Zhong Jiacheng, Shi Shuang, Peng Wen, Liu Bei, Yang Biao, Niu Wenyong, Zhang Biao, Qin Chuan, Zhong Dong, Cui Hongjuan, Zhang Zhengbao, Sun Xiaochuan
Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Department of Neurosurgery, The People's Hospital of Dazu, Chongqing, China.
Front Genet. 2022 May 20;13:792443. doi: 10.3389/fgene.2022.792443. eCollection 2022.
Our previous studies shown that syndecan-1 (SDC1) may be a novel class of biomarkers for the diagnosis and treatment of glioma, but its specific roles and the in-depth molecular mechanism remain elusive. Here, we used Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithms and single-sample Gene Set Enrichment Analysis (ssGSEA) algorithms to evaluate the immune score of tumor samples and quantify the relative infiltration of immune cells in the tumor microenvironment (TME), respectively, in different data sets obtained from the Chinese Glioma Genome Atlas and The Cancer Gene Atlas. Next, we calculate the correlation of the immune score and immune cells with SDC1, respectively. To identify the specific process regulated by SDC1, the differentially expressed genes (DEGs) analysis between the high and low expression of SDC1 of glioma samples were used to discover the hub genes through Weighted Gene Coexpression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed cardinal biological processes and pathways involved in genes and tumor grade correlation and survival analysis verified its significance in glioma. The results show that SDC1 is associated with the immune infiltration of glioma in the TME, especially activated CD4+T cells and CD8+T cells. The three data sets filter 8,887 DEGs, the genes in the blue modules were selected as hub genes in WGCNA. GO and KEGG analysis found eight genes in the blue modules involved in antigen processing and presentation in T cells in glioma. Kaplan-Meier estimator and log-rank test statistic determined that the introduced genes are associated with poor prognosis in glioma. Protein-protein network interaction analysis showed that SDC1 may regulate antigen processing and presentation through CTSL or CD4 in glioma. Finally, this study provided insights and clues for the next research direction of SDC1 and identified the key pathways and genes that might participate in the immune escape of glioma. These results might provide a new insight on the study of immune infiltration of glioma in the future.
我们之前的研究表明,Syndecan-1(SDC1)可能是一类用于胶质瘤诊断和治疗的新型生物标志物,但其具体作用和深入的分子机制仍不清楚。在此,我们使用基于表达数据的恶性肿瘤组织基质和免疫细胞估计(ESTIMATE)算法和单样本基因集富集分析(ssGSEA)算法,分别评估来自中国胶质瘤基因组图谱和癌症基因图谱的不同数据集中肿瘤样本的免疫评分,并量化肿瘤微环境(TME)中免疫细胞的相对浸润情况。接下来,我们分别计算免疫评分和免疫细胞与SDC1的相关性。为了确定SDC1调控的具体过程,通过加权基因共表达网络分析(WGCNA),利用胶质瘤样本中SDC1高表达和低表达之间的差异表达基因(DEG)分析来发现枢纽基因。基因本体(GO)和京都基因与基因组百科全书(KEGG)分析揭示了与基因和肿瘤分级相关的主要生物学过程和途径,生存分析验证了其在胶质瘤中的重要性。结果表明,SDC1与胶质瘤在TME中的免疫浸润相关,尤其是活化的CD4+T细胞和CD8+T细胞。三个数据集筛选出8887个DEG,蓝色模块中的基因在WGCNA中被选为枢纽基因。GO和KEGG分析发现蓝色模块中有八个基因参与胶质瘤中T细胞的抗原加工和呈递。Kaplan-Meier估计器和对数秩检验统计量确定引入的基因与胶质瘤的不良预后相关。蛋白质-蛋白质网络相互作用分析表明,SDC1可能通过CTSL或CD4在胶质瘤中调节抗原加工和呈递。最后,本研究为SDC1的下一步研究方向提供了见解和线索,并确定了可能参与胶质瘤免疫逃逸的关键途径和基因。这些结果可能为未来胶质瘤免疫浸润的研究提供新的见解。