Evidence map›Paper›PMID 39368016›Full record

ArticleMolecular genetics and genomics : MGG2024

Integrated bioinformatics reveals genetic links between visceral obesity and uterine tumors.

Swayamprabha Samantaray, Nidhi Joshi, Shrinal Vasa, Shan Shibu, Aditi Kaloni, Bhavin Parekh, Anupama Modi

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Article in Molecular genetics and genomics : MGG, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Swayamprabha SamantaraySchool of Applied Sciences and Technology, Gujarat Technological University, Ahmedabad, Gujarat, 382424, India.
Nidhi JoshiSchool of Applied Sciences and Technology, Gujarat Technological University, Ahmedabad, Gujarat, 382424, India.
Shrinal VasaSchool of Applied Sciences and Technology, Gujarat Technological University, Ahmedabad, Gujarat, 382424, India.
Shan ShibuSchool of Applied Sciences and Technology, Gujarat Technological University, Ahmedabad, Gujarat, 382424, India.
Aditi KaloniSchool of Applied Sciences and Technology, Gujarat Technological University, Ahmedabad, Gujarat, 382424, India.
Bhavin ParekhSchool of Applied Sciences and Technology, Gujarat Technological University, Ahmedabad, Gujarat, 382424, India. bp2580@googlemail.com.
Anupama ModiSchool of Applied Sciences and Technology, Gujarat Technological University, Ahmedabad, Gujarat, 382424, India. anupamarmodi@gmail.com.ORCID http://orcid.org/0000-0002-6488-4701

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Visceral obesity (VO), characterized by excess fat around internal organs, is a recognized risk factor for gynecological tumors, including benign uterine leiomyoma (ULM) and malignant uterine leiomyosarcoma (ULS). Despite this association, the shared molecular mechanisms remain underexplored. This study utilizes an integrated bioinformatics approach to elucidate common molecular pathways and identify potential therapeutic targets linking VO, ULM, and ULS. We analyzed gene expression datasets from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) in each condition. We found 101, 145, and 18 DEGs in VO, ULM, and ULS, respectively, with 37 genes overlapping across all three conditions. Functional enrichment analysis revealed that these overlapping DEGs were significantly enriched in pathways related to cell proliferation, immune response, and transcriptional regulation, suggesting shared biological processes. Protein-protein interaction network analysis identified 14 hub genes, of which TOP2A, APOE, and TYMS showed significant differential expression across all three conditions. Drug-gene interaction analysis identified 26 FDA-approved drugs targeting these hub genes, highlighting potential therapeutic opportunities. In conclusion, this study uncovers shared molecular pathways and actionable drug targets across VO, ULM, and ULS. These findings deepen our understanding of disease etiology and offer promising avenues for drug repurposing. Experimental validation is needed to translate these insights into clinical applications and innovative treatments.

Indexed as

Computational BiologyGene Expression Regulation, NeoplasticGene Regulatory NetworksLeiomyomaObesity, AbdominalProtein Interaction MapsUterine NeoplasmsApolipoproteins EDatabases, GeneticDNA Topoisomerases, Type IIFemaleGene Expression ProfilingHumansLeiomyosarcomaPoly-ADP-Ribose Binding ProteinsApolipoproteins EDNA Topoisomerases, Type IIPoly-ADP-Ribose Binding ProteinsTOP2A protein, humanApolipoprotein ECytoscapeDAVIDDGIdbDifferentially expressed genesDrug targetsGene Expression Omnibus datasetsSTRINGTherapeuticsThymidylate SynthaseTopoisomerase IIalphaUALCANUterine leiomyomaUterine leiomyosarcomaVisceral obesity

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.