Volume 32, Issue 1 (3-2025)
RJMS 2025, 32(1): 1-11 |
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Ethics code: IR.IAU.K.REC.1403.90
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Rahimi M, Maddah R, Shokrgozar A, Masoumi Goudarzi G. Identification of Key Genes in the Development of Major Depressive Disorder. RJMS 2025; 32 (1) :1-11
URL: http://rjms.iums.ac.ir/article-1-8699-en.html
URL: http://rjms.iums.ac.ir/article-1-8699-en.html
Clinical care and Health Promotion Research Center, Karaj Branch, Islamic Azad University, Karaj, Iran , mar.rahimi20@gmail.com
Abstract: (1479 Views)
Background & Aim: Psychiatric disorders are typically viewed as neuropsychological and neurobehavioral conditions that impair the ability to comprehend new or complex information. It is estimated that over 450 million people worldwide experience various mental disorders, with major depression anticipated to be the most disabling by 2030. Major depressive disorder (MDD), also known as unipolar disorder, is characterized by prolonged periods of depression across different situations and contexts, low self-esteem, and a lack of interest in activities that are normally enjoyable, lasting for at least two weeks. To diagnose MDD, it is essential to exclude any history of manic or hypomanic episodes. In children and adolescents, MDD may present as irritability.
MDD is influenced by both hereditary and environmental factors. Genetic contributions to MDD are well-documented through family, twin, and adoption studies. However, high discordance rates in studies of monozygotic twins suggest significant non-genetic influences, such as stressful life events, which can elevate the risk of developing depression. The heritability of MDD is estimated to be between 30% and 50%. The high prevalence, moderate heritability, and polygenic nature of MDD pose significant challenges for genetic mapping compared to other psychiatric disorders.
Disruptions in key neurobiological stress-responsive systems, such as the hypothalamic-pituitary-adrenal axis and the immune system, are observed in MDD. Treatment primarily involves psychotherapy and pharmacological interventions. For patients with treatment-resistant MDD who do not respond to multiple augmentation or combination therapies, electroconvulsive therapy has the strongest empirical support.
MDD is a complex, multifactorial disorder likely influenced by numerous individual genetic variations, similar to many other psychiatric disorders. The causes of MDD are intricate and involve a combination of genetic, immune system, and endocrine factors, often triggered by stress-related psychosocial conditions. Acute stress and major depression share similar pathophysiological changes, such as inflammatory states, metabolic alterations, and a prothrombotic state. The roles of genetic and epigenetic factors in the development and progression of MDD have been recognized, with nearly 200 genes associated with the disorder identified so far. By conducting genetic analyses to identify risk types, we can enhance our understanding of MDD's development, potentially leading to better prevention strategies and the creation of newer, more effective treatments.
Bioinformatics is emerging as a promising tool for diagnosing psychiatric disorders by identifying sensitive biomarkers and exploring related signaling pathways. Bioinformatics studies now facilitate the discovery of key genes associated with diseases like depression. Advances in bioinformatics, coupled with high-throughput assessments of brain and behavior, have provided technologies for rapidly identifying and characterizing the roles of biological systems in behavioral processes. This progress has led to the identification of new molecular targets for research, diagnostics, and therapies. Although much of this research is in its early stages, significant advancements are being made, and practical applications are already emerging. This study utilizes bioinformatics data to investigate the genes contributing to depression. In molecular biology, techniques such as signal processing and 3D imaging generate abundant raw data that can be used for disease diagnosis and treatment. Bioinformatics is also crucial for analyzing gene and protein regulation and expression. By identifying genes that contribute to MDD, rapid and cost-effective computational methods can be employed to diagnose the disorder and develop effective treatment solutions.
Methods: In the present study, microarray was used from the Gene Expression Omnibus (GEO) database on 32 samples, including 10 samples from healthy individuals, 10 samples from patients with MDD and 10 samples from patients diagnosed with MDD but recovered. Data analysis was performed using R software. In addition, inflammation-related genes were extracted from the MSigDB database for further analysis.
Finally, protein-protein interaction analysis and hub gene selection were analyzed using Cytoscape software.
Results: In the present study, a total of 1179 genes were identified as DEGs between MDD and healthy subjects, of which 648 were up-regulated and 531 were down-regulated. 18 genes are shared between DEGs and genes related to inflammatory processes, termed inflammation-related DEGs.
This study identified the genes IL-1β (interleukin 1 beta), TLR2 (toll-like receptor 2), IFNAR2 (interferon alpha and beta receptor subunit 1), TLR1 (toll-like receptor 1) and C5AR1 (complement receptor 1 C5a) as key genes in major depressive disorder.
