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Differential gene expression profile evaluation between uterine leiomyoma and leiomyosarcoma using a machine learning approach

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Gynecology and Obstetrics Clinical Medicine 2023年第3期3卷 154-162页

作者： Sonal Upadhyay Ravi Bhushan Anima Tripathi Lavina Chaubey Amita Diwakar pawan k.dubey Centre for Genetic Disorders Institute of ScienceBanaras Hindu UniversityVaranasi221005Uttar PradeshIndia Department of Zoology MMVBanaras Hindu UniversityVaranasi221005Uttar PradeshIndia Department of Obstetrics and Gynecology Institute of Medical ScienceBanaras Hindu UniversityVaranasi221005Uttar PradeshIndia

Objective The objective of this study is to differentiate between uterine leiomyomas(ULM)and uterine leiomyosarcomas(ULMS)by conducting molecular differential analysis and identifying potential prognostic biomarkers for *** The microarray datasets(GSEID:GSE64763 and GSE185543)were retrieved from the Gene Expression Omnibus *** preprocessing and differential gene expressions(DEGs)analysis were *** DEGs were further intersected to find the common DEGs in ULM and ULMS and further validation of selected DEGs were ***,a machine learning classifier was also applied in the selection of ***-protein interaction network based upon STRING v 10.5,was ***,Gene Ontology(GO)and kEGG(kyoto Encyclopedia of Genes and Genomes)pathway enrichment analyses were also performed to dissect possible functions and *** A total of 50 significant DEGs for ULM while 321 DEGs for ULMS have been identified with their official gene *** ULM and ULMS,a total of 14 common DEGs were identified of which 8 were up-regulated while 6 were *** DEGs of(GSE185543)were also analyzed and the significant genes were retrieved common in both datasets for further *** a machine learning approach,10 feature genes were *** the expression profiles of these genes,a sequential minimal optimization(SMO)prediction model was built on the training set,and it accurately and reliably classified features expression in ULM and ULMS in the independent test ***,Co-Enrichment analysis was also *** The study identified several DEGs,including ZNF365,EPYC,COL11A1,SHOX2,MMP13,TNN,GPM6A,and GATA2,through cross-validation,machine learning classifier,and Co-Enrichment *** candidate disease genes may provide valuable insight into the underlying mechanisms and could be used as potential diagnostic biomarkers for ULM and ***,further validation of the

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