همایش ملی بیوانفورماتیک ایران

صفحه اصلی / 4th international edition and 13th Iranian Conference on Bioinformatics

Dysregulated genes in the fat tissue of children confer a risk of cancer

نویسندگان :

Armita Kakavand Hamidi¹ Mahsa Mohammad Amoli²

1- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran 2- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

کلمات کلیدی :

Obesity،gene،gene expression،cancer

چکیده :

Obesity is a chronic complex disease characterized by the extreme accumulation of body fat. The prevalence of obesity has surged in recent years all over the world specifically in children. Obesity increases the risk of cardiovascular diseases, some types of cancer, type 2 diabetes, and mental problems among others (Xiao and Ding, 2024). The pathophysiology of obesity is not fully understood especially from the genetic causes of obesity. The heritability of obesity is between 40-70% and new technologies could overcome the current gap in the mechanisms of obesity (Concepción-Zavaleta and Quiroz-Aldave, 2024). In this study, we aim to explore the pathways and genes that play a role in obesity development in children. Gene Expression Omnibus (GEO) was searched for datasets in which the expression profile of fat tissue in obese children had been evaluated in comparison to lean children. The two selected datasets were analyzed using GEOquery, then two data were merged and the batch effect was removed using the SVA package. limma package was used for finding differentially expressed genes (DEGS), upregulated and downregulated DEGs. Enrichr was used to detect enriched pathways. Protein-protein interaction (PPI) network was drawn through STRING and Cytoscape software. The top 10 percent of hub genes were gained using the cytohubba plugin based on four centrality methods including degree centrality, closeness centrality, betweenness centrality and maximal clique centrality (MCC). We identified 403 DEGs (p-value<0.05) between obese and lean children from which 9 genes were upregulated (logFC>0.5) and 20 genes were downregulated (logFc<-0.5). Pathway enrichment analysis showed Neuroinflammation And Glutamatergic Signaling (WP5083) enriched pathway and Regulation Of DNA-templated Transcription (GO:0006355) was the biological process related to obesity. PPI network included 353 nodes and 879 edges. Hub gene analysis revealed 13 genes including VCP, NFKB1, CXCL1, TCP1, CHEK1, RPS20, IGF1, COL1A1, DNMT1, NFE2L2, REL, H2BC11, PGK1 linked to obesity. Enrichr-KG highlighted the role of these genes in the Transcriptional misregulation in cancer (KEGG_2021_Human) through Ras; p53; PI3K-Akt signaling pathways. It is well-known that the genetic basis of obesity in children is involved in genetic variations of genes that regulate the leptin-melanocortin pathway in the gut-brain axis so far (Loos and Yeo, 2022). The results of this meta-analysis also show that genes that are expressed differentially in the fat tissue of obese children compared to lean ones are acting through neurological pathways in general. In specific, this study shows the genes that are related to the comorbidity of cancer with obesity, making it harder and more important to manage. More genomic study of these genes for a better understanding of their role in obesity development is warranted.

لیست مقالات

لیست مقالات بایگانی شده

A Transformer-based Model for Diagnosis of Multiple Sclerosis using MRI Images

Elham Bagheri - Leila Safari

Site-Directed Mutagenesis to Optimize Anti- TIM-3 Antibody Affinity through In-Silico Modeling

Zahra Sohrabi nasr - Mehri Khatami - Mohammad mahdi Heidari

Prediction of E8 mpox virus protein structure: a potential to design inhibitor

Mahsa Kazemi - Saeide Karimi - Maryam Kheirani nasab - Maryam Kazemi - Mahboobeh Nazari

New inhibitors of the Toxoplasmosis by in-silico drug repurposing

Milad Jaberi - Masoud Aliyar - Mosleh Kadkhodamohammadi - Parva Karimimousivandi

A Novel Approach to Antimicrobial Susceptibility Testing: Automated Disk Diffusion Analysis with Smartphone Cameras and Deep Learning Techniques

Mahdiar Mansouri - Mohammadreza Najafi Disfani - Danial Ghofrani - Arash Pournaji

Identification of potential RNA interference targets in bladder cancer through bioinformatics approaches

Sana Sajjadi - Mohammad ali Takhshid

DTMP-Prime: A Deep Transformer-based Model for Predicting Prime Editing Efficiency and PegRNA Activity

Roghayyeh Alipanahi - Leila Safari - Alireza Khanteymoori

Predicting drug response using omics data and artificial intelligence approach in cancer

Abdolhamid Nazerpanahi - Mohammad Ali Hossein Beigy - Amirhossein Keyhanipour - Kaveh Kavousi - Siavash Kavousi

An efficient method based on transformers for antimicrobial peptide prediction

Alireza Khorramfard - Jamshid Pirgazi - Ali Ghanbari Sorkhi

Insilico study of CD4+ T cells epitopes in ORF1ab protein of SARS-COV-2 for Iranian common MHCII alleles

Fatemeh Hajighasem - Atefeh Shirkavand

بیشتر