An integrative bioinformatics approach in microRNA data analytics of Alzheimer’s disease

Article Sidebar

pdf
Published: Dec 28, 2023
DOI: https://doi.org/10.69598/sehs.17.23030002
Keywords:
Alzheimer’s disease microRNA differential expression analysis data analytics bioinformatics

Main Article Content

Jill Ann Chia
Bioinformatics Programme, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
Mei Sze Tan
Bioinformatics Programme, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
Siow-Wee Chang
Bioinformatics Programme, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia / Centre of Research in System Biology, Structural, Bioinformatics and Human Digital Imaging (CRYSTAL), Universiti Malaya, Kuala Lumpur, Malaysia. Corresponding author's e-mail: siowwee@um.edu.my

Abstract

Alzheimer’s disease (AD) is the most common type of dementia clinically recognized by cognitive function impairment. Recently, the blood-based biomarkers relating to AD have been intensively investigated due to the minimum invasiveness and relatively low cost in the collection of blood samples compared to the cerebrospinal fluid in the brain. In line with this, the study of the deregulation of microRNA (miRNA) levels in the blood of AD patients is also rising. In this study, data analysis was performed on the miRNA expression profiling dataset using an integrative bioinformatics approach. K-nearest neighbor imputation and quantile normalization were carried out as the data pre-processing step to remove outliers and reduce bias in the dataset. Differential expression analysis was performed to identify 10 significant dysregulated miRNAs. Subsequently, 16 pathways were determined to be involved by the selected 10 miRNA signatures, and 7 genes were predicted as the common target genes. The roles of these target genes in AD were substantiated through a review of the existing literature. Expansion of the current work on a larger scale of data analysis is needed to further validate and understand the mechanism of miRNAs in AD development.

Downloads

Download data is not yet available.

Article Details

How to Cite
Chia, J. A., Tan, M. S. ., & Chang, S.-W. (2023). An integrative bioinformatics approach in microRNA data analytics of Alzheimer’s disease. Science, Engineering and Health Studies, 17, 23030002. https://doi.org/10.69598/sehs.17.23030002
Issue
Volume 17, 2023
Section
Biological sciences
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Carmona-Saez, P., Chagoyen, M., Tirado, F., Carazo, J. M., and Pascual-Montano, A. (2007). GENECODIS: A web-based tool for finding significant concurrent annotations in gene lists. Genome Biology, 8(1), R3.

Cloonan, N., Forrest, A. R. R., Kolle, G., Gardiner, B. B. A., Faulkner, G. J., Brown, M. K., Taylor, D. F., Steptoe A. L., Wani, S., Bethel, G., Robertson, A. J., Perkins, A. C., Bruce, S. J., Lee, C. C., Ranade, S. S., Peckham, H. E., Manning, J. M., McKernan, K. J., and Grimmond, S. M. (2008). Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nature Methods, 5(7), 613–619.

Delay, C., Mandemakers, W., and Hébert, S. S. (2012). MicroRNAs in Alzheimer's disease. Neurobiology of Disease, 46(2), 285–290.

Ding, C., Wu, Z., You, H., Ge, H., Zheng, S., Lin, Y., Wu, X., Lin, Z., and Kang, D. (2019). CircNFIX promotes progression of glioma through regulating miR-378e/RPN2 axis. Journal of Experimental & Clinical Cancer Research, 38(1), 506.

Dudoit, S., Fridlyand, J., and Speed, T. P. (2002). Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American statistical Association, 97(457), 77–87.

Fransquet, P. D., and Ryan, J. (2018). Micro RNA as a potential blood-based epigenetic biomarker for Alzheimer's disease. Clinical Biochemistry, 58, 5–14.

Garmire, L. X., and Subramaniam, S. (2012). Evaluation of normalisation methods in mammalian microRNA-Seq data. RNA Society, 18(6), 1279–1288.

Griffiths-Jones, S., Grocock, R. J., van Dongen, S., Bateman, A., and Enright, A. J. (2006). miRBase: MicroRNA sequences, targets and gene nomenclature. Nucleic Acids Research, 34, D140–D144.

Hou, X., Gong, X., Zhang, L., Li, T., Yuan, H., Xie, Y., Peng, Y., Qiu, R., Xia, K., Tang, B., and Jiang, H. (2019). Identification of a potential exosomal biomarker in spinocerebellar ataxia Type 3/Machado-Joseph disease. Epigenomics, 11(9), 1037–1056.

Keller, A., Backes, C., Haas, J., Leidinger, P., Maetzler, W., Deuschle, C., Berg, D., Ruschil, C., Galata, V., Ruprecht, K., Stähler, C., Würstle, M., Sickert, D., Gogol, M., Meder, B., and Meese, E. (2016). Validating Alzheimer's disease micro RNAs using next-generation sequencing. Alzheimer's & Dementia, 12(5), 565–576.

Kovanda, A., Leonardis, L., Zidar, J., Koritnik, B., Dolenc-Groselj, L., Kovacic, S. R., Curk, T., and Rogelj, B. (2018). Differential expression of microRNAs and other small RNAs in muscle tissue of patients with ALS and healthy age-matched controls. Scientific Reports, 8(1), 5609.

