Department of Medical Oncology, the First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi Zhuang Autonomous Region, China.
Laboratory of Experimental Cardiology, Department of Cardiology, University Medical Center Utrecht, Utrecht, 3584, CX, The Netherlands.
BMC Cancer. 2020 Dec 7;20(1):1205. doi: 10.1186/s12885-020-07695-3.
Ovarian cancer (OV) is one of the most common malignant tumors of gynecology oncology. The lack of effective early diagnosis methods and treatment strategies result in a low five-year survival rate. Also, immunotherapy plays an important auxiliary role in the treatment of advanced OV patient, so it is of great significance to find out effective immune-related tumor markers for the diagnosis and treatment of OV.
Based on the consensus clustering analysis of single-sample gene set enrichment analysis (ssGSEA) score transformed via The Cancer Genome Atlas (TCGA) mRNA profile, we obtained two groups with high and low levels of immune infiltration. Multiple machine learning methods were conducted to explore prognostic genes associated with immune infiltration. Simultaneously, the correlation between the expression of mark genes and immune cells components was explored.
A prognostic classifier including 5 genes (CXCL11, S1PR4, TNFRSF17, FPR1 and DHRS95) was established and its robust efficacy for predicting overall survival was validated via 1129 OV samples. Some significant variations of copy number on gene loci were found between two risk groups and it showed that patients with fine chemosensitivity has lower risk score than patient with poor chemosensitivity (P = 0.013). The high and low-risk groups showed significantly different distribution (P < 0.001) of five immune cells (Monocytes, Macrophages M1, Macrophages M2, T cells CD4 menory and T cells CD8).
The present study identified five prognostic genes associated with immune infiltration of OV, which may provide some potential clinical implications for OV treatment.
卵巢癌（OV）是妇科肿瘤学中最常见的恶性肿瘤之一。缺乏有效的早期诊断方法和治疗策略导致五年生存率低。此外，免疫疗法在晚期 OV 患者的治疗中发挥着重要的辅助作用，因此找到有效的免疫相关肿瘤标志物对 OV 的诊断和治疗具有重要意义。
基于单样本基因集富集分析（ssGSEA）评分通过癌症基因组图谱（TCGA）mRNA 谱的共识聚类分析，我们获得了免疫浸润水平高和低的两组。采用多种机器学习方法探索与免疫浸润相关的预后基因。同时，还探讨了标记基因的表达与免疫细胞成分之间的相关性。
建立了一个包括 5 个基因（CXCL11、S1PR4、TNFRSF17、FPR1 和 DHRS95）的预后分类器，并通过 1129 个 OV 样本验证了其预测总生存期的稳健疗效。在两个风险组之间发现了基因座上的一些显著的拷贝数变化，并且表明具有良好化疗敏感性的患者比具有不良化疗敏感性的患者具有更低的风险评分（P=0.013）。高风险组和低风险组在五种免疫细胞（单核细胞、巨噬细胞 M1、巨噬细胞 M2、CD4 记忆 T 细胞和 CD8 T 细胞）的分布上存在显著差异（P<0.001）。
本研究确定了与 OV 免疫浸润相关的五个预后基因，这可能为 OV 的治疗提供一些潜在的临床意义。
