National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, PR China.
Department of Hematology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, PR China.
BMC Cancer. 2023 Sep 12;23(1):859. doi: 10.1186/s12885-023-11371-7.
Multiple myeloma (MM) is a fatal malignant tumor in hematology. Mitophagy plays vital roles in the pathogenesis and drug sensitivity of MM.
We acquired transcriptomic expression data and clinical index of MM patients from NCI public database, and 36 genes involved in mitophagy from the gene set enrichment analysis (GSEA) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted to construct a risk score prognostic model. Kaplan-Meier survival analysis and receiver operation characteristic curves (ROC) were conducted to identify the efficiency of prognosis and diagnosis. ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) was performed to uncover the level of immune infiltration. QRT-PCR was performed to verify gene expression in clinical samples of MM patients. The sensitivity to chemotherapy drugs was evaluated upon the database of the genomics of drug sensitivity in cancer (GDSC).
Fifty mitophagy-related genes were differently expressed in two independent cohorts. Ten out of these genes were identified to be related to MM overall survival (OS) rate. A prognostic risk signature model was built upon on these genes: VDAC1, PINK1, VPS13C, ATG13, and HUWE1, which predicted the survival of MM accurately and stably both in training and validation cohorts. MM patients suffered more adverse prognosis showed more higher risk core. In addition, the risk score was considered as an independent prognostic element for OS of MM patients by multivariate cox regression analysis. Functional pathway enrichment analysis of differentially expressed genes (DEGs) based on risk score showed terms of cell cycle, immune response, mTOR pathway, and MYC targets were obviously enriched. Furthermore, MM patients with higher risk score were observed lower immune scores and lower immune infiltration levels. The results of qRT-PCR verified VDAC1, PINK1, and HUWE1 were dysregulated in new diagnosed MM patients. Finally, further analysis indicated MM patients showed more susceptive to bortezomib, lenalidomide and rapamycin in high-risk group.
Our research provided a neoteric prognostic model of MM based on mitophagy genes. The immune infiltration level based on risk score paved a better understanding of the participation of mitophagy in MM.
多发性骨髓瘤（MM）是血液学中的一种致命恶性肿瘤。自噬在 MM 的发病机制和药物敏感性中起着至关重要的作用。
我们从 NCI 公共数据库中获取 MM 患者的转录组表达数据和临床指标，并从基因集富集分析（GSEA）数据库中获取 36 个与自噬相关的基因。采用最小绝对收缩和选择算子（LASSO）Cox 回归分析构建风险评分预后模型。进行 Kaplan-Meier 生存分析和接收器操作特征曲线（ROC）以确定预后和诊断的效率。使用 ESTIMATE 算法和免疫相关的单样本基因集富集分析（ssGSEA）来揭示免疫浸润水平。通过 MM 患者的临床样本进行 QRT-PCR 验证基因表达。通过癌症基因组药物敏感性数据库（GDSC）评估对化疗药物的敏感性。
两个独立队列中，50 个与自噬相关的基因表达存在差异。其中 10 个基因与 MM 总生存率（OS）相关。基于这些基因构建了一个预后风险签名模型：VDAC1、PINK1、VPS13C、ATG13 和 HUWE1，该模型在训练和验证队列中均能准确且稳定地预测 MM 的生存情况。MM 患者预后不良的风险评分更高，核心风险更高。此外，多变量 Cox 回归分析表明，风险评分是 MM 患者 OS 的独立预后因素。基于风险评分的差异表达基因（DEGs）的功能途径富集分析显示，细胞周期、免疫反应、mTOR 途径和 MYC 靶标等术语明显富集。此外，风险评分较高的 MM 患者的免疫评分和免疫浸润水平较低。qRT-PCR 验证结果显示，新诊断的 MM 患者中 VDAC1、PINK1 和 HUWE1 表达失调。最后，进一步分析表明，高危组的 MM 患者对硼替佐米、来那度胺和雷帕霉素更敏感。
本研究基于自噬基因提供了一种新的 MM 预后模型。基于风险评分的免疫浸润水平为更好地了解自噬在 MM 中的参与提供了依据。
