Table1_Integrated analysis of inflammatory response subtype-related signature to predict clinical outcomes, immune status and drug targets in lower-grade glioma.XLSX

Background: The inflammatory response in the tumor immune microenvironment has implications for the progression and prognosis in glioma. However, few inflammatory response-related biomarkers for lower-grade glioma (LGG) prognosis and immune infiltration have been identified. We aimed to construct and identify the prognostic value of an inflammatory response-related signature, immune infiltration, and drug targets for LGG.
Methods: The transcriptomic and clinical data of LGG samples and 200 inflammatory response genes were obtained from public databases. The LGG samples were separated into two inflammatory response-related subtypes based on differentially expressed inflammatory response genes between LGG and normal brain tissue. Next, inflammatory response-related genes (IRRGs) were determined through a difference analysis between the aforementioned two subtypes. An inflammatory response-related prognostic model was constructed using IRRGs by using univariate Cox regression and Lasso regression analyses and validated in an external database (CGGA database). ssGSEA and ESTIMATE algorithms were conducted to evaluate immune infiltration. Additionally, we performed integrated analyses to investigate the correlation between the prognostic signature and N 6-methyladenosine mRNA status, stemness index, and drug sensitivity. We finally selected MSR1 from the prognostic signature for further experimental validation.
Results: A total of nine IRRGs were identified to construct the prognostic signature for LGG. LGG patients in the high-risk group presented significantly reduced overall survival than those in the low-risk group. An ROC analysis confirmed the predictive power of the prognostic model. Multivariate analyses identified the risk score as an independent predictor for the overall survival. ssGSEA revealed that the immune status was definitely disparate between two risk subgroups, and immune checkpoints such as PD-1, PD-L1, and CTLA4 were significantly expressed higher in the high-risk group. The risk score was strongly correlated with tumor stemness and m6A. The expression levels of the genes in the signature were significantly associated with the sensitivity of tumor cells to anti-tumor drugs. Finally, the knockdown of MSR1 suppressed LGG cell migration, invasion, epithelial–mesenchymal transition, and proliferation.
Conclusion: The study constructed a novel signature composed of nine IRRGs to predict the prognosis, potential drug targets, and impact immune infiltration status in LGG, which hold promise for screening prognostic biomarkers and guiding immunotherapy for LGG.

研究背景：肿瘤免疫微环境中的炎症反应与胶质瘤的进展及预后密切相关。然而，目前针对低级别胶质瘤（lower-grade glioma, LGG）的预后及免疫浸润相关的炎症反应生物标志物仍较为匮乏。本研究旨在构建并验证炎症反应相关特征基因集、免疫浸润状态及药物靶点在LGG中的预后价值。
研究方法：本研究从公共数据库中获取了LGG样本的转录组与临床数据，以及200个炎症反应相关基因的信息。基于LGG与正常脑组织间的差异表达炎症反应基因，将LGG样本划分为两种炎症反应相关亚型。随后，通过上述两种亚型间的差异分析，筛选得到炎症反应相关基因（inflammatory response-related genes, IRRGs）。利用IRRGs，通过单因素Cox回归与Lasso回归分析构建炎症反应相关预后模型，并在外部数据集（CGGA数据库）中完成验证。采用单样本基因集富集分析（single-sample gene set enrichment analysis, ssGSEA）与ESTIMATE算法评估样本的免疫浸润状态。此外，本研究还开展了整合分析，以探究预后特征基因集与N⁶-甲基腺嘌呤（N⁶-methyladenosine, m⁶A）mRNA修饰状态、干细胞干性指数以及药物敏感性之间的关联。最终，从该预后特征基因集中选取MSR1开展后续实验验证。
研究结果：本研究共筛选得到9个IRRGs用于构建LGG的预后特征基因集。高风险组LGG患者的总生存期显著短于低风险组。受试者工作特征曲线（Receiver Operating Characteristic, ROC）分析证实了该预后模型的预测效能。多因素分析显示，风险评分可作为总生存期的独立预测因子。ssGSEA分析结果表明，两个风险亚组的免疫状态存在显著差异，且PD-1、PD-L1及CTLA4等免疫检查点在高风险组中的表达水平显著上调。风险评分与肿瘤干细胞干性及m⁶A修饰水平显著相关。该特征基因集中的基因表达水平与肿瘤细胞对抗肿瘤药物的敏感性显著相关。最终，MSR1敲低实验证实，其可抑制LGG细胞的迁移、侵袭、上皮间质转化（epithelial-mesenchymal transition, EMT）及增殖能力。
研究结论：本研究构建了一个由9个IRRGs组成的新型预后特征基因集，可用于预测LGG的预后、潜在药物靶点以及免疫浸润状态，有望为LGG的预后生物标志物筛选及免疫治疗指导提供参考依据。