Table4_Immune Cell Infiltration Landscape of Ovarian Cancer to Identify Prognosis and Immunotherapy-Related Genes to Aid Immunotherapy.XLSX

Ovarian cancer (OC) is the second leading cause of death in gynecological cancer. Multiple study have shown that the efficacy of tumor immunotherapy is related to tumor immune cell infiltration (ICI). However, so far, the Immune infiltration landscape of tumor microenvironment (TME) in OC has not been elucidated. In this study, We organized the transcriptome data of OC in the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, evaluated the patient’s TME information, and constructed the ICI scores to predict the clinical benefits of patients undergoing immunotherapy. Immune-related genes were further used to construct the prognostic model. After clustering analysis of ICI genes, we found that patients in ICI gene cluster C had the best prognosis, and their tumor microenvironment had the highest proportion of macrophage M1 and T cell follicular helper cells. This result was consistent with that of multivariate cox (multi-cox) analysis. The prognostic model constructed by immune-related genes had good predictive performance. By estimating Tumor mutation burden (TMB), we also found that there were multiple genes with statistically different mutation frequencies in the high and low ICI score groups. The model based on the ICI score may help to screen out patients who would benefit from immunotherapy. The immune-related genes screened may be used as biomarkers and therapeutic targets.

卵巢癌（Ovarian cancer, OC）是妇科恶性肿瘤中第二大致死病因。多项研究证实，肿瘤免疫治疗的疗效与肿瘤免疫细胞浸润（tumor immune cell infiltration, ICI）紧密相关。然而截至目前，卵巢癌肿瘤微环境（tumor microenvironment, TME）的免疫浸润图谱仍未阐明。本研究整合了癌症基因组图谱（Cancer Genome Atlas, TCGA）与基因表达综合数据库（Gene Expression Omnibus, GEO）中的卵巢癌转录组数据，对患者的肿瘤微环境信息进行评估，并构建免疫细胞浸润评分以预测接受免疫治疗患者的临床获益。研究团队进一步利用免疫相关基因构建了预后模型。通过对免疫细胞浸润相关基因开展聚类分析后发现，归属于免疫细胞浸润基因簇C的患者预后最佳，其肿瘤微环境中M1型巨噬细胞与滤泡辅助性T细胞的占比最高。该结果与多因素Cox（multi-cox）分析结果一致。基于免疫相关基因构建的预后模型具备良好的预测性能。通过评估肿瘤突变负荷（tumor mutation burden, TMB），本研究还发现，在高、低免疫细胞浸润评分组中，存在多个突变频率具有统计学差异的基因。基于免疫细胞浸润评分的模型或可助力筛选出可从免疫治疗中获益的患者，而筛选得到的免疫相关基因可作为生物标志物与治疗靶点。