背景：肿瘤免疫的特征之一是免疫抑制性肿瘤微环境（TME）。本研究使用TME基因签名来定义宫颈鳞状细胞癌（CESC）免疫亚型的特征并构建一个新的预后模型。方法：使用单样本基因集富集分析（ssGSEA）来量化通路活性。从癌症基因组图谱（TCGA）数据库获取了291个CESC的RNA-seq作为训练集。从基因表达编译（GEO）数据库获取了400个CESC的基于微阵列的数据作为独立验证集。参考以前的研究，29个与TME相关的基因签名被选中。采用Consensus Cluster Plus来鉴定分子亚型。使用单因素Cox回归分析和随机生存森林（RSF）来建立基于TCGA数据集的CESC免疫相关基因风险模型，通过GEO数据集验证预后预测的准确性。使用ESTIMATE算法对数据集进行了免疫和基质评分。结果：由于29个TME基因签名，TCGA-CESC筛选出3个分子亚型（C1、C2、C3）。其中，C3具有更好的生存结局，具有更高的免疫相关基因签名，而C1具有更坏的预后时间，具有增强的基质相关特征。C3中观察到增强的免疫浸润、抑制肿瘤相关通路、广泛的基因组突变和易感免疫治疗等现象。此外，构建了一个由五个免疫基因组成的签名，并预测了CESC的总体生存率，在GSE44001数据集中成功验证。观察到五个枢纽基因表达和甲基化之间存在积极的现象。类似地，高基质相关特征组富集了在低组富集了免疫相关基因签名。免疫细胞、免疫检查点基因表达水平与大多数TME基因签名与风险评分呈负相关，而高组更敏感于耐药性。结论：本文鉴定出三个不同的免疫亚型和一个由五个基因组成的签名，用于预测CESC患者的预后，为CESC提供了一个有前途的治疗策略。版权所有©2023年胡，张，张，黄和李。

Background: One of the features of tumor immunity is the immunosuppressive tumor microenvironment (TME). In this study, TME gene signatures were used to define the characteristics of Cervical squamous cell carcinoma (CESC) immune subtypes and construct a new prognostic model. Methods: Single sample gene set enrichment analysis (ssGSEA) was used to quantify pathway activity. RNA-seq of 291 CESC were obtained from the Cancer Genome Atlas (TCGA) database as a training set. Microarray-based data of 400 cases of CESC were obtained from the Gene Expression Compilation (GEO) database as an independent validation set. 29 TME related gene signatures were consulted from previous study. Consensus Cluster Plus was employed to identify molecular subtype. Univariate cox regression analysis and random survival forest (RSF) were used to establish the immune-related gene risk model based on the TCGA data set of CESC, and the accuracy of prognosis prediction was verified by GEO data set. ESTIMATE algorithm was used to perform immune and matrix scores on the data set. Results: three molecular subtypes (C1, C2, C3) were screened in TCGA-CESC on account of 29 TME gene signatures. Among, C3 with better survival outcome had higher immune related gene signatures, while C1 with worse prognosis time had enhanced matrix related features. Increased immune infiltration, inhibition of tumor related pathways, widespread genomic mutations and prone immunotherapy were observed in C3. Furthermore, a five immune genes signature was constructed and predicted overall survival for CESC, which successfully validated in GSE44001 dataset. A positive phenomenon was observed between five hub genes expressions and methylation. Similarly, high group enriched in matrix related features, while immune related gene signatures were enriched in low group. Immune cell, immune checkpoints genes expression levels were negatively, while most TME gene signatures were positively correlated with Risk Score. In addition, high group was more sensitive to drug resistance. Conclusion: This work identified three distinct immune subtypes and a five genes signature for predicting prognosis in CESC patients, which provided a promising treatment strategy for CESC.Copyright © 2023 Hu, Zhang, Zhang, Huang and Li.