Rac3 Expression and its Clinicopathological Significance in Patients With Bladder Cancer

Unveiling the role of RAC3 in the growth and invasion of cisplatin-resistant bladder cancer cells

Bladder cancer is one of the most prevalent cancers worldwide, and its morbidity and mortality rates have been increasing over the years. However, how RAC family small GTPase 3 (RAC3) affects the proliferation, migration and invasion of cisplatin-resistant bladder cancer cells remains unclear. Bioinformatics techniques were used to investigate the expression of RAC3 in bladder cancer tissues. Influences of RAC3 in the grade, stage, distant metastasis, and survival rate of bladder cancer were also examined. Analysis of the relationship between RAC3 expression and the immune microenvironment (TIME), genomic mutations, and stemness index. In normal bladder cancer cells (T24, 5637, and BIU-87) and cisplatin-resistant bladder cancer cells (BIU-87-DDP), the expression of RAC3 was detected separately with Western blotting. Plasmid transfection was used to overexpress or silence the expression of RAC3 in bladder cancer cells resistant to cisplatin (BIU-87-DDP). By adding activators and inhibitors, the activities of the JNK/MAPK signalling pathway were altered. Cell viability, invasion, and its level of apoptosis were measured in vitro using CCK-8, transwell, and flow cytometry. The bioinformatics analyses found RAC3 levels were elevated in bladder cancer tissues and were associated with a poor prognosis in bladder cancer. RAC3 in BIU-87-DDP cells expressed a higher level than normal bladder cancer cells. RAC3 overexpression promoted BIU-87-DDP proliferation. The growth of BIU-87-DDP cells slowed after the knockdown of RAC3, and RAC3 may have had an impact on the activation of the JNK/MAPK pathway.

Establishment of a prognostic model to predict chemotherapy response and identification of RAC3 as a chemotherapeutic target in bladder cancer

Cisplatin-based chemotherapy is considered the primary treatment option for patients with advanced bladder cancer (BCa). However, the objective response rate to chemotherapy is often unsatisfactory, leading to a poor 5-year survival rate. Furthermore, current strategies for evaluating chemotherapy response and prognosis are limited and inefficient. In this study, we aimed to address these challenges by establishing a chemotherapy response type gene (CRTG) signature consisting of 9 genes and verified the prognostic value of this signature using TCGA and GEO BCa cohorts. The risk scores based on the CRTG signature were found to be associated with advanced clinicopathological status and demonstrated favorable predictive power for chemotherapy response in the TCGA cohort. Meanwhile, tumors with high risk scores exhibited a tendency toward a "cold tumor" phenotype. These tumors showed a low abundance of T cells, CD8+ T cells and cytotoxic lymphocytes, along with a high abundance of cancer-associated fibroblasts. Moreover, they displayed higher mRNA levels of these immune checkpoints: CD200, CD276, CD44, NRP1, PDCD1LG2 (PD-L2), and TNFSF9. Furthermore, we developed a nomogram that integrated the CRTG signature with clinicopathologic risk factors. This nomogram proved to be a more effective tool for predicting the prognosis of BCa patients. Additionally, we identified Rac family small GTPase 3 (RAC3) as a biomarker in our model. RAC3 was found to be overexpressed in chemoresistant BCa tissues and enhance the chemotherapeutic resistance of BCa cells in vitro and in vivo by regulating the PAK1-ERK1/2 pathway. In conclusion, our study presents a novel CRTG model for predicting chemotherapy response and prognosis in BCa. We also highlight the potential of combining chemotherapy with immunotherapy as a promising strategy for chemoresistant BCa and that RAC3 might be a latent target for therapeutic intervention.

Early diagnosis and prognostic potential of RAC3 in bladder tumor

BACKGROUND AND PURPOSE

Bladder tumors are among the most prevalent malignancies in the urinary system, and RAC3 has been linked to various types of cancer. This article seeks to explore the potential of RAC3 as both an early diagnostic marker for bladder tumors and a novel therapeutic target.

