Lv Daojun, Wu Xiangkun, Wang Ming, Chen Wenzhe, Yang Shuxin, Liu Yongda, Zeng Guohua, Gu Di
Department of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Guangdong Key Laboratory of Urology, Guangzhou Institute of Urology, Guangzhou, China.
Front Cell Dev Biol. 2021 Mar 16;9:621618. doi: 10.3389/fcell.2021.621618. eCollection 2021.
Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma whose pathogenesis is not well understood. We aimed at identifying novel immune-related biomarkers that could be valuable in the diagnosis and prognosis of ccRCC.
The Robust Rank Aggregation (RRA) method was used to integrate differently expressed genes (DEGs) of 7 Gene Expression Omnibus (GEO) datasets and obtain robust DEGs. Weighted gene co-expression network analyses (WGCNA) were performed to identify hub genes associated with clinical traits in The Cancer Genome Atlas (TCGA) database. Comprehensive bioinformatic analyses were used to explore the role of hub genes in ccRCC.
Four hub genes IFI16, LMNB1, RHBDF2 and TACC3 were screened by the RRA method and WGCNA. These genes were found to be up-regulated in ccRCC, an upregulation that could be due to their associations with late TNM stages and tumor grades. The Receiver Operating Characteristic (ROC) curve and Kaplan-Meier survival analysis showed that the four hub genes had great diagnostic and prognostic values for ccRCC, while Gene Set Enrichment Analysis (GSEA) showed that they were involved in immune signaling pathways. They were also found to be closely associated with multiple tumor-infiltrating lymphocytes and critical immune checkpoint expressions. The results of Quantitative Real-time PCR (qRT-PCR) and immunohistochemical staining (IHC) analysis were consistent with bioinformatics analysis results.
The four hub genes were shown to have great diagnostic and prognostic values and played key roles in the tumor microenvironment of ccRCC.
透明细胞肾细胞癌（ccRCC）是肾细胞癌最常见的亚型，其发病机制尚不清楚。我们旨在鉴定对ccRCC诊断和预后有价值的新型免疫相关生物标志物。
采用稳健秩聚合（RRA）方法整合7个基因表达综合数据库（GEO）数据集的差异表达基因（DEG），获得稳健的DEG。在癌症基因组图谱（TCGA）数据库中进行加权基因共表达网络分析（WGCNA），以识别与临床特征相关的枢纽基因。采用综合生物信息学分析方法探讨枢纽基因在ccRCC中的作用。
通过RRA方法和WGCNA筛选出4个枢纽基因IFI16、LMNB1、RHBDF2和TACC3。这些基因在ccRCC中上调，这种上调可能与其与TNM晚期阶段和肿瘤分级的关联有关。受试者工作特征（ROC）曲线和Kaplan-Meier生存分析表明，这4个枢纽基因对ccRCC具有重要的诊断和预后价值，而基因集富集分析（GSEA）表明它们参与免疫信号通路。还发现它们与多种肿瘤浸润淋巴细胞和关键免疫检查点表达密切相关。定量实时PCR（qRT-PCR）和免疫组织化学染色（IHC）分析结果与生物信息学分析结果一致。
这4个枢纽基因具有重要的诊断和预后价值，在ccRCC的肿瘤微环境中起关键作用。
