PDF(Pubmed)
Abstract：
UNASSIGNED: The immune landscape, prognostic model, and molecular variations of mantle cell lymphoma (MCL) remain unclear. Hence, an integrated bioinformatics analysis of MCL datasets is required for the development of immunotherapy and the optimization of targeted therapies.
UNASSIGNED: Data were obtained from the Gene Expression Omnibus (GEO) database (GSE32018, GSE45717 and GSE93291). The differentially expressed immune-related genes were selected, and the hub genes were screened by three machine learning algorithms, followed by enrichment and correlation analyses. Next, MCL molecular clusters based on the hub genes were identified by K-Means clustering, the probably approximately correct (PAC) algorithm, and principal component analysis (PCA). The landscape of immune cell infiltration and immune checkpoint molecules in distinct clusters was explored by single-sample gene-set enrichment analysis (ssGSEA) as well as the CIBERSORT and xCell algorithms. The prognostic genes and prognostic risk score model for MCL clusters were identified by least absolute shrinkage and selection operator (LASSO)-Cox analysis and cross-validation for lambda. Correlation analysis was performed to explore the correlation between the screened prognostic genes and immune cells or immune checkpoint molecules.
UNASSIGNED: Four immune-related hub genes (CD247, CD3E, CD4, and GATA3) were screened in MCL, mainly enriched in the T-cell receptor signaling pathway. Based on the hub genes, two MCL molecular clusters were recognized. The cluster 2 group had a significantly worse overall survival (OS), with down-regulated hub genes, and a variety of activated immune effector cells declined. The majority of immune checkpoint molecules had also decreased. An efficient prognostic model was established, including six prognostic genes (LGALS2, LAMP3, ICOS, FCAMR, IGFBP4, and C1QA) differentially expressed between two MCL clusters. Patients with a higher risk score in the prognostic model had a poor prognosis. Furthermore, most types of immune cells and a range of immune checkpoint molecules were positively correlated with the prognostic genes.
UNASSIGNED: Our study identified distinct molecular clusters based on the immune-related hub genes, and showed that the prognostic model affected the prognosis of MCL patients. These hub genes, modulated immune cells, and immune checkpoint molecules might be involved in oncogenesis and could be potential prognostic biomarkers in MCL.
UNASSIGNED: Data were obtained from the Gene Expression Omnibus (GEO) database (GSE32018, GSE45717 and GSE93291). The differentially expressed immune-related genes were selected, and the hub genes were screened by three machine learning algorithms, followed by enrichment and correlation analyses. Next, MCL molecular clusters based on the hub genes were identified by K-Means clustering, the probably approximately correct (PAC) algorithm, and principal component analysis (PCA). The landscape of immune cell infiltration and immune checkpoint molecules in distinct clusters was explored by single-sample gene-set enrichment analysis (ssGSEA) as well as the CIBERSORT and xCell algorithms. The prognostic genes and prognostic risk score model for MCL clusters were identified by least absolute shrinkage and selection operator (LASSO)-Cox analysis and cross-validation for lambda. Correlation analysis was performed to explore the correlation between the screened prognostic genes and immune cells or immune checkpoint molecules.
UNASSIGNED: Four immune-related hub genes (CD247, CD3E, CD4, and GATA3) were screened in MCL, mainly enriched in the T-cell receptor signaling pathway. Based on the hub genes, two MCL molecular clusters were recognized. The cluster 2 group had a significantly worse overall survival (OS), with down-regulated hub genes, and a variety of activated immune effector cells declined. The majority of immune checkpoint molecules had also decreased. An efficient prognostic model was established, including six prognostic genes (LGALS2, LAMP3, ICOS, FCAMR, IGFBP4, and C1QA) differentially expressed between two MCL clusters. Patients with a higher risk score in the prognostic model had a poor prognosis. Furthermore, most types of immune cells and a range of immune checkpoint molecules were positively correlated with the prognostic genes.
