Molecular Prognostication in Bladder Cancer

Prognostic significance of copper metabolism-related genes as risk markers in bladder urothelial carcinoma

Bladder urothelial carcinoma (BLCA), a prevalent malignant neoplasm affecting the human urinary system, is frequently linked with an unfavorable prognosis in a significant proportion of individuals. More effective and sensitive markers are needed to provide a reference for prognostic judgment. We obtained RNA sequencing data and clinical information of individuals from TCGA, and 133 copper metabolism-related genes from literature. Prognostic genes were evaluated by univariate/multivariate Cox regression analysis and LASSO analysis, and a risk-scoring model was established and validated in the GEO dataset. The CIBERSORT method was utilized to explore immune cell infiltration in BLCA individuals. In addition, tumor immune dysfunction and exclusion (TIDE) and immunophenoscore (IPS) were utilized to verify whether the model can foretell the response of BLCA individuals to immunotherapy. We successfully constructed an 8-gene risk scoring model to foretell individuals' overall survival, and the model performed well in TCGA training and GEO validation cohorts. Lastly, a nomogram containing clinical parameters and risk scores was constructed to help individualized result prediction for individuals. Calibration curves demonstrated a high degree of concordance between the observed and projected survival durations, attesting to its exceptional predictive accuracy. Analysis utilizing CIBERSORT unveiled elevated levels of immune cell infiltration in individuals classified as low risk. TIDE and IPS analyses substantiated that low-risk individuals exhibited a more favorable response to immunotherapy. In summary, the model held immense potential for stratifying the risk of survival and guiding tailored treatment approaches for individuals with BLCA, thereby offering valuable insights for personalized therapeutic interventions.

An immune biomarker associated with EMT serves as a predictor for prognosis and drug response in bladder cancer

BACKGROUND

Bladder cancer (BLCA), which develops from the upper endometrial of the bladder, is the sixth most prevalent cancer across the globe. WDHD1 (WD repeat and HMG-box DNA binding protein 1 gene) directly affects signaling, the cell cycle, and the development of the cell skeleton. Uncertainty surrounds WDHD1's function in BLCA immunity and prognosis, though.

MATERIALS AND METHODS

Using weighed gene co-expression network analysis (WGCNA), initially, we first identified 32 risk factors in genes with differential expression for this investigation. Then, using a variety of bioinformatic techniques and experimental validation, we examined the connections between WDHD1 and BLCA expression, clinical pathological traits, WDHD1-related proteins, upper-skin-intermediate conversion (EMT), immune cell immersion, convergence factors, immune markers, and drug sensitivity.

RESULT

The findings demonstrated that we constructed a 32-gene risk-predicting model where WDHD1 was elevated as a representative gene expression in BLCA and related to a range of clinical traits. Furthermore, high WDHD1 expression was a standalone predictor associated with a worse survival rate. The most commonly recruited cells and their evolutionary patterns were highlighted to better comprehend WDHD1's function in cancer. High WDHD1 expression was associated with many aspects of immunology. Finally, the study found that individuals with high expression of WDHD1 were drug-sensitive to four different broad-spectrum anti-cancer drugs.

CONCLUSION

These results describe dynamic changes in the tumor microenvironment in BLCA and provide evidence for the hypothesis that WDHD1 is a novel biomarker of tumor development. WDHD1 may therefore be a useful target for the detection and management of BLCA.

PRR11 is a prognostic biomarker and correlates with immune infiltrates in bladder urothelial carcinoma

Abnormal proline-rich protein 11 (PRR11) expression is associated with various tumors. However, there are few reports concerning PRR11 with prognostic risk, immune infiltration, or immunotherapy of bladder urothelial carcinoma (BLCA). This study is based on online databases, such as Oncomine, GEPIA, HPA, LinkedOmics, TIMER, ESTIMATE and TISIDB, and BLCA data downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus, we employed an array of bioinformatics methods to explore the potential oncogenic roles of PRR11, including analyzing the relationship between PRR11 and prognosis, tumor mutational burden (TMB), microsatellite instability, and immune cell infiltration in BLCA. The results depict that PRR11 is highly expressed in BLCA, and BLCA patients with higher PRR11 expression have worse outcomes. In addition, there was a significant correlation between PRR11 expression and TMB and tumor immune infiltration. These findings suggest that PRR11 can be used as a potential marker for BLCA patient assessment and risk stratification to improve clinical prognosis, and its potential regulatory mechanism in the BLCA tumor microenvironment and targeted therapy is worthy of further investigation.

