Wang Shuowen, Wang Zijun, Liu Zhuo, Wu Jianxin
Capital Institute of Pediatrics, Beijing, China.
Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Front Genet. 2024 Aug 12;15:1403587. doi: 10.3389/fgene.2024.1403587. eCollection 2024.
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.
肿瘤微环境和免疫相关基因（IRGs）与肿瘤的发生、发展及预后高度相关。然而，它们在II级和III级胶质瘤（本研究中称为低级别胶质瘤，LGGs）中的作用仍有待充分阐明。我们的研究旨在开发免疫相关特征用于LGG的风险分层和预后预测。
使用单样本基因集富集分析（ssGSEA）方法，我们评估了LGG人群的免疫特征。我们使用来自TCGA数据库的LGG样本和来自GTEx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（DEIRGs）。随后，我们利用单变量Cox分析、LASSO分析和多变量Cox回归分析建立了基因预测模型和列线图预测模型。
在此，我们发现高免疫、高级别和异柠檬酸脱氢酶（IDH）野生型胶质瘤的ESTIMATE评分、免疫评分和基质评分高于相应组，且肿瘤纯度较低。较高的ESTIMATE评分、基质评分和免疫评分表明LGG患者预后较差。我们的四基因预后模型与其他分子特征相比显示出更高的准确性。使用CGGA作为测试集以及联合TCGA和CGGA队列进行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更强的预测能力。
我们的研究突出了所鉴定的四个DEIRGs（KLRC3、MR1、PDIA2和RFXAP）在LGG患者中的预后意义。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。未来的研究应集中于进一步验证这些发现，并探索这些DEIRGs在LGG肿瘤微环境中的功能作用。
