Zhan Chuanchuan, Wang Zichu, Xu Chao, Huang Xiao, Su Junzhou, Chen Bisheng, Wang Mingshan, Qi Zhihong, Bai Peiming
Shaoxing people's Hospital, Shaoxing, China.
Zhongshan Hospital, Xiamen University, Xiamen, China.
Front Mol Biosci. 2021 Apr 8;8:609865. doi: 10.3389/fmolb.2021.609865. eCollection 2021.
Clear cell renal cell carcinoma (ccRCC), one of the most common urologic cancer types, has a relatively good prognosis. However, clinical diagnoses are mostly done during the medium or late stages, when mortality and recurrence rates are quite high. Therefore, it is important to perform real-time information tracking and dynamic prognosis analysis for these patients. We downloaded the RNA-seq data and corresponding clinical information of ccRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 3,238 differentially expressed genes were identified between normal and ccRCC tissues. Through a series of Weighted Gene Co-expression Network, overall survival, immunohistochemical and the least absolute shrinkage selection operator (LASSO) analyses, seven prognosis-associated genes (AURKB, FOXM1, PTTG1, TOP2A, TACC3, CCNA2, and MELK) were screened. Their risk score signature was then constructed. Survival analysis showed that high-risk scores exhibited significantly worse overall survival outcomes than low-risk patients. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic curve and was further validated using another cohort. Gene set enrichment analysis showed that some cancer-associated phenotypes were significantly prevalent in the high-risk group. Overall, these findings prove that this risk model can potentially improve individualized diagnostic and therapeutic strategies.
透明细胞肾细胞癌(ccRCC)是最常见的泌尿系统癌症类型之一,预后相对较好。然而,临床诊断大多在中晚期进行,此时死亡率和复发率相当高。因此,对这些患者进行实时信息跟踪和动态预后分析很重要。我们从癌症基因组图谱(TCGA)和基因表达综合数据库(GEO)下载了ccRCC的RNA测序数据及相应的临床信息。在正常组织和ccRCC组织之间共鉴定出3238个差异表达基因。通过一系列加权基因共表达网络、总生存、免疫组织化学和最小绝对收缩选择算子(LASSO)分析,筛选出7个与预后相关的基因(AURKB、FOXM1、PTTG1、TOP2A、TACC3、CCNA2和MELK)。然后构建了它们的风险评分特征。生存分析表明,高风险评分患者的总生存结果明显比低风险患者差。通过受试者工作特征曲线证实了该预后特征的准确性,并使用另一个队列进一步验证。基因集富集分析表明,一些癌症相关表型在高风险组中显著普遍。总体而言,这些发现证明该风险模型可能改善个体化诊断和治疗策略。