Background: A typical cancerous growth in the urinary tract, bladder cancer (BLCA) has a dismal survival rate and a poor chance of being cured. The cytoskeleton has been shown to be tightly related to tumor invasion and metastasis. Nevertheless, the expression of genes associated with the cytoskeleton and their prognostic significance in BLCA remain unknown.Methods: In our study, we performed differential expression analysis of cytoskeleton-related genes between BLCA versus normal bladder tissues. According to the outcomes of this analysis of differentially expressed genes, all BLCA cases doing nonnegative matrix decomposition clustering analysis be classified into different molecular subtypes and were subjected to Immune cell infiltration analysis. We then constructed a cytoskeleton-associated gene prediction model for BLCA, and performed risk score independent prognostic analysis and receiver operating characteristic curve analyses to evaluate and validate the prognostic value of the model. Furthermore, enrichment analysis, clinical correlation analysis of prognostic models, and immune cell correlation analysis were carried out.Results: We identified 546 differentially expressed genes that are linked to the cytoskeleton, including 314 up-regulated genes and 232 down-regulated genes. All BLCA cases doing nonnegative matrix decomposition clustering analysis could be classified into 2 molecular subtypes, and we observed differences (P < .05) in C1 and C2 immune scores about 9 cell types. Next, we obtained 129 significantly expressed cytoskeleton-related genes. A final optimized model was constructed consisting of 11 cytoskeleton-related genes. Survival curves and risk assessment predicted the prognostic risk in both groups of patients with BLCA. Survival curves and receiver operating characteristic curves were used to evaluate and validate the prognostic value of the model. Significant enrichment pathways for cytoskeleton-associated genes in bladder cancer samples were explored by Gene set enrichment analysis enrichment analysis. After we obtained the risk scores, a clinical correlation analysis was performed to examine which clinical traits were related to the risk scores. Finally, we demonstrated a correlation between different immune cells. Conclusion:Cytoskeleton-related genes have an important predictive value for BLCA, and the prognostic model we constructed may enable personalized treatment of BLCA.

A typical cancerous growth in the urinary tract, bladder cancer (BLCA) has a dismal survival rate and a poor chance of being cured. The main cause of tumor death is tumor metastasis, cell migration is crucial in tumor metastasis, and cell-directed movement requires the involvement of the cytoskeleton, so it is said that the cytoskeleton is tightly related to tumor invasion and metastasis. Nevertheless, the expression of genes associated with the cytoskeleton and their prognostic significance in BLCA remain unknown. Differential expression of 546 genes involved in the cytoskeleton was analyzed in BLCA versus normal bladder tissues. According to the outcomes of this analysis of differentially expressed genes (DEGs), all BLCA cases doing NMF clustering analysis could be classified into two molecular subtypes and were subjected to survival analysis. Using the TCGA dataset to screen out genes with drastically differing expression levels, a cytoskeleton-associated gene prediction model for bladder cancer was constructed, and 11 genes were assigned risk formulae using the least absolute shrinkage and selection operator (LASSO) Cox regression approach. We divided all TCGA cohort patients with BLCA into low-risk groups and high-risk groups categories based on the average risk score in the middle, then analyzed survival data and ROC curves separately for each risk category. An external validation dataset (GSM340668) was used to verify the accuracy of the model. Columnar line plots were created to predict the prognostic outcome of bladder cancer cases. Significant enrichment pathways for cytoskeleton-associated genes in bladder cancer samples were explored by GSEA enrichment analysis. In addition, immune infiltration studies were conducted to help us better understand and observe the degree of bladder cancer immune cell infiltration. An independent prognostic analysis of risk score (RS) was done and proven to be a significant predictor of outcome for bladder cancer. Following this, we looked at the connection between risk score, clinical characteristics, and immune cells, and found that they are all interconnected. In conclusion, cytoskeleton-related genes have an important predictive value for bladder cancer, and the prognostic model we constructed may enable personalized treatment of bladder cancer.

A typical cancerous growth in the urinary tract, bladder cancer (BLCA) has a dismal survival rate and a poor chance of being cured. The main cause of tumor death is tumor metastasis, cell migration is crucial in tumor metastasis, and cell-directed movement requires the involvement of the cytoskeleton, so it is said that the cytoskeleton is tightly related to tumor invasion and metastasis. Nevertheless, the expression of genes associated with the cytoskeleton and their prognostic significance in BLCA remain unknown. Differential expression of 546 genes involved in the cytoskeleton was analyzed in BLCA versus normal bladder tissues. According to the outcomes of this analysis of differentially expressed genes (DEGs), all BLCA cases doing NMF clustering analysis could be classified into two molecular subtypes and were subjected to survival analysis. Using the TCGA dataset to screen out genes with drastically differing expression levels, a cytoskeleton-associated gene prediction model for bladder cancer was constructed, and 11 genes were assigned risk formulae using the least absolute shrinkage and selection operator (LASSO) Cox regression approach. We divided all TCGA cohort patients with BLCA into low-risk groups and high-risk groups categories based on the average risk score in the middle, then analyzed survival data and ROC curves separately for each risk category. An external validation dataset (GSM340668) was used to verify the accuracy of the model. Columnar line plots were created to predict the prognostic outcome of bladder cancer cases. Significant enrichment pathways for cytoskeleton-associated genes in bladder cancer samples were explored by GSEA enrichment analysis. In addition, immune infiltration studies were conducted to help us better understand and observe the degree of bladder cancer immune cell infiltration. An independent prognostic analysis of risk score (RS) was done and proven to be a significant predictor of outcome for bladder cancer. Following this, we looked at the connection between risk score, clinical characteristics, and immune cells, and found that they are all interconnected. In conclusion, cytoskeleton-related genes have an important predictive value for bladder cancer, and the prognostic model we constructed may enable personalized treatment of bladder cancer.

Background Bladder carcinoma (BLCA) is a prevalent malignancy in the urinary tract and is known for its aggressive nature and high probability of recurrence. The regulation of the biological response during tumor proliferation and metastasis is inextricably linked to liquid-liquid phase separation (LLPS). To facilitate early detection and treatment, this study utilized transcriptomic data to explore the prognostic roles of LLPS-linked genes and develop a predictive model.Methods The dataset of bladder cancer patients consisted of clinical and transcriptomic data retrieved from the GEO and TCGA databases. The study utilized a clustering algorithm that employed non-negative matrix factorization (NMF) to classify the samples, which were further compared systematically for their liquid-liquid phase separation characteristics. A multivariate Cox regression model and the Least Absolute Shrinkage Selection Operator (LASSO) algorithm were utilized to construct prognostic models to establish risk formulas for nine genes. Validation of the gene signature was conducted in the entire TCGA cohort (406 cases), TCGA testing cohort (120 cases), and the external validation dataset GSE13507. The signature system was evaluated using both receiver operating characteristic (ROC) and Kaplan-Meier curves. Furthermore, decision curve analysis including the clinicopathological parameters and genetic signature was utilized to predict individual survival.Results In the study, two distinct molecular subtypes were identified, namely C1 and C2. It was revealed that individuals with the C1 subtype had a significantly more favorable prognosis as compared to those with the C2 subtype. Patients belonging to the low-risk group had remarkably better prognoses compared to those in the high-risk groups across the entire TCGA, GEO, and TCGA training cohorts. In addition, the LLPS-related gene model constructed in the study was validated as a prognostic factor independent of other clinical traits.Conclusions This study identifies gene clusters associated with LLPS and establishes a model that can predict, accurately and independently, the prognosis of BLCA. This model can be utilized in clinical practice to assess the prognosis of BLCA.