microRNA Expression Data Research Articles

Identification of genes associated with survival of breast cancer patients.

We aimed to investigate the potential of microRNA expression profiles to predict survival in breast cancer. MicroRNA and mRNA expression data of breast cancer were downloaded from The Cancer Genome Atlas. LASSO regression was used to identify microRNAs signature predicting survival of breast cancer patients. Transfection experiment was conducted to explore the influence of microRNAs on their potential targets. We identified 56 differentially expressed microRNAs in breast cancer tissues compared to adjacent normal tissues. 10 microRNAs with non-zero coefficient were selected from the 56 microRNAs using LASSO Cox regression. After predicting the targets for the 10 microRNAs, we further obtained 155 targets that were associated with overall survival of breast cancer patients. Spearman's correlation analysis found that the expression of SCUBE2, SCRN3, YTHDF3, ITFG1, ITPRIPL2, and JAK1 was an inversely correlated with their microRNAs. Transfection experiment showed that YTHDF3 was down-regulated in cells transfected with miR-106b-5p mimics compared with those transfected with negative control of mimics (fold change 4.21; P < 0.01). In conclusion, we identified a 10-miRNA signature associated with prognosis of breast cancer patients. The expression of YTHDF3 was down-regulated by miR-106b-5p.

Evidence for a potential role of miR-1908-5p and miR-3614-5p in autoimmune disease risk using integrative bioinformatics

Genome-wide association studies (GWAS) have identified a large number of genetic risk loci for autoimmune diseases. However, the functional variants underlying these disease associations remain largely unknown. There is evidence that microRNA-mediated regulation may play an important role in this context. Therefore, we assessed whether autoimmune disease loci unfold their effects via altering microRNA expression in relevant immune cells.To this end, we performed comprehensive data integration of many large and publicly available datasets to combine information on autoimmune disease risk loci with RNA-Seq-based microRNA expression data. Specifically, we carried out microRNA expression quantitative trait loci (eQTL) analyses across 115 GWAS regions associated with 12 autoimmune diseases using next-generation sequencing data of 345 lymphoblastoid cell lines. Statistical analyses included the application and extension of a recently proposed framework (joint likelihood mapping) to microRNA expression data and microRNA target gene enrichment analyses of relevant GWAS data.Overall, only a minority of autoimmune disease risk loci may exert their pathophysiologic effects by altering microRNA expression based on JLIM. However, detailed functional fine-mapping revealed two independent GWAS regions harboring autoimmune disease risk SNPs with significant effects on microRNA expression. These relate to SNPs associated with Crohn's disease (CD; rs102275) and rheumatoid arthritis (RA; rs968567), which affect the expression of miR-1908-5p (prs102275 = 1.44e-20, prs968567 = 2.54e-14). In addition, an independent CD risk SNP, rs3853824, was found to alter the expression of miR-3614-5p (p = 5.70e-7). To support these findings, we demonstrate that GWAS signals for RA and CD were enriched in genes predicted to be targeted by both microRNAs (all with p < 0.05).In summary, our study points towards a potential pathophysiological role of miR-1908-5p and miR-3614-5p in autoimmunity.

Corrigendum] Bioinformatics analysis of differentially expressed miRNA‑related mRNAs and their prognostic value in breast carcinoma

Subsequently to the publication of this article, the authors have realized that the first of the affiliations had appeared incorrectly: 'Shying People's Hospital, Shyang Affiliated Hospital of Xuzhou Medical University, Shying' should have appeared as 'Shuyang People's Hopital, Shuyang Affiliated Hospital of Xuzhou Medical University, Shuyang'. The authors regret this error in the affiliations, and apologize for any inconvenience caused. [the original article was published in the Oncology Reports 39: 2865‑2872, 2018; DOI: 10.3892/or.2018.6393].

Discovery of two-level modular organization from matched genomic data via joint matrix tri-factorization.

