method and Hellinger method for discovering biomarkers to distinguish different breast cancer subtypes. This novel technique could be directly applied to identify biomarkers in other diseases

Potential Biomarkers for Therapeutic Monitoring and Clinical Outcome in Breast Cancer

Non-coding RNAs are a species of RNA that are not translated to proteins. These include transfer RNAs and ribosomal RNAs, microRNAs, transfer RNA-derived fragments, and long non-coding RNA. It is known that expression levels of some non-coding RNAs included microRNAs are altered in cancer cells or tumor tissues. Moreover, expression profiles of such non-coding RNAs correlate between tissues and body fluids. Therefore, several non-coding RNAs are being used as diagnostic/prognosis biomarkers or therapeutic targets in cancer. In this chapter, we review about representative non-coding RNAs and introduce especially microRNA as diagnosis/prognosis biomarkers and therapeutic targets

Non‑invasive prostate cancer detection by measuring miRNA variants (isomiRs) in urine extracellular vesicles

The authors thank J. Bossenga for collecting urine and P.P. Eijk for their technical assistance in smallRNA sequencing library preparations.In many cancer types, the expression and function of ~22 nucleotide‑long microRNAs (miRNA) is deregulated. Mature miRNAs can be stably detected in extracellular vesicles (EVs) in biofluids, therefore they are considered to have great potential as biomarkers. In the present study, we investigated whether miRNAs have a distinct expression pattern in urine‑EVs of prostate cancer (PCa) patients compared to control males. By next generation sequencing, we determined the miRNA expression in a discovery cohort of 4 control men and 9 PCa patients. miRNAs were validated by using a stemloop RT‑PCR in an independent cohort of 74 patients (26 control and 48 PCa‑patients). Whereas standard mapping protocols identified > 10 PCa associated miRNAs in urinary EVs, miR‑21, miR‑375 and miR‑204 failed to robustly discriminate for disease in a validation study with RT‑PCR‑detection of mature miRNA sequences. In contrast, we observed that miRNA isoforms (isomiRs) with 3′ end modifications were highly discriminatory between samples from control men and PCa patients. Highly differentially expressed isomiRs of miR‑21, miR‑204 and miR‑375 were subsequently validated in an independent group of 74 patients. Receiver‑operating characteristic analysis was performed to evaluate the diagnostic performance of three isomiRs, resulting in a 72.9% sensitivity with a high (88%) specificity and an area under the curve (AUC) of 0.866. In comparison, prostate specific antigen had an AUC of 0.707 and measuring the mature form of these miRNAs yielded a lower 70.8% sensitivity and 72% specificity (AUC 0.766). We propose that isomiRs may carry discriminatory information which is useful to generate stronger biomarkers.Stichting Urologie 1973VUmc-CCAWorldwide Cancer Research (AICR) 15-1005KWF-VU-5510Worldwide Cancer Research 15-100

A 10-year prediagnostic follow-up study shows that serum RNA signals are highly dynamic in lung carcinogenesis

The majority of lung cancer (LC) patients are diagnosed at a late stage, and survival is poor. Circulating RNA molecules are known to have a role in cancer; however, their involvement before diagnosis remains an open question. In this study, we investigated circulating RNA dynamics in prediagnostic LC samples, focusing on smokers, to identify if and when disease-related signals can be detected in serum. We sequenced small RNAs in 542 serum LC samples donated up to 10 years before diagnosis and 519 matched cancer-free controls coming from 905 individuals in the Janus Serum Bank. This sample size provided sufficient statistical power to independently analyze time to diagnosis, stage, and histology. The results showed dynamic changes in differentially expressed circulating RNAs specific to LC histology and stage. The greatest number of differentially expressed RNAs was identified around 7 years before diagnosis for early-stage LC and 1–4 years prior to diagnosis for locally advanced and advanced-stage LC, regardless of LC histology. Furthermore, NSCLC and SCLC histologies have distinct prediagnostic signals. The majority of differentially expressed RNAs were associated with cancer-related pathways. The dynamic RNA signals pinpointed different phases of tumor development over time. Stage-specific RNA profiles may be associated with tumor aggressiveness. Our results improve the molecular understanding of carcinogenesis. They indicate substantial opportunity for screening and improved treatment and will guide further research on early detection of LC. However, the dynamic nature of the RNA signals also suggests challenges for prediagnostic biomarker discovery

Systematic analysis of prognostic miRNAs and isomiRs in prostate cancer

There are no reliable prognostic indicators to distinguish between indolent and aggressive prostate cancer (PCa). Consequently, 42–66% of patients with indolent PCa are over-treated. Additionally, 15-45% of patients treated with radical prostatectomy (RP) experience biochemical recurrence (BCR) within 5-years, highlighting an urgent need for reliable prognostic biomarkers. MiRNAs (miRs) and isomiRs (miR isoforms) are non-coding regulatory RNAs that hold ideal biomarker properties such as detection in circulation, tissue and tumour specific expression profiles, and correlation with PCa development and progression. I hypothesised that miR species (canonical miRs and isomiRs) can be utilised as biomarkers for reliable PCa prognostication. A novel database of prognostic PCa miRs was built by performing a systematic review of relevant publications in the PubMed database. MiRs significantly associated with BCR were also identified following a meta-analysis of six datasets. MiR-148a-3p and miR-582-4p were identified as potential biomarker candidates as they were consistently prognostic in both the review and meta-analysis. The ability of miR species to predict BCR post-RP was tested with elastic net regularisation models using The Cancer Genome Atlas PCa dataset (recurrent=61, non-recurrent=330). Models based on a combination of isomiRs and clinical markers achieved marginally greater predictive power (AUC=0.795) than the model solely based on clinical markers (AUC=0.748), demonstrating that isomiRs could contribute additional prognostic value to the clinical markers currently used. The mechanism by which miR-27a-3p, a PCa-specific putative oncomiR, promotes tumour growth was investigated using RNA-seq data from LNCaP tumour xenograft models treated with a miR-27a-3p inhibitor (n=3) and control (n=3). 11 significantly dysregulated genes involved in apoptosis and oncogenic signalling were identified as likely mir-27a-3p targets. This study has not only furthered our understanding of the importance of miRs in PCa, but also identified potential prognostic miR biomarkers and showed the inclusion of miR species increases the utility of current markers.Open Acces