Identification of plasma cell infiltration-related gene signatures as a novel prognostic model for clear cell renal cell carcinoma.

This study aimed to identify key genes regulating plasma cell (PC) infiltration in kidney renal clear cell carcinoma (KIRC) and construct a novel prognostic model for predicting KIRC. Clear cell renal cell carcinoma is the most common malignant tumor type of the kidney, with an increasing incidence rate and low survival rates in advanced patients. Plasma cells (PCs), as terminally differentiated B cells, produce highly specific antibodies that effectively target and kill tumors through the antibody-dependent cellular cytotoxicity (ADCC) mechanism. Growing evidence has shown that PC infiltration is closely associated with the progression of various malignant tumors, including ccRCC. Therefore, identifying PC infiltration-related biomarkers is of great significance for the prognosis and treatment of ccRCC patients. Machine learning was used to determine PC-related key genes in KIRC patients. A prognostic model termed PC score was developed using TCGA and ArrayExpress data and validated in external cohorts. The molecular background, immune characteristics, and drug sensitivity of the high PC score group were evaluated. Single-cell sequencing was employed to assess the expression of hub genes in KIRC patients. We identified 9 hub genes associated with PC infiltration, including 3 risk genes (ADAM8, KCNN4, and TCIRG1) and 6 protective genes (RAG1, ATPEV1D, CDKL2, RUNDC3B, SLC30A9, and PPARGC1A), and constructed a PC score based on these key genes. Older age, advanced TNM stage, and higher PC score were independent predictors of shorter overall survival. A nomogram model integrating age, stage, and PC score showed significantly higher predictive value than staging alone (P < 0.01). The high PC score group exhibited a higher abundance of immune cells (e.g., activated B cells, activated CD8 + T cells) in the tumor microenvironment. Drug sensitivity analysis revealed that tyrosine kinase inhibitors (e.g., ceritinib, imatinib) potently inhibited cancer cell lines in the high PC score group, while inhibitors like acalabrutinib were effective in the low PC score group. Patients with higher risk scores showed greater sensitivity to ofloxacin and cortivazol (a cortisol hormone). Expression of hub genes in KIRC patients was validated using a local cohort and single-cell sequencing. We identified key genes regulating PC infiltration in KIRC and proposed a predictive model that effectively identifies high-risk KIRC patients.