Zhou Min, Deng Yunbo, Fu Ya, Liang Richu, Liu Yang, Liao Quan
Department of Neurosurgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.
Department of Operating Room, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China.
Transl Cancer Res. 2023 Oct 31;12(10):2898-2910. doi: 10.21037/tcr-23-322. Epub 2023 Oct 10.
Glioblastoma multiforme (GBM) is the most aggressive, common, and lethal type of primary brain tumor. Multiple cancers have been associated with abnormalities in the coagulation system that facilitate tumor invasion and metastasis. In GBM, the prognostic value and underlying mechanism of coagulation-related genes (CRGs) have not been explored.
RNA sequencing (RNA-seq) and clinical information on GBM were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), respectively. Following the identification of differentially expressed CRGs (DECRGs) between GBM and control samples, the survival-related DECRGs were selected via univariate and multivariate Cox regression analyses to establish a prognostic signature. The prognostic performance and clinical utility of the prognostic signature were assessed by the Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curve analysis, and a nomogram was constructed. The signature genes-related underlying mechanisms were analyzed according to gene set enrichment analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-cell analysis. Finally, the difference in immune cell infiltration, stromal score, immune score, and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) score were compared between different risk groups.
A 5-gene prognostic signature () was established for overall survival (OS) prediction of GBM patients. The predicted efficiency of the prognostic signature was confirmed in TGGA-GBM dataset and validated in the CGGA-GBM dataset, revealing that it could differentiate GBM patients from controls well, and high risk score was accompanied with poor prognosis. Moreover, biological process (BP) and signaling pathway analyses showed that signature genes were mainly enriched in the functions of blood coagulation and tumor invasion and metastasis. Moreover, high-risk patients exhibited higher levels of immune cell infiltration, stromal score, immune score, and ESTIMATE score than that of low-risk patients.
An analysis of coagulation-related prognostic signatures was conducted in this study, as well as how signature genes may affect GBM progress, providing information that might provide new ideas for the development of GBM-related molecular targeted therapies.
多形性胶质母细胞瘤（GBM）是最具侵袭性、最常见且致死性最高的原发性脑肿瘤类型。多种癌症与凝血系统异常有关，这些异常促进肿瘤侵袭和转移。在GBM中，凝血相关基因（CRG）的预后价值及潜在机制尚未得到探索。
分别从癌症基因组图谱（TCGA）和中国胶质瘤基因组图谱（CGGA）获取GBM的RNA测序（RNA-seq）数据和临床信息。在鉴定出GBM与对照样本之间的差异表达CRG（DECRG）后，通过单变量和多变量Cox回归分析选择与生存相关的DECRG，以建立预后特征。通过Kaplan-Meier（KM）分析和受试者工作特征（ROC）曲线分析评估预后特征的预后性能和临床实用性，并构建列线图。根据基因集富集分析（GSEA）、基因本体论（GO）、京都基因与基因组百科全书（KEGG）和单细胞分析对特征基因相关的潜在机制进行分析。最后，比较不同风险组之间免疫细胞浸润、基质评分、免疫评分以及使用表达数据评估恶性肿瘤组织中的基质和免疫细胞（ESTIMATE）评分的差异。
建立了一个用于预测GBM患者总生存期（OS）的5基因预后特征。该预后特征的预测效率在TGGA-GBM数据集中得到证实，并在CGGA-GBM数据集中得到验证，表明其能很好地将GBM患者与对照区分开来，且高风险评分与不良预后相关。此外，生物学过程（BP）和信号通路分析表明，特征基因主要富集于凝血以及肿瘤侵袭和转移功能。此外，高风险患者的免疫细胞浸润、基质评分、免疫评分和ESTIMATE评分均高于低风险患者。
本研究对凝血相关预后特征以及特征基因如何影响GBM进展进行了分析，为GBM相关分子靶向治疗的开发提供了可能提供新思路的信息。
