Establish TIIC signature score based the machine learning fusion in bladder cancer.

BACKGROUND: Bladder cancer is a prevalent malignant tumor with high heterogeneity. Current treatments, such as transurethral resection of bladder tumor (TURBT) and intravesical Bacillus Calmette-Guérin (BCG) therapy, still have limitations, with approximately 30% of non-muscle-invasive bladder cancer (NMIBC) progressing to muscle-invasive bladder cancer (MIBC), and a substantial number of MIBC patients experiencing recurrence after surgery. Immunotherapy has shown potential benefits, but accurate prediction of its prognostic effects remains challenging. METHODS: We analyzed bladder cancer RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and used various machine learning algorithms to screen for feature RNAs related to tumor-infiltrating immune cells (TIICs) from single-cell data. Based on these RNAs, we established a TIIC signature score and evaluated its relationship with overall survival (OS) and immunotherapy response in bladder cancer patients. RESULTS: The study identified 171 TIIC-RNAs and selected 11 TIIC-RNAs with prognostic value through survival analysis. The TIIC signature score established using a machine learning fusion method was significantly associated with OS and showed good predictive performance in different datasets. Additionally, the signature score was negatively correlated with immunotherapy response, with patients with low TIIC feature scores showing better survival outcomes after immunotherapy. Further biological functional analysis revealed a close association between the TIIC signature score and immune regulation processes, cellular metabolism, and genetic variations. CONCLUSION: This study successfully constructed and validated an RNA signature scoring system based on tumor-infiltrating immune cell (TIIC) features, which can effectively predict OS and the effectiveness of immunotherapy in bladder cancer patients.

Machine learning-based integration develops a mitophagy-related lncRNA signature for predicting the progression of prostate cancer: a bioinformatic analysis.

Prostate cancer remains a complex and challenging disease, necessitating innovative approaches for prognosis and therapeutic guidance. This study integrates machine learning techniques to develop a novel mitophagy-related long non-coding RNA (lncRNA) signature for predicting the progression of prostate cancer. Leveraging the TCGA-PRAD dataset, we identify a set of four key lncRNAs and formulate a riskscore, revealing its potential as a prognostic indicator. Subsequent analyses unravel the intricate connections between riskscore, immune cell infiltration, mutational landscapes, and treatment outcomes. Notably, the pan-cancer exploration of YEATS2-AS1 highlights its pervasive impact, demonstrating elevated expression across various malignancies. Furthermore, drug sensitivity predictions based on riskscore guide personalized chemotherapy strategies, with drugs like Carmustine and Entinostat showing distinct suitability for high and low-risk group patients. Regression analysis exposes significant correlations between the mitophagy-related lncRNAs, riskscore, and key mitophagy-related genes. Molecular docking analyses reveal promising interactions between Cyclophosphamide and proteins encoded by these genes, suggesting potential therapeutic avenues. This comprehensive study not only introduces a robust prognostic tool but also provides valuable insights into the molecular intricacies and potential therapeutic interventions in prostate cancer, paving the way for more personalized and effective clinical approaches.

m6A-YTHDF1 Mediated Regulation of GRIN2D in Bladder Cancer Progression and Aerobic Glycolysis.

The modification of N6-methyladenosine (m6A), primarily orchestrated by the reader protein YTHDF1, is a pivotal element in the post-transcriptional regulation of genes. While its role in various biological processes is well-documented, the specific impact of m6A-YTHDF1 on the regulation of GRIN2D, a gene implicated in cancer biology, particularly in the context of bladder cancer, is not thoroughly understood. Utilizing a series of bioinformatics analyses and experimental approaches, including cell culture, transfection, RT-qPCR, and western blotting, we investigated the m6A modification landscape in bladder cancer cells. The relationship between m6A-YTHDF1 and GRIN2D expression was examined, followed by functional assays to assess their roles in cancer progression and glycolytic activity. Our analysis identified a significant upregulation of m6A modification in bladder cancer tissues. YTHDF1 was found to regulate GRIN2D expression positively. Functionally, GRIN2D was implicated in promoting bladder cancer cell proliferation and enhancing aerobic glycolysis. Inhibition of the m6A-YTHDF1-GRIN2D axis resulted in the suppression of cancer progression and metabolic alterations. Through this research, we have elucidated the significant influence of the m6A-YTHDF1 axis on the modulation of GRIN2D expression, which in turn markedly impacts the progression of bladder cancer and its metabolic pathways, particularly aerobic glycolysis. Our findings uncover critical molecular dynamics within bladder cancer cells, offering a deeper understanding of its pathophysiology. Furthermore, the insights gained from this study underscore the potential of targeting the m6A-YTHDF1-GRIN2D pathway for the development of innovative therapeutic strategies in the treatment of bladder cancer.

Integrative analysis of the bladder cancer from a cell cycle NCAM1 perspective at both single cell and bulk resolution.

INTRODUCTION: Bladder cancer (BLCA) is a prevalent and deadly form of urinary cancer, and there is a need for effective therapies, particularly for muscle-invasive bladder cancer (MIBC). Cell cycle inhibitors show promise in restoring control of the cell cycle in BLCA cells, but their clinical prognosis evaluation is limited. METHODS: Transcriptome and scRNA-seq data were collected from the Cancer Genome Atlas Program (TCGA)-BLCA and GSE190888 cohort, respectively. R software and the Seurat package were used for data analysis, including cell quality control, dimensionality reduction, and identification of differentially expressed genes. Genes related to the cell cycle were obtained from the genecards website, and a protein-protein interaction network analysis was performed using cytoscape software. Functional enrichment analysis, immune infiltration analysis, drug sensitivity analysis, and molecular docking were conducted using various tools and packages. BLCA cell lines were cultured and transfected for in vitro experimental assays, including RT-qPCR analysis, and CCK-8 cell viability assays. RESULTS: We identified 32 genes as independent risk or protective factors for BLCA prediction. Functional enrichment analysis revealed their involvement in cell cycle regulation, apoptosis, and various signaling pathways. Using these genes, we developed a nomogram for predicting BLCA survival, which displayed high prognosis stratification efficacy in BLCA patients. Four cell cycle associated key genes identified, including NCAM1, HBB, CKD6, and CTLA4. We also identified the main cell types in BLCA patients and investigated the functional differences between epithelial cells based on their expression levels of key genes. Furthermore, we observed a high positive correlative relationship between the infiltration of cancer-associated fibroblasts and the risk score value. Finally, we conducted in vitro experiments to demonstrate the suppressive role of NCAM1 in BLCA cell proliferation. CONCLUSION: These findings suggest that cell cycle associated genes could serve as potential biomarkers for predicting BLCA prognosis and may represent therapeutic targets for the development of more effective therapies. Hopefully, these findings provide valuable insights into the molecular mechanisms and potential therapeutic targets in BLCA from the perspective of cell cycle. Moreover, NCAM1 was a novel cell proliferation suppressor in the BLCA carcinogenesis.