Medical Journal of The Islamic Republic of Iran (MJIRI)
Iran University of Medical Sciences
Thu, Dec 26, 2024
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Volume 38, Issue 1 (1-2024)
Med J Islam Repub Iran 2024 |
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Hejrati A, Maddah R, Parvin S, Gholami A, Hejrati L, Shateri Amiri B et al . Evaluation of Genes and Molecular Pathways Common between Diffuse Large B-cell Lymphoma (DLBCL) and Systemic Lupus Erythematosus (SLE): A Systems Biology Approach. Med J Islam Repub Iran 2024; 38 (1) :934-941
URL: http://mjiri.iums.ac.ir/article-1-9302-en.html
URL: http://mjiri.iums.ac.ir/article-1-9302-en.html
Alireza Hejrati 
, Reza Maddah , Sadaf Parvin , Abbas Gholami , Lina Hejrati , Bahareh Shateri Amiri , Anousheh Haghighi
, Reza Maddah , Sadaf Parvin , Abbas Gholami , Lina Hejrati , Bahareh Shateri Amiri , Anousheh Haghighi
Department of Internal Medicine, School of Medicine, Hazrat-e Rasool General Hospital, Iran University of Medical Sciences, Tehran, Iran , shateri.b@iums.ac.ir
Abstract: (162 Views)
Background: Diffuse large B-cell lymphoma (DLBL) and systemic lupus erythematosus (SLE) are complex autoimmune disorders that present unique clinical challenges. These conditions may share underlying genetic and signaling pathways despite their distinct manifestations. Uncovering these commonalities could offer invaluable insights into disease pathogenesis, paving the way for more targeted and effective therapeutic interventions. This study embarks on a comprehensive investigation of the common genes and signaling pathways between SLE and DLBL.
Methods: The researchers scoured the Gene Expression Omnibus database, meticulously gathering microarray datasets for SLE (GSE61635) and DLBL (GSE56315). Differential expression analysis was performed, allowing the team to identify the genes that were commonly dysregulated across these 2 autoimmune conditions. To delve deeper into the biological significance of these shared genes, the researchers conducted functional enrichment analysis, network analysis, and core gene identification. Notably, the diagnostic potential of the identified hub genes was assessed using a cutting-edge neural network model.
Results: The data analysis revealed a remarkable 146 genes that were shared between SLE and DLBL, of which 111 were upregulated and 45 downregulated. Functional enrichment analysis unveiled the involvement of these shared genes in vital immune system-related processes—such as defense response to viruses, interferon signaling, and broader immune system pathways. Network analysis pinpointed 5 hub genes (IFIT3, IFIT1, DDX58, CCL2, and OASL) that emerged as central players, exhibiting a high degree of centrality and predicted to hold crucial roles in the underlying molecular mechanisms. Remarkably, the neural network model demonstrated exceptional diagnostic accuracy in distinguishing between the disease states (DLBL and SLE) based solely on the expression patterns of these hub genes.
Conclusion: The identified hub genes and their associated pathways hold immense potential as diagnostic biomarkers and may serve as valuable targets for future therapeutic explorations.
Methods: The researchers scoured the Gene Expression Omnibus database, meticulously gathering microarray datasets for SLE (GSE61635) and DLBL (GSE56315). Differential expression analysis was performed, allowing the team to identify the genes that were commonly dysregulated across these 2 autoimmune conditions. To delve deeper into the biological significance of these shared genes, the researchers conducted functional enrichment analysis, network analysis, and core gene identification. Notably, the diagnostic potential of the identified hub genes was assessed using a cutting-edge neural network model.
Results: The data analysis revealed a remarkable 146 genes that were shared between SLE and DLBL, of which 111 were upregulated and 45 downregulated. Functional enrichment analysis unveiled the involvement of these shared genes in vital immune system-related processes—such as defense response to viruses, interferon signaling, and broader immune system pathways. Network analysis pinpointed 5 hub genes (IFIT3, IFIT1, DDX58, CCL2, and OASL) that emerged as central players, exhibiting a high degree of centrality and predicted to hold crucial roles in the underlying molecular mechanisms. Remarkably, the neural network model demonstrated exceptional diagnostic accuracy in distinguishing between the disease states (DLBL and SLE) based solely on the expression patterns of these hub genes.
Conclusion: The identified hub genes and their associated pathways hold immense potential as diagnostic biomarkers and may serve as valuable targets for future therapeutic explorations.
Keywords: Diffuse Large B-cell Lymphoma, Systemic Lupus Erythematous, Interferons, OASL, DDX58, CCL2, IFIT1, IFIT3
Type of Study: Original Research |
Subject:
Medical Genetics
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