Table_8_A novel tumor mutational burden-based risk model predicts prognosis and correlates with immune infiltration in ovarian cancer.xls

Tumor mutational burden (TMB) has been reported to determine the response to immunotherapy, thus affecting the patient’s prognosis in many cancers. However, it is unclear whether TMB or TMB-related signature could be used as prognostic indicators for ovarian cancer (OC), as its potential association with immune infiltration remains poorly understood. Therefore, this study aimed to develop a novel TMB-related risk model (TMBrisk) to predict the prognosis of OC patients on the basis of exploring TMB-related genes, and to explore the potential association between TMB/TMBrisk and immune infiltration. The mutational landscape, TMB scores, and correlations between TMB and clinical characteristics and immune infiltration were investigated in The Cancer Genome Atlas (TCGA)-OV cohort. Differentially expressed gene (DEG) analyses and weighted gene co-expression network analysis (WGCNA) were performed to derive TMB-related genes. TMBrisk was constructed by Cox regression and further validated in Gene Expression Omnibus (GEO) datasets. The mRNA and protein expression levels and biological functions of TMBrisk hub genes were verified through Gene Expression Profiling Interactive Analysis (GEPIA), GSCA Lite, the Human Protein Atlas (HPA) database, and RT-qPCR. TMBrisk-related biological phenotypes were analyzed in function enrichment and tumor immune infiltration signature. Potential therapeutic regimens were inferred utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). According to our results, higher TMB was associated with better survival and higher CD8+ T cell, regulatory T cell, and NK cell infiltration. TMBrisk was developed based on CBWD1, ST7L, RFX5-AS1, C3orf38, LRFN1, LEMD1, and HMGB1. High TMBrisk was identified as a poor factor for prognosis in TCGA and GEO datasets; the high-TMBrisk group comprised more higher-grade (G2 and G3) and advanced clinical stage (stage III/IV) tumors. Meanwhile, higher TMBrisk was associated with an immunosuppressive phenotype, with less infiltration of a majority of immunocytes and less expression of several genes of the human leukocyte antigen (HLA) family. Moreover, a nomogram containing TMBrisk showed a strong predictive ability demonstrated by time-dependent ROC analysis. Overall, this novel TMB-related risk model (TMBrisk) could predict prognosis, evaluate immune infiltration, and discover new therapeutic regimens in OC, which is very promising in clinical promotion.

已有研究表明，肿瘤突变负荷（Tumor mutational burden, TMB）可决定免疫治疗应答，进而影响多种癌症患者的预后。然而，肿瘤突变负荷或其相关特征能否作为卵巢癌（Ovarian Cancer, OC）的预后指标仍不明确，因其与免疫浸润的潜在关联尚未得到充分阐释。本研究旨在通过探索肿瘤突变负荷相关基因，构建一种新型肿瘤突变负荷相关风险模型（TMBrisk）以预测卵巢癌患者预后，并探究肿瘤突变负荷/肿瘤突变负荷相关风险模型与免疫浸润的潜在关联。本研究于癌症基因组图谱（The Cancer Genome Atlas, TCGA）-OV队列中分析了突变景观、肿瘤突变负荷评分，以及肿瘤突变负荷与临床特征、免疫浸润的相关性。通过差异表达基因（Differentially expressed gene, DEG）分析与加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）筛选得到肿瘤突变负荷相关基因。采用Cox回归构建TMBrisk模型，并在基因表达综合数据库（Gene Expression Omnibus, GEO）数据集内完成进一步验证。通过基因表达谱交互分析数据库（Gene Expression Profiling Interactive Analysis, GEPIA）、GSCA Lite、人类蛋白质图谱（Human Protein Atlas, HPA）数据库及实时定量聚合酶链反应（Real-time Quantitative Polymerase Chain Reaction, RT-qPCR）验证了TMBrisk核心基因的mRNA与蛋白表达水平及生物学功能。对TMBrisk相关的生物学表型进行了功能富集分析与肿瘤免疫浸润特征分析。利用癌症药物敏感性基因组学数据库（Genomics of Drug Sensitivity in Cancer, GDSC）与连接图数据库（Connectivity Map, CMap）推断潜在治疗方案。研究结果显示，较高的肿瘤突变负荷与更佳的生存结局、更高的CD8阳性T细胞、调节性T细胞及自然杀伤细胞浸润水平相关。本研究基于CBWD1、ST7L、RFX5-AS1、C3orf38、LRFN1、LEMD1及HMGB1构建了TMBrisk模型。在TCGA与GEO数据集内，高TMBrisk被鉴定为预后不良的危险因素；高TMBrisk组中更多为高级别（G2与G3级）及晚期临床分期（Ⅲ/Ⅳ期）的肿瘤。同时，较高的TMBrisk与免疫抑制表型相关，表现为多数免疫细胞浸润减少及多种人类白细胞抗原（Human Leukocyte Antigen, HLA）家族基因表达下调。此外，包含TMBrisk的列线图经时间依赖性受试者工作特征曲线分析显示出较强的预测能力。综上，本研究构建的新型肿瘤突变负荷相关风险模型（TMBrisk）可用于预测卵巢癌患者预后、评估免疫浸润状态并发掘潜在治疗方案，具备良好的临床应用前景。