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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: Sep 8, 2025

Isolation and Flow Cytometric Assessment of Neuroimmune Interactions in a Mini-Stroke Murine Model

Isolation and Flow Cytometric Assessment of Neuroimmune Interactions in a Mini-Stroke Murine Model

Published on: June 20, 2025

Exploring biomarkers for ischemic stroke through integrated microarray data analysis.

Miao Lv¹, Wanting He¹, Tian Liang¹

¹School of Public Health, Guangxi Medical University, 22 Shuangyong Road, Nanning 530021, Guangxi, China.

|June 12, 2022

Summary

This summary is machine-generated.

This study identified 10 high-confidence candidate genes for ischemic stroke (IS) using bioinformatics. FAM102A showed high diagnostic value, offering potential biomarkers and gene-drug interactions for IS diagnosis and treatment.

Keywords:

Diagnostic biomarkers Gene-drug interactions High-confidence candidate genes Ischemic stroke Least absolute shrinkage and selection operator Weighted gene coexpression network analysis

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Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke

Published on: June 2, 2023

Area of Science:

Genomics
Bioinformatics
Stroke Research

Background:

Stroke is a leading cause of disability worldwide with limited treatment options.
Identifying reliable biomarkers for ischemic stroke (IS) is crucial for timely diagnosis and intervention.

Purpose of the Study:

To identify high-confidence candidate genes associated with ischemic stroke (IS) using bioinformatics.
To discover potential diagnostic biomarkers and gene-drug interactions for IS.

Main Methods:

Integrated weighted gene coexpression network analysis (WGCNA) and differentially expressed genes (DEGs).
Employed least absolute shrinkage and selection operator (LASSO) regression for gene screening.
Utilized Receiver Operating Characteristic (ROC) curves for diagnostic value assessment and NetworkAnalyst/DGIdb for network and drug interaction analysis.

Main Results:

Identified 10 high-confidence candidate genes (ARG1, LY96, ABCA1, SLC22A4, CD163, TPM2, SLC25A42, ID3, FAM102A, CD79B) for IS.
FAM102A demonstrated significant diagnostic value (AUC=0.974, sensitivity=0.919, specificity=0.936).
Discovered 8 gene-drug interactions and confirmed gene expression in normal human brain and blood.

Conclusions:

Identified novel diagnostic biomarkers for ischemic stroke.
Uncovered potential gene-drug interactions offering new therapeutic avenues for IS.