Network based approach to identify interactions between Type 2 diabetes and cancer comorbidities.

Autor: Nayan SI; Dept. of Computer Science & Engineering, University of Global Village, Barisal 8200, Bangladesh., Rahman MH; Department of Computer Science and Engineering, Islamic University, Kushtia 7003, Bangladesh; Center for Advanced Bioinformatics and Artificial Intelligence Research, Islamic University, Kushtia 7003, Bangladesh., Hasan MM; Dept. of Computer Science & Engineering, University of Global Village, Barisal 8200, Bangladesh., Raj SMRH; Dept. of Computer Science & Engineering, University of Global Village, Barisal 8200, Bangladesh., Almoyad MAA; Department of Basic Medical Sciences, College of Applied Medical Sciences in Khamis Mushyt, King Khalid University, 47 Abha, Mushait, PO Box. 4536, 61412, Saudi Arabia., Liò P; Computer Laboratory, The University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK., Moni MA; Artificial Intelligence and Cyber Futures Institute, Charles Stuart University, Bathurst, NSW, 2795, Australia. Electronic address: mmoni@csu.edu.au.
Jazyk: angličtina
Zdroj: Life sciences [Life Sci] 2023 Dec 15; Vol. 335, pp. 122244. Date of Electronic Publication: 2023 Nov 10.
DOI: 10.1016/j.lfs.2023.122244
Abstrakt: High blood sugar and insulin insensitivity causes the lifelong chronic metabolic disease called Type 2 diabetes (T2D) which has a higher chance of developing different malignancies. T2D with comorbidities like Cancers can make normal medications for those disorders more difficult. There may be a significant correlation between comorbidities and have an impact on one another's health. These associations may be due to a number of direct and indirect mechanisms. Such molecular mechanisms that underpin T2D and cancer are yet unknown. However, the large volumes of data available on these diseases allowed us to use analytical tools for uncovering their interrelated pathways. Here, we tried to present a system for investigating potential comorbidity relationships between T2D and Cancer disease by looking at the molecular processes involved, analyzing a huge number of freely accessible transcriptomic datasets of various disorders using bioinformatics. Using semantic similarity and gene set enrichment analysis, we created an informatics pipeline that evaluates and integrates Gene Ontology (GO), expression of genes, and biological process data. We discovered genes that are common in T2D and Cancer along with molecular pathways and GOs. We compared the top 200 Differentially Expressed Genes (DEGs) from each selected T2D and cancer dataset and found the most significant common genes. Among all the common genes 13 genes were found most frequent. We also found 4 common GO terms: GO:0000003, GO:0000122, GO:0000165, and GO:0000278 among all the common GO terms between T2d and different cancers. Using these genes and GO term semantic similarity, we calculated the distance between these two diseases. The semantic similarity results of our study showed a higher association of Liver Cancer (LiC), Breast Cancer (BreC), Colorectal Cancer (CC), and Bladder Cancer (BlaC) with T2D. Furthermore we found KIF4A, NUSAP1, CENPF, CCNB1, TOP2A, CCNB2, RRM2, HMMR, NDC80, NCAPG, and IGFBP5 common hub proteins among different cancers correlated to T2D. AGE-RAGE signaling pathway in diabetic complications, Osteoclast differentiation, TNF signaling pathway, IL-17 signaling pathway, p53 signaling pathway, MAPK signaling pathway, Human T-cell leukemia virus 1 infection, and Non-alcoholic fatty liver disease are the 8 most significant pathways found among 18 common pathways between T2D and selected cancers. As a result of our technique, we now know more about disease pathways that are critical between T2D and cancer.
Competing Interests: Declaration of competing interest This research was supported by the Deanship of Scientific Research Large Groups at King Khalid University, Kingdom of Saudi Arabia (RGP.2/219/43).
(Copyright © 2023. Published by Elsevier Inc.)
Databáze: MEDLINE
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