Pheochromocytoma/paraganglioma (PGPG) is a rare neuroendocrine tumor. Amino acid metabolism is crucial for energy production, redox balance, and metabolic pathways in tumor cell proliferation. This study aimed to build a risk model using amino acid metabolism-related genes, enhancing PGPG diagnosis and treatment decisions. We analyzed RNA-sequencing data from the PCPG cohort in the GEO dataset as our training set and validated our findings using the TCGA dataset and an additional clinical cohort. WGCNA and LASSO were utilized to identify hub genes and develop risk prediction models. The single-sample gene set enrichment analysis, MCPCOUNTER, and ESTIMATE algorithm calculated the relationship between amino acid metabolism and immune cell infiltration in PCPG. The TIDE algorithm predicted the immunotherapy efficacy for PCPG patients. The analysis identified 292 genes with differential expression, which are involved in amino acid metabolism and immune pathways. Six genes (DDC, SYT11, GCLM, PSMB7, TYRO3, AGMAT) were identified as crucial for the risk prediction model. Patients with a high-risk profile demonstrated reduced immune infiltration but potentially higher benefits from immunotherapy. Notably, DDC and SYT11 showed strong diagnostic and prognostic potential. Validation through quantitative Real-Time Polymerase Chain Reaction and immunohistochemistry confirmed their differential expression, underscoring their significance in PCPG diagnosis and in predicting immunotherapy response. This study’s integration of amino acid metabolism-related genes into a risk prediction model offers critical clinical insights for PCPG risk stratification, potential immunotherapy responses, drug development, and treatment planning, marking a significant step forward in the management of this complex condition.

Background Previous observational studies have indicated potential associations between certain dietary factors and the incidence of kidney cancer. However, the causal relationship between these elements remains unknown. This study is aimed to investigate the causal relationship between dietary factors and the incidence of kidney cancer through Mendelian randomization (MR) analysis. Methods The study utilized genome-wide association study (GWAS) summary data from the GWAS Catalog and the IEU Open GWAS project databases. The exposure factors included consumption of dessert (n = 20,622), fruit (n = 421,155), alcohol (n = 462,346), and salt (n = 323,995). The outcomes were based on GWAS data for kidney cancer (cases = 1,103, controls = 455,245). Three MR methods, including Inverse Variance Weighted (IVW), MR-Egger, and Weighted Median, were used in the study, with IVW serving as the primary method. Sensitivity analyses such as the MR-Egger intercept test, Cochran’s Q test, and leave-one-out analysis were performed to detect heterogeneity and pleiotropy. Results The IVW method demonstrated a significant causal relationship between frequent alcohol consumption (OR = 1.61, 95% CI: 1.02–2.54, P = 0.040) and kidney cancer. In contrast, no causal associations were found with the consumption of alcohol (OR = 1.12, 95% CI: 0.91–1.37, P = 0.280), dessert (OR = 1.23, 95% CI: 0.71–2.12, P = 0.465), fruit (OR = 1.33, 95% CI: 0.59-3.00, P = 0.494) or salt (OR = 0.01, 95% CI: 0.00-1.49, P = 0.073). The sensitivity analyses did not reveal any evidence of pleiotropy. Conclusion This MR study suggests that frequent alcohol consumption can increase the risk of kidney cancer, whereas no significant causal relationships were detected between the consumption of dessert, fruit or salt and the risk of kidney cancer.