肿瘤微环境和IRG与肿瘤的发生、进展和预后高度相关。然而，它们在 II 级和 III 级神经胶质瘤（本研究中称为 LGG）中的作用仍有待充分阐明。我们的研究旨在开发 LGG 风险分层和预后预测的免疫相关特征。使用 ssGSEA 方法，我们评估了 LGG 人群的免疫特征。我们使用TCGA数据库中的LGG样本和GTEx中的正常样本进行差异分析，鉴定出412个差异表达的免疫相关基因（DEIRG）。随后，我们利用单变量Cox、LASSO和多变量Cox回归分析建立了基因预测模型和列线图预测模型。在这里，我们发现高免疫、高等级和野生型胶质瘤异柠檬酸脱氢酶（IDH）含量高于相应组，肿瘤纯度较低。较高的估计评分、基质评分和免疫评分表明 LGG 患者预后较差。与其他分子特征相比，我们的四基因预后模型表现出卓越的准确性。使用 CGGA 作为测试集以及 TCGA 和 CGGA 组合队列进行的验证证实了其强大的预后价值。此外，整合预后模型和临床变量的列线图显示出增强的预测能力。我们的研究强调了 LGG 患者中已确定的四种 DEIRG（KLRC3、MR1、PDIA2 和 RFXAP）的预后意义。本文开发的预测模型和列线图为 LGG 的个性化治疗策略提供了有价值的工具。未来的研究应侧重于进一步验证这些发现，并探索这些 DEIRG 在 LGG 肿瘤微环境中的功能作用。版权所有 © 2024 Wang、Wang、Liu 和 Wu。

The tumor microenvironment and IRGs are highly correlated with tumor occurrence, progression, and prognosis. However, their roles in grade II and III gliomas, termed LGGs in this study, remain to be fully elucidated. Our research aims to develop immune-related features for risk stratification and prognosis prediction in LGG.Using the ssGSEA method, we assessed the immune characteristics of the LGG population. We conducted differential analysis using LGG samples from the TCGA database and normal samples from GTEx, identifying 412 differentially expressed immune-related genes (DEIRGs). Subsequently, we utilized univariate Cox, LASSO, and multivariate Cox regression analyses to establish both a gene predictive model and a nomogram predictive model.Here, we found that the ESTIMATE score, immune score and stromal score of high-immunity, high-grade and isocitrate dehydrogenase (IDH) wild-type glioma were higher than those of the corresponding group, and the tumor purity was lower. Higher ESTIMATE scores, stromal scores and immune scores indicated a poor prognosis in patients with LGG. Our four-gene prognostic model demonstrated superior accuracy compared to other molecular features. Validation using the CGGA as a testing set and the combined TCGA and CGGA cohort confirmed its robust prognostic value. Additionally, a nomogram integrating the prognostic model and clinical variables showed enhanced predictive capability.Our study highlights the prognostic significance of the identified four DEIRGs (KLRC3, MR1, PDIA2, and RFXAP) in LGG patients. The predictive model and nomogram developed herein offer valuable tools for personalized treatment strategies in LGG. Future research should focus on further validating these findings and exploring the functional roles of these DEIRGs within the LGG tumor microenvironment.Copyright © 2024 Wang, Wang, Liu and Wu.