Wang Jing, Su Xiaoling, Wang Chao, Xu Mingjuan
Department of Obstetrics and Gynecology, Changhai Hospital, Navy Medical University, Shanghai, China.
Department of Obstetrics and Gynecology, PLA Navy Medical Center, Shanghai, China.
Ann Transl Med. 2022 Jan;10(2):91. doi: 10.21037/atm-21-7014.
Ovarian cancer (OC) is a major cause of most gynecological cancer deaths, and the rates of incidence and mortality are increasing worldwide. However, factors in the tumor microenvironment (TME) related to OC and certain prognostic markers of OC are still unknown. We aimed to identify biomarkers connected to prognostic immunity based on clinical patients' data from The Cancer Genome Atlas (TCGA).
We used the ESTIMATE algorithm to compute the immune and matrix scores of OC patients from TCGA. Next, differentially expressed genes (DEGs) according to the immune and matrix scores were obtained. Subsequently, genes (, , , and ) connected with prognostic immunity were determined. Moreover, functional enrichment analysis and the protein-protein interaction network showed that these genes were enriched in many biological processes related to immune function. The Tumor Immune Estimation Resource (TIMER) algorithm was also used to analyze the immune prognostic genes according to six immuno-infiltrating cells.
According to high/low immune-scores and matrix-score groups, 682 common genes were identified, within 420 upregulated genes and 262 downregulated genes. Gene ontology (GO) analysis of biological process primarily enriched in T cell activation, regulation of lymphocyte activation and lymphocyte differentiation. OS analysis showed 45 genes (6.6%) were relevant in the final results. The Kaplan-Meier plotter database verified the top 10 genes related to prognosis, but only , , and were related to overall survival (OS).
, , and may influence prognosis via their effects on the infiltration of immune cells and therefore represent potential targets for OC immunotherapy.
卵巢癌（OC）是大多数妇科癌症死亡的主要原因，且全球发病率和死亡率都在上升。然而，与OC相关的肿瘤微环境（TME）因素以及OC的某些预后标志物仍不清楚。我们旨在根据来自癌症基因组图谱（TCGA）的临床患者数据识别与预后免疫相关的生物标志物。
我们使用ESTIMATE算法计算来自TCGA的OC患者的免疫和基质评分。接下来，根据免疫和基质评分获得差异表达基因（DEG）。随后，确定与预后免疫相关的基因（、、、和）。此外，功能富集分析和蛋白质-蛋白质相互作用网络表明这些基因在许多与免疫功能相关的生物学过程中富集。还使用肿瘤免疫估计资源（TIMER）算法根据六种免疫浸润细胞分析免疫预后基因。
根据高/低免疫评分和基质评分组，共鉴定出682个常见基因，其中420个上调基因和262个下调基因。生物学过程的基因本体（GO）分析主要富集于T细胞活化、淋巴细胞活化调节和淋巴细胞分化。OS分析显示最终结果中有45个基因（6.6%）相关。Kaplan-Meier绘图仪数据库验证了与预后相关的前10个基因，但只有、、和与总生存期（OS）相关。
、和可能通过影响免疫细胞浸润来影响预后，因此代表OC免疫治疗的潜在靶点。
