基于 Silico 分析的 TGF-β 评分可准确预测下级胶质瘤的预后和免疫学特征:来自多中心研究的证据

IF 2.5 4区 医学 Q3 ONCOLOGY
Weizhong Zhang, Zhiyuan Yan, Feng Zhao, Qinggui He, Hongbo Xu
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引用次数: 0

摘要

前言如今,越来越多的证据表明,TGF-β 信号通路相关成分的变化会影响肿瘤的发展。目前的研究专利描述了使用抗 TGF-β 抗体和检查点抑制剂治疗增殖性疾病。重要的是,TGF-β 信号通路对低级别胶质瘤(LGG)逃避宿主免疫非常重要。特定肿瘤抗原的缺失和专业抗原递呈细胞活性的关闭可能会使 LGG 患者的抗肿瘤反应无效。然而,TGF-β相关基因在LGG中的预后意义尚不清楚:方法:我们收集了 GTEx 数据库(正常皮质组织)、癌症基因组图谱数据库(TCGA-LGG)和中国胶质瘤基因组图谱数据库(CGGA-693 和 CGGA-325)中的 RNA-seq 数据进行研究:此外,我们还对以往发表的223个TGF-β信号通路调控因子进行了研究,发现了30个在TCGA和GTEx数据库中异常表达的调控因子。为了确定枢纽预后调节因子,研究人员采用了最小绝对收缩和选择算子(LASSO)回归和多变量考克斯回归分析法,从差异表达基因(DEGs)中进行筛选。根据 LASSO-Cox 回归分析得出的 11 个基因(NEDD8、CHRD、TGFBR1、TP53、BMP2、LRRC32、THBS2、ID1、NOG、TNF 和 SERPINE1),计算出 TGF-β 评分。多种统计方法验证了 TGF-β 评分对训练队列和两个外部验证队列的预测价值。考虑到 TGF-β 信号通路在免疫调节中的重要性,我们使用六种算法(TIMER、CIBERSORT、QUANTISEQ、MCP-counter、XCELL 和 EPIC)和三种免疫疗法队列(GSE78820、Imvigor-210 和 PRJEB23709)评估了 TGF-β 评分对免疫学特征和免疫治疗反应可能应用的预测。值得注意的是,我们将我们的风险特征与元队列(TCGA-LGG、CGGA-693和CGGA-325)中十篇论文的特征进行了比较,TGF-β评分的预测效率最高(C-指数=0.812):总之,我们的研究结果表明,TGF-β信号通路相关特征是LGG的预后生物标志物,并为肿瘤微环境(TME)评估提供了一种新工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TGF-β Score based on Silico Analysis can Robustly Predict Prognosis and Immunological Characteristics in Lower-grade Glioma: The Evidence from Multicenter Studies.

Introduction: Nowadays, mounting evidence shows that variations in TGF-β signaling pathway-related components influence tumor development. Current research has patents describing the use of anti-TGF-β antibodies and checkpoint inhibitors for the treatment of proliferative diseases. Importantly, TGF-β signaling pathway is significant for lower-grade glioma (LGG) to evade host immunity. Loss of particular tumor antigens and shutdown of professional antigenpresenting cell activity may render the anti-tumor response ineffective in LGG patients. However, the prognostic significance of TGF-β related genes in LGG is still unknown.

Methods: We collected RNA-seq data from the GTEx database (normal cortical tissues), the Cancer Genome Atlas database (TCGA-LGG), and the Chinese Glioma Genome Atlas database (CGGA-693 and CGGA-325) for conducting our investigation.

Results: In addition, previous publications were explored for the 223 regulators of the TGF-β signaling pathway, and 30 regulators with abnormal expression in TCGA and GTEx database were identified. In order to identify hub prognostic regulators, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were used to screen from differentially expressed genes (DEGs). On the basis of 11 genes from LASSO-Cox regression analysis (NEDD8, CHRD, TGFBR1, TP53, BMP2, LRRC32, THBS2, ID1, NOG, TNF, and SERPINE1), TGF-β score was calculated. Multiple statistical approaches verified the predictive value of the TGF-β score for the training cohort and two external validation cohorts. Considering the importance of the TGF-β signaling pathway in immune regulation, we evaluated the prediction of the TGF-β score for immunological characteristics and the possible application of the immunotherapeutic response using six algorithms (TIMER, CIBERSORT, QUANTISEQ, MCP-counter, XCELL and EPIC) and three immunotherapy cohorts (GSE78820, Imvigor-210 and PRJEB23709). Notably, we compared our risk signature with the signature in ten publications in the meta-cohort (TCGA-LGG, CGGA-693 and CGGA-325), and the TGF-β score had the best predictive efficiency (C-index =0.812).

Conclusion: In conclusion, our findings suggest that TGF-β signaling pathway-related signatures are prognostic biomarkers in LGG and provide a novel tool for tumor microenvironment (TME) assessment.

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来源期刊
CiteScore
4.50
自引率
7.10%
发文量
55
审稿时长
3 months
期刊介绍: Aims & Scope Recent Patents on Anti-Cancer Drug Discovery publishes review and research articles that reflect or deal with studies in relation to a patent, application of reported patents in a study, discussion of comparison of results regarding application of a given patent, etc., and also guest edited thematic issues on recent patents in the field of anti-cancer drug discovery e.g. on novel bioactive compounds, analogs, targets & predictive biomarkers & drug efficacy biomarkers. The journal also publishes book reviews of eBooks and books on anti-cancer drug discovery. A selection of important and recent patents on anti-cancer drug discovery is also included in the journal. The journal is essential reading for all researchers involved in anti-cancer drug design and discovery. The journal also covers recent research (where patents have been registered) in fast emerging therapeutic areas/targets & therapeutic agents related to anti-cancer drug discovery.
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