Affiliations: [a] School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China | [b] The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, Henan, China
Correspondence:
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Corresponding author: Jingxin Zhang, School of Life Sciences, Zhengzhou University, Zhengzhou, China, School of Life Sciences, Zhengzhou University, No. 100, Science Avenue, Zhongyuan District, Zhengzhou, Henan, China. Tel.: +86 371 67783235; E-mail: zhangjx@zzu.edu.cn.
Note: [1] These authors contributed equally to this work and share first authorship.
Abstract: 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.
