Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence

ABSTRACT

Bladder cancer (BLCA) is one of the most common cancers worldwide with high recurrence rate. Hence, we intended to establish a recurrence-related long non-coding RNA (lncRNA) model of BLCA as a potential biomarker based on multi-omics analysis. Multi-omics data including copy number variation (CNV) data, mutation annotation files, RNA expression profiles and clinical data of The Cancer Genome Atlas (TCGA) BLCA cohort (303 cases) and GSE31684 (93 cases) were downloaded from public database. With multi-omics analysis, twenty lncRNAs were identified as the candidates related with BLCA recurrence, CNVs and mutations in training set. Ten-lncRNA signature were established using least absolute shrinkage and selection operation (LASSO) and Cox regression. Then, various survival analysis was used to assess the power of lncRNA model in predicting BLCA recurrence. The results showed that the recurrence-free survival time of high-risk group was significantly shorter than that of low-risk group in training and testing sets, and the predictive value of ten-lncRNA signature was robust and independent of other clinical variables. Gene Set Enrichment Analysis (GSEA) showed this signature were associated with immune disorders, indicating this signature may be involved in tumor immunology. After compared with the other reported lncRNA signatures, ten-lncRNA signature was validated as a superior prognostic model in predicting the recurrence of BLCA. The effectiveness of the model was also evaluated in bladder cancer samples via qRT-PCR. Thus, the novel ten-lncRNA signature, constructed based on multi-omics data, had robust prognostic power in predicting the recurrence of BLCA and potential clinical implications as biomarkers.

KEYWORDS:

• Multi-omics data
• copy number variation
• mutation
• bladder cancer recurrence
• long non-coding rnas¹

Research highlights

Multi-omics analysis on CNV, mutation annotation, RNA expression and clinical data.

Constructing a novel and robust lncRNA signature to predict BLCA recurrence.

The lncRNA signature may be associated with tumor immunology.

AGAP2-AS1 knockdown could inhibit cell proliferation and migration in BLCA cells.

Acknowledgements

We acknowledge TCGA and GEO database for providing their platforms and contributors for uploading their meaningful datasets. The authors wish to thank Dr. Wang Keyi from Shanghai Tenth People’s Hospital of Tongji University for his help in collecting clinical samples and relevant experimental validation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Original data were uploaded to a recognized data repository (URL: https://zenodo.org/record/5153530#.YZtEZU5ByUk; DOI: 10.5281/zenodo.5153530)

Author statement

Zhipeng Xu: Conceptualization, Methodology, Software, Writing - original draft preparation. Hui Chen: Data curation, Formal analysis, Writing - original draft preparation. Jin Sun: Validation, Writing - review and editing. Weipu Mao: Software, Visualization. Shuqiu Chen: Funding acquisition, Supervision. Ming Chen: Conceptualization, Funding acquisition, Supervision.

Supplementary material

Supplemental data for this article can be accessed here

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (81872089, 81672551, 81670632, 81871157, 82070773, 82102831); Jiangsu Provincial Key Research and Development Program (BE2019751), Natural Science Foundation of Jiangsu Province (BK20201271) and The National Key Research and Development Program of China (SQ2017YFSF090096).