Significance of tumor mutation burden combined with immune infiltrates in the progression and prognosis of ovarian cancer
Significance of tumor mutation burden combined with immune infiltrates in the progression and prognosis of ovarian cancer
复制标题
肿瘤突变负荷与免疫浸润相结合在卵巢癌进展和预后中的意义
DOI：
10.1186/s12935-020-01472-9
复制
发表时间：
2020-08-05
影响因子：
5.8
通讯作者：
Yang, Qing
中科院分区：
文献类型：
--
作者：
Bi, Fangfang;Chen, Ying;Yang, Qing
研究背景卵巢癌（Ovarian cancer，OC）是女性生殖系统最常见的恶性肿瘤。约75%的OC在临床症状完全缓解后仍会复发。因此，寻找新的治疗方法对改善OC的预后具有重要作用。方法我们从TCGA数据库中下载MAF文件、RNA-seq数据和临床信息。使用R软件中的“maftools”包来可视化OC突变数据。我们计算了OC的肿瘤突变负荷（TMB），并分析了其与临床病理参数和预后价值的相关性。结果卵巢癌患者的TMB与FIGO分期、分级及肿瘤残留大小有统计学相关性。Kaplan-Meier曲线表明，高TMB与OC的临床结局较好相关。差异分析显示，高TMB组与低TMB组相比，有24个基因表达上调，619个基因表达下调。此外，构建了基于RBMS 3、PLA 2G 5、CDH 2、AMHR 2和ADAMTS 8 5个中枢基因的TMBRS模型，预测OC的OS。ROC曲线和验证数据集均表明TMBRS模型在预测复发风险方面是可靠的。免疫微环境分析表明TMB与浸润免疫细胞之间的相关性。结论TMB在OC的预后和指导免疫治疗中起重要作用。通过检测OC的TMB，临床医生可以更准确地对患者进行免疫治疗，从而提高其生存率。
BackgroundOvarian cancer (OC) is the most malignant tumor in the female reproductive system. About 75% of OC in complete remission of clinical symptoms still develop a recurrence. Therefore, searching for new treatment methods plays an important role in improving the prognosis of OC.MethodsWe downloaded the MAF files, RNA-seq data and clinical information from the TCGA database. The "maftools" package in R software was used to visualize the OC mutation data. We calculated the tumor mutation burden (TMB) of OC and analyzed its correlation with clinicopathological parameters and prognostic value. Tumor mutation burden related signature model was constructed to predict the overall survival (OS) of OC.ResultsThe results revealed that there was a statistical correlation between TMB and FIGO stage, grade and tumor residual size of ovarian cancer patients. The Kaplan-Meier curve indicated that a high TMB is associated with better clinical outcomes of OC. The difference analysis indicated 24 upregulated genes and 619 downregulated genes in the high-TMB group compared with the low-TMB group. Besides, the TMBRS model based on five hub genes (RBMS3, PLA2G5, CDH2, AMHR2 and ADAMTS8) was constructed to predict the OS of OC. The ROC curve and validation data sets all revealed that the TMBRS model was reliable in predicting recurrence risk. Immune microenvironment analysis indicated the correlations between TMB and infiltrating immune cells.ConclusionsOur results suggest that TMB plays an important role in the prognosis and guiding immunotherapy of OC. By detecting the TMB of OC, clinicians can more accurately treat patients with immunotherapy, thereby improving their survival rate.