Identification and experimental validation of a sialylation-related long noncoding RNA signature for prognosis of bladder cancer

鉴定和实验验证与唾液酸化相关的长链非编码RNA特征在膀胱癌预后中的作用

阅读:1

Abstract

BACKGROUND: The dysregulation of sialylation plays a pivotal role in cancer progression and metastasis, impacting various aspects of tumor behavior. This study aimed to investigate the prognostic significance of long non-coding RNAs (lncRNAs) in relation to sialylation. Additionally, we aimed to develop a signature of sialylation-related lncRNAs in the context of bladder cancer. METHODS: This study used transcriptomic data and clinical information from the TCGA (the Cancer Genome Atlas) database to screen for sialylation-related lncRNAs and constructed a prognostic model. The relationships between these lncRNAs and biological pathways, immune cell infiltration, drug sensitivity, etc., were analyzed, and the expression of some lncRNAs was validated at the cellular level. RESULTS: This study identified 6 prognostic lncRNAs related to sialylation and constructed a risk score model with high predictive accuracy and reliability. The survival period of patients in the high-risk group was significantly lower than that of the low-risk group, and it was related to various biological pathways and immune functions. In addition, this study found differences in the sensitivity of patients in different risk groups to chemotherapy drugs, providing a reference for personalized treatment. CONCLUSION: In this study, we examined the relationship between sialylation-related lncRNA and the prognosis of bladder cancer, providing new molecular markers and potential targets for diagnosis and treatment. Our research revealed correlations between sialylation-related lncRNA characteristics and clinicopathological features, potential mechanisms, somatic mutations, immune microenvironment, chemotherapy response, and predicted drug sensitivity in bladder cancer. Additionally, in vitro cellular studies were conducted to validate these findings and lay the groundwork for future clinical applications.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。