Table_2_A Novel Prognostic Signature Associated With the Tumor Microenvironment in Kidney Renal Clear Cell Carcinoma.xlsx

BackgroundThe tumor microenvironment (TME) is a complex and evolving environment, and the tumor immune microenvironment in kidney renal clear cell carcinoma (KIRC) has a strong suppressive profile. This study investigates the potential prognostic role and value of genes of the tumor microenvironment in KIRC.
MethodsThe transcriptome sequencing data of 530 cases and 39 cases of KIRC and the corresponding clinical prognosis information were downloaded from TCGA data and GEO data, respectively, and TME-related gene expression profiles were extracted. A prognostic signature was constructed and evaluated using univariate Cox regression analysis and LASSO regression analysis. Gene set enrichment analysis (GSEA) was used to obtain the biological process of gene enrichment in patients with high and low-risk groups.
ResultsA prognostic signature consisting of eight TME-related genes (LRFN1, CSF1, UCN, TUBB2B, SERPINF1, ADAM8, ABCB4, CCL22) was constructed. Kaplan-Meier survival analysis yielded significantly lower survival times for patients in the high-risk group than in the low-risk group, and the AUC values for the ROC curves of this prognostic signature were essentially greater than 0.7, and univariate and multifactorial Cox regression analyses indicated that the risk score was independent risk factors for KIRC prognosis. GSEA analysis showed that immune-related biological processes were enriched in the high-risk group and that risk values were strongly associated with multiple immune cell scores and immune checkpoint-related genes (PDCD1, CTLA4).
ConclusionsThe prognostic signature can accurately predict the prognosis of KIRC patients, which may provide new ideas for future precision immunotherapy of KIRC.

背景 肿瘤微环境（tumor microenvironment, TME）是一类复杂且动态演变的微环境，肾透明细胞癌（kidney renal clear cell carcinoma, KIRC）的肿瘤免疫微环境呈现极强的免疫抑制特性。本研究旨在探讨肿瘤微环境相关基因在肾透明细胞癌中的潜在预后作用与价值。
方法 分别从癌症基因组图谱（The Cancer Genome Atlas, TCGA）与基因表达综合数据库（Gene Expression Omnibus, GEO）下载530例及39例肾透明细胞癌患者的转录组测序数据与对应临床预后信息，并提取肿瘤微环境相关基因表达谱。采用单变量Cox回归分析与最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）回归分析构建并验证预后特征模型。通过基因集富集分析（Gene Set Enrichment Analysis, GSEA）明确高低风险组患者的基因富集生物学过程。
结果 本研究构建了一套包含8个肿瘤微环境相关基因的预后特征模型，涉及基因分别为LRFN1、CSF1、UCN、TUBB2B、SERPINF1、ADAM8、ABCB4、CCL22。Kaplan-Meier生存分析显示，高风险组患者的生存时长显著低于低风险组；该预后特征的受试者工作特征（Receiver Operating Characteristic, ROC）曲线下面积（Area Under Curve, AUC）均大于0.7；单因素及多因素Cox回归分析表明，风险评分是肾透明细胞癌预后的独立危险因素。基因集富集分析结果显示，高风险组富集了免疫相关生物学过程，且风险值与多种免疫细胞评分及免疫检查点相关基因（PDCD1、CTLA4）密切相关。
结论 该预后特征模型可精准预测肾透明细胞癌患者的预后，可为未来肾透明细胞癌的精准免疫治疗提供新思路。