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Fig. 5 | Genome Medicine

Fig. 5

From: A novel molecular signature identifies mixed subtypes in renal cell carcinoma with poor prognosis and independent response to immunotherapy

Fig. 5

Risk prediction for RCC based on PSA and the RCC-R score. AC PSA were used as predictors of survival in C3 (n = 864). A prognostic index (PI), which differentiated the individual risk of patients based on ccRCC- and pRCC-score, was calculated as detailed in the “Methods” section. For samples without available survival data, the PI was predicted. Samples with \({P}_{psa}>0.05\) are marked in gray. Hazard ratios (HR) were obtained by exponentiating the PI. A Combinations of ccRCC- and pRCC-score values are colored according to their HR. Points in the corners represent 201 (bottom right), 165 (top left), and 48 (bottom left) cases, respectively. B Principal component analysis plot is shown with samples colored according to their HR. C Distributions of HR in distinct, pathologically defined subtypes (n = 805) are displayed. Samples with \({P}_{psa}>0.05\) are not shown here. Per histological subtype, a Cox regression of cancer-specific survival (CSS) on the respective subset of PI was conducted. Log-rank P-values are indicated by the level of significance: “***” P < 0.001, “**” P < 0.01, “*” P < 0.05, “.” P < “0.1”. DF Relationship of cancer-specific survival (CSS) and the ccRCC-score, termed as RCC-R score, is shown. D The curve displays the estimated relationship as specified in Eq. 1 in the “Methods” section between the RCC-R score, modeled via cubic polynomials, and the PI in C3 (n = 828). Using conditional inference trees with endpoint CSS, the PI was categorized into three risk groups (good (n = 290), intermediate (n = 480), and poor (n = 58)). Corresponding P-values from recursive binary splitting are indicated. E Kaplan–Meier curves of CSS for risk groups based on the RCC-R score are shown for the discovery cohort (C3). Additionally, HR with the good group as reference are specified for the intermediate and the poor group. F Kaplan–Meier curves of CSS for risk groups based on the RCC-R score in the validation cohort (C5, n = 241) are shown by colored curves. Corresponding Kaplan–Meier curves for C3 are added for comparison. Indicated HR and log-rank test P-value result from Cox regression analysis in C5. GI Relationship of RCC-R score with established molecular signatures. Risk groups derived from the RCC-R score in cohort C3 were compared with different molecular-based classifications or signatures available for the combined TCGA RCC or the KIRC cohort. G Bar chart showing distribution (%) of nine major genomic subtypes of RCC (as established by multi-omics analysis [67]) per risk group (good (n = 290), intermediate (n = 480), and poor (n = 58)). H Boxplots showing immune infiltration as predicted by the ESTIMATE method [70] per risk group (good (n = 290), intermediate (n = 480), and poor (n = 58)). I Bar chart showing distribution (%) of four immune subtypes of ccRCC [71] per risk group (good (n = 17), intermediate (n = 421), and poor (n = 15))

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