Volume 12, Issue 3 (2024)
Health Educ Health Promot 2024, 12(3): 537-545 |
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Mohammadi P, Noruzinia M, Ebadi M, Ghoraeian P. Identification of Blood Biomarkers for Veno-Occlusive Disease for Early Diagnosis and Enhancing Patients Quality of Life. Health Educ Health Promot 2024; 12 (3) :537-545
URL: http://hehp.modares.ac.ir/article-5-76901-en.html
URL: http://hehp.modares.ac.ir/article-5-76901-en.html
1- Department of Biology, Damghan Branch, Islamic Azad University, Damghan, Iran
2- Department of Medical Genetics, Faculty of Medicine, Tarbiat Modares University, Tehran, Iran
3- Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
2- Department of Medical Genetics, Faculty of Medicine, Tarbiat Modares University, Tehran, Iran
3- Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
Abstract: (183 Views)
Aims: Sinusoidal obstruction syndrome/veno-occlusive disease is a severe complication that can develop in up to 15% of adults following hematopoietic cell transplantation, resulting in congestion and damage due to the occlusion of small veins in the liver. This study aimed to identify novel blood biomarkers associated with veno-occlusive disease through bioinformatics analysis to improve early diagnosis and treatment outcomes.
Materials & Methods: This retrospective cohort study analyzed GSE164635 using R packages, specifically employing the affy package for data normalization before applying the Limma package for differential expression analysis in 2024. To identify significant miRNAs, a log fold change filter was set at greater than +1 or less than -1, with an adjusted p-value of less than 0.05. The multiMiR package was used to predict gene targets for the identified miRNAs, with data sourced from the mirTarBase and Tarbase databases. Pathway enrichment and gene ontology analyses of the common genes were performed using Funrich 3.1.3, and Cytoscape software was used to construct networks of commonly shared genes. Target gene prediction for these miRNAs was conducted using the multiMiR package in R, with data sourced from the mirTarBase and Tarbase databases.
Findings: Two upregulated miRNAs (hsa-miR-194-5p and hsa-miR-148a-3p) and two downregulated miRNAs (hsa-miR-342-3p and hsa-miR-150-5p) were identified. For the upregulated miRNAs, the network analysis revealed interactions with key genes, such as AGO2, CDKN1A, HSP90AA1, HSPA4, EP300, IGF1R, MYC, SMAD2, DICER1, and IL10. For the downregulated miRNAs, the interaction network identified significant genes, including EEF2, IGF1R, EP300, CCN2, DNMT1, SREBF1, CANX, ZEB1, SP1, and JUN.
Conclusion: The pathophysiology of VOD is greatly influenced by microRNAs, which play a crucial role in regulating inflammation, fibrosis, endothelial function, and cellular survival.
Materials & Methods: This retrospective cohort study analyzed GSE164635 using R packages, specifically employing the affy package for data normalization before applying the Limma package for differential expression analysis in 2024. To identify significant miRNAs, a log fold change filter was set at greater than +1 or less than -1, with an adjusted p-value of less than 0.05. The multiMiR package was used to predict gene targets for the identified miRNAs, with data sourced from the mirTarBase and Tarbase databases. Pathway enrichment and gene ontology analyses of the common genes were performed using Funrich 3.1.3, and Cytoscape software was used to construct networks of commonly shared genes. Target gene prediction for these miRNAs was conducted using the multiMiR package in R, with data sourced from the mirTarBase and Tarbase databases.
Findings: Two upregulated miRNAs (hsa-miR-194-5p and hsa-miR-148a-3p) and two downregulated miRNAs (hsa-miR-342-3p and hsa-miR-150-5p) were identified. For the upregulated miRNAs, the network analysis revealed interactions with key genes, such as AGO2, CDKN1A, HSP90AA1, HSPA4, EP300, IGF1R, MYC, SMAD2, DICER1, and IL10. For the downregulated miRNAs, the interaction network identified significant genes, including EEF2, IGF1R, EP300, CCN2, DNMT1, SREBF1, CANX, ZEB1, SP1, and JUN.
Conclusion: The pathophysiology of VOD is greatly influenced by microRNAs, which play a crucial role in regulating inflammation, fibrosis, endothelial function, and cellular survival.
Article Type: Original Research |
Subject:
Health Communication
Received: 2024/09/7 | Accepted: 2024/10/20 | Published: 2024/10/30
Received: 2024/09/7 | Accepted: 2024/10/20 | Published: 2024/10/30
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