Department of Gynecology and Obstetrics, Shanghai Key Laboratory of Gynecology Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
Aging (Albany NY). 2021 Apr 4;13(7):10289-10311. doi: 10.18632/aging.202792.
The immune response is associated with the progression and prognosis of epithelial ovarian cancer (EOC). However, the roles of infiltrated immune cells and immune-related genes (IRGs) in EOC have not been reported comprehensively. In the current study, the differentially expressed genes (DEGs) were filtered based on the integrated gene expression data acquired from The University of California at Santa Cruz (UCSC) Genome Browser. Then, IRGs and transcriptional factors (TFs) were screened based on the ImmPort database and Cistrome database. A total of 501 differentially expressed IRGs, and 76 TFs were detected. A TF-mediated network was constructed by univariate Cox analysis to reveal the potential regulatory mechanisms of IRGs. Next, a nine immune-based prognostic risk model using nine IRGs (PI3, CXCL10, CXCL11, LCN6, CCL17, CCL25, MIF, CX3CR1, and CSPG5) was established. Based on the risk score worked out from the signature, the EOC patients could be classified into low-risk and high-risk groups. Furthermore, the immune landscapes, elevated by the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm and the Tumor Immune Estimation Resource (TIMER) database, effectuated different patterns in two groups. Thus, an immune-based prognostic risk model of EOC elucidates the immune status in the tumor microenvironment, and hence, could be used for prognosis.
免疫反应与上皮性卵巢癌 (EOC) 的进展和预后相关。然而，浸润性免疫细胞和免疫相关基因 (IRGs) 在 EOC 中的作用尚未得到全面报道。在本研究中，基于从加利福尼亚大学圣克鲁兹分校 (UCSC) 基因组浏览器获取的综合基因表达数据筛选差异表达基因 (DEGs)。然后，根据 ImmPort 数据库和 Cistrome 数据库筛选 IRGs 和转录因子 (TFs)。共检测到 501 个差异表达的 IRGs 和 76 个 TFs。通过单变量 Cox 分析构建 TF 介导的网络，揭示 IRGs 的潜在调控机制。接下来，使用 9 个 IRGs（PI3、CXCL10、CXCL11、LCN6、CCL17、CCL25、MIF、CX3CR1 和 CSPG5）构建了基于 9 种免疫的预后风险模型。基于该特征得出的风险评分，EOC 患者可分为低风险和高风险组。此外，通过估计相对 RNA 转录物子集的细胞类型鉴定算法 (CIBERSORT) 和肿瘤免疫估计资源 (TIMER) 数据库对免疫景观进行了分类，两组的模式不同。因此，EOC 的基于免疫的预后风险模型阐明了肿瘤微环境中的免疫状态，因此可用于预后。
