| 引用本文: | 张钧栋,卢迪,张皓旻,陈浩然,王紫宁,卢学春.多发性骨髓瘤预后相关自噬基因筛选及建模[J].生物信息学,2023,21(2):114-120. |
| ZHANG Jundong,LU Di,ZHANG Haomin,CHEN Haoran,WANG Zining,LU Xuechun.Screening of autophagy-related genes and establishment of a prognostic model for multiple myeloma[J].Chinese Journal of Bioinformatics,2023,21(2):114-120. |
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| 多发性骨髓瘤预后相关自噬基因筛选及建模 |
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张钧栋 1,2,卢迪1,张皓旻2,陈浩然3,王紫宁 1,2,卢学春2
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(1．解放军医学院,北京 100853;2.解放军总医院 第二医学中心血液科,国家老年疾病临床医学研究中心,北京 100853;3. 山西医科大学 管理学院,太原 030604)
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| 摘要: |
| 为探讨自噬相关基因(ARGs)在MM发生发展中的作用机制并建立相关的预后模型。基于MMRF与HADb数据库,通过R语言确定多发性骨髓瘤中自噬相关基因的差异表达,GO和KEGG分析自噬相关基因与多发性骨髓瘤发生发展的关系,使用COX回归算法建立多基因预后模型,Kaplan-Meier方法绘制生存曲线,ROC曲线评价预后模型的可靠性。最终从764例多发性骨髓瘤患者骨髓样本及4例正常骨髓样本中共发现104个基因的表达在多发性骨髓瘤样本中具有显著差异,其中上调基因46个,下调基因58个。GO富集主要集中在巨自噬、自噬调节、细胞对外部刺激的反应等本体学注释。KEGG富集主要集中在自噬、细胞凋亡、NOD样受体信号通路、PI3K-Akt信号通路。单因素COX分析发现33个自噬相关基因与多发性骨髓瘤患者整体生存明显相关。多因素COX回归筛选出13个预后相关自噬相关基因(NKX2-3、NCKAP1、BIRC5、PEX3、HGS、RUBCN、PARP1、ARSA、DNAJB9、HSP90AB1、EEF2、FKBP1B和CD46)建立多发性骨髓瘤自噬相关基因预后模型。Kaplan-Meier 生存曲线分析显示,高、低风险组患者的生存率具有显著差异 ( P<0.001),多因素COX 回归分析表明,年龄、ISS分期和风险值是多发性骨髓瘤患者的独立预后指标( P<0.05),ROC曲线下面积分别为0.561、0.685和0.719。得出如下结论:多发性骨髓瘤自噬相关基因预后模型可用于预测多发性骨髓瘤患者的生存情况,但需进一步的临床研究加以证实。 |
| 关键词: 多发性骨髓瘤 自噬相关基因 预后 |
| DOI：10.12113/202110019 |
| 分类号:Q786 |
| 文献标识码:A |
| 基金项目:2019年度保健专项科研课题(No.19BJZ28)；国家重点研发计划(No.2020YFC2002706). |
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| Screening of autophagy-related genes and establishment of a prognostic model for multiple myeloma |
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ZHANG Jundong1,2, LU Di1, ZHANG Haomin2, CHEN Haoran3, WANG Zining1,2, LU Xuechun2
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(1.Medical School of Chinese PLA, Beijing 100853, China;2.National Clinical Research Center for Geriatric Disease,Department of Hematology of the Second Medical Center, Chinese PLA General Hospital, Beijing 100853, China; 3. School of Management, Shanxi Medical University, Taiyuan 030604, China)
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| Abstract: |
| The mechanism of an autophagy-related gene (ARG) in the occurrence and development of multiple myeloma (MM) was explored and a related prognostic model was established. On the basis of the MMRF and HADb databases, the differential expression of ARGs in MM was determined by R language, the relationship between ARGs and the occurrence and development of MM was analyzed by GO and KEGG, the multi-gene prognostic model was established by COX regression algorithm, the survival curve was drawn by Kaplan-Meier method, and the reliability of the prognostic model was evaluated by ROC curve. Finally, a total of 104 differentially expressed ARGs were extracted from bone marrow samples of 764 MM patients and four normal bone marrow samples. It was found that there were significant differences in the expression of 104 genes in MM samples, including 46 up-regulated genes and 58 down-regulated genes. GO enrichment mainly focused on ontology annotations such as macrophagy, autophagy regulation, and cell response to external stimuli. KEGG enrichment was mainly concentrated in autophagy, apoptosis, NOD-like receptor signal pathway, and PI3K-Akt signal pathway. Univariate COX analysis showed that 33 ARGs were significantly correlated with the overall survival of MM patients. Thirteen prognostic ARGs (NKX2-3, NCKAP1, BIRC5, PEX3, HGS, RUBCN, PARP1, ARSA, DNAJB9, HSP90AB1, EEF2, FKBP1B, and CD46) were selected by multivariate COX regression to establish ARGs prognostic model for MM. Kaplan-Meier survival curve analysis showed a significant difference in survival rate between high risk group and low risk group (P <0.001). Multivariate COX regression analysis showed that age, ISS stage, and risk value were independent prognostic indicators of MM patients (P<0.005). The area under the ROC curve was 0.561, 0.685, and 0.719. The following conclusions are drawn: the ARGs prognostic model of MM can be used to predict the survival of patients with MM, but it needs to be verified by further clinical studies. |
| Key words: Multiple myeloma Autophagy-related genes Prognosis |
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