Li Xiaohui, Zhang Ling, Liu Chengcheng, He Yi, Li Xudong, Xu Yichuan, Gu Cuiyin, Wang Xiaozhen, Wang Shuoting, Zhang Jingwen, Liu Jiajun
Hematology Department, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Biofactors. 2025 Jan-Feb;51(1):e2135. doi: 10.1002/biof.2135. Epub 2024 Oct 24.
Mitochondrial quality regulation plays an important role in affecting the treatment sensitivity of multiple myeloma (MM). We aimed to develop a mitochondrial quality regulation genes (MQRGs)-related prognostic model for MM patients. The Genomic Data Commons-MM of bulk RNA-seq, mutation, and single-cell RNA-seq (scRNA-seq) dataset were downloaded, and the MQRGs gene set was collected previous study. "maftools" and CIBERSORT were used for mutation and immune-infiltration analysis. Subsequently, the "ConsensusClusterPlus" was used to perform the unsupervised clustering analysis, "survminer" and "ssGSEA" R package was used for the Kaplan-Meier survival and enrichment analysis, "limma" R, univariate and Least Absolute Shrinkage and Selection Operator Cox were used for RiskScore model. The "timeROC" R package was used for Receiver Operating Characteristic Curve analysis. Finally, the "Seurat" R package was used for scRNA-seq analysis. These MQRGs are mainly located on chromosome-1,2,3,7, and 22 and had significant expression differences among age, gender, and stage groups, in which PPARGC1A and PPARG are the high mutation genes. Most MQRGs expression are closely associated with the plasma cells infiltration and can divide the patients into 2 different prognostic clusters (C1, C2). Then, 8 risk models were screened from 60 DEGs for RiskScore, which is an independent prognostic factor and effectively divided the patients into high and low risk groups with significant difference of immune checkpoint expression. Nomogram containing RiskScore can accurately predict patient prognosis, and a series of specific transcription factor PRDM1 and IRF1 were identified. We described the based molecular features and developed a high effective MQRGs-related prognostic model in MM.
线粒体质量调控在影响多发性骨髓瘤（MM）的治疗敏感性方面发挥着重要作用。我们旨在为MM患者开发一种与线粒体质量调控基因（MQRGs）相关的预后模型。下载了批量RNA测序、突变和单细胞RNA测序（scRNA-seq）数据集的基因组数据共享库-MM，并收集了先前研究中的MQRGs基因集。使用“maftools”和CIBERSORT进行突变和免疫浸润分析。随后，使用“ConsensusClusterPlus”进行无监督聚类分析，使用“survminer”和“ssGSEA”R包进行Kaplan-Meier生存和富集分析，使用“limma”R、单变量和最小绝对收缩和选择算子Cox进行风险评分模型分析。使用“timeROC”R包进行受试者工作特征曲线分析。最后，使用“Seurat”R包进行scRNA-seq分析。这些MQRGs主要位于1、2、3、7和22号染色体上，在年龄、性别和分期组之间存在显著的表达差异，其中PPARGC1A和PPARG是高突变基因。大多数MQRGs表达与浆细胞浸润密切相关，并可将患者分为2个不同的预后簇（C1、C2）。然后，从60个差异表达基因中筛选出8个风险模型用于风险评分，这是一个独立的预后因素，并有效地将患者分为免疫检查点表达有显著差异的高风险和低风险组。包含风险评分的列线图可以准确预测患者预后，并鉴定出一系列特异性转录因子PRDM1和IRF1。我们描述了MM的基础分子特征，并开发了一种高效的与MQRGs相关的预后模型。
