Lipid metabolism subtypes reflects the prognosis and immune profiles of bladder cancer patients by NMF clustering and building related signature

Figures

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  Fig. 1. The analysis process of this study

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  Fig. 2. Identification of lipid metabolism subtypes in BLCA patients by NMF analysis. A Heatmap of DEGs related with lipid metabolism; B Two molecular subtypes (cluster 1 and cluster 2) were identified based on K = 2 as the optimal value; OS (C) and PFS (D) between cluster 1 and cluster 2

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  Fig. 3. The immune cell infiltration among two clusters (cluster 1 and cluster 2). A The immune scores of cluster 1 vs cluster 2 using MCPcounter algorithm; B Immunophenotyping analysis of cluster 1 and cluster 2

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  Fig. 4. Construction and validation of LMGs-related signature. A, B LASSO regression analysis and regression coefficient; C Kaplan–Meier curve of OS in training dataset; D AUC values of 1-, 3-, 5-year OS in training dataset; E Kaplan–Meier curve of OS in testing dataset; F AUC values of 1-, 3-, 5-year OS in testing dataset; G Kaplan–Meier curve of OS in overall dataset; H AUC values of 1-, 3-, 5-year OS in overall dataset; I Kaplan–Meier curve of OS in GEO dataset; J AUC values of 1-, 3-, 5-year OS in GEO dataset

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  Fig. 5. The survival outcomes of HR and LR groups were stratified by various clinicopathological features including age (A, B), gender (C, D), stage (E, F), T stage (G, H), N stage (I, J) and M stage (K, L)

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  Fig. 6. Nomogram construction in TCGA database. A Construction of a prognostic nomogram to predict 1-, 3-, 5-year OS of BLCA patients; B AUC values of nomogram, risk and clinicopathological features; C the calibration curve of nomogram; D decision curve of nomogram and other clinical indicators; C-index (E) and RMS curves (F) analysis of our model and several models

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  Fig. 7. Clinical influences of risk score for BLCA patients including age (A), gender (B), stage (C), grade (D), T stage (E), N stage (F), M stage (G) and response (H)

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  Fig. 8. Immune checkpoints (A), immunotherapy response (B) and anti-tumor drug sensitivity (C) between the LR group and the HR group

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  Fig. 9. Tumor mutational burden between the LR group and the HR group. A, B The top 10 most frequently mutated genes; C low-TMB had a shorter OS than high-TMB; D high-TMB had a better OS in both the LR and HR groups than low-TMB

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  Fig. 10. The relationship between risk score, TMB score and immune cells (A); the correlation of risk score and immune cells (B); the functional enrichment analysis of DEGs including GO analysis (C, D) and KEGG analysis (E–F)

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  Fig. 11. Validation expression patterns of signature-related eight LMGs including AKR1B1, SCD, CYP27B1, UGCG, SGPL1, FASN, TNFAIP8L3, PLA2G2A between BLCA tissues and paracancerous tissues in TCGA database

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  Fig. 12. Verify the expression of AKR1B1, SCD, UGCG, SGPL1, FASN, TNFAIP8L3, PLA2G2A in BLCA and normal bladder tissues from HPA database