Integrative analyses of biomarkers and pathways for heart failure

Functional Enrichment ◽

Ppi Networks ◽

Abstract Background: Heart failure (HF) is the most common potential cause of death, causing a huge health and economic burden all over the world. So far, some impressive progress has been made in the study of pathogenesis. However, the underlying molecular mechanisms leading to this disease remain to be fully elucidated. Methods: The microarray data sets of GSE76701, GSE21610 and GSE8331 were retrieved from the gene expression comprehensive database (GEO). After merging all microarray data and adjusting batch effects, differentially expressed genes (DEG) were determined. Functional enrichment analysis was performed based on Gene Ontology (GO) resources, Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, gene set enrichment analysis (GSEA), response pathway database and Disease Ontology (DO). Protein protein interaction (PPI) network was constructed using string database. Combined with the above important bioinformatics information, the potential key genes were selected. The comparative toxicological genomics database (CTD) is used to explore the interaction between potential key genes and HF. Results: We identified 38 patients with heart failure and 16 normal controls. There were 315 DEGs among HF samples, including 278 up-regulated genes and 37 down-regulated genes. Pathway enrichment analysis showed that most DEGs were significantly enriched in BMP signal pathway, transmembrane receptor protein serine / threonine kinase signal pathway, extracellular matrix, basement membrane, glycosaminoglycan binding, sulfur compound binding and so on. Similarly, GSEA enrichment analysis showed that DEGs were mainly enriched in extracellular matrix and extracellular matrix related proteins. BBS9, CHRD, BMP4, MYH6, NPPA and CCL5 are central genes in PPI networks and modules. Conclusions: the enrichment pathway of DEGs and go ontology may reveal the molecular mechanism of HF. Among them, target genes EIF1AY, RPS4Y1, USP9Y, KDM5D, DDX3Y, NPPA, HBB, TSIX, LOC28556 and XIST are expected to become new targets for heart failure. Our findings provide potential biomarkers or therapeutic targets for the further study of heart failure and contribute to the development of advanced prediction, diagnosis and treatment strategies.

Identification of Diagnostic Markers Correlated With HIV+ Immune Non-response Based on Bioinformatics Analysis

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.809085 ◽

2021 ◽

Vol 8 ◽

Author(s):

Ruojing Bai ◽

Zhen Li ◽

Yuying Hou ◽

Shiyun Lv ◽

Ran Wang ◽

...

Keyword(s):

T Cell ◽

Microarray Data ◽

Immune Cells ◽

Mononuclear Cells ◽

Enrichment Analysis ◽

Diagnostic Markers ◽

Functional Enrichment ◽

Clinical Samples ◽

Pathway Enrichment Analysis

Background: HIV-infected immunological non-responders (INRs) are characterized by their inability to reconstitute CD4+ T cell pools after antiretroviral therapy. The risk of non-AIDS-related diseases in INRs is increased, and the outcome and prognosis of INRs are inferior to that of immunological responders (IRs). However, few markers can be used to define INRs precisely. In this study, we aim to identify further potential diagnostic markers associated with INRs through bioinformatic analyses of public datasets.Methods: This study retrieved the microarray data sets of GSE106792 and GSE77939 from the Gene Expression Omnibus (GEO) database. After merging two microarray data and adjusting the batch effect, differentially expressed genes (DEGs) were identified. Gene Ontology (GO) resource and Kyoto Encyclopedia of Genes and Genomes (KEGG) resource were conducted to analyze the biological process and functional enrichment. We performed receiver operating characteristic (ROC) curves to filtrate potential diagnostic markers for INRs. Gene Set Enrichment Analysis (GSEA) was conducted to perform the pathway enrichment analysis of individual genes. Single sample GSEA (ssGSEA) was performed to assess scores of immune cells within INRs and IRs. The correlations between the diagnostic markers and differential immune cells were examined by conducting Spearman’s rank correlation analysis. Subsequently, miRNA-mRNA-TF interaction networks in accordance with the potential diagnostic markers were built with Cytoscape. We finally verified the mRNA expression of the diagnostic markers in clinical samples of INRs and IRs by performing RT-qPCR.Results: We identified 52 DEGs in the samples of peripheral blood mononuclear cells (PBMC) between INRs and IRs. A few inflammatory and immune-related pathways, including chronic inflammatory response, T cell receptor signaling pathway, were enriched. FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1 were considered as potential diagnostic markers. ssGSEA results showed that the IRs had significantly higher enrichment scores of seven immune cells compared with IRs. The miRNA-mRNA-TF network was constructed with 97 miRNAs, 6 diagnostic markers, and 26 TFs, which implied a possible regulatory relationship.Conclusion: The six potential crucial genes, FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1, may be associated with clinical diagnosis in INRs. Our study provided new insights into diagnostic and therapeutic targets.

HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening

BMC Musculoskeletal Disorders ◽

10.1186/s12891-021-03958-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Zhixin Wu ◽

Yinxian Wen ◽

Guanlan Fan ◽

Hangyuan He ◽

Siqi Zhou ◽

...

Keyword(s):

Femoral Head ◽

Roc Curve ◽

Enrichment Analysis ◽

Diagnostic Value ◽

Functional Enrichment ◽

Overlapping Genes ◽

Hub Genes ◽

Diagnostic Biomarkers ◽

Abstract Background Steroid-induced osteonecrosis of the femoral head (SONFH) is a chronic and crippling bone disease. This study aims to reveal novel diagnostic biomarkers of SONFH. Methods The GSE123568 dataset based on peripheral blood samples from 10 healthy individuals and 30 SONFH patients was used for weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. The genes in the module related to SONFH and the DEGs were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Genes with |gene significance| > 0.7 and |module membership| > 0.8 were selected as hub genes in modules. The DEGs with the degree of connectivity ≥5 were chosen as hub genes in DEGs. Subsequently, the overlapping genes of hub genes in modules and hub genes in DEGs were selected as key genes for SONFH. And then, the key genes were verified in another dataset, and the diagnostic value of key genes was evaluated by receiver operating characteristic (ROC) curve. Results Nine gene co-expression modules were constructed via WGCNA. The brown module with 1258 genes was most significantly correlated with SONFH and was identified as the key module for SONFH. The results of functional enrichment analysis showed that the genes in the key module were mainly enriched in the inflammatory response, apoptotic process and osteoclast differentiation. A total of 91 genes were identified as hub genes in the key module. Besides, 145 DEGs were identified by DEGs screening and 26 genes were identified as hub genes of DEGs. Overlapping genes of hub genes in the key module and hub genes in DEGs, including RHAG, RNF14, HEMGN, and SLC2A1, were further selected as key genes for SONFH. The diagnostic value of these key genes for SONFH was confirmed by ROC curve. The validation results of these key genes in GSE26316 dataset showed that only HEMGN and SLC2A1 were downregulated in the SONFH group, suggesting that they were more likely to be diagnostic biomarkers of SOFNH than RHAG and RNF14. Conclusions Our study identified that two key genes, HEMGN and SLC2A1, might be potential diagnostic biomarkers of SONFH.

Identification of Key Genes and miRNA-mRNA Regulatory Pathways in Bronchopulmonary Dysplasia in Preterm Infants by Bioinformatics Methods

10.21203/rs.3.rs-50763/v1 ◽

2020 ◽

Author(s):

Tong Sun ◽

Haiyang Yu ◽

Jianhua Fu

Keyword(s):

Gene Expression ◽

Preterm Infants ◽

Bronchopulmonary Dysplasia ◽

Regulatory Networks ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Regulatory Pathways ◽