Kumar, P., Dezso Z., MacKenzie, C., Oestreicher, J., Agoulnik, S., Byrne, M., Bernier, F., Yanagimachi, M., Aoshima, K., and Oda, Y. (2013). Circulating miRNA biomarkers for Alzheimer’s disease. PLoS One, 8(7), e69807.

Kumar, S., and Reddy, P. H. (2016). Are circulating microRNAs peripheral biomarkers for Alzheimer's disease? Biochimica et Biophysica Acta, 1862(9), 1617–1627.

Lee, T., and Lee, H. (2020). Prediction of Alzheimer’s disease using blood gene expression data. Scientific Reports, 10, 3485.

Leidinger, P., Backes, C., Deutscher, S., Schmitt, K., Mueller, S. C., Frese, K., Haas, J., Ruprecht, K., Paul, F., Stähler, C., Lang, C. JG., Meder, B., Bartfai, T., Meese, E., and Keller, A. (2013). A blood based 12-miRNA signature of Alzheimer disease patients. Genome Biology, 14, R78.

Liu, S., Fan, M., Zheng, Q., Hao, S., Yang, L., Xia, Q., Qi, C., and Ge, J. (2022). MicroRNAs in Alzheimer's disease: Potential diagnostic markers and therapeutic targets. Biomedicine & Pharmacotherapy, 148, 112681.

Liu, X., Erikson, C., and Brun, A. (1996). Cortical synaptic changes and gliosis in normal aging, Alzheimer's disease and frontal lobe degeneration. Dementia, 7(3), 128–134.

National Collaborating Centre for Mental Health (UK). (2007). Dementia: A NICE-SCIE Guideline on Supporting People with Dementia and Their Carers in Health and Social Care. Leicester: British Psychological Society, p. 42.

Mucke, L. (2009). Neuroscience: Alzheimer's disease. Nature, 461(7266), 895–897.

Prajapati, P., Sripada, L., Singh, K., Roy, M., Bhatelia, K., Dalwadi, P., and Singh, R. (2018). Systemic analysis of miRNAs in PD stress condition: miR-5701 modulates mitochondrial-lysosomal cross talk to regulate neuronal death. Molecular Neurobiology, 55(6), 4689–4701.

Prince, M., Wimo, A., Guerchet, M., Ali, G.-C., Wu, Y.-T., and Prina, M. (2015). World Alzheimer Report 2015, The Global Impact of Dementia: An Analysis of Prevalence, Incidence, Cost and Trend. London: Alzheimer’s Disease International, p. 2.

Roden, C., Mastriano, S., Wang, N., and Lu, J. (2015). microRNA expression profiling: Technologies, insights, and prospects. In microRNA: Medical Evidence. Advances in Experimental Medicine and Biology, Vol. 888 (Santulli, G., Ed.), pp. 409–421. Cham: Springer.

Satoh, J.-I., Kino, Y., and Niida, S. (2015). MicroRNA-Seq data analysis pipeline to identify blood biomarkers for Alzheimer's disease from public data. Biomarker Insights, 10, 21–31.

Soneson, C., and Delorenzi, M. (2013). A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics, 14, 91.

Sproviero, D., Gagliardi, S., Zucca, S., Arigoni, M., Giannini, M., Garofalo, M., Olivero M., Dell'Orco, M., Pansarasa, O., Bernuzzi, S., Avenali, M., Ramusino, M. C., Diamanti, L., Minafra, B., Perini, G., Zangaglia, R., Costa, A., Ceroni, M., Perrone-Bizzozero, N. I., Calogero, R. A., and Cereda, C. (2021). Different miRNA profiles in plasma derived small and large extracellular vesicles from patients with neurodegenerative diseases. International Journal of Molecular Sciences, 22(5), 2737.

Sturmberg, J. P., Bennett, J. M., Picard, M., and Seely, A. J. E. (2015). The trajectory of life. Decreasing physiological network complexity through changing fractal patterns. Frontiers in Physiology, 6, 169.

Swarbrick, S., Wragg, N., Ghosh, S., and Stolzing, A. (2019). Systematic review of miRNA as biomarkers in Alzheimer’s disease. Molecular Neurobiology, 56(9), 6156–6167.

Takousis, P., Sadlon, A., Schulz, J., Wohlers, I., Dobricic, V., Middleton, L., Lill, C. M., Perneczky, R., and Bertram, L. (2019). Differential expression of microRNAs in Alzheimer's disease brain, blood, and cerebrospinal fluid. Alzheimer's & Dementia, 15(11), 1468–1477.

Wang, M., Qin, L., and Tang, B. (2019). MicroRNAs in Alzheimer’s disease. Frontiers in Genetics, 10, 153.

Yaari, G., Bolen, C. R., Thakar, J., and Kleinstein, S. H. (2013). Quantitative set analysis for gene expression: A method to quantify gene set differential expression including gene-gene correlations. Nucleic Acids Research, 41(18), e170.

Zhou, Y., Wang, Z.-F., Li, W., Hong, H., Chen, J., Tian, Y., and Liu, Z.-Y. (2018). Protective effects of microRNA‐330 on amyloid β‐protein production, oxidative stress, and mitochondrial dysfunction in Alzheimer's disease by targeting VAV1 via the MAPK signaling pathway. Journal of Cellular Biochemistry, 119(7), 5437–5448.