摘要

      卵巢癌症（OC）是一种高度异质性疾病，据报道其细胞起源不同；因此，OC患者迫切需要精确的预后策略和有效的新疗法。越来越多的研究表明，大多数恶性肿瘤具有密集的血管生成和快速生长。因此，血管生成在肿瘤转移的发展中起着重要作用。然而，血管生成相关基因（ARGs）在OC中的预后价值仍有待进一步阐明。在本研究中，使用UCSC XENA下载OC患者和正常对照样本的表达数据和相应的临床数据。共筛选了1960个差异表达的ARG，并通过基因本体论（GO）术语和京都基因和基因组百科全书（KEGG）途径进行了功能注释。进行单变量Cox回归分析，以确定与预后相关的ARGs。基于最小绝对收缩和选择算子（LASSO）和多变量Cox回归分析，构建了新的ARGs特征（包括ESM1、CXCL13、TPCN2、PTPRD、FOXO1和ELK3），用于预测OC的总生存率（OS）。根据患者的中位风险评分对患者进行分组。在癌症基因组图谱（TCGA）训练数据集中，生存分析表明，高危组的总生存率低于低危组（p < 0.0001）。使用国际癌症基因组联合会（ICGC）数据库进行验证，接收器工作特性（ROC）曲线显示出良好的性能。进行单变量和多变量Cox分析，以确定OS的独立预测因素。列线图包括风险评分、年龄、阶段、级别和位置，不仅可以显示出良好的预测能力，而且可以探索基于ARGs的免疫原性、免疫成分和免疫表型与风险评分的相关性分析。风险评分与免疫浸润类型密切相关。此外，同源重组缺陷（HRD）、NtAI评分、LOH评分、LSTm评分、干性指数（mRNAsi）和基质细胞与风险评分显著相关。本研究表明，由六个ARG构建的新信号可能作为OC的有效预后生物标志物，并有助于OC的临床决策和个性化预后监测。Ovarian cancer (OC) is a highly heterogeneous disease with different cellular origins reported; thus, precise prognostic strategies and effective new therapies are urgently needed for patients with OC. A growing number of studies have shown that most malignancies have intensive angiogenesis and rapid growth. Therefore, angiogenesis plays an important role in the development of tumor metastasis. However, the prognostic value of angiogenesis-related genes (ARGs) in OC remains to be further elucidated. In this study, the expression data and corresponding clinical data from patients with OC and normal control samples were downloaded with UCSC XENA. A total of 1,960 differentially expressed ARGs were screened and functionally annotated through Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Univariate Cox regression analysis was performed to identify ARGs associated with prognosis. New ARGs signatures (including ESM1, CXCL13, TPCN2, PTPRD, FOXO1, and ELK3) were constructed for the prediction of overall survival (OS) in OC based on the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. Patients were divided based on their median risk score. In the The Cancer Genome Atlas (TCGA) training dataset, the survival analysis showed that overall survival was lower in the high-risk group than that in the low-risk group (p < 0.0001). The International Cancer Genome Consortium (ICGC) database was used for validation, and the receiver operating characteristic (ROC) curves showed good performance. Univariate and multivariate Cox analyses were conducted to identify independent predictors of OS. The nomogram, including the risk score, age, stage, grade, and position, can not only show good predictive ability but also can explore the correlation analysis based on ARGs for immunogenicity, immune components, and immune phenotypes with risk score. Risk scores were correlated strongly with the type of immune infiltration. Furthermore, homologous recombination defect (HRD), NtAIscore, LOH score, LSTm score, stemness index (mRNAsi), and stromal cells were significantly correlated with risk score. The present study suggests that the novel signature constructed from six ARGs may serve as effective prognostic biomarkers for OC and contribute to clinical decision making and personalized prognostic monitoring of OC.

https://www.frontiersin.org/articles/10.3389/fonc.2021.783666/full