Identification of Key Genes and Pathways in Myeloma side population cells by Bioinformatics Analysis
Identification of Key Genes and Pathways in Myeloma side population cells by Bioinformatics Analysis
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生物信息学分析鉴定骨髓瘤侧群细胞关键基因和通路
DOI：
10.7150/ijms.48244
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发表时间：
2020-01-01
影响因子：
3.6
通讯作者：
Liu, Jing
中科院分区：
文献类型：
--
作者：
Yang, Qin;Li, Kaihu;Liu, Jing
背景资料：多发性骨髓瘤（multiple myeloma，MM）是第二常见的血液系统恶性肿瘤，目前仍无法治愈，复发不可避免，需要进一步了解其可能的发病机制。侧群（SP）细胞是一组富集的祖细胞，显示干细胞样表型，具有明显的Hoechst 33342低染色模式。与主群（MP）细胞相比，SP细胞的基本分子特征仍不清楚。该生物信息学分析旨在确定骨髓瘤SP细胞中的关键基因和途径，以提供新的生物标志物，预测MM预后并推进潜在的治疗靶点。基因表达谱GSE 109651从Gene Expression Omnibus数据库获得，然后通过骨髓瘤轻链（LC）限制性SP（LC/SP）的比较，选择P值2的差异表达基因（DEG），细胞和MP CD 138+细胞。随后，采用基因本体（GO）和京都基因与基因组百科全书（KEGG）途径富集分析、蛋白质-蛋白质相互作用（PPI）网络分析对DEG进行功能富集分析，筛选枢纽基因。使用考克斯比例风险回归来选择训练数据集（GSE 2658）中的潜在预后DEG。采用Kaplan-Meier曲线分析潜在预后基因的预后价值，并在另一个外部数据集（TCGA的MMRF-CoMMpass队列）中进行验证。GO分析显示，表达上调的DEGs主要集中在天然免疫反应、炎症反应、质膜和膜的组成部分，表达下调的DEGs主要参与原卟啉原IX和血红素的生物合成过程、血红蛋白复合物和红细胞分化。KEGG通路分析表明，DEG在破骨细胞分化、卟啉和叶绿素代谢以及嘌呤-细胞因子受体相互作用中显著富集。通过Cytoscape软件的插件cytoHubba使用最大团中心性（MCC）算法鉴定的前10个枢纽基因是ITGAM、MMP 9、ITGB 2、FPR 2、C3 AR 1、CXCL 1、CYBB、LILRB 2、HP和FCER 1G。模块和相应的GO富集分析表明，骨髓瘤LC/SP细胞与免疫系统、免疫应答和细胞周期显著相关。包括TFF 3，EPDR 1，MACROD 1，ARHGEF 12，AMMECR 1，NFATC 2，HES 6，PLEK 2和SNCA的预后模型的预测值被确定，并在另一个外部数据集（来自TCGA的MMRF-CoMMpass队列）中验证。结论：总之，本研究为骨髓瘤LC/SP细胞的筛选，预后以及新的治疗靶点提供了可靠的分子生物标志物。
Background: Multiple myeloma (MM) is the second most common hematological malignancy, which is still incurable and relapses inevitably, highlighting further understanding of the possible mechanisms. Side population (SP) cells are a group of enriched progenitor cells showing stem-like phenotypes with a distinct low-staining pattern with Hoechst 33342. Compared to main population (MP) cells, the underlying molecular characteristics of SP cells remain largely unclear. This bioinformatics analysis aimed to identify key genes and pathways in myeloma SP cells to provide novel biomarkers, predict MM prognosis and advance potential therapeutic targets.Methods: The gene expression profile GSE109651 was obtained from Gene Expression Omnibus database, and then differentially expressed genes (DEGs) with P-value 2 were selected by the comparison of myeloma light-chain (LC) restricted SP (LC/SP) cells and MP CD138+ cells. Subsequently, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis, protein-protein interaction (PPI) network analysis were performed to identify the functional enrichment analysis of the DEGs and screen hub genes. Cox proportional hazards regression was used to select the potential prognostic DEGs in training dataset (GSE2658). The prognostic value of the potential prognostic genes was evaluated by Kaplan-Meier curve and validated in another external dataset (MMRF-CoMMpass cohort from TCGA).Results: Altogether, 403 up-regulated and 393 down-regulated DEGs were identified. GO analysis showed that the up-regulated DEGs were significantly enriched in innate immune response, inflammatory response, plasma membrane and integral component of membrane, while the down-regulated DEGs were mainly involved in protoporphyrinogen IX and heme biosynthetic process, hemoglobin complex and erythrocyte differentiation. KEGG pathway analysis suggested that the DEGs were significantly enriched in osteoclast differentiation, porphyrin and chlorophyll metabolism and cytokine-cytokine receptor interaction. The top 10 hub genes, identified by the plug-in cytoHubba of the Cytoscape software using maximal clique centrality (MCC) algorithm, were ITGAM, MMP9, ITGB2, FPR2, C3AR1, CXCL1, CYBB, LILRB2, HP and FCER1G. Modules and corresponding GO enrichment analysis indicated that myeloma LC/SP cells were significantly associated with immune system, immune response and cell cycle. The predictive value of the prognostic model including TFF3, EPDR1, MACROD1, ARHGEF12, AMMECR1, NFATC2, HES6, PLEK2 and SNCA was identified, and validated in another external dataset (MMRF-CoMMpass cohort from TCGA).Conclusions: In conclusion, this study provides reliable molecular biomarkers for screening, prognosis, as well as novel therapeutic targets for myeloma LC/SP cells.