Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China.
Department of Anesthesiology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China.
Biomolecules. 2022 Oct 30;12(11):1598. doi: 10.3390/biom12111598.
Glioma is the most common primary tumor of the central nervous system with a high lethality rate. This study aims to mine fibroblast-related genes with prognostic value and construct a corresponding prognostic model.
A glioma-related TCGA (The Cancer Genome Atlas) cohort and a CGGA (Chinese Glioma Genome Atlas) cohort were incorporated into this study. Variance expression profiling was executed via the "limma" R package. The "clusterProfiler" R package was applied to perform a GO (Gene Ontology) analysis. The Kaplan-Meier (K-M) curve, LASSO regression analysis, and Cox analyses were implemented to determine the prognostic genes. A fibroblast-related risk model was created and affirmed by independent cohorts. We derived enriched pathways between the fibroblast-related high- and low-risk subgroups using gene set variation analysis (GSEA). The immune infiltration cell and the stromal cell were calculated using the microenvironment cell populations-counter (MCP-counter) method, and the immunotherapy response was assessed with the SubMap algorithm. The chemotherapy sensitivity was estimated using the "pRRophetic" R package.
A total of 93 differentially expressed fibroblast-related genes (DEFRGs) were uncovered in glioma. Seven prognostic genes were filtered out to create a fibroblast-related gene signature in the TCGA-glioma cohort training set. We then affirmed the fibroblast-related risk model via TCGA-glioma cohort and CGGA-glioma cohort testing sets. The Cox regression analysis proved that the fibroblast-related risk score was an independent prognostic predictor in prediction of the overall survival of glioma patients. The fibroblast-related gene signature revealed by the GSEA was applicable to the immune-relevant pathways. The MCP-counter algorithm results pointed to significant distinctions in the tumor microenvironment between fibroblast-related high- and low-risk subgroups. The SubMap analysis proved that the fibroblast-related risk score could predict the clinical sensitivity of immunotherapy. The chemotherapy sensitivity analysis indicated that low-risk patients were more sensitive to multiple chemotherapeutic drugs.
Our study identified prognostic fibroblast-related genes and generated a novel risk signature that could evaluate the prognosis of glioma and offer a theoretical basis for clinical glioma therapy.
神经胶质瘤是中枢神经系统最常见的原发性肿瘤，致死率较高。本研究旨在挖掘具有预后价值的成纤维细胞相关基因，并构建相应的预后模型。
本研究纳入了胶质瘤相关 TCGA（癌症基因组图谱）队列和 CGGA（中国胶质瘤基因组图谱）队列。使用“limma”R 包进行方差表达谱分析。使用“clusterProfiler”R 包进行 GO（基因本体论）分析。通过 Kaplan-Meier（K-M）曲线、LASSO 回归分析和 Cox 分析确定预后基因。利用独立队列构建并验证了成纤维细胞相关风险模型。通过基因集变异分析（GSEA）发现成纤维细胞相关的高低风险亚组之间富集的通路。使用微环境细胞群体计数器（MCP-counter）方法计算免疫浸润细胞和基质细胞，并使用 SubMap 算法评估免疫治疗反应。使用“pRRophetic”R 包估计化疗敏感性。
在胶质瘤中发现了 93 个差异表达的成纤维细胞相关基因（DEFRGs）。从 TCGA-胶质瘤队列训练集中筛选出 7 个预后基因，构建成纤维细胞相关基因特征。然后，我们利用 TCGA-胶质瘤队列和 CGGA-胶质瘤队列的测试集验证了成纤维细胞相关风险模型。Cox 回归分析表明，成纤维细胞相关风险评分是预测胶质瘤患者总生存期的独立预后预测因子。GSEA 揭示的成纤维细胞相关基因特征适用于免疫相关途径。MCP-counter 算法结果表明，成纤维细胞相关的高低风险亚组之间的肿瘤微环境存在显著差异。SubMap 分析表明，成纤维细胞相关风险评分可以预测免疫治疗的临床敏感性。化疗敏感性分析表明，低风险患者对多种化疗药物更敏感。
本研究鉴定了具有预后价值的成纤维细胞相关基因，并构建了一个新的风险特征，可以评估胶质瘤的预后，并为临床胶质瘤治疗提供理论依据。
