基于综合分析鉴定乳头状肾细胞肿瘤微环境中的 4 基因模型。
Identification of 4-genes model in papillary renal cell tumor microenvironment based on comprehensive analysis.
机构信息
Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, Tianhe Road 600, Guangzhou, 510630, China.
Department of Gynecology of Traditional Chinese Medicine, Shanxi Academy of Traditional Chinese Medicine, Taiyuan, 030000, China.
出版信息
BMC Cancer. 2021 May 17;21(1):553. doi: 10.1186/s12885-021-08319-0.
BACKGROUND
The tumor microenvironment acts a pivotal part in the occurrence and development of tumor. However, there are few studies on the microenvironment of papillary renal cell carcinoma (PRCC). Our study aims to explore prognostic genes related to tumor microenvironment in PRCC.
METHODS
PRCC expression profiles and clinical data were extracted from The Cancer Gene Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Immune/stromal scores were performed utilizing the ESTIMATE algorithm. Three hundred fifty-seven samples were split into two groups on the basis of median immune/stromal score, and comparison of gene expression was conducted. Intersect genes were obtained by Venn diagrams. Hub genes were selected through protein-protein interaction (PPI) network construction, and relevant functional analysis was conducted by DAVID. We used Kaplan-Meier analysis to identify the correlations between genes and overall survival (OS) and progression-free survival (PFS). Univariate and multivariate cox regression analysis were employed to construct survival model. Cibersort was used to predict the immune cell composition of high and low risk group. Combined nomograms were built to predict PRCC prognosis. Immune properties of PRCC were validated by The Cancer Immunome Atlas (TCIA).
RESULTS
We found immune/stromal score was correlated with T pathological stages and PRCC subtypes. Nine hundred eighty-nine differentially expressed genes (DEGs) and 1169 DEGs were identified respectively on the basis of immune and stromal score. Venn diagrams indicated that 763 co-upregulated genes and 4 co-downregulated genes were identified. Kaplan-Meier analysis revealed that 120 genes were involved in tumor prognosis. Then PPI network analysis identified 22 hub genes, and four of which were significantly related to OS in patients with PRCC confirmed by cox regression analysis. Finally, we constructed a prognostic nomogram which combined with influence factors.
CONCLUSIONS
Four tumor microenvironment-related genes (CD79A, CXCL13, IL6 and CCL19) were identified as biomarkers for PRCC prognosis.
背景
肿瘤微环境在肿瘤的发生和发展中起着关键作用。然而，关于乳头状肾细胞癌（PRCC）的微环境研究较少。本研究旨在探讨与 PRCC 肿瘤微环境相关的预后基因。
方法
从癌症基因图谱（TCGA）和基因表达综合（GEO）数据库中提取 PRCC 表达谱和临床数据。利用 ESTIMATE 算法进行免疫/基质评分。根据中位免疫/基质评分将 357 例样本分为两组，比较基因表达。通过 Venn 图获取交集基因。通过构建蛋白质-蛋白质相互作用（PPI）网络选择枢纽基因，并通过 DAVID 进行相关功能分析。我们使用 Kaplan-Meier 分析来确定基因与总生存期（OS）和无进展生存期（PFS）之间的相关性。采用单因素和多因素 Cox 回归分析构建生存模型。使用 Cibersort 预测高低风险组的免疫细胞组成。构建联合列线图预测 PRCC 预后。通过癌症免疫图谱（TCIA）验证 PRCC 的免疫特性。
结果
我们发现免疫/基质评分与 T 病理分期和 PRCC 亚型相关。基于免疫和基质评分，分别鉴定出 989 个差异表达基因（DEGs）和 1169 个 DEGs。Venn 图表明，鉴定出 763 个共同上调基因和 4 个共同下调基因。Kaplan-Meier 分析显示，有 120 个基因与肿瘤预后相关。然后，通过 PPI 网络分析确定了 22 个枢纽基因，其中 4 个通过 Cox 回归分析证实与 PRCC 患者的 OS 显著相关。最后，我们构建了一个预后列线图，结合了影响因素。
结论
鉴定出 4 个与肿瘤微环境相关的基因（CD79A、CXCL13、IL6 和 CCL19）作为 PRCC 预后的生物标志物。
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