He Yang, Xu Abai, Xiao Li, Yang Ying, Li Boping, Liu Zhe, Rao Peng, Wang Yicheng, Ruan Li, Zhang Tao
Department of Urology, Guangzhou Red Cross Hospital, Jinan University, Guangzhou, China.
Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Iran J Public Health. 2024 Sep;53(9):1935-1950. doi: 10.18502/ijph.v53i9.16449.
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.
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.
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 (, and ) 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.
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.
磷代谢可能与肿瘤的发生和进展有关。我们旨在筛选出与膀胱癌相关的磷代谢基因，并构建一个临床预后模型。
用于分析的数据集来自TCGA数据库。随后对差异表达基因进行GO和KEGG富集分析。采用一致性聚类，并比较肿瘤免疫微环境的不同聚类和其他特征。通过单因素Cox回归、LASSO回归和多因素Cox回归分析筛选出与预后相关的磷代谢基因，并构建列线图。分别使用TCGA数据集和GEO数据集验证列线图的性能。
总体而言，从TCGA数据库中鉴定出405个与磷代谢相关的差异表达基因，这些基因与磷酸化、细胞增殖、白细胞活化和信号通路有关。通过一致性聚类获得两个聚类。经过肿瘤免疫微环境分析，发现聚类1和聚类2之间免疫细胞浸润存在显著差异。四个与磷代谢相关的基因（ 、 和 ）与膀胱癌（BLCA）患者的预后相关。我们构建了一个预后模型，并将该模型可视化为列线图。校准曲线展示了该列线图的性能，与ROC曲线所示一致。
我们成功鉴定出四个与预后相关的磷代谢基因，为生物标志物和治疗提供了潜在靶点。基于这些基因开发了一个列线图。然而，本研究基于生物信息学，实验验证仍然至关重要。
