Su Jiaqi, Tian Xi, Zhang Zihao, Xu Wenhao, Anwaier Aihetaimujiang, Ye Shiqi, Zhu Shuxuan, Wang Yue, Shi Guohai, Qu Yuanyuan, Zhang Hailiang, Ye Dingwei
Department of Urology, Fudan University Shanghai Cancer Center, School of Life Sciences, Fudan University, Shanghai, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Front Oncol. 2022 Oct 14;12:1019949. doi: 10.3389/fonc.2022.1019949. eCollection 2022.
Renal cancer is one of the most lethal cancers because of its atypical symptoms and metastatic potential. The metabolism of amino acids and their derivatives is essential for cancer cell survival and proliferation. Thus, the construction of the amino acid metabolism-related risk signature might enhance the accuracy of the prognostic model and shed light on the treatments of renal cancers.
RNA expression and clinical data were downloaded from Santa Cruz (UCSC) Xena, GEO, and ArrayExpress databases. The "DESeq2" package identified the differentially expressed genes. Univariate COX analysis selected prognostic genes related to the metabolism of amino acids. Patients were divided into two clusters using the "ConsensusClusterPlus" package, and the CIBERSORT, ESTIMATE methods were explored to assess the immune infiltrations. The LASSO regression analysis constructed a risk model which was evaluated the prediction accuracy in two independent cohorts. The genomic alterations and drug sensitivity of 18-LASSO-genes were assessed. The differentially expressed genes between two clusters were used to perform functional enrichment analysis and weighted gene co-expression network analysis (WGCNA). Furthermore, external validation of expression was conducted in the FUSCC cohort containing 33 ccRCC patients.
The amino acid metabolism-related genes had significant correlations with prognosis. The patients in Cluster A demonstrated better survival, lower Treg cell proportion, higher ESTIMATE scores, and higher cuproptosis-related gene expressions. Amino acid metabolism-related genes with prognostic values were used to construct a risk model and patients in the low risk group were associated with improved outcomes. The Area Under Curve of the risk model was 0.801, 0.777, and 0.767 at the first, second, and third year respectively. The external validation cohort confirmed the stable prognostic value of the risk model. WGCNA identified four gene modules correlated with immune cell infiltrations and cuproptosis. We found that was downregulated in tumors by using TCGA, GEO datasets (p<0.001) and the FUSCC cohort (p=0.002).
Our study firstly constructed an 18 amino acid metabolism related signature to predict the prognosis in clear cell renal cell carcinoma. We also identified four potential gene modules potentially correlated with cuproptosis and identified downregulation in ccRCC which deserved further studies.
肾癌因其非典型症状和转移潜能而成为最致命的癌症之一。氨基酸及其衍生物的代谢对于癌细胞的存活和增殖至关重要。因此，构建与氨基酸代谢相关的风险特征可能会提高预后模型的准确性，并为肾癌的治疗提供线索。
从圣克鲁斯（UCSC）Xena、GEO和ArrayExpress数据库下载RNA表达和临床数据。“DESeq2”软件包识别差异表达基因。单变量COX分析选择与氨基酸代谢相关的预后基因。使用“ConsensusClusterPlus”软件包将患者分为两个聚类，并采用CIBERSORT、ESTIMATE方法评估免疫浸润情况。LASSO回归分析构建风险模型，并在两个独立队列中评估其预测准确性。评估18个LASSO基因的基因组改变和药物敏感性。利用两个聚类之间的差异表达基因进行功能富集分析和加权基因共表达网络分析（WGCNA）。此外，在包含33例ccRCC患者的FUSCC队列中进行了表达的外部验证。
与氨基酸代谢相关的基因与预后有显著相关性。A聚类中的患者表现出更好的生存率、更低的调节性T细胞比例、更高的ESTIMATE评分以及更高的铜死亡相关基因表达。使用具有预后价值的氨基酸代谢相关基因构建风险模型，低风险组患者的预后较好。风险模型在第一年、第二年和第三年的曲线下面积分别为0.801、0.777和0.767。外部验证队列证实了风险模型稳定的预后价值。WGCNA识别出与免疫细胞浸润和铜死亡相关的四个基因模块。我们发现，通过使用TCGA、GEO数据集（p<0.001）和FUSCC队列（p=0.002）发现该基因在肿瘤中表达下调。
我们的研究首次构建了一个包含18个与氨基酸代谢相关的特征来预测透明细胞肾细胞癌的预后。我们还识别出四个与铜死亡潜在相关的潜在基因模块，并发现该基因在ccRCC中表达下调，值得进一步研究。
