通过综合生物信息学分析鉴定与多发性骨髓瘤细胞黏附相关的关键生物标志物。
Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis.
作者信息
Peng Yue, Wu Dong, Li Fangmei, Zhang Peihua, Feng Yuandong, He Aili
机构信息
Department of Hematology, The Second Affiliated Hospital of Xi'an Jiaotong University, 157, 5th West Road, 710004 Xi'an, Shaanxi China.
出版信息
Cancer Cell Int. 2020 Jun 22;20:262. doi: 10.1186/s12935-020-01355-z. eCollection 2020.
BACKGROUND
Multiple Myeloma (MM) is a hematologic malignant disease whose underlying molecular mechanism has not yet fully understood. Generally, cell adhesion plays an important role in MM progression. In our work, we intended to identify key genes involved in cell adhesion in MM.
METHODS
First, we identified differentially expressed genes (DEGs) from the mRNA expression profiles of GSE6477 dataset using GEO2R with cut-off criterion of p < 0.05 and [logFC] ≥ 1. Then, GO and KEGG analysis were performed to explore the main function of DEGs. Moreover, we screened hub genes from the protein-protein interaction (PPI) network analysis and evaluated their prognostic and diagnostic values by the PrognoScan database and ROC curves. Additionally, a comprehensive analysis including clinical correlation analysis, GSEA and transcription factor (TF) prediction, pan-cancer analysis of candidate genes was performed using both clinical data and mRNA expression data.
RESULTS
First of all, 1383 DEGs were identified. Functional and pathway enrichment analysis suggested that many DEGs were enriched in cell adhesion. 180 overlapped genes were screened out between the DEGs and genes in GO terms of cell adhesion. Furthermore, 12 genes were identified as hub genes based on a PPI network analysis. ROC curve analysis demonstrated that ITGAM, ITGB2, ITGA5, ITGB5, CDH1, IL4, ITGA9, and LAMB1 were valuable biomarkers for the diagnosis of MM. Further study demonstrated that ITGA9 and LAMB1 revealed prognostic values and clinical correlation in MM patients. GSEA and transcription factor (TF) prediction suggested that MYC may bind to ITGA9 and repress its expression and HIF-1 may bind to LAMB1 to promote its expression in MM. Additionally, pan-cancer analysis showed abnormal expression and clinical outcome associations of LAMB1 and ITGA9 in multiple cancers.
CONCLUSION
In conclusion, ITGA9 and LAMB1 were identified as potent biomarkers associated with cell adhesion in MM.
背景
多发性骨髓瘤（MM）是一种血液系统恶性疾病，其潜在分子机制尚未完全明确。一般来说，细胞黏附在MM进展中起重要作用。在我们的研究中，我们旨在鉴定MM中参与细胞黏附的关键基因。
方法
首先，我们使用GEO2R从GSE6477数据集的mRNA表达谱中鉴定差异表达基因（DEG），截断标准为p < 0.05且[logFC]≥1。然后，进行GO和KEGG分析以探索DEG的主要功能。此外，我们通过蛋白质-蛋白质相互作用（PPI）网络分析筛选枢纽基因，并通过PrognoScan数据库和ROC曲线评估其预后和诊断价值。另外，使用临床数据和mRNA表达数据进行了包括临床相关性分析、基因集富集分析（GSEA）和转录因子（TF）预测、候选基因的泛癌分析在内的综合分析。
结果
首先，鉴定出1383个DEG。功能和通路富集分析表明许多DEG富集于细胞黏附。在DEG与细胞黏附GO术语中的基因之间筛选出180个重叠基因。此外，基于PPI网络分析鉴定出12个基因作为枢纽基因。ROC曲线分析表明整合素αM（ITGAM）、整合素β2（ITGB2）、整合素α5（ITGA5）、整合素β5（ITGB5）、钙黏蛋白1（CDH1）、白细胞介素4（IL4）、整合素α9（ITGA9）和层粘连蛋白β1（LAMB1）是MM诊断的有价值生物标志物。进一步研究表明ITGA9和LAMB1在MM患者中显示出预后价值和临床相关性。GSEA和转录因子预测表明，在MM中，MYC可能与ITGA9结合并抑制其表达，而缺氧诱导因子-1（HIF-1）可能与LAMB1结合以促进其表达。此外，泛癌分析显示LAMB1和ITGA9在多种癌症中存在异常表达和临床结局关联。
结论
总之，ITGA9和LAMB1被鉴定为与MM中细胞黏附相关的有效生物标志物。
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