With the rapid development of biotechnology, multi-dimensional genomic data are available for us to study the regulatory associations among multiple levels. Thus, it is essential to develop a tool to identify not only the modular patterns from multiple levels, but also the relationships among these modules. In this study, we adopt a novel non-negative matrix factorization framework (NetNMF) to integrate pairwise genomic data in a network manner. NetNMF could reveal the modules of each dimension and the connections within and between both types of modules. We first demonstrated the effectiveness of NetNMF using a set of simulated data and compared it with two typical NMF methods. Further, we applied it to two different types of pairwise genomic datasets including microRNA (miRNA) and gene expression data from The Cancer Genome Atlas and gene expression and pharmacological data from the Cancer Genome Project. We respectively identified a two-level miRNA–gene module network and a two-level gene–drug module network. Not only have the majority of identified modules significantly functional implications, but also the three types of module pairs have closely biological associations. This module discovery tool provides us comprehensive insights into the mechanisms of how the two levels of molecules cooperate with each other.

Time Series Integrative Analysis of RNA Sequencing and MicroRNA Expression Data Reveals Key Biologic Wound Healing Pathways in Keloid-Prone Individuals

Time Series Integrative Analysis of RNA Sequencing and MicroRNA Expression Data Reveals Key Biologic Wound Healing Pathways in Keloid-Prone Individuals

MicroRNA expression data analysis to identify key miRNAs associated with Alzheimer's disease.

MicroRNAs (miRNAs) have become increasingly prevalent as a result of the association of their deregulation with neurodegenerative disorders, especially Alzheimer's disease (AD). However, the association between miRNAs and AD remains unclear. In the present study, Nine representative miRNA datasets were selected for the identification of the critical miRNAs by analyzing the overlapping relationships among them. TargetScan software (http://www.targetscan.org) was used to predict the target genes of these miRNAs. In addition, the Database for Annotation Visualization and Integrated Discovery (DAVID; http://david.abcc.ncifcrf.gov) and TfactS (http://www.tfacts.org) datasets were used for combined analysis of functional enrichment and transcription factor (TF) analysis. Thirteen key miRNAs were identified, of which four were significantly up-regulated (hsa-miR-101,hsa-miR-155, has-miR-34a, has-miR-9) and eight were found to be significantly down-regulated (hsa-let-7d-5p, hsa-let-7g-5p, hsa-miR-15b, has-miR-191-5p, hsa-miR-125b, has-miR-26b-5p, hsa-miR-29b, hsa-miR-342-3p). The functional enrichment analysis indicated that up-regulated signature miRNA targets were associated with transcription from the RNA polymerase II promoter process and the chemical synaptic transmission process. Down-regulated signature miRNA targets were mostly enriched with respect to positive regulation of transcription from the RNA polymerase II promoter process, p53 signaling, and microRNAs in cancer pathways. TF analysis showed that 87 TFs were influenced by the up-regulated miRNAs, and 134 TFs were influenced by the down-regulated miRNAs. In total, 70 (45.5%) TFs were affected by both up-regulated and down-regulated miRNAs. In summary, 13 key miRNAs were found to have a vital function in the pathological progress of AD, as well as the target genes and TFs of these miRNAs. The potential functions of these miRNAs as diagnostic and therapeutic targets of the AD are revealed by the present study.

Systematic prediction of target genes and pathways in cervical cancer from microRNA expression data.

Cervical cancer (CC) is a leading cause of cancer-associated mortality in women; thus, the present study aimed to investigated potential target genes and pathways in patients with CC by utilizing an ensemble method and pathway enrichment analysis. The ensemble method integrated a correlation method [Pearson's correlation coefficient (PCC)], a causal inference method (IDA) and a regression method [least absolute shrinkage and selection operator (Lasso)] using the Borda count election algorithm, forming the PCC, IDA and Lasso (PIL) method. Subsequently, the PIL method was validated to be a feasible approach to predict microRNA (miRNA) targets by comparing predicted miRNA targets against those from a confirmed database. Finally, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was conducted for target genes in the 1,000 most frequently predicted miRNA-mRNA interactions to determine target pathways. A total of 10 target genes were obtained that were predicted >5 times, including secreted frizzled-related protein 4, maternally expressed 3 and NIPA like domain containing 4. Additionally, a total of 17 target pathways were identified, of which cytokine-cytokine receptor interaction (P=8.91×10-7) was the most significantly associated with CC of all pathways. In conclusion, the present study predicted target genes and pathways for patients with CC based on miRNA expression data, the PIL method and pathway analysis. The results of the present study may provide an insight into the pathological mechanisms underlying CC, and provide potential biomarkers for the diagnosis and treatment of this tumor type. However, these biomarkers have yet to be validated; these validations will be performed in future studies.