OLFML2B Is a Robust Prognostic Biomarker in Bladder Cancer Through Genome-Wide Screening: A Study Based on Seven Cohorts

Background

Bladder cancer lacks useful and robust prognostic markers to stratify patients at risk. Our study is to identify a robust prognostic marker for bladder cancer.

Methods

The transcriptome and clinical data of bladder cancer were downloaded from multiple databases. We searched for genes with robust prognosis by Kaplan-Meier analysis of the whole genome. CIBERSORT and TIMER algorithm was used to calculate the degree of immune cell infiltration.

Results

We identified OLFML2B as a robust prognostic marker for bladder cancer in five cohorts. Kaplan-Meier analysis showed that patients with a high level of OLFML2B expression had a poor prognosis. The expression of OLFML2B increased with the increase of stage and grade. We found that patients with high expression of OLFML2B still had a poor prognosis in two small bladder cancer cohorts. OLFML2B also has the prognostic ability in ten other tumors, and the prognosis is poor in high expression. The correlation analysis between OLFML2B and immune cells showed that it was positively correlated with the degree of macrophage infiltration and highly co-expressed with tumor-associated macrophage markers. Finally, the Wound-healing assay and Colony formation assay results showed that the migration and proliferation ability of bladder cancer cell lines decreased after the knockdown of OLFML2B.

Conclusions

In summary, OLFML2B is a robust risk prognostic marker, and it can help patients with bladder cancer improve individualized treatment.

Characterization of the immune cell infiltration landscape in bladder cancer to aid immunotherapy

The tumor microenvironment (TME) is composed mainly of tumor cells, tumorinfiltrating immune cells, and matrix components. Recent clinical studies have indicated that tumor immune cell infiltration (ICI) is related to the sensitivity of immunotherapy and the prognosis of patients with bladder cancer (BC). Nevertheless, up to now, the landscape of immune infiltration in BC has not been clearly defined. Here we present two algorithms to reveal the landscape of ICI in 277 cases of BC. Two kinds of ICI patterns were established, and ICI scores were based on the analysis of the main components. In sub-types with high ICI scores, we found highly expressed immunecheckpoint and activated transforming growth factor b and WNT signal pathways. These might be the cause of poor prognosis. A low ICI score indicated a better prognosis. Our study showed that ICI scores in immunotherapy could be a valid biomarker for the prognosis of patients and a predictive indicator. The evaluation of ICI patterns of a larger cohort of samples would expand our cognition of TME, and the present study might guide the strategies of immunotherapy for patients with BC.

Construction of Protein-related Risk Score Model in Bladder Urothelial Carcinoma

Background

Though there are several prognostic models, there is no protein-related prognostic model. The aim of this study is to identify possible prognostic-related proteins in bladder urothelial carcinoma and to try to predict the prognosis of bladder urothelial carcinoma based on these proteins.

Methods

Profile data and corresponding clinical traits were obtained from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA) expression. Survival-associated protein in bladder urothelial carcinoma patients were estimated with Kaplan-Meier (KM) test and COX regression analysis. The potential molecular mechanisms and properties of these bladder urothelial carcinoma-specific proteins were also explored with the help of computational skills. The risk score model was validated in different clinical traits. Sankey diagram representation is for protein correlation. A new prognostic-related risk model based on proteins was developed by using multivariable COX analysis. Next, the alteration of the corresponding genes to the 6 prognostic-related proteins was analyzed. Finally, the relation between the corresponding genes and the immune infiltration was analyzed using the TIMER.

Results

Six proteins were identified to be associated with the prognosis of bladder urothelial carcinoma. A prognostic signature based on proteins (BECLIN, EGFR, PKCALPHA, SRC, ANNEXIN1, and AXL) performed moderately in prognostic predictions. The alteration of corresponding genes was in 31(24%) sequenced cases. ANXA1, AXL, and EGFR were positively related to CD8+ T cell.

Conclusion

Our results screened six proteins of clinical significance. The importance of a personalized protein signature model in the recognition, surveillance. The abnormal expression of six prognostic-related proteins may be caused by corresponding gene alteration. Furthermore, these proteins may affect survival via the immune infiltration.