Biomarker Candidates for Alzheimer’s Disease Unraveled through In Silico Differential Gene Expression Analysis
Date
2022-05-07Author
Silva-Lucero, Maria-del-Carmen
Rivera-Osorio, Jared
Gómez-Virgilio, Laura
Lopez-Toledo, Gustavo
Luna-Muñoz, José
Montiel-Sosa, Francisco
Soto-Rojas, Luis O.
Pacheco-Herrero, Mar
Cardenas-Aguayo, Maria-del-Carmen
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Show full item recordAbstract
Alzheimer’s disease (AD) is neurodegeneration that accounts for 60–70% of dementia cases.
Symptoms begin with mild memory difficulties and evolve towards cognitive impairment. The
underlying risk factors remain primarily unclear for this heterogeneous disorder. Bioinformatics
is a relevant research tool that allows for identifying several pathways related to AD. Open-access
databases of RNA microarrays from the peripheral blood and brain of AD patients were analyzed
after background correction and data normalization; the Limma package was used for differential
expression analysis (DEA) through statistical R programming language. Data were corrected with
the Benjamini and Hochberg approach, and genes with p-values equal to or less than 0.05 were
considered to be significant. The direction of the change in gene expression was determined by its
variation in the log2-fold change between healthy controls and patients. We performed the functional
enrichment analysis of GO using goana and topGO-Limma. The functional enrichment analysis
of DEGs showed upregulated (UR) pathways: behavior, nervous systems process, postsynapses,
enzyme binding; downregulated (DR) were cellular component organization, RNA metabolic process,
and signal transduction. Lastly, the intersection of DEGs in the three databases showed eight shared
genes between brain and blood, with potential use as AD biomarkers for blood tests.
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