Identifying the molecular dynamics of stress in chronic fatigue syndrome
Stress is a significant contributor to various health conditions, among which chronic fatigue syndrome (CFS), also known as myalgic encephalomyelitis (ME), is particularly noteworthy. This condition, marked by intense fatigue and cognitive impairments, has shown a strong correlation with...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Academia.edu Journals
2024-08-01
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| Series: | Academia Molecular Biology and Genomics |
| Online Access: | https://www.academia.edu/123294877/Molecular_Dynamics_of_Stress_in_Chronic_Fatigue_Syndrome_Through_a_Gene_Level_Analysis |
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| Summary: | Stress is a significant contributor to various health conditions, among which chronic fatigue syndrome (CFS), also known as myalgic encephalomyelitis (ME), is particularly noteworthy. This condition, marked by intense fatigue and cognitive impairments, has shown a strong correlation with stress. Recent progress in molecular biology, especially through methods like RNA-sequencing, has opened new avenues for investigating the influence of stress on disorders such as ME/CFS. These advancements in technology enable a more in-depth exploration of how stress affects gene expression and cellular processes in ME/CFS, potentially guiding the development of innovative treatment approaches. Toward this, we introduce an in silico method aimed at identifying key genes that establish a connection between stress and ME/CFS. Our process focuses on two essential criteria: the presence of strong differential gene expression and the formation of ligand-receptor (LR) pairs. These criteria are crucial for distinguishing genes that are not only statistically significant but also biologically meaningful. By applying this methodology to relevant RNA-seq data, we identified 40 key genes forming LR pairs. Our findings suggest potential biomarkers and therapeutic targets for ME/CFS, which warrant further in vitro investigation. This computational framework is designed to uncover potential gene biomarkers for a given disease, utilizing data from RNA-seq experiments. |
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| ISSN: | 3064-9765 |