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MicroRNA-Seq Data Analysis Pipeline to Identify Blood Biomarkers for Alzheimer's Disease from Public Data.

Satoh J, Kino Y, Niida S - Biomark Insights (2015)

Bottom Line: By using omiRas, we identified 27 miRNAs expressed differentially between both groups, including upregulation in AD of miR-26b-3p, miR-28-3p, miR-30c-5p, miR-30d-5p, miR-148b-5p, miR-151a-3p, miR-186-5p, miR-425-5p, miR-550a-5p, miR-1468, miR-4781-3p, miR-5001-3p, and miR-6513-3p and downregulation in AD of let-7a-5p, let-7e-5p, let-7f-5p, let-7g-5p, miR-15a-5p, miR-17-3p, miR-29b-3p, miR-98-5p, miR-144-5p, miR-148a-3p, miR-502-3p, miR-660-5p, miR-1294, and miR-3200-3p.DIANA miRPath indicated that miRNA-regulated pathways potentially downregulated in AD are linked with neuronal synaptic functions, while those upregulated in AD are implicated in cell survival and cellular communication.The simple web-based miRNA data analysis pipeline helps us to effortlessly identify candidates for miRNA biomarkers and pathways of AD from the complex small RNA-Seq data.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics and Molecular Neuropathology, Meiji Pharmaceutical University, Tokyo, Japan.

ABSTRACT

Background: Alzheimer's disease (AD) is the most common cause of dementia with no curative therapy currently available. Establishment of sensitive and non-invasive biomarkers that promote an early diagnosis of AD is crucial for the effective administration of disease-modifying drugs. MicroRNAs (miRNAs) mediate posttranscriptional repression of numerous target genes. Aberrant regulation of miRNA expression is implicated in AD pathogenesis, and circulating miRNAs serve as potential biomarkers for AD. However, data analysis of numerous AD-specific miRNAs derived from small RNA-sequencing (RNA-Seq) is most often laborious.

Methods: To identify circulating miRNA biomarkers for AD, we reanalyzed a publicly available small RNA-Seq dataset, composed of blood samples derived from 48 AD patients and 22 normal control (NC) subjects, by a simple web-based miRNA data analysis pipeline that combines omiRas and DIANA miRPath.

Results: By using omiRas, we identified 27 miRNAs expressed differentially between both groups, including upregulation in AD of miR-26b-3p, miR-28-3p, miR-30c-5p, miR-30d-5p, miR-148b-5p, miR-151a-3p, miR-186-5p, miR-425-5p, miR-550a-5p, miR-1468, miR-4781-3p, miR-5001-3p, and miR-6513-3p and downregulation in AD of let-7a-5p, let-7e-5p, let-7f-5p, let-7g-5p, miR-15a-5p, miR-17-3p, miR-29b-3p, miR-98-5p, miR-144-5p, miR-148a-3p, miR-502-3p, miR-660-5p, miR-1294, and miR-3200-3p. DIANA miRPath indicated that miRNA-regulated pathways potentially downregulated in AD are linked with neuronal synaptic functions, while those upregulated in AD are implicated in cell survival and cellular communication.

Conclusions: The simple web-based miRNA data analysis pipeline helps us to effortlessly identify candidates for miRNA biomarkers and pathways of AD from the complex small RNA-Seq data.

No MeSH data available.


Related in: MedlinePlus

ROC analysis of miRNAs differentially expressed in blood of AD and NC. By omiRas, we identified the set of 27 miRNAs differentially expressed between AD and NC blood samples. ROC analysis was performed by importing normalized counts of individual miRNAs into SPSS in the setting of the group discrimination number 1 for AD and 0 for NC, where 1 is defined as a state variable for miRNAs upregulated in AD. The representative profiles of (A) miR-26b-3p, (B) miR-148b-5p, (C) miR-186–5p, and (D) the entire 27 miRNA panel are shown. All 27 profiles are shown in Supplementary Figures 4 and 5.
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f3-bmi-10-2015-021: ROC analysis of miRNAs differentially expressed in blood of AD and NC. By omiRas, we identified the set of 27 miRNAs differentially expressed between AD and NC blood samples. ROC analysis was performed by importing normalized counts of individual miRNAs into SPSS in the setting of the group discrimination number 1 for AD and 0 for NC, where 1 is defined as a state variable for miRNAs upregulated in AD. The representative profiles of (A) miR-26b-3p, (B) miR-148b-5p, (C) miR-186–5p, and (D) the entire 27 miRNA panel are shown. All 27 profiles are shown in Supplementary Figures 4 and 5.

Mentions: By ROC analysis, the set of differentially expressed miRNAs, except for let-7e-5p and miR-29b-3p, showed statistically significant levels of the sensitivity and the specificity for discrimination between AD and NC (Fig. 3A–C and Supplementary Figs. 4 and 5). ROC analysis of the entire 27 miRNA panel revealed the area under the curve (AUC) = 0.801 (P = 0.00006) with the sensitivity = 70.8% and the specificity = 81.8% for discrimination of both groups (Fig. 3D).