Ppi Networks ◽

Abstract Background: Bronchopulmonary dysplasia (BPD) remains a severe respiratory complication of preterm infants in neonatal intensive care units (NICUs). However, its pathogenesis has been unclear. Bioinformatics analysis, which can help us explore genetic alternations and recognize latent diagnostic biomarkers, has recently promoted the comprehension of the molecular mechanisms underlying disease occurrence and development. Methods: In this study, we identified key genes and miRNA-mRNA regulatory networks in BPD in preterm infants to elucidate the pathogenesis of BPD. We downloaded and analyzed miRNA and gene expression microarray datasets from the Gene Expression Omnibus database (GEO). Differentially expressed miRNA (DEMs) and differentially expressed genes (DEGs) were obtained through NetworkAnalyst. We performed pathway enrichment analysis using the Database for Annotation, Visualization and Integrated Discovery program (DAVID), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then we used the STRING to establish protein–protein interactions and the Cytoscape tool to establish miRNA–mRNA regulatory networks. Results: We identified 19 significant DEMs and 140 and 33 significantly upregulated and downregulated DEGs, respectively. Functional enrichment analysis indicated that significant DEGs were associated with the antigen processing and presentation, and B-cell receptor signaling pathways in BPD. Key DEGs, such as CD19, CD79B, MS4A1, and FCGR2B were selected as hub genes in PPI networks. Conclusions: In this study, we screened out 19 DEMs that might play important roles in the regulatory networks of BPD. Higher expression of miRNAs such as miR-15b-5p, hsa-miR-32-5p, miR-3613-3p, and miR-33a-5p and lower expression of miRNAs such as miR-3960, miR-425-5p, and miR-3202 might be correlated with the process of BPD.

Analysis of the Prognosis Related Genes in HER2+ Breast Cancer Based on Weighted Gene Co-expression Network Analysis

10.21203/rs.3.rs-704894/v1 ◽

2021 ◽

Author(s):

Yujie WENG ◽

RONG JIA ◽

ZHONGXIAN LI ◽

WEI LIANG ◽

YUCHENG JI ◽

...

Keyword(s):

Breast Cancer ◽

Extracellular Matrix ◽

Survival Analysis ◽

Cox Regression ◽

Functional Enrichment ◽

Receptor Interaction ◽

Screening Methods ◽

Her2 Breast Cancer ◽

Ppi Networks ◽

Abstract Background: Breast cancer is one of the malignant tumors that threaten women's health, with HER2+ breast cancer being more aggressive. In this study, bioinformatics methods were used to find potential key genes in HER2 + for diagnosis and treatment.Methods: Datasets of HER2+ breast cancer and normal tissue samples retrieved from TCGA databases were subjected to DEGs analysis using R software. Then WGCNA is constructed for DEGs. The key gene co-expression modules were then subjected to GO and KEGG pathway enrichment analyses, as well as construction of PPI networks using the STRING database for identifying key genes. Finally, key genes were further validated by survival analysis, protein expression, and COX regression models.Results: We identified 2063 DEGs and 4 gene co-expression modules. Functional enrichment analysis showed that these key co-expression modules were mainly associated with extracellular matrix organization, extracellular matrix structural constituent and neuroactive ligand−receptor interaction. PPI network visualization identified 100 key genes, 3 of which were not present in the other subtypes of breast cancer. UTS2 DRD4 and GLP1R are key genes specific to the HER2+ subtype. Survival analysis showed that UTS2 are prognosis-related key genes in HER2+ breast cancer. Finally, UTS2 combined with clinical data to construct Cox regression model.Conclusions: Combined with the two screening methods, 3 key genes closely related to HER2 + breast cancer were identified. UTS2 is a new potential key gene and may become a new therapeutic target for HER2 + breast cancer.

Global transcriptional regulation of STAT3- and MYC-mediated sepsis-induced ARDS

Therapeutic Advances in Respiratory Disease ◽

10.1177/1753466619879840 ◽

2019 ◽

Vol 13 ◽

pp. 175346661987984 ◽

Cited By ~ 2

Author(s):

Jianfeng Zhang ◽

Yifeng Luo ◽

Xiaoling Wang ◽

Jieyun Zhu ◽

Qian Li ◽

...

