Table_10_Comprehensive Genomic Characterization of Tumor Microenvironment and Relevant Signature in Clear Cell Renal Cell Carcinoma.xlsx

PurposeTo systematically investigate the characterization of tumor microenvironment (TME) in clear cell renal cell carcinoma (ccRCC), we performed a comprehensive analysis incorporating genomic alterations, cellular interactions, infiltrating immune cells, and risk signature.
Patients and MethodsMulti-omics data including RNA-seq, single-nucleotide variant (SNV) data, copy number variation (CNV) data, miRNA, and corresponding prognostic data were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database. The CIBERSORT algorithm was utilized to identify prognostic TME subclusters, and TMEscore was further quantified. Moreover, the mutational landscape of TCGA-KIRC was explored. Lastly, TIDE resource was applied to assess the significance of TMEscore in predicting immunotherapeutic benefits.
ResultsWe analyzed the TME infiltration patterns from 621 ccRCC patients and identified 5 specific TME subclusters associated with clinical outcomes. Then, we found that TMEcluster5 was significantly related to favorable prognosis and enriched memory B-cell infiltration. Accordingly, we depicted the clustering landscape of TMEclusters, TMEscore levels, tumor mutation burden (TMB), tumor grades, purity, and ploidy in all patients. Lastly, TIDE was used to assess the efficiency of immune checkpoint blockers (ICBs) and found that the TMEscore has superior predictive significance to TMB, making it an essential independent prognostic biomarker and drug indicator for clinical use.
ConclusionsOur study depicted the clustering landscape of TMEclusters, TMEscore levels, TMB, tumor grades, purity, and ploidy in total ccRCC patients. The TMEscore was proved to have promising significance for predicting prognosis and ICB responses, in accordance with the goal of developing rationally individualized therapeutic interventions.

研究目的 为系统解析透明细胞肾细胞癌（clear cell renal cell carcinoma, ccRCC）的肿瘤微环境（tumor microenvironment, TME）特征，本研究开展了整合基因组变异、细胞互作、浸润免疫细胞及风险特征的全面分析。
患者与方法 本研究从癌症基因组图谱（The Cancer Genome Atlas, TCGA）与国际癌症基因组联盟（International Cancer Genome Consortium, ICGC）数据库中，获取了涵盖RNA测序（RNA-seq）、单核苷酸变异（single-nucleotide variant, SNV）数据、拷贝数变异（copy number variation, CNV）数据、微小RNA（miRNA）及对应预后信息的多组学数据集。采用CIBERSORT算法识别与预后相关的TME亚群，并对TMEscore进行量化分析；此外，本研究还探索了TCGA-KIRC队列的突变图谱。最后，借助TIDE（Tumor Immune Dysfunction and Exclusion）资源评估TMEscore在预测免疫治疗获益中的价值。
研究结果 本研究分析了621例ccRCC患者的TME浸润模式，鉴定出5个与临床预后相关的特异性TME亚群。进一步研究发现，TMEcluster5与良好预后显著相关，且存在记忆性B细胞浸润富集现象。据此，本研究绘制了所有患者的TME簇、TMEscore水平、肿瘤突变负荷（tumor mutation burden, TMB）、肿瘤分级、肿瘤纯度及倍性的聚类图谱。最后，通过TIDE资源评估免疫检查点阻断剂（immune checkpoint blockers, ICBs）的临床疗效，结果显示TMEscore的预测效能优于TMB，可作为重要的独立预后生物标志物及临床用药指导指标。
研究结论 本研究描绘了全部ccRCC患者的TME簇、TMEscore水平、TMB、肿瘤分级、肿瘤纯度及倍性的聚类图谱。研究证实TMEscore在预测患者预后及ICBs应答方面具有良好应用价值，契合了开发合理个体化治疗干预手段的研究目标。