MicroRNA-Seq Data Analysis Pipeline to Identify Blood Biomarkers for Alzheimer's Disease from Public Data.

Satoh J, Kino Y, Niida S - Biomark Insights (2015)

ROC analysis of miRNAs differentially expressed in blood of AD and NC. By omiRas, we identified the set of 27 miRNAs differentially expressed between AD and NC blood samples. ROC analysis was performed by importing normalized counts of individual miRNAs into SPSS in the setting of the group discrimination number 1 for AD and 0 for NC, where 1 is defined as a state variable for miRNAs upregulated in AD. The representative profiles of (A) miR-26b-3p, (B) miR-148b-5p, (C) miR-186–5p, and (D) the entire 27 miRNA panel are shown. All 27 profiles are shown in Supplementary Figures 4 and 5.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4401249&req=5

f3-bmi-10-2015-021: ROC analysis of miRNAs differentially expressed in blood of AD and NC. By omiRas, we identified the set of 27 miRNAs differentially expressed between AD and NC blood samples. ROC analysis was performed by importing normalized counts of individual miRNAs into SPSS in the setting of the group discrimination number 1 for AD and 0 for NC, where 1 is defined as a state variable for miRNAs upregulated in AD. The representative profiles of (A) miR-26b-3p, (B) miR-148b-5p, (C) miR-186–5p, and (D) the entire 27 miRNA panel are shown. All 27 profiles are shown in Supplementary Figures 4 and 5.
Mentions: By ROC analysis, the set of differentially expressed miRNAs, except for let-7e-5p and miR-29b-3p, showed statistically significant levels of the sensitivity and the specificity for discrimination between AD and NC (Fig. 3A–C and Supplementary Figs. 4 and 5). ROC analysis of the entire 27 miRNA panel revealed the area under the curve (AUC) = 0.801 (P = 0.00006) with the sensitivity = 70.8% and the specificity = 81.8% for discrimination of both groups (Fig. 3D).

Bottom Line: By using omiRas, we identified 27 miRNAs expressed differentially between both groups, including upregulation in AD of miR-26b-3p, miR-28-3p, miR-30c-5p, miR-30d-5p, miR-148b-5p, miR-151a-3p, miR-186-5p, miR-425-5p, miR-550a-5p, miR-1468, miR-4781-3p, miR-5001-3p, and miR-6513-3p and downregulation in AD of let-7a-5p, let-7e-5p, let-7f-5p, let-7g-5p, miR-15a-5p, miR-17-3p, miR-29b-3p, miR-98-5p, miR-144-5p, miR-148a-3p, miR-502-3p, miR-660-5p, miR-1294, and miR-3200-3p.DIANA miRPath indicated that miRNA-regulated pathways potentially downregulated in AD are linked with neuronal synaptic functions, while those upregulated in AD are implicated in cell survival and cellular communication.The simple web-based miRNA data analysis pipeline helps us to effortlessly identify candidates for miRNA biomarkers and pathways of AD from the complex small RNA-Seq data.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics and Molecular Neuropathology, Meiji Pharmaceutical University, Tokyo, Japan.

ABSTRACT

Background: Alzheimer's disease (AD) is the most common cause of dementia with no curative therapy currently available. Establishment of sensitive and non-invasive biomarkers that promote an early diagnosis of AD is crucial for the effective administration of disease-modifying drugs. MicroRNAs (miRNAs) mediate posttranscriptional repression of numerous target genes. Aberrant regulation of miRNA expression is implicated in AD pathogenesis, and circulating miRNAs serve as potential biomarkers for AD. However, data analysis of numerous AD-specific miRNAs derived from small RNA-sequencing (RNA-Seq) is most often laborious.

Methods: To identify circulating miRNA biomarkers for AD, we reanalyzed a publicly available small RNA-Seq dataset, composed of blood samples derived from 48 AD patients and 22 normal control (NC) subjects, by a simple web-based miRNA data analysis pipeline that combines omiRas and DIANA miRPath.

Results: By using omiRas, we identified 27 miRNAs expressed differentially between both groups, including upregulation in AD of miR-26b-3p, miR-28-3p, miR-30c-5p, miR-30d-5p, miR-148b-5p, miR-151a-3p, miR-186-5p, miR-425-5p, miR-550a-5p, miR-1468, miR-4781-3p, miR-5001-3p, and miR-6513-3p and downregulation in AD of let-7a-5p, let-7e-5p, let-7f-5p, let-7g-5p, miR-15a-5p, miR-17-3p, miR-29b-3p, miR-98-5p, miR-144-5p, miR-148a-3p, miR-502-3p, miR-660-5p, miR-1294, and miR-3200-3p. DIANA miRPath indicated that miRNA-regulated pathways potentially downregulated in AD are linked with neuronal synaptic functions, while those upregulated in AD are implicated in cell survival and cellular communication.

Conclusions: The simple web-based miRNA data analysis pipeline helps us to effortlessly identify candidates for miRNA biomarkers and pathways of AD from the complex small RNA-Seq data.

No MeSH data available.


Related in: MedlinePlus