背景:免疫调节药物调节泛素-蛋白酶体系统,是目前多发性骨髓瘤(MM)的主要治疗手段。在这项研究中,生物信息学分析用于构建风险模型并评估MM中泛素化相关基因的预后价值。
结果:泛素化相关基因和MM样品的数据从癌症基因组图谱(TCGA)和基因表达综合(GEO)数据库下载。使用一致的聚类分析和ESTIMATE算法来创建不同的聚类。通过单因素和多因素分析构建MM预后风险模型。绘制ROC曲线以比较高危组和低危组之间的生存差异。列线图用于验证风险模型的预测能力。共获得87个泛素化相关基因,有47个基因在MM组中显示高表达。根据一致性聚类分析,确定了4个簇。免疫浸润,生存,4个簇之间的预后差异显着。簇1和簇3的肿瘤纯度高于簇2和簇4,而簇1和簇3的免疫评分和基质评分较低。B细胞记忆的比例,浆细胞,T细胞CD4在第4组中最低。模型基因KLHL24,HERC6,USP3,TNIP1和CISH在高危组中高表达。AICAr和BMS.754,807在低风险组中表现出更高的药物敏感性,而博来霉素在高危人群中表现出更高的药物敏感性。风险模型的列线图显示了使用TCGA和GEO数据集预测MM患者生存的良好疗效。
结论:泛素化相关基因构建的风险模型可有效预测MM患者的预后。MM中的KLHL24,HERC6,USP3,TNIP1和CISH基因值得进一步研究作为治疗靶标并对抗耐药性。
BACKGROUND: Immunoregulatory drugs regulate the ubiquitin-proteasome system, which is the main treatment for multiple myeloma (MM) at present. In this study, bioinformatics analysis was used to construct the risk model and evaluate the prognostic value of ubiquitination-related genes in MM.
RESULTS: The data on ubiquitination-related genes and MM samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The consistent cluster analysis and ESTIMATE algorithm were used to create distinct clusters. The MM prognostic risk model was constructed through single-factor and multiple-factor analysis. The ROC curve was plotted to compare the survival difference between high- and low-risk groups. The nomogram was used to validate the predictive capability of the risk model. A total of 87 ubiquitination-related genes were obtained, with 47 genes showing high expression in the MM group. According to the consistent cluster analysis, 4 clusters were determined. The immune infiltration, survival, and prognosis differed significantly among the 4 clusters. The tumor purity was higher in clusters 1 and 3 than in clusters 2 and 4, while the immune score and stromal score were lower in clusters 1 and 3. The proportion of B cells memory, plasma cells, and T cells CD4 naïve was the lowest in cluster 4. The model genes KLHL24, HERC6, USP3, TNIP1, and CISH were highly expressed in the high-risk group. AICAr and BMS.754,807 exhibited higher drug sensitivity in the low-risk group, whereas Bleomycin showed higher drug sensitivity in the high-risk group. The nomogram of the risk model demonstrated good efficacy in predicting the survival of MM patients using TCGA and GEO datasets.
CONCLUSIONS: The risk model constructed by ubiquitination-related genes can be effectively used to predict the prognosis of MM patients. KLHL24, HERC6, USP3, TNIP1, and CISH genes in MM warrant further investigation as therapeutic targets and to combat drug resistance.