Bioinformatics analysis of differentially expressed miRNA-related mRNAs and their prognostic value in breast carcinoma.

Breast carcinoma is one of the most common types of malignant neoplasms, and is associated with high rates of morbidity and mortality. Altered gene expression is critical in the development of breast cancer. To identify the important differentially expressed genes and microRNAs in breast carcinoma, mRNA (GSE26910, GSE42568, and GSE89116) and microRNA (GSE35412) microarray datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed microRNA expression data were extracted with GEO2R online software. The DAVID online database was used to perform a function and pathway enrichment analysis of the key identified differentially expressed genes. A protein-protein interaction (PPI) network was constructed using the STRING online database, and visualized in Cytoscape software. The effect of the expression level of the key identified genes on overall survival (OS) time was analyzed by using the Kaplan-Meier Plotter online database. Furthermore, the online miRNA databases TargetScan, microT-CDS, and TarBase were used to identify the target genes of the differentially expressed miRNAs. A total of 254 differentially expressed genes were identified, which were enriched in cell adhesion, polysaccharide binding, extracellular region part and ECM-receptor interactions. The PPI network contained 250 nodes and 375edges. Five differentially expressed genes were found to be significantly negatively correlated with the differentially expressed miRNAs, which were potentially also target genes for miRNAs. Four of the five genes, including AKAP12, SOPB, TCF7L2, COL12A1 and TXNIP were downregulated, and were associated with the OS of patients with breast carcinoma. In addition, a total of 130 differentially expressed miRNAs were identified. In conclusion, these results constitute a novel model for miRNA-mRNA differential expression patterns, and further studies may provide potential targets for diagnosing and understanding the mechanisms of breast carcinoma.

MOBCdb: a comprehensive database integrating multi-omics data on breast cancer for precision medicine.

Breast cancer is one of the most frequently diagnosed cancers among women worldwide, characterized by diverse biological heterogeneity. It is well known that complex and combined gene regulation of multi-omics is involved in the occurrence and development of breast cancer. In this paper, we present the Multi-Omics Breast Cancer Database (MOBCdb), a simple and easily accessible repository that integrates genomic, transcriptomic, epigenomic, clinical, and drug response data of different subtypes of breast cancer. MOBCdb allows users to retrieve simple nucleotide variation (SNV), gene expression, microRNA expression, DNA methylation, and specific drug response data by various search fashions. The genome-wide browser /navigation facility in MOBCdb provides an interface for visualizing multi-omics data of multi-samples simultaneously. Furthermore, the survival module provides survival analysis for all or some of the samples by using data of three omics. The approved public drugs with genetic variations on breast cancer are also included in MOBCdb. In summary, MOBCdb provides users a unique web interface to the integrated multi-omics data of different subtypes of breast cancer, which enables the users to identify potential novel biomarkers for precision medicine.

GEView (Gene Expression View) Tool for Intuitive and High Accessible Visualization of Expression Data for Non-Programmer Biologists

This article describes how the last decade has been characterized by the production of huge amounts of different types of biological data. Following that, a flood of bioinformatics tools have been published. However, many of these tools are commercial, or require computational skills. In addition, not all tools provide intuitive and highly accessible visualization of the results. The authors have developed GEView (Gene Expression View), which is a free, user-friendly tool harboring several existing algorithms and statistical methods for the analysis of high-throughput gene, microRNA or protein expression data. It can be used to perform basic analysis such as quality control, outlier detection, batch correction and differential expression analysis, through a single intuitive graphical user interface. GEView is unique in its simplicity and highly accessible visualization it provides. Together with its basic and intuitive functionality it allows Bio-Medical scientists with no computational skills to independently analyze and visualize high-throughput data produced in their own labs.

