Dataset Information

A clustering-based approach for efficient identification of microRNA combinatorial biomarkers.

ABSTRACT: MicroRNAs (miRNAs) have great potential serving as tumor biomarkers and therapeutic targets. As the rapid development of high-throughput experimental technology, gene expression experiments have become more and more specialized and diversified. The complex data structure has brought great challenge for the identification of biomarkers. In the meantime, current statistical and machine learning methods for detecting biomarkers have the problem of low reliability and biased criteria.This study aims to select combinatorial miRNA biomarkers, which have higher sensitivity and specificity than single-gene biomarkers. In order to avoid exhaustive search and redundant information, miRNAs are firstly clustered, then the combinations of representative cluster members are assessed as potential biomarkers. Both the criteria for the partition of clusters and selection of representative members are based on Fisher linear discriminant analysis (FDA). The FDA-based criterion has been demonstrated to be superior to three other criteria in selecting representative members, and also good at refining clusters. In the comparison with eight common feature selection methods, this clustering-based method performs the best with regard to the discriminative ability of selected biomarkers.Our experimental results demonstrate that the clustering-based method can identify microRNA combinatorial biomarkers with high accuracy and efficiency. Our method and data are available to the public upon request.

SUBMITTER: Yang Y

PROVIDER: S-EPMC5374636 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A clustering-based approach for efficient identification of microRNA combinatorial biomarkers.

Yang Yang Y Huang Ning N Hao Luning L Kong Wei W

BMC genomics 20170314 Suppl 2

<h4>Background</h4>MicroRNAs (miRNAs) have great potential serving as tumor biomarkers and therapeutic targets. As the rapid development of high-throughput experimental technology, gene expression experiments have become more and more specialized and diversified. The complex data structure has brought great challenge for the identification of biomarkers. In the meantime, current statistical and machine learning methods for detecting biomarkers have the problem of low reliability and biased crite ...[more]

PMID: 28361698

Similar Datasets

Project description:The discovery and characterization of blood-based disease biomarkers are clinically important because blood collection is easy and involves relatively little stress for the patient. However, blood generally reflects not only targeted diseases, but also the whole body status of patients. Thus, the selection of biomarkers may be difficult. In this study, we considered miRNAs as biomarker candidates for several reasons. First, since miRNAs were discovered relatively recently, they have not yet been tested extensively. Second, since the number of miRNAs is relatively limited, selection is expected to be easy. Third, since they are known to play critical roles in a wide range of biological processes, their expression may be disease specific. We applied a newly proposed method to select combinations of miRNAs that discriminate between healthy controls and each of 14 diseases that include 5 cancers. A new feature selection method is based on principal component analysis. Namely this method does not require knowledge of whether each sample was derived from a disease patient or a healthy control. Using this method, we found that hsa-miR-425, hsa-miR-15b, hsa-miR-185, hsa-miR-92a, hsa-miR-140-3p, hsa-miR-320a, hsa-miR-486-5p, hsa-miR-16, hsa-miR-191, hsa-miR-106b, hsa-miR-19b, and hsa-miR-30d were potential biomarkers; combinations of 10 of these miRNAs allowed us to discriminate each disease included in this study from healthy controls. These 12 miRNAs are significantly up- or downregulated in most cancers and other diseases, albeit in a cancer- or disease-specific combinatory manner. Therefore, these 12 miRNAs were also previously reported to be cancer- and disease-related miRNAs. Many disease-specific KEGG pathways were also significantly enriched by target genes of up-/downregulated miRNAs within several combinations of 10 miRNAs among these 12 miRNAs. We also selected miRNAs that could discriminate one disease from another or from healthy controls. These miRNAs were found to be largely overlapped with miRNAs that discriminate each disease from healthy controls.

Project description:Accumulating evidence indicates that human circulating microRNAs (miRNAs) could serve as diagnostic and prognostic biomarkers in various cancers. We aimed to explore novel miRNA biomarkers in the blood of breast cancer patients based on miRNA profiling. A miRCURY™ LNA Array was used to identify differentially altered miRNAs in the whole blood of breast cancer patients (n = 6) and healthy controls (n = 6). Levels of candidate miRNAs were quantified by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) in whole blood specimens of 15 breast cancer patients and 13 age-matched healthy control individuals. The miRWalk database was used to predict miRNA targets and the DAVID tool was used to identify significant enrichment pathways. A total of 171 differentially expressed miRNAs were identified by microarray, including 169 up-regulated and 2 down-regulated miRNAs in breast cancer. Five upregulated miRNAs (miR-30b-5p, miR-96-5p, miR-182-5p, miR-374b-5p, and miR-942-5p) were confirmed by qRT-PCR. The areas under the receiver operating characteristic curve of miR-30b-5p, miR-96-5p, miR-182-5p, miR-374b-5p, and miR-942-5p were 0.9333, 0.7692, 0.7590, 0.8256, and 0.8128, respectively. A total of 855 genes were predicted to be targeted by the five miRNAs, and the one cut domain family member 2 gene (ONECUT2) was a shared target of the five miRNAs. Analysis of publicly available data revealed that these dysregulated miRNAs and target genes were associated with the survival of breast cancer patients. Furthermore, the five miRNAs were significantly enriched in numerous cancer-related pathways, including “MicroRNAs in cancer”, “Pathways in cancer”, “FoxO signaling pathway”, “Ras signaling pathway”, “Rap1 signaling pathway”, “MAPK signaling pathway”, and “PI3K-Akt signaling pathway”. Our data support the potential of the five identified miRNAs as novel biomarkers for the detection of breast cancer, and indicate that they may be involved in breast cancer development and progression.

Project description:Data storage in DNA has recently emerged as a promising archival solution, offering space-efficient and long-lasting digital storage solutions. Recent studies suggest leveraging the inherent redundancy of synthesis and sequencing technologies by using composite DNA alphabets. A major challenge of this approach involves the noisy inference process, obstructing large composite alphabets. This paper introduces a novel approach for DNA-based data storage, offering, in some implementations, a 6.5-fold increase in logical density over standard DNA-based storage systems, with near-zero reconstruction error. Combinatorial DNA encoding uses a set of clearly distinguishable DNA shortmers to construct large combinatorial alphabets, where each letter consists of a subset of shortmers. We formally define various combinatorial encoding schemes and investigate their theoretical properties. These include information density and reconstruction probabilities, as well as required synthesis and sequencing multiplicities. We then propose an end-to-end design for a combinatorial DNA-based data storage system, including encoding schemes, two-dimensional (2D) error correction codes, and reconstruction algorithms, under different error regimes. We performed simulations and show, for example, that the use of 2D Reed-Solomon error correction has significantly improved reconstruction rates. We validated our approach by constructing two combinatorial sequences using Gibson assembly, imitating a 4-cycle combinatorial synthesis process. We confirmed the successful reconstruction, and established the robustness of our approach for different error types. Subsampling experiments supported the important role of sampling rate and its effect on the overall performance. Our work demonstrates the potential of combinatorial shortmer encoding for DNA-based data storage and describes some theoretical research questions and technical challenges. Combining combinatorial principles with error-correcting strategies, and investing in the development of DNA synthesis technologies that efficiently support combinatorial synthesis, can pave the way to efficient, error-resilient DNA-based storage solutions.

Dataset Information

A clustering-based approach for efficient identification of microRNA combinatorial biomarkers.

Publications

A clustering-based approach for efficient identification of microRNA combinatorial biomarkers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets