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. 2024 Jan 3;24(1):6.
doi: 10.1186/s12894-023-01354-y.

Identification of clinical prognostic factors and analysis of ferroptosis-related gene signatures in the bladder cancer immune microenvironment

Jiafu Ma #  1 , Jianting Hu #  2 , Leizuo Zhao  3 , Zixuan Wu  4 , Rongfen Li #  5 , Wentao Deng  6
Affiliations

Identification of clinical prognostic factors and analysis of ferroptosis-related gene signatures in the bladder cancer immune microenvironment

Jiafu Ma et al. BMC Urol. .

Abstract

Background: Bladder cancer (BLCA) is a prevalent malignancy affecting the urinary system and poses a significant burden in terms of both incidence and mortality rates on a global scale. Among all BLCA cases, non-muscle invasive bladder cancer constitutes approximately 75% of the total. In recent years, the concept of ferroptosis, an iron-dependent form of regulated cell death marked by the accumulation of lipid peroxides, has captured the attention of researchers worldwide. Nevertheless, the precise involvement of ferroptosis-related genes (FRGs) in the anti-BLCA response remains inadequately elucidated.

Methods: The integration of BLCA samples from the TCGA and GEO datasets facilitated the quantitative evaluation of FRGs, offering potential insights into their predictive capabilities. Leveraging the wealth of information encompassing mRNAsi, gene mutations, CNV, TMB, and clinical features within these datasets further enriched the analysis, augmenting its robustness and reliability. Through the utilization of Lasso regression, a prediction model was developed, enabling accurate prognostic assessments within the context of BLCA. Additionally, co-expression analysis shed light on the complex relationship between gene expression patterns and FRGs, unraveling their functional relevance and potential implications in BLCA.

Results: FRGs exhibited increased expression levels in the high-risk cohort of BLCA patients, even in the absence of other clinical indicators, suggesting their potential as prognostic markers. GSEA revealed enrichment of immunological and tumor-related pathways specifically in the high-risk group. Furthermore, notable differences were observed in immune function and m6a gene expression between the low- and high-risk groups. Several genes, including MYBPH, SOST, SPRR2A, and CRNN, were found to potentially participate in the oncogenic processes underlying BLCA. Additionally, CYP4F8, PDZD3, CRTAC1, and LRTM1 were identified as potential tumor suppressor genes. Significant discrepancies in immunological function and m6a gene expression were observed between the two risk groups, further highlighting the distinct molecular characteristics associated with different prognostic outcomes. Notably, strong correlations were observed among the prognostic model, CNVs, SNPs, and drug sensitivity profiles.

Conclusions: FRGs are associated with the onset and progression of BLCA. A FRGs signature offers a viable alternative to predict BLCA, and these FRGs show a prospective research area for BLCA targeted treatment in the future.

Keywords: BLCA; CNV; Drug prediction; FRGs; Immunity; SNP; m6a and immune checkpoint.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Framework based on an integration strategy of FRGs
Fig. 2
Fig. 2
Expressions of the 146 FRGs and their interactions (a): A PPI network illustrating the interactions of FRGs (interaction score = 0.9). b: The ferroptosis-related gene correlation network (red line: positive correlation; blue line: negative correlation). c: Mutations in FRGs. 11 genes had a 10% mutation rate, with TP53 being the most often modified (53%)
Fig. 3
Fig. 3
CNV, SNP and mutation analysis. a: Correlation analysis between the expression of genes (TP53, ELF3, KMT2C and SPTAN1) in prognostic signatures and SNP. b The survival analysis of TP53. c, d: The mutation distribution of genes in prognostic signatures. (e): CNV analysis
Fig. 4
Fig. 4
The drug prediction of the model
Fig. 5
Fig. 5
Correlation analysis between the expression of genes (MYBPH, SPRR2A, SOST, and BHMT) in prognostic signatures and drug sensitivity
Fig. 6
Fig. 6
Tumor categorization based on DEGs associated with ferroptosis. a: The consensus clustering matrix (k = 2) was used to divide 414 BLCA patients into two groups. Heatmap (b). The heatmap and clinicopathologic features of the two clusters identified by these DEGs (T, Grade, and Stage indicate the degree of tumor differentiation. P values were showed as:*P < 0.05; **P < 0.01; ***P < 0.001. c: Kaplan–Meier OS curves for the two clusters
Fig. 7
Fig. 7
The development of a risk signature in the TCGA cohort. a: A Univariate Cox regression analysis of OS for each ferroptosis-related gene, with P < 0.05 for 14 genes. b: Regression of the 14 OS-related genes using LASSO. c: Cross-validation is used in the LASSO regression to fine-tune parameter selection. d: The patient's chance of survival (low-risk population: on the left side of the dotted line; high-risk population: on the right side of the dotted line). e: Kaplan–Meier curves for patients in the high- and low-risk groups' OS. f: The AUC for predicting the 1-, 3-, and 5-year survival rates of BLCA. g: A PCA plot based on the risk score for BLCAs. h: A t-SNE plot based on the risk score for BLCAs
Fig. 8
Fig. 8
The risk model was validated in the GEO cohort. a: Each patient's chance of survival (low-risk population: on the left side of the dotted line; high-risk population: on the right side of the dotted line). b: Kaplan–Meier curves for patients in the high- and low-risk groups' overall survival. c: The AUC for predicting the 1-, 3-, and 5-year survival rates of BLCA. d: A PCA plot based on the risk score for BLCA. e: A t-SNE plot based on the risk score for BLCA
Fig. 9
Fig. 9
Cox regression analysis, both univariate and multivariate. a TCGA cohort multivariate analysis. b: TCGA cohort univariate analysis. c: GEO cohort multivariate analysis. d: GEO cohort univariate analysis. e: Heatmap (green: low expression; red: high expression) illustrating the relationships between clinicopathologic characteristics and risk groups (*P < 0.05; **P < 0.01; ***P < 0.001)
Fig. 10
Fig. 10
For FRGs, GO, and KEGG analyses were performed. a: The GO circle illustrates the scatter map of the selected gene's logFC. b: the KEGG circle illustrates the scatter map of the logFC of the indicated gene. The greater the Z-score value, the greater the expression of the enriched pathway
Fig. 11
Fig. 11
FRG gene set enrichment studies. The top six enriched functions or pathways of each cluster were provided to illustrate the distinction between related activities or pathways in various samples. The 'nod like receptor signaling pathway' was the most enriched. FDR q-value and FWER p-value were both < 0.05
Fig. 12
Fig. 12
The ssGSEA scores are compared. a + b: Comparison of the enrichment scores of 16 kinds of immune cells and 13 immune-related pathways in the TCGA cohort between the low-risk (green box) and high-risk (red box) groups. c + d: In the GEO cohort, tumor immunity was compared between the low-risk (blue box) and high-risk (red box) groups. P values were shown as follows: ns not significant; *P < 0.05; **P < 0.01; ***P < 0.001
Fig. 13
Fig. 13
a Immune checkpoint expression in high and low BLCA risk groups. b. The expression of m6a-related genes differed across groups with high and low BLCA risk
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