MiR-195/miR-497 Regulate CD274 Expression of Immune Regulatory Ligands in Triple-Negative Breast Cancer.

PurposeImmune suppression is common in patients with advanced breast cancer but the mechanisms underlying this phenomenon have not been sufficiently studied. In this study, we aimed to identify B7 family members that were able to predict the immune status of patients, and which may serve as potential targets for the treatment of breast cancer. We also aimed to identify microRNAs that may regulate the expression of B7 family members.MethodsThe Cancer Genome Atlas data from 1,092 patients with breast cancer, including gene expression, microRNA expression and survival data, were used for statistical and survival analyses. Polymerase chain reaction and Western blot were used to measure messenger RNA and protein expression, respectively. Luciferase assay was used to investigate direct microRNA target.ResultsBioinformatic analysis predicted that microRNA (miR)-93, miR-195, miR-497, and miR-340 are potential regulators of the immune evasion of breast cancer cells, and that they exert this function by targeting CD274, PDCD1LG2, and NCR3LG1. We chose CD274 for further investigations. We found that miR-195, miR-497, and CD274 expression levels were inversely correlated in MDA-MB-231 cells, and miR-195 and miR-497 expressions mimic inhibited CD274 expression in vitro. Mechanistic investigations demonstrated that miR-195 and miR-497 directly target CD274 3′ untranslated region.ConclusionOur data indicated that the level of B7 family members can predict the prognosis of breast cancer patients, and miR-195/miR-497 regulate CD274 expression in triple negative breast cancer. This regulation may further influence tumor progression and the immune tolerance mechanism in breast cancer and may be able to predict the effect of immunotherapy on patients.

Elementary screening of lymph node metastatic-related genes in gastric cancer based on the co-expression network of messenger RNA, microRNA and long non-coding RNA.

Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer-related deaths worldwide. The high mortality might be attributed to delay in detection and is closely related to lymph node metastasis. Therefore, it is of great importance to explore the mechanism of lymph node metastasis and find strategies to block GC metastasis. Messenger RNA (mRNA), microRNA (miRNA) and long non-coding RNA (lncRNA) expression data and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 908 differentially expressed factors with variance >0.5 including 542 genes, 42 miRNA, and 324 lncRNA were screened using significant analysis microarray algorithm, and interaction networks were constructed using these differentially expressed factors. Furthermore, we conducted functional modules analysis in the network, and found that yellow and turquoise modules could separate samples efficiently. The groups classified in the yellow and turquoise modules had a significant difference in survival time, which was verified in another independent GC mRNA dataset (GSE62254). The results suggested that differentially expressed factors in the yellow and turquoise modules may participate in lymph node metastasis of GC and could be applied as potential biomarkers or therapeutic targets for GC.

Urinary and Blood MicroRNA-126 and -770 are Potential Noninvasive Biomarker Candidates for Diabetic Nephropathy: a Meta-Analysis

