Sunghwan Hwang, Eunhye Flavin, Ji-Eun Lee
Exploring research trends of technology use in mathematics education: A scoping review using topic modeling
Education and information technologies
endoplasmic reticulum er stress arises accumulation misfolded unfolded proteins within cell intricately linked initiation progression various tumors therapeutic strategies however precise role er stress uterine corpus endometrial cancer ucec remains unclear data patients ucec control subjects obtained cancer genome atlas tcga gene expression omnibus geo databases using differential expression analysis weighted gene coexpression network analysis wgcna identified pivotal differentially expressed er stressrelated genes deergs validation significance genes ucec achieved consensus clustering bioinformatic analyses using cox regression analysis several machine learning algorithms least absolute shrinkage selection operator lasso extreme gradient boosting xgboost support vector machine recursive feature elimination svmrfe random forest hub deergs associated patient prognosis effectively identified based four identified hub genes prognostic model nomogram constructed additionally drug sensitivity analysis vitro validation experiments performed total 94 deergs identified patients ucec healthy controls consensus clustering analysis revealed significant differences prognosis typical immune checkpoints tumor microenvironments subtypes using cox regression analysis machine learning four hub deergs mybl2 radx rusc2 cyp46a1 identified construct prognostic model reliability model validated using receiver operating characteristic roc curves decision curve analysis dca demonstrated superior predictive ability nomogram terms 3 5year survival compared clinical indicators drug sensitivity analysis revealed increased sensitivity dactinomycin docetaxel selumetinib trametinib lowrisk group expressions radx rusc2 cyp46a1 downregulated whereas mybl2 upregulated ucec tissues demonstrated reverse transcriptionquantitative polymerase chain reaction rtqpcr immunofluorescence assays study developed stable accurate prognostic model based multiple bioinformatics analyses used assess prognosis ucec model may contribute future research risk stratification patients ucec formulation novel treatment strategies