Clinical Medical College, Guizhou Medical University, 550004 Guiyang, Guizhou Province, China.
Department of Hematology, Affiliated Hospital of Guizhou Medical University, Guizhou Province Institute of Hematology, Guizhou Province Laboratory of Hematopoietic Stem Cell Transplantation Centre, 550004 Guiyang, Guizhou Province, China.
J Immunol Res. 2022 May 29;2022:3922739. doi: 10.1155/2022/3922739. eCollection 2022.
Myeloma (MM) is a malignant plasma cell disorder, which is incurable owing to its drug resistance. Autophagy performs an integral function in homeostasis, survival, and drug resistance in multiple myeloma (MM). Therefore, the purpose of the present research was to identify potential autophagy-related genes (ARGs) in patients with MM. We downloaded the transcriptomic data (GSE136400) of patients with MM, as well as the corresponding clinical data from the Gene Expression Omnibus (GEO); the patients were classified at random into two groups in a ratio of 6: 4, with 212 samples in the training dataset and 142 samples in the test dataset. Both multivariate and univariate Cox regression analyses were performed to identify autophagy-related genes. The univariate Cox regression analysis demonstrated that 26 ARGs had a significant correlation with overall survival (OS). We constructed an autophagy-related risk prognostic model based on six ARGs: EIF2AK2 (ENSG00000055332), KIF5B (ENSG00000170759), MYC (ENSG00000136997), NRG2 (ENSG00000158458), PINK1 (ENSG00000158828), and VEGFA (ENSG00000112715) using LASSO-Cox regression analysis to predict risk outcomes, which revealed substantially shortened OS duration in the high-risk cohort in contrast with that in the low-risk cohort. Therefore, the ARG-based model significantly predicted the MM patients' prognoses and was verified in an internal test set. Differentially expressed genes were found to be predominantly enriched in pathways associated with inflammation and immune regulation. Immune infiltration of tumor cells resulted in the formation of a strong immunosuppressive microenvironment in high-risk patients. The potential therapeutic targets of ARGs were subsequently analyzed via protein-drug network analysis. Therefore, a prognostic model for MM was established via a comprehensive analysis of ARGs, through using the clinical models; we have further revealed the molecular landscape features of multiple myeloma.
骨髓瘤（MM）是一种恶性浆细胞疾病，由于其耐药性，该病无法治愈。自噬在多发性骨髓瘤（MM）的体内平衡、存活和耐药性中发挥着重要作用。因此，本研究旨在鉴定 MM 患者中潜在的自噬相关基因（ARGs）。我们从基因表达综合数据库（GEO）下载了 MM 患者的转录组数据（GSE136400）及其相应的临床数据；将患者随机分为两组，比例为 6:4，训练数据集有 212 个样本，测试数据集有 142 个样本。采用多变量和单变量 Cox 回归分析鉴定自噬相关基因。单变量 Cox 回归分析表明，26 个 ARG 与总生存期（OS）显著相关。我们基于 6 个 ARG 构建了一个自噬相关风险预后模型：EIF2AK2（ENSG00000055332）、KIF5B（ENSG00000170759）、MYC（ENSG00000136997）、NRG2（ENSG00000158458）、PINK1（ENSG00000158828）和 VEGFA（ENSG00000112715），使用 LASSO-Cox 回归分析预测风险结果，结果表明高危队列的 OS 明显缩短。因此，基于 ARG 的模型显著预测了 MM 患者的预后，并在内部测试集中得到验证。差异表达基因主要富集在与炎症和免疫调节相关的途径中。肿瘤细胞的免疫浸润导致高危患者形成强大的免疫抑制微环境。随后通过蛋白药物网络分析对 ARG 的潜在治疗靶点进行了分析。因此，通过综合分析 ARG，建立了 MM 的预后模型，进一步揭示了多发性骨髓瘤的分子特征。