Background/Aims: Diabetic nephropathy (DN), a major diabetic microvascular complication, has a long and growing list of biomarkers, including microRNA biomarkers, which have not been consistent across preclinical and clinical studies. This meta-analysis aims to identify significant blood- and urine-incident microRNAs as diagnostic/prognostic biomarker candidates for DN. Methods: PubMed, Web of Science, and Cochrane Library were searched from their earliest records through 12th Dec 2016. Relevant publications for the meta-analysis included (1) human participants; (2) microRNAs in blood and urine; (3) DN studies; and (4) English language. Four reviewers, including two physicians, independently and blindly extracted published data regarding microRNA profiles in blood and/or urine from subjects with diabetic nephropathy. A random-effect model was used to pool the data. Statistical associations between diabetic nephropathy and urinary or blood microRNA expression levels were assessed. Results: Fourteen out of 327 studies (n=2,747 patients) were selected. Blood or urinary microRNA expression data of diabetic nephropathy were pooled for this analysis. The hsa-miR-126 family was significantly (OR: 0.57; 95% CI: 0.44-0.74; p-value < 0.0001) downregulated in blood from patients with diabetic kidney disease, while its urinary level was upregulated (OR: 2931.12; 95% CI: 9.96-862623.21; p-value = 0.0059). The hsa-miR-770 family microRNA were significantly (OR: 10.24; 95% CI: 2.37-44.25; p-value = 0.0018) upregulated in both blood and urine from patients with diabetic nephropathy. Conclusions: Our meta-analysis suggests that hsa-miR-126 and hsa-miR-770 family microRNA may have important diagnostic and pathogenetic implications for DN, which warrants further systematic clinical studies.

Subtype identification from heterogeneous TCGA datasets on a genomic scale by multi-view clustering with enhanced consensus

BackgroundThe Cancer Genome Atlas (TCGA) has collected transcriptome, genome and epigenome information for over 20 cancers from thousands of patients. The availability of these diverse data types makes it necessary to combine these data to capture the heterogeneity of biological processes and phenotypes and further identify homogeneous subtypes for cancers such as breast cancer. Many multi-view clustering approaches are proposed to discover clusters across different data types. The problem is challenging when different data types show poor agreement of clustering structure.ResultsIn this work, we first propose a multi-view clustering approach with consensus (CMC), which tries to find consensus kernels among views by using Hilbert Schmidt Independence Criterion. To tackle the problem when poor agreement among views exists, we further propose a multi-view clustering approach with enhanced consensus (ECMC) to solve this problem by decomposing the kernel information in each view into a consensus part and a disagreement part. The consensus parts for different views are supposed to be similar, and the disagreement parts should be independent with the consensus parts. Both the CMC and ECMC models can be solved by alternative updating with semi-definite programming. Our experiments on both simulation datasets and real-world benchmark datasets show that ECMC model could achieve higher clustering accuracies than other state-of-art multi-view clustering approaches. We also apply the ECMC model to integrate mRNA expression, DNA methylation and microRNA (miRNA) expression data for five cancer data sets, and the survival analysis show that our ECMC model outperforms other methods when identifying cancer subtypes. By Fisher’s combination test method, we found that three computed subtypes roughly correspond to three known breast cancer subtypes including luminal B, HER2 and basal-like subtypes.ConclusionIntegrating heterogeneous TCGA datasets by our proposed multi-view clustering approach ECMC could effectively identify cancer subtypes.

Role of Melt Curve Analysis in Interpretation of Nutrigenomics' MicroRNA Expression Data.

This article illustrates the importance of melt curve analysis (MCA) in interpretation of mild nutrogenomic micro(mi)RNA expression data, by measuring the magnitude of the expression of key miRNA molecules in stool of healthy human adults as molecular markers, following the intake of Pomegranate juice (PGJ), functional fermented sobya (FS), rich in potential probiotic lactobacilli, or their combination. Total small RNA was isolated from stool of 25 volunteers before and following a three-week dietary intervention trial. Expression of 88 miRNA genes was evaluated using Qiagen's 96 well plate RT2 miRNA qPCR arrays. Employing parallel coordinates plots, there was no observed significant separation for the gene expression (Cq) values, using Roche 480® PCR LightCycler instrument used in this study, and none of the miRNAs showed significant statistical expression after controlling for the false discovery rate. On the other hand, melting temperature profiles produced during PCR amplification run, found seven significant genes (miR-184, miR-203, miR-373, miR-124, miR-96, miR-373 and miR-301a), which separated candidate miRNAs that could function as novel molecular markers of relevance to oxidative stress and immunoglobulin function, for the intake of polyphenol (PP)-rich, functional fermented foods rich in lactobacilli (FS), or their combination. We elaborate on these data, and present a detailed review on use of melt curves for analyzing nutigenomic miRNA expression data, which initially appear to show no significant expressions, but are actually more subtle than this simplistic view, necessitating the understanding of the role of MCA for a comprehensive understanding of what the collective expression and MCA data collectively imply.

