Integrative analysis of protein-protein interaction networks: linking cellular functions to cancer and diabetes mechanisms
DOI:
https://doi.org/10.18203/2320-6012.ijrms20253950Keywords:
FUCA2, NDUFAF7, CYP26B1, FKBP4, M6PR, ARF5, Insulin signaling, Protein-protein interaction network, DiabetesAbstract
Background: Protein-protein interactions (PPIs) are vital in regulating cellular functions, including signal transduction, metabolic control, and intracellular transport. In diseases such as diabetes and cancer, analyzing PPI networks provides valuable insights into underlying molecular mechanisms and potential therapeutic targets. Focusing on disease-specific subnetworks enables the identification of key proteins and their functional relationships.
Methods: A subset of the human PPI network associated with diabetes and cancer was analyzed to identify proteins involved in signaling, trafficking, and metabolism. Proteins such as ARF5, M6PR, FKBP4, CYP26B1, NDUFAF7, and FUCA2 were included in the dataset annotations. We utilized visualization techniques to generate PPI (protein-protein interaction) maps, isolate diabetes modules with key genes like INS, GCK, IGF1, PPARG, and GLUT4, and investigate their relationships with different cancer types using the adjusted p-value threshold.
Results: In this study, each protein contributed a unique aspect and function to the overall analysis. M6PR regulated the transport of lysosomal enzymes, while ARF5 played a role in Golgi-associated vesicle trafficking. Hormone receptor microtubule FCBP4, along with hormone-receptor microtubule dynamics, was involved in retinoic acid metabolism and germ cell development. NDUFAF7 played a crucial role in mitochondrial complex I assembly, while FUCA2 participated in glycoprotein catabolism. Furthermore, the diabetes-specific interaction modules revealed a core regulatory axis, which comprised INS, GCK, IGF1, PPARG, and GLUT4. These genes are significant insulin signaling and glucose metabolism genes. Beside that, notable associations were observed with endometrial, melanoma, and colorectal cancers.
Conclusions: Recording the interplay of metabolic pathways underscores the importance of ppi subnetworks in metabolic regulation, signal transduction, and associated disorders, which supports further drug target investigations in diabetes and cancer.
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References
Calimlioglu B, Karagoz K, Sevimoglu T, Kilic E, Gov E, Arga KY. Tissue-specific molecular biomarker signatures of type 2 diabetes: an integrative analysis of transcriptomics and protein–protein interaction data. Omics. 2015;19(9):563-73. DOI: https://doi.org/10.1089/omi.2015.0088
Kasera H, Shekhawat RS, Yadav P, Singh P. Gene expression profiling and protein–protein network analysis revealed prognostic hub biomarkers linking cancer risk in type 2 diabetic patients. Scientific Rep. 2023;13(1):22605. DOI: https://doi.org/10.1038/s41598-023-49715-9
Tang X, Hu X, Yang X, Fan Y, Li Y, Hu W. Predicting diabetes mellitus genes via protein-protein interaction and protein subcellular localization information. BMC Genomics, 2016;17:397-405. DOI: https://doi.org/10.1186/s12864-016-2795-y
Poluri KM, Gulati K, Sarkar S. Protein-protein interactions. Springer. 2021. DOI: https://doi.org/10.1007/978-981-16-1594-8
Suresh NT, Ravindran VE, Krishnakumar U. A computational framework to identify cross association between complex disorders by protein-protein interaction network analysis. Current Bioinformatics. 2021;16(3):433-45. DOI: https://doi.org/10.2174/1574893615999200724145434
Khokhar M, Roy D, Tomo S, Gadwal A, Sharma P, Purohit P. Novel molecular networks and regulatory microRNAs in type 2 diabetes mellitus: multiomics integration and interactomics study. JMIR. 2022;3(1):e32437. DOI: https://doi.org/10.2196/32437
Tiwari S, Dwivedi UN. Discovering innovative drugs targeting both cancer and cardiovascular disease by shared protein–protein interaction network analyses. Omics. 2019;23(9):417-25. DOI: https://doi.org/10.1089/omi.2019.0095
Soofi A, Taghizadeh M, Tabatabaei SM, Tavirani MR, Shakib H, Namaki S, et al. Centrality analysis of protein-protein interaction networks and molecular docking prioritize potential drug-targets in type 1 diabetes. IJPR. 2020;19(4):121.
Durrani IA, John P, Bhatti A, Khan JS. Network medicine based approach for identifying the type 2 diabetes, osteoarthritis and triple negative breast cancer interactome: Finding the hub of hub gnes. Heliyon. 2024;10:17. DOI: https://doi.org/10.1016/j.heliyon.2024.e36650
Sabir JS, El Omri A, Shaik NA, Banaganapalli B, Al-Shaeri MA, Alkenani NA, et al. Identification of key regulatory genes connected to NF-κB family of proteins in visceral adipose tissues using gene expression and weighted protein interaction network. PLoS One. 2019;14(4):e0214337. DOI: https://doi.org/10.1371/journal.pone.0214337
Saik OV, Klimontov VV. Bioinformatic reconstruction and analysis of gene networks related to glucose variability in diabetes and its complications. Int J Molecular Sci. 2020;21(22):8691. DOI: https://doi.org/10.3390/ijms21228691