Integrated protein-protein interaction and gene ontology enrichment analysis reveals strong association with carbohydrate metabolism pathways
DOI:
https://doi.org/10.18203/2319-2003.ijbcp20253370Keywords:
Bioinformatics, Carbohydrate metabolism, Diabetes, Functional enrichment analysis, Gene ontology, Glucose metabolism, Metabolic disorders, Protein-protein interaction, STRINGdbAbstract
Background: Carbohydrate metabolism is very important for keeping cellular energy balance, but it is not working properly in people with diabetes and other metabolic disorders. The molecular characteristics of this pathway can be elucidated through the integrated analysis of gene functions and protein-protein interactions. The study utilized computational techniques to examine the functional and relational characteristics of specific genes that govern carbohydrate metabolism.
Methods: The researchers utilized a dual-stage bioinformatics system. The first step was to use the STRINGdb R package to look at PPI with Homo sapiens (Ensembl ID: 9606) and set a cutoff of 500 for results with a lot of confidence. The research team utilized R-based tools to perform gene ontology enrichment analysis, aiming to identify statistically significant functional associations within biological processes, molecular functions, and cellular component domains. We only accepted GO terms when the q-value was less than 0.05.
Results: STRINGdb analysis revealed six high-confidence protein-protein interactions (PPIs) with interaction values ranging from 590 to 995, indicating a robust connection. GO enrichment analysis showed that there was a total gene overlap (GeneRatio =60/60) in the top biological processes related to carbohydrate metabolism. These processes included glucose metabolic process (GO:0006006), hexose metabolic process (GO:0019318), and monosaccharide metabolic process (GO:0005996). These processes exhibited highly significant adjusted p-values (p adjust <1.0E-114) and substantial fold enrichment (>78).
Conclusions: The integrated analysis unequivocally demonstrates that the examined gene set functions as a cohesive network primarily involved in glucose and sugar metabolism. This particularly emphasizes their importance in metabolic disease mechanisms and energy regulation, notably diabetes. These findings provide a robust foundation for potential therapeutic exploration and subsequent experimental verification.
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