Fig 2: Immunotherapy efficacy, TIME characterization, and prognostic implications of RBCK1 in ccRCC patients from the Fudan University Shanghai Cancer Center (FUSCC) proteomics cohort. (A) The TIDE algorithm is used to evaluate two different tumor immune escape mechanisms and was developed in RBCK1high compared with low RBCK1 expression group in both ccRCC and RCC cohorts using Student’s t-test. (B) The expression heatmap of immune checkpoint-related genes and RBCK1 in ccRCC and normal tissues was tested by Kruskal–Wallis, and the different colors represent the expression trend in different samples. (C, D) Immunohistochemistry staining showed RBCK1 expression in tumor (n = 50) and normal kidney samples (n = 40) from a real-world cohort, FUSCC, Shanghai. (E, F) Opal multi-marker immunohistochemistry was used on 10 ccRCC specimens to achieve six biomarker staining. The significance of the two groups of samples passed the Wilcoxon test. (G, H) Prognostic value of RBCK1 in predicting prognosis was assessed for 232 ccRCC patients from the FUSCC cohort based on proteomics sequencing data using the Kaplan–Meier method (**p < 0.01, ***p < 0.001, ****p < 0.0001).

Fig 3: Differentially expressed 24 IFN-γ response genes and its association with clinical features of renal cell carcinoma (RCC). (A) We compared the mRNA expression in clear cell RCC (ccRCC) (n = 530), papillary RCC (pRCC) (n = 323), chromophobia RCC (chRCC) (n = 91), and adjacent normal kidney tissues displayed using a bubble chart. The size of the circle represents statistical significance; red represents significantly high expression in tumor tissue and blue represents low. (B–D) Expressions of RBCK1, PNP, and SELP in RCC samples were observed in comparison with normal samples using unpaired t-test. (E) We summarized the difference of IFN-γ response signatures of mRNA expression between clinical stages in ccRCC, pRCC, and chRCC using Wilcoxon non-parametric test. (F) The univariate Cox survival analysis emphasized the prognostic significance of TRAFD1, SOD2, RIPK2, RBCK1, and MT2A as cancer-promoting factors of ccRCC and pRCC.

Fig 4: Pan-cancer analysis of RBCK1 mRNA differential expression and correlation analysis of the tumor immune microenvironment. (A) The expression distribution of RBCK1 in tumor and normal samples from the TCGA database and GTEx Portal, where the horizontal axis represents different tumor tissues, and the vertical axis represents the gene expression distribution using the Wilcoxon test (*p < 0.05, **p < 0.01, ***p < 0.001). (B) Spearman correlation analysis heatmap of immune score and RBCK1 expression in over 13,000 pan-cancer tissues, where the horizontal axis represents different tumor tissues, and the vertical axis represents different immune scores. The stronger the correlation, the darker the color. (C) The heatmap represents the correlation between RBCK1 expression and immune checkpoints (SIGLEC15, IDO1, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2) in pan-cancer tissues, where the horizontal axis represents different immune checkpoint genes, and the vertical axis represents different tumor tissues (*p < 0.05, **p < 0.01, ***p < 0.001).

Fig 5: Protein networks and single-cell localization of RBCK1. (A) Protein–protein interaction (PPI) network of the 24 IFN-γ response signatures was constructed using GeneMANIA. (B) Three scRNA-seq datasets with detailed cell-type annotation at the single-cell level focusing on the tumor microenvironment for ccRCC. GSE111360 (n = 2, number of cells = 23,130), GSE139555 (n = 3, number of cells = 49,907), and GSE145281_PDL1 (n = 4, number of cells = 44,220) were enrolled with correlation analyzed between RBCK1 expression and abundance of immune cell infiltrations. The higher the expression of RBCK1 in a single-cell subpopulation, the darker the red color is in the heatmap. (C–E) The heatmap showed the relatively high expression of RBCK1 in different cell types, such as CD4+, CD8+ T cells, monocytes, macrophages, and NK cells across the three scRNA-seq datasets.