PDF(Pubmed)
Abstract：
Tryptophan metabolism is indirectly involved in immune tolerance and promotes response to anticancer drugs. However, the mechanisms underlying tryptophan metabolism and immune landscape in bladder urothelial carcinoma (BLCA) are not fully understood.
A BLCA dataset containing 406 tumor samples with clinical survival information and 19 normal samples were obtained from the Cancer Genome Atlas database. The validation set, GSE32894, contained 223 BLCA tumor samples with survival information, and the single-cell dataset, GSE135337, included seven BLCA tumor samples; both were obtained from the gene expression omnibus database. Univariate and multivariate Cox regression analyses were conducted to evaluate clinical parameters and risk scores. Immune infiltration and checkpoint analyses were performed to explore the immune landscape of BLCA. Single-cell analysis was conducted to further identify the roles of model genes in BLCA. Finally, NAMPT expression in BLCA and adjacent tissues was detected using RT-qPCR, CCK-8 and Transwell assays were conducted to determine the role of NAMPT in BLCA cells.
Six crossover genes (TDO2, ACAT1, IDO1, KMO, KYNU, and NAMPT) were identified by overlap analysis of tryptophan metabolism-related genes, immune-related genes, and differentially expressed genes (DEGs). Three biomarkers, NAMPT, IDO1, and ACAT1, were identified using Cox regression analysis. Accordingly, a tryptophan metabolism- and immune-related gene risk model was constructed, and the patients were divided into high- and low-risk groups. There were significant differences in the clinical parameters, prognosis, immune infiltration, and immunotherapy response between the risk groups. RT-qPCR revealed that NAMPT was upregulated in BLCA samples. Knocking down NAMPT significantly inhibited BLCA cell proliferation, migration, and invasion.
In our study, we constructed a tryptophan metabolism- and immune-related gene risk model based on three biomarkers, namely NAMPT, IDO1, and ACAT1, that were significantly associated with the progression and immune landscape of BLCA. The risk model could effectively predict patient prognosis and immunotherapy response and can guide individualized immunotherapy.
A BLCA dataset containing 406 tumor samples with clinical survival information and 19 normal samples were obtained from the Cancer Genome Atlas database. The validation set, GSE32894, contained 223 BLCA tumor samples with survival information, and the single-cell dataset, GSE135337, included seven BLCA tumor samples; both were obtained from the gene expression omnibus database. Univariate and multivariate Cox regression analyses were conducted to evaluate clinical parameters and risk scores. Immune infiltration and checkpoint analyses were performed to explore the immune landscape of BLCA. Single-cell analysis was conducted to further identify the roles of model genes in BLCA. Finally, NAMPT expression in BLCA and adjacent tissues was detected using RT-qPCR, CCK-8 and Transwell assays were conducted to determine the role of NAMPT in BLCA cells.
Six crossover genes (TDO2, ACAT1, IDO1, KMO, KYNU, and NAMPT) were identified by overlap analysis of tryptophan metabolism-related genes, immune-related genes, and differentially expressed genes (DEGs). Three biomarkers, NAMPT, IDO1, and ACAT1, were identified using Cox regression analysis. Accordingly, a tryptophan metabolism- and immune-related gene risk model was constructed, and the patients were divided into high- and low-risk groups. There were significant differences in the clinical parameters, prognosis, immune infiltration, and immunotherapy response between the risk groups. RT-qPCR revealed that NAMPT was upregulated in BLCA samples. Knocking down NAMPT significantly inhibited BLCA cell proliferation, migration, and invasion.
In our study, we constructed a tryptophan metabolism- and immune-related gene risk model based on three biomarkers, namely NAMPT, IDO1, and ACAT1, that were significantly associated with the progression and immune landscape of BLCA. The risk model could effectively predict patient prognosis and immunotherapy response and can guide individualized immunotherapy.
摘要：
■色氨酸代谢间接参与免疫耐受并促进对抗癌药物的反应。然而,膀胱尿路上皮癌(BLCA)中色氨酸代谢和免疫景观的潜在机制尚不完全清楚.
从癌症基因组图谱数据库获得包含406个具有临床存活信息的肿瘤样品和19个正常样品的BLCA数据集。验证集，GSE32894，包含223个具有生存信息的BLCA肿瘤样本，和单细胞数据集，GSE135337包括七个BLCA肿瘤样品;两者均从基因表达综合数据库获得。进行单变量和多变量Cox回归分析以评估临床参数和风险评分。进行免疫浸润和检查点分析以探索BLCA的免疫景观。进行单细胞分析以进一步鉴定模型基因在BLCA中的作用。最后,使用RT-qPCR检测BLCA和邻近组织中的NAMPT表达，进行CCK-8和Transwell测定以确定NAMPT在BLCA细胞中的作用。
■六个交叉基因（TDO2，ACAT1，IDO1，KMO，KYNU,和NAMPT）通过色氨酸代谢相关基因的重叠分析鉴定，免疫相关基因，和差异表达基因(DEGs)。三种生物标志物，NAMPT,使用Cox回归分析鉴定IDO1和ACAT1。因此，构建了色氨酸代谢和免疫相关基因风险模型，将患者分为高危组和低危组。临床参数有显著差异,预后,免疫浸润，和风险组之间的免疫疗法反应。RT-qPCR显示NAMPT在BLCA样品中上调。敲低NAMPT显著抑制BLCA细胞增殖,迁移,和入侵。
■在我们的研究中，我们构建了基于三种生物标志物的色氨酸代谢和免疫相关基因风险模型，即NAMPT，IDO1和ACAT1与BLCA的进展和免疫状况显着相关。该风险模型可有效预测患者预后和免疫治疗反应,指导个体化免疫治疗。
从癌症基因组图谱数据库获得包含406个具有临床存活信息的肿瘤样品和19个正常样品的BLCA数据集。验证集，GSE32894，包含223个具有生存信息的BLCA肿瘤样本，和单细胞数据集，GSE135337包括七个BLCA肿瘤样品;两者均从基因表达综合数据库获得。进行单变量和多变量Cox回归分析以评估临床参数和风险评分。进行免疫浸润和检查点分析以探索BLCA的免疫景观。进行单细胞分析以进一步鉴定模型基因在BLCA中的作用。最后,使用RT-qPCR检测BLCA和邻近组织中的NAMPT表达，进行CCK-8和Transwell测定以确定NAMPT在BLCA细胞中的作用。
■六个交叉基因（TDO2，ACAT1，IDO1，KMO，KYNU,和NAMPT）通过色氨酸代谢相关基因的重叠分析鉴定，免疫相关基因，和差异表达基因(DEGs)。三种生物标志物，NAMPT,使用Cox回归分析鉴定IDO1和ACAT1。因此，构建了色氨酸代谢和免疫相关基因风险模型，将患者分为高危组和低危组。临床参数有显著差异,预后,免疫浸润，和风险组之间的免疫疗法反应。RT-qPCR显示NAMPT在BLCA样品中上调。敲低NAMPT显著抑制BLCA细胞增殖,迁移,和入侵。
■在我们的研究中，我们构建了基于三种生物标志物的色氨酸代谢和免疫相关基因风险模型，即NAMPT，IDO1和ACAT1与BLCA的进展和免疫状况显着相关。该风险模型可有效预测患者预后和免疫治疗反应,指导个体化免疫治疗。
