Dataset Information

Identification of differentially expressed miRNAs in individual breast cancer patient and application in personalized medicine.

ABSTRACT: MicroRNAs (miRNAs) have key roles in breast cancer progression, and their expression levels are heterogeneous across individual breast cancer patients. Traditional methods aim to identify differentially expressed miRNAs in populations rather than in individuals and are affected by the expression intensities of miRNAs in different experimental batches or platforms. Thus it is urgent to conduct miRNA differential expression analysis at an individual level for further personalized medicine research. We proposed a straightforward method to determine the differential expression of each miRNA in an individual patient by utilizing the reversal expression order of miRNA pairs between two conditions (cancer and normal tissue). We applied our method to breast cancer miRNA expression profiles from The Cancer Genome Atlas and two other independent data sets. In total, 292 miRNAs were differentially expressed in individual breast cancer patients. Using the differential expression profile of miRNAs in individual patients, we found that the deregulations of miRNA tend to occur in specific breast cancer subtypes. We investigated the coordination effect between the miRNA and its target, based on the hypothesis that one gene function can be changed by copy number alterations of the corresponding gene or deregulation of the miRNA. We revealed that patients exhibiting an upregulation of hsa-miR-92b and patients with deletions of PTEN did not tend to overlap, and hsa-miR-92b and PTEN coordinately regulated the pathway of 'cell cycle' and so on. Moreover, we discovered a new prognostic signature, hsa-miR-29c, whose downregulation was associated with poor survival of breast cancer patients.

SUBMITTER: Peng F

PROVIDER: S-EPMC5154351 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of differentially expressed miRNAs in individual breast cancer patient and application in personalized medicine.

Peng F F Zhang Y Y Wang R R Zhou W W Zhao Z Z Liang H H Qi L L Zhao W W Wang H H Wang C C Guo Z Z Gu Y Y

Oncogenesis 20160215

MicroRNAs (miRNAs) have key roles in breast cancer progression, and their expression levels are heterogeneous across individual breast cancer patients. Traditional methods aim to identify differentially expressed miRNAs in populations rather than in individuals and are affected by the expression intensities of miRNAs in different experimental batches or platforms. Thus it is urgent to conduct miRNA differential expression analysis at an individual level for further personalized medicine research ...[more]

PMID: 26878388

Similar Datasets

Project description:BACKGROUND: The miRNAs, a class of short approximately 22-nucleotide non-coding RNAs, often act post-transcriptionally to inhibit mRNA expression. In effect, they control gene expression by targeting mRNA. They also help in carrying out normal functioning of a cell as they play an important role in various cellular processes. However, dysregulation of miRNAs is found to be a major cause of a disease. It has been demonstrated that miRNA expression is altered in many human cancers, suggesting that they may play an important role as disease biomarkers. Multiple reports have also noted the utility of miRNAs for the diagnosis of cancer. Among the large number of miRNAs present in a microarray data, a modest number might be sufficient to classify human cancers. Hence, the identification of differentially expressed miRNAs is an important problem particularly for the data sets with large number of miRNAs and small number of samples. RESULTS: In this regard, a new miRNA selection algorithm, called ?HEM, is presented based on rough hypercuboid approach. It selects a set of miRNAs from a microarray data by maximizing both relevance and significance of the selected miRNAs. The degree of dependency of sample categories on miRNAs is defined, based on the concept of hypercuboid equivalence partition matrix, to measure both relevance and significance of miRNAs. The effectiveness of the new approach is demonstrated on six publicly available miRNA expression data sets using support vector machine. The.632+ bootstrap error estimate is used to minimize the variability and biasedness of the derived results. CONCLUSIONS: An important finding is that the ?HEM algorithm achieves lowest B.632+ error rate of support vector machine with a reduced set of differentially expressed miRNAs on four expression data sets compare to some existing machine learning and statistical methods, while for other two data sets, the error rate of the ?HEM algorithm is comparable with the existing techniques. The results on several microarray data sets demonstrate that the proposed method can bring a remarkable improvement on miRNA selection problem. The method is a potentially useful tool for exploration of miRNA expression data and identification of differentially expressed miRNAs worth further investigation.

Project description:MicroRNAs (miRNAs) control multiple biological processes including the innate immune responses by negative post-transcriptional regulation of gene expression. As there were no studies on the role(s) of miRNAs in viral diseases in Atlantic salmon, we aimed to identify miRNAs responding to salmonid alphavirus (SAV) infection. Their expression were studied at different time points post infection with SAV isolates associated with different mortalities. Furthermore, the genome sequences of the identified miRNAs were analysed to reveal putative cis-regulatory elements, and, finally, their putative target genes were predicted.Twenty differentially expressed miRNAs (DE miRNAs) were identified. The expression of the majority of these increased post infection with maximum levels reached after the viral load were stabilized or decreasing. On the other hand, some miRNAs (e.g. the miRNA-21 family) showed decreased expression at the early time points post infection. There were significant differences in the temporal expression of individual miRNA associated with different SAV isolates. Target gene prediction in SAV responsive immune network genes showed that seventeen of the DE miRNAs could target 24 genes (e.g. IRF3, IRF7). Applying the Atlantic salmon transcriptome as input 28 more immune network genes were revealed as putative targets (e.g. IRF5, IRF4). The majority of the predicted target genes promote inflammatory response. The upstream sequences of the miRNA genes revealed a high density of cis-regulatory sequences known as binding sites for immune network transcription factors (TFs). A high expression in the late phase could therefore be due to increased transcription promoted by immune response activated TFs. Based on the in silico target predictions, we discuss their putative roles as early promotors or late inhibitors of inflammation. We propose that the differences in expressions associated with different SAV isolates could contribute to their differences in mortality rates.This study represents the first steps in exploring miRNAs important in viral-host interaction in Atlantic salmon. We identified several miRNAs responding to SAV infection. Some likely to prohibit harmful inflammation while other may promote an early immune response. Their predicted functions need to be validated and further studied in functional assays to fully understand their roles in immune homeostasis.

Dataset Information

Identification of differentially expressed miRNAs in individual breast cancer patient and application in personalized medicine.

Publications

Identification of differentially expressed miRNAs in individual breast cancer patient and application in personalized medicine.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure