Phosphorus Metabolism-Related Genes Serve as Novel Biomarkers for Predicting Prognosis in Bladder Cancer: A Bioinformatics Analysis

Yang  He; Abai  Xu; Li  Xiao; Ying  Yang; Boping  Li; Zhe  Liu; Peng  Rao; Yicheng Wang; Li Ruan; Tao  Zhang

doi:10.18502/ijph.v53i9.16449

Yang He Department of Urology, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China
Abai Xu Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
Li Xiao Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
Ying Yang Department of Hematology, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China
Boping Li Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
Zhe Liu Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
Peng Rao Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
Yicheng Wang Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
Li Ruan Department of Urology, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China
Tao Zhang Department of Urology, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China

DOI: https://doi.org/10.18502/ijph.v53i9.16449

Keywords: Bladder cancer (BLCA); Phosphorus metabolism; Prognosis; Nomogram

Abstract

Background: Phosphorus metabolism might be associated with tumor initiation and progression. We aimed to screen out the phosphorus metabolism genes related to bladder cancer and construct a clinical prognosis model.

Methods: The dataset used for the analysis was obtained from TCGA database. GO and KEGG enrichment analyses were subsequently applied to differentially expressed genes. Consensus clustering was utilized, and different clusters of the tumor immune microenvironment and other features were compared. The phosphorus metabolism-related genes involved in prognosis were screened out by univariate Cox regression, LASSO regression and multivariate Cox regression analysis, and a nomogram was constructed. The performance of the nomogram was validated using TCGA dataset and the GEO dataset, respectively.

Results: Overall, 405 phosphorus metabolism-related differentially expressed genes from TCGA database were identified, which were associated with phosphorylation, cell proliferation, leukocyte activation, and signaling pathways. Two clusters were obtained by consistent clustering. After tumor immune microenvironment analysis, significant differences in immune cell infiltration between cluster 1 and cluster 2 were found. Four phosphorus metabolism-related genes (LIME1, LRP8, SPDYA, and MST1R) were associated with the prognosis of bladder cancer (BLCA) patients. We built a prognostic model and visualized the model as a nomogram. Calibration curves demonstrated the performance of this nomogram, in agreement with that shown by the ROC curves.

Conclusion: We successfully identified four phosphorus metabolism-related genes associated with prognosis, providing potential targets for biomarkers and therapeutics. A nomogram based on these genes was developed. Nevertheless, this study is based on bioinformatics, and experimental validation remains essential.