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. 2024 Nov 6;15(1):622.
doi: 10.1007/s12672-024-01505-z.

Prognosis and immunotherapy significances of a cancer-associated fibroblasts-related gene signature in bladder urothelial carcinoma

Xiaobin Chen  1 , Yugen Li  1 , Jing Huang  1 , Qiang Zhang  1 , Chunlin Tan  1 , Yang Liu  1 , Zhongbo Du  2
Affiliations

Prognosis and immunotherapy significances of a cancer-associated fibroblasts-related gene signature in bladder urothelial carcinoma

Xiaobin Chen et al. Discov Oncol. .

Abstract

Background: The biological significance of cancer-associated fibroblasts (CAFs) in bladder urothelial carcinoma (BUC) warrants further investigation. There is an urgent need to explore the predictive utility of CAF-related genes for prognosis in BUC.

Methods: The transcriptome and clinical data of 407 BUC patients in The Cancer Genome Atlas (TCGA) database were analyzed and a prognostic model was established. A total of 476 BUC cases from the E-MTAB-4321 database were used for validation. A risk model was constructed utilizing CAF-related genes through LASSO Cox regression, investigating its association with prognosis, gene mutations, immune cell infiltration, and drug sensitivity in BUC.

Results: We identified five CAF-related genes (EGFL6, NRSN2, SEMA3D, TM4SF1 and TPST1) in both the TCGA and E-MTAB-4321 datasets, and established a prognostic model using LASSO Cox regression. The high-risk group showed a significant correlation with poor survival. Furthermore, the low-risk group exhibited higher tumor mutational burden and lower levels of immune cell infiltration, and this model holds promise for guiding drug selection in BUC patients.

Conclusions: These findings underscore the pivotal role of CAF-related genes in prognostic prediction for BUC patients. Clinical decision-making and tailored therapeutics stand to benefit from these results, providing a valuable reference for future research endeavors.

Keywords: Bioinformatics; Bladder urothelial carcinoma; Immune checkpoint; Prognosis; Tumor immune microenvironment.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Overall survival and WGCNA analysis based on CAF scoring in TCGA and E-MTAB-4321 databases. A Survival analysis in TCGA cohort. B Survival analysis in E-MTAB-4321 cohort. C WGCNA analysis of TCGA cohort. D Correlation analysis between module and CAF scoring. E, F WGCNA analysis of the E-MTAB-432 cohort
Fig. 2
Fig. 2
Construction of the CAF-related gene prognostic signature. A Gene intersections in modules significantly associated with CAFs of TCGA and E-MTAB-4321 cohorts. B The LASSO Cox analysis of CAF-related genes in the training cohort. C The value of the penalty parameter (λ) was determined based on the minimum partial likelihood deviationby tenfold cross-validation. D, E Differential expression of candidate genes between high-risk and low-risk group in training cohort (D) and validation cohort (E)
Fig. 3
Fig. 3
Prognostic analysis of 5 genes signature. A-C Heatmap of 5 CAF-related genes in the training (A), test (B) cohort of TCGA cohort and E-MTAB-4321 (C) cohort. D-F The distributions of the risk score in the training (D), test (E) cohort of TCGA cohort and E-MTAB-4321 (F) cohort. G-L The distributions of OS status and OS of patients between high-risk and low-risk groups, patients in the high-risk group had higher score values and mortality in the training (G, J), test (H, K) cohort of TCGA cohort and E-MTAB-4321 (I, L) cohort. M–O ROCs for 1, 3, and 5 year survival time based on the risk score in the training (M), test (N) cohort of TCGA cohort and E-MTAB-4321 (O) cohort
Fig. 4
Fig. 4
Independent prognostic analysis of the signature and construction of nomogram. A, E The univariate Cox regression analysis in the TCGA (A) and E-MTAB-4321 (E) cohort. B, F The multivariate Cox regression analysis in the TCGA (B) and E-MTAB-4321 (F) cohort. C, G ROC curve of the prognosis of BUC patients in the TCGA and E-MTAB-4321 cohort. D, H c-index curve of the prognosis of BUC patients in the TCGA and E-MTAB-4321 cohort. I The nomogram that includes the risk score and clinic characteristics. J The calibration curves for the predicted 1-, 3-, and 5-years OS rates
Fig. 5
Fig. 5
Functional enrichment analysis. A, B GO enrichment analysis in the TCGA cohort. C, D KEGG enrichment analysis in the TCGA cohort. E, F GO enrichment analysis in the E-MTAB-4321 cohort. G, H KEGG enrichment analysis in the E-MTAB-4321 cohort
Fig. 6
Fig. 6
TME and checkpoint related genes analysis. A The comparison of immune cell fractions between groups of the TCGA cohorts via the ssGSEA method. B, C Radar plots show diferences in immune checkpoints between the high- and low-risk group in the TCGA cohort
Fig. 7
Fig. 7
Tumor mutational burden (TMB) analysis. A, B Top 20 mutated genes shown between the high- and low-risk groups. C Boxplot illustrating TMB differences between the groups. D Survival analysis of overall survival between the TMB subgroups. E TMB score and risk score were combined for survival analysis
Fig. 8
Fig. 8
Differences in TIDE (A), MDSC (B), CAF (C), and TAM M2 (D) between high- and low-risk groups of TCGA cohort. Differences in TIDE (E), MDSC (F), CAF (G), and TAM M2 (H) between high- and low-risk groups of E-MTAB-4321 cohort
Fig. 9
Fig. 9
Drug sensitivity analysis. Correlation analysis between risk scores and IC50 levels of A AKT inhibitor VIII, C Pazopanib, E Sunitinib, G Dasatinib I JNK Inhibitor VIII, and K Mitomycin C. Boxplots depict differences in estimated IC50 levels of B AKT inhibitor VIII, D Pazopanib, F Sunitinib, H Dasatinib J JNK Inhibitor VIII, and L Mitomycin C between high- and low-risk groups
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