Keyword(s):

Expression Profiles ◽

Enrichment Analysis ◽

Regulatory Genes ◽

Functional Enrichment ◽

Hypergeometric Test ◽

Functional Modules ◽

Global Regulation ◽

Ppi Networks ◽

Regulation Network

Background: In recent years, sepsis-induced acute respiratory distress syndrome (ARDS) has remained a major clinical challenge for patients in intensive care units. While some progress has been reported over the years, the pathogenesis of ARDS still needs to be further expounded. Methods: In the present study, gene set enrichment analysis, differentially expressed genes analysis, short time-series expression miner, protein–protein interaction (PPI) networks, module analysis, hypergeometric test, and functional enrichment analysis were performed in whole blood gene expression profiles of sepsis and induced-sepsis ARDS to explore the molecular mechanism of sepsis-induced ARDS. Results: Further dysregulated genes in the process evolving from healthy control through sepsis to sepsis-induced ARDS were identified and organized into 10 functional modules based on their PPI networks. These functional modules were significantly involved in cell cycle, ubiquitin mediated proteolysis, spliceosome, and other pathways. MYC, STAT3, LEF1, and BRCA1 were potential transcription factors (TFs) regulating these modules. A TF-module-pathway global regulation network was constructed. In particular, our findings suggest that MYC and STAT3 may be the key regulatory genes in the underlying dysfunction of sepsis-induced ARDS. Receiver operating characteristic curve analysis showed the core genes in the global regulation network may be biomarkers for sepsis or sepsis-induced ARDS. Conclusions: We found that MYC and STAT3 may be the key regulatory genes in the underlying dysfunction of sepsis-induced ARDS. The reviews of this paper are available via the supplementary material section.

Identification of Potential Crucial Genes in Atrial Fibrillation: A Bioinformatic Analysis

10.21203/rs.3.rs-15436/v1 ◽

2020 ◽

Author(s):

Junguo Zhang ◽

Xin Huang ◽

Xiaojie Wang ◽

Yanhui Gao ◽

Li Liu ◽

...

Keyword(s):

Gene Expression ◽

Atrial Fibrillation ◽

Growth Factor ◽

Microarray Data ◽

Therapeutic Targets ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Functional Enrichment ◽

Insulin Like Growth Factor

Abstract Background Atrial fibrillation (AF) is clearly heritable, affecting 2-3% of the population in Europe and the USA. However, a substantial proportion of heritability is still lacking. In the present study, we aim to identify potential crucial genes associated with AF through bioinformatic analyses of public datasets.Methods Microarray data sets of GSE115574, GSE31821, GSE79768, GSE41177 and GSE14975 from the Gene Expression Omnibus (GEO) database were enrolled. After merging all microarray data and adjusted batch effect, differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource, Gene Set Enrichment Analysis (GSEA), Reactome Pathway Database and Disease Ontology (DO) were carried out for DEGs. Protein-protein interaction (PPI) network was constructed using the STRING database. Combined with aforementioned significant bioinformatics information, potential crucial genes were subsequently selected. The potential crucial genes coupled with corresponding predicted microRNAs involved in AF were then assessed.Result We identified 27 of DEGs with gene expression fold change (FC) ≥ 1.5 and 5 with FC ≥ 2 of AF patients compared with sinus rhythm controls. The most significantly enriched pathway was regulation of insulin-like growth factor transport and uptake by insulin-like growth factor binding proteins. IGFBP2, C1orf105, FHL2, CHGB, ATP1B4, IGFBP3, SLC26A9, CXCR4 and HTR2B were considered the potential crucial genes. Sixteen corresponding predicted microRNAs, of which 5 targeting IGFBP3 and 8 FHL2, might be associated with AF. The comparative toxicogenomics database (CTD) database showed CXCR4, IGFBP2, IGFBP3 and FHL2 had higher scores with AF.Conclusions The 9 potential crucial genes, especially CXCR4, IGFBP2, IGFBP3 and FHL2, may be associated with risk of AF. MicroRNAs targeting IGFBP3 and FHL2 may be potential biomarkers or therapeutic targets for AF. Our study provided new insights of AF into genetics, molecular pathogenesis and new therapeutic targets.

Identification of Potential Crucial Genes in Atrial Fibrillation: A Bioinformatic Analysis

10.21203/rs.3.rs-15436/v4 ◽

2020 ◽

Author(s):

Junguo Zhang ◽

Xin Huang ◽

Xiaojie Wang ◽

Yanhui Gao ◽

Li Liu ◽

...

