ArticleCancer biomarkers : section A of Disease markers2023
Comprehensive analysis of thirteen-gene panel with prognosis value in Multiple Myeloma.
Article in Cancer biomarkers : section A of Disease markers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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13 authors.
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Abstract
backgroundAlthough there are many treatments for Multiple myeloma (MM), patients with MM still unable to escape the recurrence and aggravation of the disease.
objectiveWe constructed a risk model based on genes closely associated with MM prognosis to predict its prognostic value.
methodsGene function enrichment and signal pathway enrichment analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, univariate and multivariate Cox regression analysis, Kaplan-Meier (KM) survival analysis and Receiver Operating Characteristic (ROC) analysis were used to identify the prognostic gene signature for MM. Finally, the prognostic gene signature was validated using the Gene Expression Omnibus (GEO) database.
resultsThirteen prognostic genes were screened by univariate Cox analysis and LASSO regression analysis. Multivariate Cox analysis revealed risk score to be an independent prognostic factor for patients with MM [Hazard Ratio (HR) = 2.564, 95% Confidence Interval (CI) = 2.223-2.958, P< 0.001]. The risk score had a high level of predictive value according to ROC analysis, with an area under the curve (AUC) of 0.744.
conclusionsThe potential prognostic signature of thirteen genes were assessed and a risk model was constructed that significantly correlated with prognosis in MM patients.
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