Conclusion: This study had limitations such as lack of access to other databases and the inability to cite some articles. It is suggested that clinical and laboratory studies be carried out to confirm and complete the present study and to use this information for better diagnosis and treatment of this disorder. The key genes obtained by analysing the data in the present study provide important information for revealing the molecular mechanism and targeted treatment of depression disorder, and in the future, with more studies, the aforementioned genes can be used in the diagnosis and correct treatment of this disease. However, depression is still a major public health problem and governments should support the necessary research to develop interventions and better prevention and treatment.
MDD is influenced by both hereditary and environmental factors. Genetic contributions to MDD are well-documented through family, twin, and adoption studies. However, high discordance rates in studies of monozygotic twins suggest significant non-genetic influences, such as stressful life events, which can elevate the risk of developing depression. The heritability of MDD is estimated to be between 30% and 50%. The high prevalence, moderate heritability, and polygenic nature of MDD pose significant challenges for genetic mapping compared to other psychiatric disorders.
Disruptions in key neurobiological stress-responsive systems, such as the hypothalamic-pituitary-adrenal axis and the immune system, are observed in MDD. Treatment primarily involves psychotherapy and pharmacological interventions. For patients with treatment-resistant MDD who do not respond to multiple augmentation or combination therapies, electroconvulsive therapy has the strongest empirical support.
MDD is a complex, multifactorial disorder likely influenced by numerous individual genetic variations, similar to many other psychiatric disorders. The causes of MDD are intricate and involve a combination of genetic, immune system, and endocrine factors, often triggered by stress-related psychosocial conditions. Acute stress and major depression share similar pathophysiological changes, such as inflammatory states, metabolic alterations, and a prothrombotic state. The roles of genetic and epigenetic factors in the development and progression of MDD have been recognized, with nearly 200 genes associated with the disorder identified so far. By conducting genetic analyses to identify risk types, we can enhance our understanding of MDD's development, potentially leading to better prevention strategies and the creation of newer, more effective treatments.
Bioinformatics is emerging as a promising tool for diagnosing psychiatric disorders by identifying sensitive biomarkers and exploring related signaling pathways. Bioinformatics studies now facilitate the discovery of key genes associated with diseases like depression. Advances in bioinformatics, coupled with high-throughput assessments of brain and behavior, have provided technologies for rapidly identifying and characterizing the roles of biological systems in behavioral processes. This progress has led to the identification of new molecular targets for research, diagnostics, and therapies. Although much of this research is in its early stages, significant advancements are being made, and practical applications are already emerging. This study utilizes bioinformatics data to investigate the genes contributing to depression. In molecular biology, techniques such as signal processing and 3D imaging generate abundant raw data that can be used for disease diagnosis and treatment. Bioinformatics is also crucial for analyzing gene and protein regulation and expression. By identifying genes that contribute to MDD, rapid and cost-effective computational methods can be employed to diagnose the disorder and develop effective treatment solutions.
Methods: In the present study, microarray was used from the Gene Expression Omnibus (GEO) database on 32 samples, including 10 samples from healthy individuals, 10 samples from patients with MDD and 10 samples from patients diagnosed with MDD but recovered. Data analysis was performed using R software. In addition, inflammation-related genes were extracted from the MSigDB database for further analysis.
Finally, protein-protein interaction analysis and hub gene selection were analyzed using Cytoscape software.
Results: In the present study, a total of 1179 genes were identified as DEGs between MDD and healthy subjects, of which 648 were up-regulated and 531 were down-regulated. 18 genes are shared between DEGs and genes related to inflammatory processes, termed inflammation-related DEGs.
This study identified the genes IL-1β (interleukin 1 beta), TLR2 (toll-like receptor 2), IFNAR2 (interferon alpha and beta receptor subunit 1), TLR1 (toll-like receptor 1) and C5AR1 (complement receptor 1 C5a) as key genes in major depressive disorder.
Conclusion: This study had limitations such as lack of access to other databases and the inability to cite some articles. It is suggested that clinical and laboratory studies be carried out to confirm and complete the present study and to use this information for better diagnosis and treatment of this disorder. The key genes obtained by analysing the data in the present study provide important information for revealing the molecular mechanism and targeted treatment of depression disorder, and in the future, with more studies, the aforementioned genes can be used in the diagnosis and correct treatment of this disease. However, depression is still a major public health problem and governments should support the necessary research to develop interventions and better prevention and treatment.
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