Background Ovarian cancer is one of the leading causes of female deaths worldwide. Ovarian serous cystadenocarcinoma occupies about 90% of it. Effective and accurate biomarkers for diagnosis, outcome prediction and personalized treatment are needed urgently Methods Gene expression profile for OSC patients was obtained from the TCGA database. The ESTIMATE algorithm was used to calculate immune scores and stromal scores of expression data of ovarian serous cystadenocarcinoma samples. Survival results between high and low groups of immune and stromal score were compared and differentially expressed genes (DEGs) were screened out by limma package. The Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and the protein-protein interaction (PPI) network analysis were performed with the g:Profiler database, the Cytoscape and Search Tool for the Retrieval of Interacting Genes (STRING-DB). Survival results between high and low immune and stromal score groups were compared. Kaplan-Meier plots based on TCGA follow up information were generated to evaluate patients’ overall survival. Results Eighty-six upregulated DEGs and one downregulated DEG were identified. Three modules, which included 49 nodes were chosen as important networks. Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were considered to be correlated with poor overall survival. Conclusion Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were correlated with poor overall survival in our study. This new set of genes can become strong predictor of survival, individually or combined. Further investigation of these genes is needed to validate the conclusion to provide novel understanding of tumor microenvironment with ovarian serous cystadenocarcinoma prognosis and treatment.

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卵巢浆液性囊腺癌微环境中基因表达的TCGA数据库挖掘

背景 卵巢癌是全球女性死亡的主要原因之一。卵巢浆液性囊腺癌约占其90%。迫切需要用于诊断、结果预测和个性化治疗的有效和准确的生物标志物。方法从 TCGA 数据库获得 OSC 患者的基因表达谱。使用ESTIMATE算法计算卵巢浆液性囊腺癌样本表达数据的免疫评分和基质评分。比较免疫和基质评分高低组之间的存活结果，并通过limma包筛选出差异表达基因（DEGs）。使用 g:Profiler 数据库进行基因本体论 (GO)、京都基因和基因组百科全书 (KEGG) 通路富集分析和蛋白质-蛋白质相互作用 (PPI) 网络分析，用于检索相互作用基因的 Cytoscape 和搜索工具 (STRING-DB)。比较高、低免疫和基质评分组之间的生存结果。生成基于 TCGA 随访信息的 Kaplan-Meier 图以评估患者的总体生存率。结果鉴定出86个上调的DEG和1个下调的DEG。包括 49 个节点的三个模块被选为重要网络。7 个 DEG（VSIG4、TGFBI、DCN、F13A1、ALOX5AP、GPX3、SFRP4）被认为与较差的总体存活率相关。结论 在我们的研究中，7 个 DEG（VSIG4、TGFBI、DCN、F13A1、ALOX5AP、GPX3、SFRP4）与较差的总生存期相关。这组新的基因可以单独或组合成为生存的强有力预测因子。