PAICS as a diagnostic and prognostic biomarker in bladder cancer: bioinformatics and molecular dynamics insights

Bladder cancer (BCa) is a common urinary malignancy characterized by high recurrence and mortality rates. Metabolic reprogramming, including dysregulation of nucleotide metabolism, contributes to tumor cell proliferation and disease progression. PAICS (phosphoribosylaminoimidazole succinocarboxamide synthetase) is a key enzyme involved in de novo purine biosynthesis; however, its expression pattern, clinical significance, and potential biological relevance in BCa remain incompletely elucidated. This study integrated high-throughput sequencing data from the TCGA, GEO, and GTEx databases. Differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning algorithms, including LASSO regression and Random Forest, were used to screen candidate genes and identify PAICS as a key gene for further analysis. Survival analysis, immune infiltration profiling, Gene Set Enrichment Analysis (GSEA), ssGSEA, somatic mutation analysis, single-cell transcriptomic analysis, drug sensitivity correlation analysis, molecular docking, and molecular dynamics simulations were further performed to evaluate the clinical value, biological relevance, and potential drug-binding characteristics of PAICS in BCa. PAICS was significantly upregulated in BCa tissues and showed predominant expression in tumor epithelial cells. PAICS demonstrated favorable diagnostic performance and was included in a PAICS/S100A6-based prognostic model associated with patient survival. Bioinformatics analyses suggested that PAICS overexpression may be associated with metabolic and biosynthetic programs, including oxidative phosphorylation, ribosome, proteasome, aerobic respiration, cellular respiration, protein–RNA complex assembly, and rRNA processing. PAICS expression was also associated with altered immune infiltration patterns, including positive correlations with Macrophages M0 and resting mast cells and negative correlations with activated mast cells, follicular helper T cells, monocytes, CD8 + T cells, and regulatory T cells. In addition, in silico drug sensitivity analysis, molecular docking, and molecular dynamics simulations suggested that YM201636 may represent a candidate PAICS-binding compound with favorable predicted binding affinity and structural stability. This study identifies PAICS as a potential diagnostic and prognostic biomarker for BCa. The findings suggest that PAICS may be associated with metabolic/biosynthetic activity, immune microenvironment remodeling, and tumor epithelial cell biology in BCa. YM201636 was identified as a candidate PAICS-binding compound through in silico analyses. Further in vitro and in vivo studies are required to validate the biological function and therapeutic relevance of PAICS in bladder cancer.