Identification and functional analysis of risk-related microRNAs for the prognosis of patients with bladder urothelial carcinoma.

The aim of the present study was to investigate risk-related microRNAs (miRs) for bladder urothelial carcinoma (BUC) prognosis. Clinical and microRNA expression data downloaded from the Cancer Genome Atlas were utilized for survival analysis. Risk factor estimation was performed using Cox's proportional regression analysis. A microRNA-regulated target gene network was constructed and presented using Cytoscape. In addition, the Database for Annotation, Visualization and Integrated Discovery was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment, followed by protein-protein interaction (PPI) network analysis. Finally, the K-clique method was applied to analyze sub-pathways. A total of 16 significant microRNAs, including hsa-miR-3622a and hsa-miR-29a, were identified (P<0.05). Following Cox's proportional regression analysis, hsa-miR-29a was screened as a prognostic marker of BUC risk (P=0.0449). A regulation network of hsa-miR-29a comprising 417 target genes was constructed. These target genes were primarily enriched in GO terms, including collagen fibril organization, extracellular matrix (ECM) organization and pathways, such as focal adhesion (P<0.05). A PPI network including 197 genes and 510 interactions, was constructed. The top 21 genes in the network module were enriched in GO terms, including collagen fibril organization and pathways, such as ECM receptor interaction (P<0.05). Finally, 4 sub-pathways of cysteine and methionine metabolism, including paths 00270_4, 00270_1, 00270_2 and 00270_5, were obtained (P<0.01) and identified to be enriched through DNA (cytosine-5)-methyltransferase (DNMT)3A, DNMT3B, methionine adenosyltransferase 2α (MAT2A) and spermine synthase (SMS). The identified microRNAs, particularly hsa-miR-29a and its 4 associated target genes DNMT3A, DNMT3B, MAT2A and SMS, may participate in the prognostic risk mechanism of BUC.

Toward the human cellular microRNAome.

MicroRNAs are short RNAs that serve as regulators of gene expression and are essential components of normal development as well as modulators of disease. MicroRNAs generally act cell-autonomously, and thus their localization to specific cell types is needed to guide our understanding of microRNA activity. Current tissue-level data have caused considerable confusion, and comprehensive cell-level data do not yet exist. Here, we establish the landscape of human cell-specific microRNA expression. This project evaluated 8 billion small RNA-seq reads from 46 primary cell types, 42 cancer or immortalized cell lines, and 26 tissues. It identified both specific and ubiquitous patterns of expression that strongly correlate with adjacent superenhancer activity. Analysis of unaligned RNA reads uncovered 207 unknown minor strand (passenger) microRNAs of known microRNA loci and 495 novel putative microRNA loci. Although cancer cell lines generally recapitulated the expression patterns of matched primary cells, their isomiR sequence families exhibited increased disorder, suggesting DROSHA- and DICER1-dependent microRNA processing variability. Cell-specific patterns of microRNA expression were used to de-convolute variable cellular composition of colon and adipose tissue samples, highlighting one use of these cell-specific microRNA expression data. Characterization of cellular microRNA expression across a wide variety of cell types provides a new understanding of this critical regulatory RNA species.

MicroTarget: MicroRNA target gene prediction approach with application to breast cancer.

