Related Experiment Video
Updated: Jul 10, 2025
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Comprehensive analysis of thirteen-gene panel with prognosis value in Multiple Myeloma
Tingting Zheng1,1, Panpan Chen1,1, Yuanlin Xu2
1School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China.
Background:
Although there are many treatments for Multiple myeloma (MM), patients with MM still unable to escape the recurrence and aggravation of the disease.
Objective:
We constructed a risk model based on genes closely associated with MM prognosis to predict its prognostic value.
Methods:
Gene 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.
Results:
Thirteen 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.
Conclusions:
The potential prognostic signature of thirteen genes were assessed and a risk model was constructed that significantly correlated with prognosis in MM patients.