Keyword(s):

Gene Expression ◽

Atrial Fibrillation ◽

Growth Factor ◽

Microarray Data ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Insulin Like Growth Factor

Abstract Background Atrial fibrillation (AF) is at least partially heritable, affecting 2-3% of the population in Europe and the USA. However, a substantial proportion of heritability is still lacking. In the present study, we aim to identify potential crucial genes associated with AF through bioinformatic analyses of public datasets. Methods Microarray data sets of GSE115574, GSE31821, GSE79768, GSE41177 and GSE14975 from the Gene Expression Omnibus (GEO) database were retrieved. After merging all microarray data and adjusting batch effect, differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource, Gene Set Enrichment Analysis (GSEA), Reactome Pathway Database and Disease Ontology (DO) were carried out. Protein-protein interaction (PPI) network was constructed using the STRING database. Combined with aforementioned significant bioinformatics information, potential crucial genes were subsequently selected. The comparative toxicogenomics database (CTD) was carried out to explore the interaction between potential crucial genes and AF. Result We identified 27 of DEGs with gene expression fold change (FC) ≥ 1.5 or ≤ 2/3 (|log2 FC| ≥ 0.58) and 5 with FC ≥ 2 or ≤ 0.5 (|log2 FC| ≥ 1) of AF patients compared with sinus rhythm controls. The most significantly enriched pathway was regulation of insulin-like growth factor transport and uptake by insulin-like growth factor binding proteins. IGFBP2, C1orf105, FHL2, CHGB, ATP1B4, IGFBP3, SLC26A9, CXCR4 and HTR2B were considered the potential crucial genes. CTD showed CXCR4, IGFBP2, IGFBP3 and FHL2 had higher scores with AF. Conclusions The 9 potential crucial genes, especially CXCR4, IGFBP2, IGFBP3 and FHL2 , may be associated with risk of AF. Our study provided new insights of AF into genetics, molecular pathogenesis and new therapeutic targets.

Study on Mechanism of Jiawei Chaiqin Wendan Decoction in Treatment of Vestibular Migraine Based on Network Pharmacology and Molecular Docking Technology

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/5528403 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Menglin Liu ◽

Genhao Fan ◽

Daopei Zhang ◽

Mingjun Zhu ◽

Huailiang Zhang

Keyword(s):

Molecular Docking ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Signal Pathway ◽

Vestibular Migraine ◽

Functional Enrichment ◽

Network Pharmacology ◽

Active Ingredients ◽

Drug Compound

Objective. To predict the main active ingredients, potential targets, and key pathways of Jiawei Chaiqin Wendan decoction treatment in vestibular migraine and explore possible mechanisms by network pharmacology and molecular docking technology. Methods. The active ingredients and related targets of Jiawei Chaiqin Wendan decoction were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The corresponding genes of the target were queried by UniProt database, and the “drug-compound-target-disease” network was constructed by Cytoscape 3.7.2 software. GO functional enrichment analysis and KEGG pathway enrichment analysis were carried out by R software and Bioconductor, and column chart and bubble chart were drawn by Prism software and OmicShare database for visualization. Finally, the mechanism and potential targets of Jiawei Chaiqin Wendan decoction in the treatment of vestibular migraine were predicted. Results. The “drug-compound-target-disease” network contains 154 active ingredients and 85 intersection targets. The key targets include AKT1, IL6, MAPK3, VEGFA, EGFR, CASP3, EGF, MAPK1, PTGS2, and ESR1. A total of 1939 items were obtained by GO functional enrichment analysis ( P < 0.05). KEGG pathway enrichment analysis screened 156 signal pathways ( P < 0.05), involving PI3K-Akt signal pathway, AGE-RAGE signal pathway in diabetes complications, MAPK signal pathway, HIF-1 signal pathway, IL-17 signal pathway, etc. Molecular docking results showed that quercetin, luteolin, kaempferol, tanshinone IIa, wogonin, naringenin, nobiletin, dihydrotanshinlactone, beta-sitosterol, and salviolone have good affinity with core target proteins IL6, PTGS2, MAPK1, MAPK3, and CGRP1. Conclusion. The active ingredients in Jiawei Chaiqin Wendan decoction may regulate the levels of inflammatory factors and neurotransmitters by acting on multiple targets such as IL6, MAPK3, MAPK1, and PTGS2, so as to play a therapeutic role in vestibular migraine.

