Comprehensive analysis of mitochondria-related genes indicates that PPP2R2B is a novel biomarker and promotes the progression of bladder cancer via Wnt signaling pathway

Abstract

Bladder cancer (BC) is the fourth and tenth most common malignancy in men and women worldwide, respectively. The complexity of the molecular biological mechanism behind BC is a major contributor to the lack of effective treatment management of the disease. The development and genesis of BC are influenced by mitochondrial retrograde control and mitochondria-nuclear cross-talk. However, the role of mitochondrial-related genes in BC remains unclear. In this study, we analyzed TCGA datasets and identified 752 DE-MRGs in BC samples, including 313 down-regulated MRGs and 439 up-regulated MRGs. Then, the results of machine-learning screened four critical diagnostic genes, including GLRX2, NMT1, PPP2R2B and TRAF3IP3. Moreover, we analyzed their prognostic value and confirmed that only PPP2R2B was associated with clinical prognosis of BC patients and Cox regression assays validated that PPP2R2B expression was a distinct predictor of overall survival in BC patients. Them, we performed RT-PCR and found that PPP2R2B expression was distinctly decreased in BC specimens and cell lines. Functional experiments revealed that overexpression of PPP2R2B distinctly suppressed the proliferation, migration and invasion of BC cells via Wnt signaling pathway. In summary, these research findings offer potential molecular markers for the diagnosis and prognosis of BC, with the discovery of PPP2R2B particularly holding significant biological and clinical significance. This study provides valuable clues for future in-depth investigations into the molecular mechanisms of BC, as well as the development of new diagnostic markers and therapeutic targets.

Keywords: Biomarker; Bladder cancer; Metastasis; Mitochondrial-related genes; PPP2R2B; Wnt signaling pathway.

Fig. 1

Identification and Functional Analysis of DE-MRGs in BC patients. A Through the application of the student’s t-test to GSE13507 datasets, a total of 752 DE-MRGs were discerned in BC specimens when compared to their normal counterparts. B DO analysis of DE-MRGs. C–E Gene Ontology (GO) analysis highlighted the functional roles of the 752 DE-MRGs. F KEGG pathway analysis revealed that the 752 DE-MRGs were significantly linked to various pathways

Fig. 2

Identification and Validation of Diagnostic Genes for BC using Machine Learning Algorithms. A, B Utilizing the LASSO logistic regression algorithm with penalty parameter tuning via tenfold cross-validation, 49 BC-related features were selected from the DE-MRGs (A, B). C KEGG analysis of the 49 diagnostic genes. D, E The SVM-RFE algorithm identified an optimal combination of 14 feature genes to distinguish BC samples from normal samples. F KEGG analysis of the 14 genes. G Intersection of marker genes obtained from the LASSO and SVM-RFE models revealed four common genes (GLRX2, NMT1, PPP2R2B, and TRAF3IP3) for subsequent analysis. H The interacting network of GLRX2, NMT1, PPP2R2B, and TRAF3IP3 was constructed using the Genemania database, illustrating potential functional connections. I A logistic regression model incorporating the seven marker genes demonstrated robust performance, as indicated by ROC curves. J The diagnostic value of the model was validated in the GSE3167 dataset. K The expression patterns of GLRX2, NMT1, PPP2R2B, and TRAF3IP3 in BC samples and normal samples, based on GSE13507 datasets. ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05. NS, not significant

Fig. 3

The expression pattern of GLRX2, NMT1, PPP2R2B and TRAF3IP3 in BC using TCGA datasets. A, B Dysregulated Expression of PPP2R2B and NMT1 in BC. C–F Pan-cancer analysis reveals dysregulation of GLRX2, NMT1, PPP2R2B, and TRAF3IP3 in various tumor types. ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05

Fig. 4

PPP2R2B as a Prognostic Biomarker in BC Patients. A, B Prognostic association of PPP2R2B with five-year overall survival and progression-free survival using Kaplan–Meier survival analysis. C Univariate Cox Proportional Hazard Analysis for PPP2R2B and other clinical factors. D Multivariate Cox Regression Analysis for PPP2R2B. E Construction of nomogram by the PPP2R2B expressions and clinical factors. F The calibration plot. ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05

Fig. 5

Association between PPP2R2B expression and clinical parameters in BC patients. A Scatter plot illustrating the relationship between PPP2R2B expression levels and age and grade in BC patients. Low PPP2R2B expression is associated with advanced age and higher tumor grade. B Heatmap Analysis of Clinical Characteristics in BC Patients Stratified by PPP2R2B Expression. Heatmap representation displaying the distribution of clinical characteristics in BC patients with high and low PPP2R2B expression levels. C, D Pancer analysis of PPP2R2B expression based on the clinical stage and grade. ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05

Fig. 6

Construction of a prognostic signature based on DEGs between BC patients with low or high PPP2R2B expressions. A Differential Expression Analysis: A total of 761 DEGs were identified in BC patients with either high or low PPP2R2B expression levels. B KEGG Pathway Enrichment Analysis of 761 DEGs. C LASSO regression was performed, and the coefficients of the 11 selected DEGs were determined. D The model achieved the best fit when considering 11 out of the 19 DEGs. E The risk score formula was established based on the coefficients of the 11 selected DEGs. F The high-risk group, determined by the risk score model, exhibited significantly poorer overall survival compared to the low-risk group. G ROC analysis was conducted to evaluate the prognostic model's performance over time

Fig. 7

PPP2R2B was low expressed in BC and its overexpression suppressed the proliferation of BC cells. A The expression of PPP2R2B in 10 pairs of BC and normal specimens using RT-PCR. B ROC analysis determining the diagnostic value of PPP2R2B expression in BC patients. C RT-PCR and western blot for the levels of PPP2R2B in five BC cell lines and normal cells. D The expression of PPP2R2B was distinctly increased in RT4 and J82 cells after the transfection of PPP2R2B plasmid. E RT4 and J82 cells that had been transfected with the PPP2R2B plasmid or vector were subjected to CCK-8 assay. F colony formation assays of RT4 and J82 cells. G Apoptosis was identified through the utilization of the TUNEL test. ***p < 0.001, **p < 0.01, *p < 0.05

Fig. 8

PPP2R2B overexpression inhibited tumor growth in vivo. A The image of xenografts isolated from mice one month. B The tumor weights. **p < 0.01

Fig. 9

Overexpression of PPP2R2B suppressed migration and invasion of BC cells via Wnt/β‐catenin/EMT pathway. A The evaluation of cell migration of RT4 and J82 was carried out using wound healing tests following the overexpression of PPP2R2B. B In order to investigate the process of cell invasion in RT4 and J82 cells, transwell invasion experiments were utilized. C Following the overexpression of PPP2R2B, the levels of N‐cadherin, E‐cadherin, MMP‐9, and β‐catenin in RT4 and J82 were determined through the utilization of Western blot detection. **p < 0.01, *p < 0.05