Liu Jia, Li Guilin
Queen Mary School, Medical School of Nanchang University, Nanchang, Jiangxi, PR China.
Medicine (Baltimore). 2021 Apr 23;100(16):e25603. doi: 10.1097/MD.0000000000025603.
Gliomas have the highest incidence among primary brain tumors, and the extracellular matrix (ECM) plays a vital role in tumor progression. We constructed a risk signature using ECM-related genes to predict the prognosis of patients with gliomas.mRNA and clinical data from glioma patients were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Chinese Glioma Genome Atlas (CGGA) databases. Differentially expressed ECM-related genes were screened, and a risk signature was built using least absolute shrinkage and selection operator (LASSO) Cox regression. Cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to assess immune infiltration in different risk groups. Gene set enrichment analysis (GSEA) was performed to explore the molecular mechanisms of the genes employed in the risk score.Differentially expressed ECM-related genes were identified, and their associated regulatory mechanisms were predicted via analysis of protein-protein interaction (PPI), transcription factor (TF) regulatory and TF coexpression networks. The established risk signature considered 17 ECM-related genes. The prognosis of the high-risk group was significantly worse than that of the low-risk group. We used the CGGA database to validate the signature. CIBERSORT indicated that the levels of naive B cells, activated memory CD4 T cells, regulatory T cells, gamma delta T cells, activated NK cells, monocytes, activated dendritic cells and activated mast cells were higher in the high-risk group. The levels of plasma cells, CD8 T cells, naive CD4 T cells, resting memory CD4 T cells, M0 macrophages, M1 macrophages, resting mast cells, and neutrophils were lower in the high-risk group. Ultimately, GSEA showed that the terms intestinal immune network for IgA production, primary immunodeficiency, and ECM receptor interaction were the top 3 terms enriched in the high-risk group. The terms Wnt signaling pathway, ErbB signaling pathway, mTOR signaling pathway, and calcium signaling pathway were enriched in the low-risk group.We built a risk signature to predict glioma prognosis using ECM-related genes. By evaluating immune infiltration and biofunctions, we gained a further understanding of this risk signature. This risk signature could be an effective tool for predicting glioma prognosis.This study did not require ethical approval. We will disseminate our findings by publishing results in a peer-reviewed journal.
神经胶质瘤在原发性脑肿瘤中发病率最高，细胞外基质（ECM）在肿瘤进展中起着至关重要的作用。我们使用与ECM相关的基因构建了一个风险特征，以预测神经胶质瘤患者的预后。从癌症基因组图谱（TCGA）、基因型-组织表达（GTEx）和中国神经胶质瘤基因组图谱（CGGA）数据库下载神经胶质瘤患者的mRNA和临床数据。筛选差异表达的与ECM相关的基因，并使用最小绝对收缩和选择算子（LASSO）Cox回归构建风险特征。通过估计RNA转录本的相对子集进行细胞类型鉴定（CIBERSORT）来评估不同风险组中的免疫浸润。进行基因集富集分析（GSEA）以探索风险评分中所使用基因的分子机制。鉴定出差异表达的与ECM相关的基因，并通过分析蛋白质-蛋白质相互作用（PPI）、转录因子（TF）调控和TF共表达网络预测其相关调控机制。所建立的风险特征考虑了17个与ECM相关的基因。高风险组的预后明显比低风险组差。我们使用CGGA数据库验证了该特征。CIBERSORT表明，高风险组中幼稚B细胞、活化记忆CD4 T细胞、调节性T细胞、γδ T细胞、活化NK细胞、单核细胞、活化树突状细胞和活化肥大细胞的水平较高。高风险组中浆细胞、CD8 T细胞、幼稚CD4 T细胞、静息记忆CD4 T细胞、M0巨噬细胞、M1巨噬细胞、静息肥大细胞和中性粒细胞的水平较低。最终，GSEA显示，高风险组中富集程度最高的前3个条目是IgA产生的肠道免疫网络、原发性免疫缺陷和ECM受体相互作用。低风险组中富集的条目是Wnt信号通路、ErbB信号通路、mTOR信号通路和钙信号通路。我们使用与ECM相关的基因构建了一个风险特征来预测神经胶质瘤的预后。通过评估免疫浸润和生物学功能，我们对该风险特征有了更深入的了解。这个风险特征可能是预测神经胶质瘤预后的有效工具。本研究无需伦理批准。我们将通过在同行评审期刊上发表结果来传播我们的发现。