Identification of Potential Key Genes and Pathways in Enzalutamide-Resistant Prostate Cancer Cell Lines: A Bioinformatics Analysis with Data from the Gene Expression Omnibus (GEO) Database

BioMed Research International ◽

10.1155/2020/8341097 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Long Zheng ◽

Xiaojie Dou ◽

Xiaodong Ma ◽

Wei Qu ◽

Xiaoshuang Tang

Keyword(s):

Gene Expression ◽

Prostate Cancer ◽

Cell Lines ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Key Genes ◽

Geo Database

Enzalutamide (ENZ) has been approved for the treatment of advanced prostate cancer (PCa), but some patients develop ENZ resistance initially or after long-term administration. Although a few key genes have been discovered by previous efforts, the complete mechanisms of ENZ resistance remain unsolved. To further identify more potential key genes and pathways in the development of ENZ resistance, we employed the GSE104935 dataset, including 5 ENZ-resistant (ENZ-R) and 5 ENZ-sensitive (ENZ-S) PCa cell lines, from the Gene Expression Omnibus (GEO) database. Integrated bioinformatics analyses were conducted, such as analysis of differentially expressed genes (DEGs), Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein-protein interaction (PPI) analysis, gene set enrichment analysis (GSEA), and survival analysis. From these, we identified 201 DEGs (93 upregulated and 108 downregulated) and 12 hub genes (AR, ACKR3, GPER1, CCR7, NMU, NDRG1, FKBP5, NKX3-1, GAL, LPAR3, F2RL1, and PTGFR) that are potentially associated with ENZ resistance. One upregulated pathway (hedgehog pathway) and seven downregulated pathways (pathways related to androgen response, p53, estrogen response, TNF-α, TGF-β, complement, and pancreas β cells) were identified as potential key pathways involved in the occurrence of ENZ resistance. Our findings may contribute to further understanding the molecular mechanisms of ENZ resistance and provide some clues for the prevention and treatment of ENZ resistance.

Inflammation as a risk factor for stroke in atrial fibrillation: data from a microarray data analysis

Journal of International Medical Research ◽

10.1177/0300060520921671 ◽

2020 ◽

Vol 48 (5) ◽

pp. 030006052092167

Author(s):

Yingyuan Li ◽

Wulin Tan ◽

Fang Ye ◽

Shihong Wen ◽

Rong Hu ◽

...

Keyword(s):

Atrial Fibrillation ◽

Microarray Data ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Microarray Data Analysis ◽

Protein Protein Interaction ◽

Inflammatory Processes ◽

Objective Stroke is a severe complication of atrial fibrillation (AF). We aimed to discover key genes and microRNAs related to stroke risk in patients with AF using bioinformatics analysis. Methods GSE66724 microarray data, including peripheral blood samples from eight patients with AF and stroke and eight patients with AF without stroke, were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AF patients with and without stroke were identified using the GEO2R online tool. Functional enrichment analysis was performed using the DAVID database. A protein–protein interaction (PPI) network was obtained using the STRING database. MicroRNAs (miRs) targeting these DEGs were obtained from the miRNet database. A miR–DEG network was constructed using Cytoscape software. Results We identified 165 DEGs (141 upregulated and 24 downregulated). Enrichment analysis showed enrichment of certain inflammatory processes. The miR–DEG network revealed key genes, including MEF2A, CAND1, PELI1, and PDCD4, and microRNAs, including miR-1, miR-1-3p, miR-21, miR-21-5p, miR-192, miR-192-5p, miR-155, and miR-155-5p. Conclusion Dysregulation of certain genes and microRNAs involved in inflammation may be associated with a higher risk of stroke in patients with AF. Evaluating these biomarkers could improve prediction, prevention, and treatment of stroke in patients with AF.