UNASSIGNED: Our study identified distinct molecular clusters based on the immune-related hub genes, and showed that the prognostic model affected the prognosis of MCL patients. These hub genes, modulated immune cells, and immune checkpoint molecules might be involved in oncogenesis and could be potential prognostic biomarkers in MCL.
摘要：
未经证实：免疫景观，预后模型，套细胞淋巴瘤（MCL）的分子变异仍不清楚。因此，MCL数据集的综合生物信息学分析对于免疫治疗的开发和靶向治疗的优化是必需的.
UNASSIGNED：数据来自基因表达综合（GEO）数据库（GSE32018，GSE45717和GSE93291）。选择差异表达的免疫相关基因，集线器基因通过三种机器学习算法进行筛选，其次是富集和相关性分析。接下来,通过K-Means聚类鉴定了基于hub基因的MCL分子簇，可能近似正确(PAC)算法，和主成分分析(PCA)。通过单样品基因集富集分析（ssGSEA）以及CIBERSORT和xCell算法探索了不同簇中免疫细胞浸润和免疫检查点分子的景观。通过最小绝对收缩和选择算子（LASSO）-Cox分析和lambda交叉验证，确定了MCL簇的预后基因和预后风险评分模型。对筛选出的预后基因与免疫细胞或免疫检查点分子进行相关性分析。
未经证实：四个免疫相关hub基因（CD247，CD3E，在MCL中筛选CD4和GATA3），主要富集在T细胞受体信号通路。基于枢纽基因，两个MCL分子簇被识别。第2组的总生存期（OS）明显较差，下调的中枢基因，和多种激活的免疫效应细胞下降。大多数免疫检查点分子也减少了。建立了一个有效的预后模型，包括六个预后基因(LGALS2、LAMP3、ICOS、FCAMR,IGFBP4和C1QA）在两个MCL簇之间差异表达。预后模型中风险评分较高的患者预后较差。此外，大多数类型的免疫细胞和一系列免疫检查点分子与预后基因呈正相关。
未经证实：我们的研究基于免疫相关中枢基因确定了不同的分子簇，并显示预后模型影响MCL患者的预后。这些枢纽基因，调节免疫细胞，和免疫检查点分子可能参与肿瘤的发生，并可能是MCL的潜在预后生物标志物。
UNASSIGNED：数据来自基因表达综合（GEO）数据库（GSE32018，GSE45717和GSE93291）。选择差异表达的免疫相关基因，集线器基因通过三种机器学习算法进行筛选，其次是富集和相关性分析。接下来,通过K-Means聚类鉴定了基于hub基因的MCL分子簇，可能近似正确(PAC)算法，和主成分分析(PCA)。通过单样品基因集富集分析（ssGSEA）以及CIBERSORT和xCell算法探索了不同簇中免疫细胞浸润和免疫检查点分子的景观。通过最小绝对收缩和选择算子（LASSO）-Cox分析和lambda交叉验证，确定了MCL簇的预后基因和预后风险评分模型。对筛选出的预后基因与免疫细胞或免疫检查点分子进行相关性分析。
未经证实：四个免疫相关hub基因（CD247，CD3E，在MCL中筛选CD4和GATA3），主要富集在T细胞受体信号通路。基于枢纽基因，两个MCL分子簇被识别。第2组的总生存期（OS）明显较差，下调的中枢基因，和多种激活的免疫效应细胞下降。大多数免疫检查点分子也减少了。建立了一个有效的预后模型，包括六个预后基因(LGALS2、LAMP3、ICOS、FCAMR,IGFBP4和C1QA）在两个MCL簇之间差异表达。预后模型中风险评分较高的患者预后较差。此外，大多数类型的免疫细胞和一系列免疫检查点分子与预后基因呈正相关。
未经证实：我们的研究基于免疫相关中枢基因确定了不同的分子簇，并显示预后模型影响MCL患者的预后。这些枢纽基因，调节免疫细胞，和免疫检查点分子可能参与肿瘤的发生，并可能是MCL的潜在预后生物标志物。
