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.