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Abstract：
BACKGROUND: Aberrant glycosylation, catalyzed by the specific glycosyltransferase, is one of the dominant features of cancers. Among the glycosyltransferase subfamilies, sialyltransferases (SiaTs) are an essential part which has close linkages with tumor-associated events, such as tumor growth, metastasis and angiogenesis. Considering the relationship between SiaTs and cancer, the current study attempted to establish an effective prognostic model with SiaTs-related genes (SRGs) to predict patients\' outcome and therapeutic responsiveness of bladder cancer.
METHODS: RNA-seq data, clinical information and genomic mutation data were downloaded (TCGA-BLCA and GSE13507 datasets). The comprehensive landscape of the 20 SiaTs was analyzed, and the differentially expressed SiaTs-related genes were screened with \"DESeq2\" R package. ConsensusClusterPlus was applied for clustering, following with survival analysis with Kaplan-Meier curve. The overall survival related SRGs were determined with univariate Cox proportional hazards regression analysis, and the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to generate a SRGs-related prognostic model. The predictive value was estimated with Kaplan-Meier plot and the receiver operating characteristic (ROC) curve, which was further validated with the constructed nomogram and decision curve.
RESULTS: In bladder cancer tissues, 17 out of the 20 SiaTs were differentially expressed with CNV changes and somatic mutations. Two SiaTs_Clusters were determined based on the expression of the 20 SiaTs, and two gene_Clusters were identified based on the expression of differentially expressed genes between SiaTs_Clusters. The SRGs-related prognostic model was generated with 7 key genes (CD109, TEAD4, FN1, TM4SF1, CDCA7L, ATOH8 and GZMA), and the accuracy for outcome prediction was validated with ROC curve and a constructed nomogram. The SRGs-related prognostic signature could separate patients into high- and low-risk group, where the high-risk group showed poorer outcome, more abundant immune infiltration, and higher expression of immune checkpoint genes. In addition, the risk score derived from the SRGs-related prognostic model could be utilized as a predictor to evaluate the responsiveness of patients to the medical therapies.
CONCLUSIONS: The SRGs-related prognostic signature could potentially aid in the prediction of the survival outcome and therapy response for patients with bladder cancer, contributing to the development of personalized treatment and appropriate medical decisions.
METHODS: RNA-seq data, clinical information and genomic mutation data were downloaded (TCGA-BLCA and GSE13507 datasets). The comprehensive landscape of the 20 SiaTs was analyzed, and the differentially expressed SiaTs-related genes were screened with \"DESeq2\" R package. ConsensusClusterPlus was applied for clustering, following with survival analysis with Kaplan-Meier curve. The overall survival related SRGs were determined with univariate Cox proportional hazards regression analysis, and the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to generate a SRGs-related prognostic model. The predictive value was estimated with Kaplan-Meier plot and the receiver operating characteristic (ROC) curve, which was further validated with the constructed nomogram and decision curve.
RESULTS: In bladder cancer tissues, 17 out of the 20 SiaTs were differentially expressed with CNV changes and somatic mutations. Two SiaTs_Clusters were determined based on the expression of the 20 SiaTs, and two gene_Clusters were identified based on the expression of differentially expressed genes between SiaTs_Clusters. The SRGs-related prognostic model was generated with 7 key genes (CD109, TEAD4, FN1, TM4SF1, CDCA7L, ATOH8 and GZMA), and the accuracy for outcome prediction was validated with ROC curve and a constructed nomogram. The SRGs-related prognostic signature could separate patients into high- and low-risk group, where the high-risk group showed poorer outcome, more abundant immune infiltration, and higher expression of immune checkpoint genes. In addition, the risk score derived from the SRGs-related prognostic model could be utilized as a predictor to evaluate the responsiveness of patients to the medical therapies.
CONCLUSIONS: The SRGs-related prognostic signature could potentially aid in the prediction of the survival outcome and therapy response for patients with bladder cancer, contributing to the development of personalized treatment and appropriate medical decisions.
摘要：
背景：糖基化异常，由特定的糖基转移酶催化，是癌症的主要特征之一。在糖基转移酶亚家族中，唾液酸转移酶（SiaTs）是与肿瘤相关事件密切相关的重要组成部分，如肿瘤生长，转移和血管生成。考虑到SiaTs和癌症之间的关系，本研究试图利用SiaTs相关基因(SRGs)建立有效的预后模型,以预测膀胱癌患者的预后和治疗反应性.
方法：RNA-seq数据，我们下载了临床信息和基因组突变数据(TCGA-BLCA和GSE13507数据集).分析了20个SiaTs的综合景观，用“DESeq2”R包筛选差异表达的SiaTs相关基因。ConsensusClusterPlus被应用于聚类，随后用Kaplan-Meier曲线进行生存分析。用单变量Cox比例风险回归分析确定总生存相关的SRGs，并进行最小绝对收缩和选择算子(LASSO)回归分析以生成SRGs相关的预后模型。预测值用Kaplan-Meier图和受试者工作特征(ROC)曲线估计,通过构建的列线图和决策曲线进一步验证。
结果：在膀胱癌组织中，20个SiaTs中的17个在CNV变化和体细胞突变的情况下差异表达。根据20个SiaTs的表达式确定了两个SiaTs_簇，并根据SiaTs_Clusters之间差异表达基因的表达鉴定了两个基因_Clusters。SRGs相关的预后模型由7个关键基因(CD109、TEAD4、FN1、TM4SF1、CDCA7L、ATOH8和GZMA），结果预测的准确性通过ROC曲线和构建的列线图进行验证。与SRGs相关的预后特征可以将患者分为高危和低危组，高危人群的预后较差，更丰富的免疫浸润，和更高的免疫检查点基因表达。此外,来自SRGs相关预后模型的风险评分可用作评估患者对药物治疗反应性的预测指标.
结论：SRGs相关的预后特征可能有助于预测膀胱癌患者的生存结果和治疗反应，促进个性化治疗和适当医疗决策的发展。
方法：RNA-seq数据，我们下载了临床信息和基因组突变数据(TCGA-BLCA和GSE13507数据集).分析了20个SiaTs的综合景观，用“DESeq2”R包筛选差异表达的SiaTs相关基因。ConsensusClusterPlus被应用于聚类，随后用Kaplan-Meier曲线进行生存分析。用单变量Cox比例风险回归分析确定总生存相关的SRGs，并进行最小绝对收缩和选择算子(LASSO)回归分析以生成SRGs相关的预后模型。预测值用Kaplan-Meier图和受试者工作特征(ROC)曲线估计,通过构建的列线图和决策曲线进一步验证。
结果：在膀胱癌组织中，20个SiaTs中的17个在CNV变化和体细胞突变的情况下差异表达。根据20个SiaTs的表达式确定了两个SiaTs_簇，并根据SiaTs_Clusters之间差异表达基因的表达鉴定了两个基因_Clusters。SRGs相关的预后模型由7个关键基因(CD109、TEAD4、FN1、TM4SF1、CDCA7L、ATOH8和GZMA），结果预测的准确性通过ROC曲线和构建的列线图进行验证。与SRGs相关的预后特征可以将患者分为高危和低危组，高危人群的预后较差，更丰富的免疫浸润，和更高的免疫检查点基因表达。此外,来自SRGs相关预后模型的风险评分可用作评估患者对药物治疗反应性的预测指标.
结论：SRGs相关的预后特征可能有助于预测膀胱癌患者的生存结果和治疗反应，促进个性化治疗和适当医疗决策的发展。
