Identifying PLAU as a shared pathogenic gene in type 2 diabetes and bladder urothelial carcinoma through integrated transcriptomic analysis and machine learning for diagnostic and therapeutic value.

内容摘要

Type 2 diabetes mellitus (T2DM) and bladder urothelial carcinoma (BLCA) are two kinds of diseases that seriously threaten human health. Their pathogenesis is complex and involves the interaction of multiple genes and multiple pathways. Recent epidemiological studies have shown that the risk of BLCA in patients with T2DM is significantly higher than that in non-diabetic people, suggesting that there may be a potential biological correlation between the two. Genomic studies have opened up new ways to reveal the common genetic characteristics of T2DM and BLCA. However, most of the current studies only focus on a single disease, and the comorbidity mechanism of these two diseases still needs to be further explored. Firstly, the datasets of BLCA and T2DM were downloaded from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases respectively. Differential expression genes (DEGs) were identified using the Limma package. Weighted gene co-expression network analysis (WGCNA) was employed to determine the co-expression modules related to BLCA and T2DM, and the common potential target genes were obtained. Correlation analysis and enrichment analysis were conducted on these target genes. Then, the best diagnostic biomarker - plasminogen activator (PLAU) was selected using machine learning algorithms. Additionally, the role of PLAU in the progression of T2DM and BLCA was confirmed through immunohistochemistry, Western Blot, and Edu experiments. Finally, small molecule compounds targeting PLAU were discovered through molecular docking and virtual screening, and the inhibitory effect of these small molecules on the progression of bladder urothelial carcinoma was verified through experiments. This study conducted a combined limma and WGCNA analysis on the T2DM and BLCA datasets to identify 42 common potential target genes, which were enriched in pathways such as innate immunity. Using machine learning algorithms such as LASSO and SVM, PLAU was identified as the best diagnostic marker for T2DM combined with BLCA. It was significantly highly expressed in both T2DM and BLCA samples, and high expression of PLAU predicted a shorter overall survival period for BLCA patients. Experimental results confirmed that PLAU was highly expressed in BLCA tissues and increased with the severity of malignancy. Knockdown (sh-PLAU) of PLAU could inhibit cancer cell proliferation and migration in a high-glucose environment, while overexpression (oe-PLAU) still promoted cancer cell progression in a low-glucose environment. Finally, molecular docking virtual screening revealed that the small molecule compound epigallocatechin gallate (EGCG) could target and inhibit PLAU, and effectively inhibited the proliferation and invasion of BLCA cells in experiments. The results of this study reveal the role of PLAU, a common characteristic gene of T2DM and BLCA, whose high expression drives tumor progression and poor prognosis. Moreover, small molecule drugs targeting PLAU, such as EGCG, have therapeutic potential. This study provides a new direction for accurate diagnosis and treatment of BLCA patients with T2DM.