Pyrimidine metabolism regulator-mediated molecular subtypes display tumor microenvironmental hallmarks and assist precision treatment in bladder cancer

Abstract

Background: Bladder cancer (BLCA) is a common urinary system malignancy with a significant morbidity and death rate worldwide. Non-muscle invasive BLCA accounts for over 75% of all BLCA cases. The imbalance of tumor metabolic pathways is associated with tumor formation and proliferation. Pyrimidine metabolism (PyM) is a complex enzyme network that incorporates nucleoside salvage, de novo nucleotide synthesis, and catalytic pyrimidine degradation. Metabolic reprogramming is linked to clinical prognosis in several types of cancer. However, the role of pyrimidine metabolism Genes (PyMGs) in the BLCA-fighting process remains poorly understood.

Methods: Predictive PyMGs were quantified in BLCA samples from the TCGA and GEO datasets. TCGA and GEO provided information on stemness indices (mRNAsi), gene mutations, CNV, TMB, and corresponding clinical features. The prediction model was built using Lasso regression. Co-expression analysis was conducted to investigate the relationship between gene expression and PyM.

Results: PyMGs were overexpressed in the high-risk sample in the absence of other clinical symptoms, demonstrating their predictive potential for BLCA outcome. Immunological and tumor-related pathways were identified in the high-risk group by GSWA. Immune function and m6a gene expression varied significantly between the risk groups. In BLCA patients, DSG1, C6orf15, SOST, SPRR2A, SERPINB7, MYBPH, and KRT1 may participate in the oncology process. Immunological function and m6a gene expression differed significantly between the two groups. The prognostic model, CNVs, single nucleotide polymorphism (SNP), and drug sensitivity all showed significant gene connections.

Conclusions: BLCA-associated PyMGs are available to provide guidance in the prognostic and immunological setting and give evidence for the formulation of PyM-related molecularly targeted treatments. PyMGs and their interactions with immune cells in BLCA may serve as therapeutic targets.

Keywords: BLCA; CNV; PyMGs; SNP; drug prediction; immunity; m 6 A and immune checkpoint.

Figure 1

Framework based on an integration strategy of PyMGs. The data of BLCA patients were obtained from TCGA and GEO databases, and then the PyMGs were matched to carry out difference analysis and risk model construction, respectively. TCGA data set was used as the main body and GEO data were used to verify the model with good grouping, and PyMGs related to the prognosis of BLCA patients were obtained. Then, GO, KEGG and GSEA analyses were performed with multiple databases to obtain the functions related to PyMGs. Last, the immune cells, function and RNA changes were analyzed.

Figure 2

Expressions of the 76 PyMGs and their interactions (A) A PPI network illustrating the interactions of PyMGs (interaction score=0.7). (B): The PyMGs correlation network (red line: positive correlation; blue line: negative correlation). (C) Mutations in PyMGs. 13 genes over a 5% mutation rate, with POLR2K being the most often modified (15%). (D) The correlation network of the PyMGs (red line: positive correlation; blue line: negative correlation. The depth of the colors reflects the strength of the relevance).

Figure 3

CNV, SNP and mutation analysis.(A) Correlation study of gene expression in prognostic signatures and SNP. (B) The survival analysis of TP53. (C, D): The mutation distribution of genes in prognostic signatures. (E) CNV analysis.

Figure 4

Tumor categorization based on DEGs associated with PyM. (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. (C) Kaplan-Meier OS curves for the two clusters.

Figure 5

The development of a risk signature in the TCGA cohort. (A) A Univariate Cox regression analysis of OS for each PyMGs, with P<0.05 for 7 genes. (B) Regression of OS-related genes. (C): Cross-validation is used in the LASSO regression to fine-tune parameter selection. (D) The patient’s chance of survival. (E) Kaplan-Meier curves. (F) The AUC for predicting the 1-, 3-, and 5-year survival rates. (G) A PCA plot. (H) A t-SNE plot. (J, K) Nomogram.

Figure 6

The risk model was validated in the GEO cohort. (A) Each patient’s chance of survival. (B) Kaplan-Meier curves for patients. (C) The AUC for predicting the 1-, 3-, and 5-year survival rates of BLCA. (D): A PCA plot. (E): A t-SNE plot.

Figure 7

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 illustrating the relationships between clinicopathologic characteristics and risk groups P values were showed as: ns not significant; *P < 0.05; **P < 0.01; ***P < 0.001.

Figure 8

For PyMGs, GO, and KEGG analyses were performed. GO and KEGG analyses for genes participating in autophagy. (A): Barplot graph for KEGG pathways (the longer bar means the more genes enriched, and the increasing depth of red means the differences were more obvious); The KEGG circle shows the scatter map of the logFC of the specified gene. The higher the Z-score value indicated, the higher expression of the enriched pathway. (B): Bubble graph for GO enrichment (the bigger bubble means the more genes enriched, and the increasing depth of red means the differences were more obvious; q-value: the adjusted p-value); The GO circle shows the scatter map of the logFC of the specified gene.

Figure 9

PyMGs 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.

Figure 10

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. (E) Immune checkpoint.

Figure 11

mRNA chemical modifications. (A) m6A (HNRNPC, FTO, ALKBH5, WTAP, and RBM15 were significantly more significant in the high-risk group. In the low-risk group, YTHDC2, METTL3, and RBM15 were significantly more significant). (B) M1A (ALKBH3 was substantially more significant in the high-risk group). (C) M7G (IFIT5, AGO2, GEMIN5, LARP1, NCBP1, NUDT11, NSUN2, and EIF4E were substantially more significant in the high-risk group). (D) M5C (TRDMT1, DNMT1, YBX1, and ALYREF were substantially more significant in the high-risk group). P values were showed as: ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001.