Yongkun Luan

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: Disulfidptosis and the disulfidptosis-related gene SLC7A11 have recently attracted significant attention for their role in tumorigenesis and tumour management. However, its association with adrenocortical carcinoma (ACC) is rarely discussed. Methods: Differential analysis, Cox regression analysis, and survival analysis were used to screen for the hub gene SLC7A11 in the TCGA and GTEx databases and disulfidptosis-related gene sets. Then, we performed an association analysis between SLC7A11 and clinically relevant factors in ACC patients. Univariate and multivariate Cox regression analyses were performed to evaluate the prognostic value of SLC7A11 and clinically relevant factors. Weighted gene coexpression analysis was used to find genes associated with SLC7A11. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and the LinkedOmics database were used to analyse the functions of SLC7A11-associated genes. The CIBERSORT and Xcell algorithms were used to analyse the relationship between SLC7A11 and immune cell infiltration in ACC. The TISIDB database was applied to search for the correlation between SLC7A11 expression and immune chemokines. In addition, we performed a correlation analysis for SLC7A11 expression and tumour mutational burden and immune checkpoint-related genes and assessed drug sensitivity based on SLC7A11 expression. Immunohistochemistry and RT‒qPCR were used to validate the upregulation of SLC7A11 in the ACC. Results: SLC7A11 is highly expressed in multiple urological tumours, including ACC. SLC7A11 expression is strongly associated with clinically relevant factors (M-stage and MYL6 expression) in ACC. SLC7A11 and the constructed nomogram can accurately predict ACC patient outcomes. The functions of SLC7A11 and its closely related genes are tightly associated with the occurrence of disulfidptosis in ACC. SLC7A11 expression was tightly associated with various immune cell infiltration disorders in the ACC tumour microenvironment (TME). It was positively correlated with the expression of immune chemokines (CXCL8, CXCL3, and CCL20) and negatively correlated with the expression of immune chemokines (CXCL17 and CCL14). SLC7A11 expression was positively associated with the expression of immune checkpoint genes (NRP1, TNFSF4, TNFRSF9, and CD276) and tumour mutation burden. The expression level of SLC7A11 in ACC patients is closely associated withcthe drug sensitivity. Conclusion: In ACC, high expression of SLC7A11 is associated with migration, invasion, drug sensitivity, immune infiltration disorders, and poor prognosis, and its induction of disulfidptosis is a promising target for the treatment of ACC.