Identification and Experimental Validation of Immune-Associate lncRNAs for Predicting Prognosis in Cervical Cancer

免疫相关 lncRNA 的鉴定及用于预测宫颈癌预后的实验验证

阅读:6
作者:Jing Ye, Xiaojing Chen, Weiguo Lu

Conclusion

Our finding demonstrated the value of lncRNAs in evaluating the immune infiltrate of the tumor. The 8-lncRNA signature could predict the prognosis of CC and contribute to decisions regarding the immunotherapeutic strategy.

Methods

We first calculated immune scores of CC patients in the Cancer Genome Atlas (TCGA) database. Univariate Cox, Lasso Cox and multivariate Cox regression analyses were perfumed to establish an immune-relative lncRNA signature. In addition, we processed pathway enrichment analysis and immune infiltration analysis between patients with higher or lower risk. Finally, T-cell Chemotaxis assays were processed to verify the function of 2 key lncRNAs.

Purpose

Cervical cancer (CC) is a major risk for health of modern women. Immune-related long non-coding RNAs (lncRNAs) can also serve as prognostic markers of overall survival (OS) in patients with CC. This study aimed to identify an immune-related lncRNA signature for the prospective assessment of prognosis in CC patients.

Results

Our results suggested that patients with higher immune scores had longer survival time and some lncRNAs expressed differentially between two groups. Eight lncRNAs (LINC02802, LINC01877, RBAKDN, LINC02480, WWC2-AS2, LINC01281, ZBTB20-AS1, IFNG-AS1) were identified as prognostic signatures for CC. The immune-related lncRNA signature was correlated with disease progression and worse prognosis. Immune infiltration analysis indicated that the expression of 8-lncRNA signatures were corrected with infiltration level of immune cell subtypes. In addition, T-cell Chemotaxis assay validated that 2 key lncRNAs (ZBTB20-AS1 and LINC01281) could significantly promote the migration ability of T cells to CC cells.

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。