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Background: Bladder cancer is very common worldwide. PIGT is a subunit of the glycosylphosphatidylinositol transamidase which involves in tumorigenesis and invasiveness. m6A modification of mRNA has been linked to cell proliferation, tumor progression and other biological events. However, how PIGT is regulated and what is the function of PIGT in bladder cancer remains to be elucidated. Methods: PIGT was silenced or overexpressed to study its role in regulating bladder cancer. Cell proliferation and invasion were examined with the Cell Counting Kit-8, colony formation and Transwell assay, respectively. Cellular oxygen consumption rates or extracellular acidification rates were detected by a XF24 Analyzer. Quantitative RT-PCR and immunoblots were performed to detect mRNA and protein levels. Results: PIGT was overexpressed in bladder cancer. Silencing PIGT inhibited cell proliferation, oxidative phosphorylation, and glycolysis. Overexpressing PIGT promoted cell proliferation, oxidative phosphorylation, glycolysis in vitro and tumor metastasis in vivo by activating glucose transporter 1 (GLUT1). PIGT also promoted GLUT1 glycosylation and membrane trafficking. Wilms' tumor 1-associated protein (WTAP) mediated PIGT m6A modification, and m6A reader, insulin-like growth factor 2 mRNA-binding protein (IGF2BP2), binds to the methylated PIGT to promote the stability of PIGT, leading to up-regulation of PIGT. Conclusion: WTAP mediates PIGT m6A modification to increase the stability of PIGT via the IGF2BP2, which enhances cell proliferation, glycolysis, and metastasis in bladder cancer by modulating GLUT1 glycosylation and membrane trafficking. [ABSTRACT FROM AUTHOR]

Background: Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms.Method: RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs).Results: Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = - 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them.Conclusion: In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers. [ABSTRACT FROM AUTHOR]