A Five-Gene Risk Score Model for Predicting the Prognosis of Multiple Myeloma Patients Based on Gene Expression Profiles

Multiple myeloma is a heterogeneous plasma cell malignancy that remains incurable because of the tendency of relapse for most patients. Survival outcomes may vary widely due to patient and disease variables; therefore, it is necessary to establish a more accurate prognostic model to improve prognost...

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Autores principales:	Xiaotong Chen, Lintao Liu, Mengping Chen, Jing Xiang, Yike Wan, Xin Li, Jinxing Jiang, Jian Hou
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	multiple myeloma prognosis risk score model overall survival prediction Genetics QH426-470
Acceso en línea:	https://doaj.org/article/60180c643933496c808c7541fc404078
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spelling	oai:doaj.org-article:60180c643933496c808c7541fc4040782021-12-01T16:55:22ZA Five-Gene Risk Score Model for Predicting the Prognosis of Multiple Myeloma Patients Based on Gene Expression Profiles1664-802110.3389/fgene.2021.785330https://doaj.org/article/60180c643933496c808c7541fc4040782021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.785330/fullhttps://doaj.org/toc/1664-8021Multiple myeloma is a heterogeneous plasma cell malignancy that remains incurable because of the tendency of relapse for most patients. Survival outcomes may vary widely due to patient and disease variables; therefore, it is necessary to establish a more accurate prognostic model to improve prognostic precision and guide clinical therapy. Here, we developed a risk score model based on myeloma gene expression profiles from three independent datasets: GSE6477, GSE13591, and GSE24080. In this model, highly survival-associated five genes, including EPAS1, ERC2, PRC1, CSGALNACT1, and CCND1, are selected by using the least absolute shrinkage and selection operator (Lasso) regression and univariate and multivariate Cox regression analyses. At last, we analyzed three validation datasets (including GSE2658, GSE136337, and MMRF datasets) to examine the prognostic efficacy of this model by dividing patients into high-risk and low-risk groups based on the median risk score. The results indicated that the survival of patients in low-risk group was greatly prolonged compared with their counterparts in the high-risk group. Therefore, the five-gene risk score model could increase the accuracy of risk stratification and provide effective prediction for the prognosis of patients and instruction for individualized clinical treatment.Xiaotong ChenLintao LiuMengping ChenJing XiangYike WanXin LiJinxing JiangJian HouFrontiers Media S.A.articlemultiple myelomaprognosisrisk score modeloverall survivalpredictionGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multiple myeloma prognosis risk score model overall survival prediction Genetics QH426-470
spellingShingle	multiple myeloma prognosis risk score model overall survival prediction Genetics QH426-470 Xiaotong Chen Lintao Liu Mengping Chen Jing Xiang Yike Wan Xin Li Jinxing Jiang Jian Hou A Five-Gene Risk Score Model for Predicting the Prognosis of Multiple Myeloma Patients Based on Gene Expression Profiles
description	Multiple myeloma is a heterogeneous plasma cell malignancy that remains incurable because of the tendency of relapse for most patients. Survival outcomes may vary widely due to patient and disease variables; therefore, it is necessary to establish a more accurate prognostic model to improve prognostic precision and guide clinical therapy. Here, we developed a risk score model based on myeloma gene expression profiles from three independent datasets: GSE6477, GSE13591, and GSE24080. In this model, highly survival-associated five genes, including EPAS1, ERC2, PRC1, CSGALNACT1, and CCND1, are selected by using the least absolute shrinkage and selection operator (Lasso) regression and univariate and multivariate Cox regression analyses. At last, we analyzed three validation datasets (including GSE2658, GSE136337, and MMRF datasets) to examine the prognostic efficacy of this model by dividing patients into high-risk and low-risk groups based on the median risk score. The results indicated that the survival of patients in low-risk group was greatly prolonged compared with their counterparts in the high-risk group. Therefore, the five-gene risk score model could increase the accuracy of risk stratification and provide effective prediction for the prognosis of patients and instruction for individualized clinical treatment.
format	article
author	Xiaotong Chen Lintao Liu Mengping Chen Jing Xiang Yike Wan Xin Li Jinxing Jiang Jian Hou
author_facet	Xiaotong Chen Lintao Liu Mengping Chen Jing Xiang Yike Wan Xin Li Jinxing Jiang Jian Hou
author_sort	Xiaotong Chen
title	A Five-Gene Risk Score Model for Predicting the Prognosis of Multiple Myeloma Patients Based on Gene Expression Profiles
title_short	A Five-Gene Risk Score Model for Predicting the Prognosis of Multiple Myeloma Patients Based on Gene Expression Profiles
title_full	A Five-Gene Risk Score Model for Predicting the Prognosis of Multiple Myeloma Patients Based on Gene Expression Profiles
title_fullStr	A Five-Gene Risk Score Model for Predicting the Prognosis of Multiple Myeloma Patients Based on Gene Expression Profiles
title_full_unstemmed	A Five-Gene Risk Score Model for Predicting the Prognosis of Multiple Myeloma Patients Based on Gene Expression Profiles
title_sort	five-gene risk score model for predicting the prognosis of multiple myeloma patients based on gene expression profiles
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/60180c643933496c808c7541fc404078
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A Five-Gene Risk Score Model for Predicting the Prognosis of Multiple Myeloma Patients Based on Gene Expression Profiles

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