MicroRNAs are known to play an essential role in gene regulation in plants and animals. The standard method for understanding microRNA-gene interactions is randomized controlled perturbation experiments. These experiments are costly and time consuming. Therefore, use of computational methods is essential. Currently, several computational methods have been developed to discover microRNA target genes. However, these methods have limitations based on the features that are used for prediction. The commonly used features are complementarity to the seed region of the microRNA, site accessibility, and evolutionary conservation. Unfortunately, not all microRNA target sites are conserved or adhere to exact seed complementary, and relying on site accessibility does not guarantee that the interaction exists. Moreover, the study of regulatory interactions composed of the same tissue expression data for microRNAs and mRNAs is necessary to understand the specificity of regulation and function. We developed MicroTarget to predict a microRNA-gene regulatory network using heterogeneous data sources, especially gene and microRNA expression data. First, MicroTarget employs expression data to learn a candidate target set for each microRNA. Then, it uses sequence data to provide evidence of direct interactions. MicroTarget scores and ranks the predicted targets based on a set of features. The predicted targets overlap with many of the experimentally validated ones. Our results indicate that using expression data in target prediction is more accurate in terms of specificity and sensitivity. Available at: https://bioinformatics.cs.vt.edu/~htorkey/microTarget .

Abstract 1484: Genome wide study reveal microrna based methylation phenotype in oral cancer

Abstract Introduction: Oral cancer is one of the leading causes of cancer burden in India. Studies over the years have enforced the role of deregulated microRNA expression in oral cancer development and progression. Incidentally not much is known about the cause of microRNA deregulation or to what extent can it be attributed to methylation. Established genome wide microarray platforms like 450K Illumina Bead Chip have probes for very limited number of microRNAs along with restriction on distance of the probes from microRNA transcription start site. We have used genome wide methylation sequencing approach RRBS to get deeper insight on microRNA methylation. Aim: Genome wide methylation profile of 15 paired oral cancer and adjacent normal tissue was compared to generate a detailed map of microRNA methylation in oral cancer. The methylation results were further correlated to microRNA expression data generated on a subset of these samples. Methodology: RRBS based global methylation sequencing of 15 pairs of samples was conducted on HiSeq 2500 with an average 10X depth. Analyses were done using DMAP software. Previously generated TLDA based microRNA expression data was used for correlation analysis. Result: Similar to protein coding genes, microRNAs also showed methylation based clustering. Interestingly majority of methylation hits were more than 2kb upstream of miRNA transcription start site which is generally not considered in known microarrays. Distribution of hypo and hyper methylation sites is different across the various CpG features like cores and shelves as well as across chromosomes. MicroRNAs like mir-181c, miR-99b, miR-23a among others showed significant negative correlation between methylation frequency and expression level. IPA based pathway analysis emphasized repression of apoptosis and cell death pathways along with activation of cell proliferation and colony formation. Conclusion: Understanding the role of methylation in microRNA deregulation could help us detect and possibly prevent a chain of events, which lead to disrupted pathways eventually leading to cancer, at the onset. Note: This abstract was not presented at the meeting. Citation Format: Roshni Roy, Aniruddha Chatterjee, Navonil De Sarkar, Bidyut Roy. Genome wide study reveal microrna based methylation phenotype in oral cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1484. doi:10.1158/1538-7445.AM2017-1484

A prognostic model for lung adenocarcinoma patient survival with a focus on four miRNAs.

There is currently no effective biomarker for determining the survival of patients with lung adenocarcinoma. The purpose of the present study was to construct a prognostic survival model using microRNA (miRNA) expression data from patients with lung adenocarcinoma. miRNA data were obtained from The Cancer Genome Atlas, and patients with lung adenocarcinoma were divided into either the training or validation set based on the random allocation principle. The prognostic model focusing on miRNA was constructed, and patients were divided into high-risk or low-risk groups as per the scores, to assess their survival time. The 5-year survival rate from the subgroups within the high- and low-risk groups was assessed. P-values of the prognostic model in the total population, the training set and validation set were 0.0017, 0.01986 and 0.02773, respectively, indicating that the survival time of the lung adenocarcinoma high-risk group was less than that of the low-risk group. Thus, the model had a good assessment effectiveness for the untreated group (P=0.00088) and the Caucasian patient group (P=0.00043). In addition, the model had the best prediction effect for the 5-year survival rate of the Caucasian patient group (AUC=0.629). In conclusion, the prognostic model developed in the present study can be used as an independent prognostic model for patients with lung adenocarcinoma.