Unknown

Dataset Information

0

A prognostic index based on a fourteen long non-coding RNA signature to predict the recurrence-free survival for muscle-invasive bladder cancer patients.


ABSTRACT:

Background

Bladder cancer (BC) is regarded as one of the most fatal cancer around the world. Nevertheless, there still lack of sufficient markers to predict the prognosis of BC patients. Herein, we aim to establish a prognosis predicting signature based on long-noncoding RNA (lncRNA) for the invasive BC patients.

Methods

The lncRNA expression profile was downloaded from The Cancer Genome Atlas (TCGA) database, along with the correlated clinicopathological information. The univariate Cox regression test was employed to screen out the recurrence-free survival (RFS)-related lncRNAs. Then, the LASSO method was conducted to construct the signature based on these RFS-related lncRNA candidates. Genes correlated with these fourteen lncRNAs were extracted from the mRNA expression profile, with the Pearson correlation coefficient?>?0.60 or?ResultsWe established an lncRNA-based RFS predicting signature by the LASSO Cox regression test, and proved its usage and stability on both the training and validation cohorts by the Kaplan-Meier and receiver operating characteristic (ROC) curves. Notably, the multivariate Cox regression analysis found that our classifier was an independent indicator for muscle-invasive BC patients rather than sex, age and tumor grade, with higher predictive value than the existing ones. Besides, we did the pathway analyses for these genes that highly correlated with the proposed fourteen lncRNAs, as well as the differentially expressed genes (DEGs) derived from the high-risk vs. low-risk groups, and the recurrence vs. non-recurrence groups, respectively. Notably, these results were consistent, and these genes were mostly enriched in the transcription factors, G protein-coupled receptors, MAPK signaling pathways, which were proved significantly associated with tumor progression and drug resistance.

Conclusions

Our results suggested that the fourteen-lncRNA-based RFS predicting signature is an independent indicator for BC patients. Further prospective studies with more samples are needed to verify our findings.

SUBMITTER: Zhang X 

PROVIDER: S-EPMC7346316 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

altmetric image

Publications

A prognostic index based on a fourteen long non-coding RNA signature to predict the recurrence-free survival for muscle-invasive bladder cancer patients.

Zhang Xiaolong X   Zhang Meng M   Zhang Xuanping X   Zhu Xiaoyan X   Wang Jiayin J  

BMC medical informatics and decision making 20200709 Suppl 3


<h4>Background</h4>Bladder cancer (BC) is regarded as one of the most fatal cancer around the world. Nevertheless, there still lack of sufficient markers to predict the prognosis of BC patients. Herein, we aim to establish a prognosis predicting signature based on long-noncoding RNA (lncRNA) for the invasive BC patients.<h4>Methods</h4>The lncRNA expression profile was downloaded from The Cancer Genome Atlas (TCGA) database, along with the correlated clinicopathological information. The univaria  ...[more]

Similar Datasets

2024-02-01 | GSE224248 | GEO
| S-EPMC5760628 | biostudies-literature
2014-04-07 | E-MEXP-3625 | biostudies-arrayexpress
| S-EPMC7044477 | biostudies-literature
| S-EPMC8602174 | biostudies-literature
| S-EPMC6756879 | biostudies-literature
| S-EPMC7917237 | biostudies-literature
| S-EPMC8328735 | biostudies-literature