构建并验证多发性骨髓瘤中与血管生成相关基因的预后模型。

Construction and validation of a prognostic model of angiogenesis-related genes in multiple myeloma.

机构信息

Department of Hematology, The First People's Hospital of Yunnan Province, Yunnan Province Clinical Research Center for Hematologic Disease, Hu Yu Expert Workstation, Kunming, China.

The Affiliated Hospital of Kunming University of Science and Technology, Yunnan Provincial Clinical Medical Center for Blood Diseases and Thrombosis Prevention and Treatment, Kunming, Yunnan, China.

出版信息

BMC Cancer. 2024 Oct 11;24(1):1269. doi: 10.1186/s12885-024-13024-9.

DOI:10.1186/s12885-024-13024-9

PMID:39394121

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11470605/

Abstract

BACKGROUND

Angiogenesis is associated with tumour growth, infiltration, and metastasis. This study aimed to detect the mechanisms of angiogenesis-related genes (ARGs) in multiple myeloma (MM) and to construct a new prognostic model.

METHODS

MM research foundation (MMRF)-CoMMpass cohort, GSE47552, GSE57317, and ARGs were sourced from public databases. Differentially expressed genes (DEGs) in the tumour and control cohorts in GSE47552 were determined through differential expression analysis and were enriched with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Weighted gene coexpression network analysis (WGCNA) was applied to derive modules linked to the ARG scores and obtain module genes in GSE47552. Differentially expressed ARGs (DE-ARGs) were selected for subsequent analyses by overlapping DEGs and module genes. Furthermore, prognostic genes were selected using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Depending on the prognostic genes, a risk model was constructed, and risk scores were determined. Moreover, MM samples from MMRF-CoMMpass were sorted into high- and low-risk teams on account of the median risk score. Additionally, correlations among clinical characteristics, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), immune analysis, immunotherapy predictions and the mRNA‒miRNA‒lncRNA network were carried out.

RESULTS

A total of 898 DEGs, 211 module genes, 24 DE-ARGs and three prognostic genes (AKAP12, C11orf80 and EMP1) were selected for this study. Enrichment analysis revealed that the DEGs were related to 86 GO terms, such as 'cytoplasmic translation', and 41 KEGG pathways, such as 'small cell lung cancer'. A prognostic gene-based risk model was created in MMRF-CoMMpass and confirmed with the GSE57317 dataset. Moreover, a nomogram was established on the basis of independent prognostic factors that have proven to be good predictors. In addition, the immune cell infiltration results suggested that memory B cells were enriched in the high-risk group and that immature B cells were enriched in the low-risk group. Finally, the mRNA‒miRNA‒lncRNA network demonstrated that hsa-miR-508-5p was tightly associated with EMP1 and AKAP12. RT‒qPCR was used to validate the expression of the genes associated with prognosis.

CONCLUSION

A new prognostic model of MM associated with ARGs was created and validated, providing a new perspective for exploring the connection between ARGs and MM.

摘要

背景

血管生成与肿瘤的生长、浸润和转移有关。本研究旨在检测多发性骨髓瘤（MM）中与血管生成相关基因（ARGs）的机制，并构建一个新的预后模型。

方法

从公共数据库中获取 MM 研究基金会（MMRF）-CoMMpass 队列、GSE47552、GSE57317 和 ARGs。通过差异表达分析确定 GSE47552 中肿瘤和对照队列中的差异表达基因（DEGs），并进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析富集。应用加权基因共表达网络分析（WGCNA）从 GSE47552 中获得与 ARG 分数相关的模块和模块基因。通过重叠 DEGs 和模块基因选择进一步分析差异表达的 ARGs（DE-ARGs）。此外，使用单变量 Cox 和最小绝对收缩和选择算子（LASSO）回归分析选择预后基因。根据预后基因构建风险模型并确定风险评分。此外，根据中位风险评分将 MMRF-CoMMpass 中的 MM 样本分为高风险和低风险组。此外，还进行了临床特征、基因集变异分析（GSVA）、基因集富集分析（GSEA）、免疫分析、免疫治疗预测以及 mRNA-miRNA-lncRNA 网络之间的相关性分析。

结果

共筛选出 898 个 DEGs、211 个模块基因、24 个 DE-ARGs 和 3 个预后基因（AKAP12、C11orf80 和 EMP1）。富集分析表明，DEGs 与 86 个 GO 术语相关，如“细胞质翻译”，与 41 个 KEGG 途径相关，如“小细胞肺癌”。在 MMRF-CoMMpass 中建立了基于预后基因的风险模型，并通过 GSE57317 数据集进行了验证。此外，还基于独立的预后因素建立了一个预测效果良好的列线图。此外，免疫细胞浸润结果表明，高风险组中记忆 B 细胞富集，低风险组中幼稚 B 细胞富集。最后，mRNA-miRNA-lncRNA 网络表明 hsa-miR-508-5p 与 EMP1 和 AKAP12 密切相关。实时荧光定量 PCR（RT-qPCR）用于验证与预后相关的基因的表达。