A Prognostic Model of Bladder Cancer Based on Metabolism-Related Long Non-Coding RNAs

Jintao Hu; Cong Lai; Zefeng Shen; Hao Yu; Junyi Lin; Weibin Xie; Huabin Su; Jianqiu Kong; Jinli Han

doi:10.3389/fonc.2022.833763

A Prognostic Model of Bladder Cancer Based on Metabolism-Related Long Non-Coding RNAs

Front Oncol. 2022 Feb 25:12:833763. doi: 10.3389/fonc.2022.833763. eCollection 2022.

Authors

Jintao Hu^{1

2}, Cong Lai^{1

2}, Zefeng Shen^{1

2}, Hao Yu^{1

2}, Junyi Lin^{1

2}, Weibin Xie^{1

2}, Huabin Su^{1

2}, Jianqiu Kong^{1

2}, Jinli Han^{1

2}

Affiliations

¹ Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
² Guangdong Provincial Clinical Research Center for Urological Diseases, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.

Abstract

Background: Some studies have revealed a close relationship between metabolism-related genes and the prognosis of bladder cancer. However, the relationship between metabolism-related long non-coding RNAs (lncRNA) regulating the expression of genetic material and bladder cancer is still blank. From this, we developed and validated a prognostic model based on metabolism-associated lncRNA to analyze the prognosis of bladder cancer.

Methods: Gene expression, lncRNA sequencing data, and related clinical information were extracted from The Cancer Genome Atlas (TCGA). And we downloaded metabolism-related gene sets from the human metabolism database. Differential expression analysis is used to screen differentially expressed metabolism-related genes and lncRNAs between tumors and paracancer tissues. We then obtained metabolism-related lncRNAs associated with prognosis by correlational analyses, univariate Cox analysis, and logistic least absolute shrinkage and selection operator (LASSO) regression. A risk scoring model is constructed based on the regression coefficient corresponding to lncRNA calculated by multivariate Cox analysis. According to the median risk score, patients were divided into a high-risk group and a low-risk group. Then, we developed and evaluated a nomogram including risk scores and Clinical baseline data to predict the prognosis. Furthermore, we performed gene-set enrichment analysis (GSEA) to explore the role of these metabolism-related lncRNAs in the prognosis of bladder cancer.

Results: By analyzing the extracted data, our research screened out 12 metabolism-related lncRNAs. There are significant differences in survival between high and low-risk groups divided by the median risk scoring model, and the low-risk group has a more favorable prognosis than the high-risk group. Univariate and multivariate Cox regression analysis showed that the risk score was closely related to the prognosis of bladder cancer. Then we established a nomogram based on multivariate analysis. After evaluation, the modified model has good predictive efficiency and clinical application value. Furthermore, the GSEA showed that these lncRNAs affected bladder cancer prognosis through multiple links.

Conclusions: A predictive model was established and validated based on 12 metabolism-related lncRNAs and clinical information, and we found these lncRNA affected bladder cancer prognosis through multiple links.

Keywords: TCGA; bladder cancer; lncRNA; metabolism; nomogram; prognosis.