Role of 8-autophagy-related long non-coding RNAs signature in prognosis for bladder cancer

Hang TONG; Tinghao LI; Honghao CAO; Xudong LIU; Junlong ZHU; Zijia QIN; Siwen YIN; Weiyang HE

doi:10.16016/j.1000-5404.202106120

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Role of 8-autophagy-related long non-coding RNAs signature in prognosis for bladder cancer

Monographic Reports: Basic and clinical researches on bladder cancer | 更新时间：2026-01-09

- Role of 8-autophagy-related long non-coding RNAs signature in prognosis for bladder cancer
- Journal of Third Military Medical University Vol. 43, Issue 23, Pages: 2532-2541(2021)
- 作者机构：
  
  1.400016 重庆，重庆医科大学附属第一医院泌尿外科
  2.402460 重庆，重庆市荣昌中医院泌尿外科
  3.402760 重庆，重庆市璧山中医院泌尿外科
- 作者简介：
- 基金信息：
- DOI：10.16016/j.1000-5404.202106120
  CLC： R181.23;R730.7;R737.14
- Received：16 June 2021，
  
  Revised：2021-09-21，
  
  Published：15 December 2021
- 稿件说明：
移动端阅览
Hang TONG, Tinghao LI, Honghao CAO, et al. Role of 8-autophagy-related long non-coding RNAs signature in prognosis for bladder cancer[J]. Journal of Third Military Medical University, 2021, 43(23): 2532-2541.
DOI：

Hang TONG, Tinghao LI, Honghao CAO, et al. Role of 8-autophagy-related long non-coding RNAs signature in prognosis for bladder cancer[J]. Journal of Third Military Medical University, 2021, 43(23): 2532-2541. DOI： 10.16016/j.1000-5404.202106120.

摘要

目的

探究自噬相关lncRNA风险预测模型在膀胱癌患者中的潜在预后作用与价值。

方法

从TCGA数据库中获取膀胱癌患者的转录组数据和相应的临床资料。通过R软件将402例膀胱癌样本按3 ∶1的比例随机分为训练集及验证集。通过Pearson相关性分析筛选自噬相关lncRNA，并通过单因素和多因素Cox回归分析构建自噬相关lncRNA预后模型。通过绘制ROC曲线评估模型的准确性。进一步计算样本风险评分并分析其与患者临床特征、生存预后的关系。另外通过构建列线图评估该模型对膀胱癌患者预后的预测能力。最后，通过GSEA分析不同风险分组可能富集的KEGG通路。采用RT-qPCR验证模型中高危lncRNA的表达情况。

结果

成功构建了一个8-自噬相关lncRNA模型，在训练集、验证集和总体集中，该预后模型ROC曲线的AUC值均大于0.75。模型风险评分与患者年龄、AJCC分期和T分期有关(

0.05)，与患者性别无明显关系。通过整合临床变量和风险评分建立的列线图证实该模型具有良好的预后预测能力。GSEA分析显示，一些肿瘤和自噬相关通路在高风险组富集，而免疫相关通路在低风险组富集。模型中lncRNA AC005229.4在膀胱癌细胞系中高表达。

结论

8-自噬相关lncRNA模型对膀胱癌患者的预后具有较好的预测效能。

Abstract

Objective

To explore the potential prognostic role and value of autophagy-related lncRNA risk prediction signature in patients with bladder cancer.

Methods

The transcriptome data and corresponding clinical data of patients with bladder cancer were obtained from the Cancer Genome Atlas (TCGA) database. Four hundreds and two bladder cancer samples were randomly divided into training set and verification set according to the ratio of 3 ∶1 by R software. Autophagy-related lncRNA

was screened by Pearson correlation analysis

and autophagy-related lncRNA prognostic signature was constructed by univariate and multivariate Cox regression analyses. The accuracy of the signature was evaluated by drawing ROC curve. The sample risk score was further calculated

and the relationship of the sample risk score with clinical characteristics and survival prognosis was analyzed. In addition

the ability of the signature to predict the prognosis of patients with bladder cancer was evaluated by constructing a nomogram. Finally

the possible enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis in different risk groups were analyzed by gene set enrichment analysis (GSEA). Finally

the expression of high-risk lncRNA was verified in the signature by RT-qPCR.

Results

An 8-autophagy-related lncRNA signature was successfully constructed. In the training set

validation set and whole set

the AUC values of the ROC curve of the prognostic signature were all greater than 0.75. The risk score of the signature was related to age

American Joint Committee on Cancer (AJCC) stage and T stage of the patients (

0.05)

but not to their gender. The nomogram established by integrating clinical variables and risk scores confirmed that the signature had a good ability to predict prognosis. GSEA showed that some tumor and autophagy-related pathways were enriched in the high-risk group

while immune-related pathways were enriched in the low-risk group. lncRNA AC005229.4 was highly expressed in bladder cancer cell lines.

Conclusion

An 8-autophagy-related lncRNA signature is effective in the prognostic prediction of patients with bladder cancer.

关键词

Keywords

references

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Tinghao LI

Hang TONG

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Related Institution

Department of Urology, Rongchang Hospital of Traditional Chinese Medicine

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