METHODS/PATIENTS

The expression of RAC3 in bladder tissue was detected using immunohistochemical staining. Additionally, the protein expression of RAC3 was measured and quantified through enzyme-linked immunosorbent assay (ELISA). Subsequently, the correlation between the expression level of RAC3 and bladder tumors was investigated through multifactorial analysis and survival analysis.

RESULTS

Our findings revealed that RAC3 expression was upregulated in bladder tumor tissues. Moreover, we observed higher levels of RAC3 expression in the serum and urine of patients with bladder tumors compared to those with non-bladder tumors. Additionally, we identified a significant positive correlation between RAC3 expression levels and the stage, degree of differentiation, and infiltration of bladder tumors. Importantly, high RAC3 expression emerged as an influential factor in the poor prognosis of bladder tumors, as patients with high RAC3 expression exhibited a lower overall survival rate than those with low RAC3 expression.

CONCLUSION

Based on our results, RAC3 shows promise as both a marker for early diagnosis of bladder tumors and a potential therapeutic target.

A prognostic model for bladder cancer based on cytoskeleton-related genes

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.

Development of a polyamine gene expression score for predicting prognosis and treatment response in clear cell renal cell carcinoma

Backgrounds

Polyamine metabolism (PM) is closely related to the tumor microenvironment (TME) and is involved in antitumor immunity. Clear cell renal cell carcinoma (ccRCC) not only has high immunogenicity but also has significant metabolic changes. However, the role of PM in the immune microenvironment of ccRCC remains unclear. This study aimed to reveal the prognostic value of PM-related genes (PMRGs) expression in ccRCC and their correlation with the TME.

Methods

The expression levels PMRGs in different cells were characterized with single-cell sequencing analysis. The PMRG expression pattern of 777 ccRCC patients was evaluated based on PMRGs. Unsupervised clustering analysis was used in identifying PMRG expression subtypes, and Lasso regression analysis was used in developing polyamine gene expression score (PGES), which was validated in external and internal data sets. The predictive value of PGES for immunotherapy was validated in the IMvigor210 cohort. Multiple algorithms were used in analyzing the correlation between PGES and immune cells. The sensitivity of PGES to chemotherapeutic drugs was analyzed with the "pRRophetic" package. We validated the genes that develop PGES in tissue samples. Finally, weighted gene co-expression network analysis was used in identifying the key PMRGs closely related to ccRCC, and cell function experiments were carried out.

Results

PMRGs were abundantly expressed on tumor cells, and PMRG expression was active in CD8⁺ T cells and fibroblasts. We identified three PMRG expression subtypes. Cancer and immune related pathways were active in PMRG expression cluster A, which had better prognosis. PGES exhibited excellent predictive value. The high-PGES group was characterized by high immune cell infiltration, high expression of T cell depletion markers, high tumor mutation burden and tumor immune dysfunction and exclusion, was insensitive to immunotherapy but sensitive to sunitinib, temsirolimus, and rapamycin, and had poor prognosis. Spermidine synthetase (SRM) has been identified as a key gene and is highly expressed in ccRCC at RNA and protein levels. SRM knockdown can inhibit ccRCC cell proliferation, migration, and invasion.

Conclusions

We revealed the biological characteristics of PMRG expression subtypes and developed PGES to accurately predict the prognosis of patients and response to immunotherapy.

m7G regulator-mediated molecular subtypes and tumor microenvironment in kidney renal clear cell carcinoma

Background: RNA methylation modification plays an important role in immune regulation. m7G RNA methylation is an emerging research hotspot in the RNA methylation field. However, its role in the tumor immune microenvironment of kidney renal clear cell carcinoma (KIRC) is still unclear.

Methods: We analyzed the expression profiles of 29 m7G regulators in KIRC, integrated multiple datasets to identify a novel m7G regulator-mediated molecular subtype, and developed the m7G score. We evaluated the immune tumor microenvironments in m7G clusters and analyzed the correlation of the m7G score with immune cells and drug sensitivity. We tested the predictive power of the m7G score for prognosis of patients with KIRC and verified the predictive accuracy of the m7G score by using the GSE40912 and E-MTAB-1980 datasets. The genes used to develop the m7G score were verified by qRT-PCR. Finally, we experimentally analyzed the effects of WDR4 knockdown on KIRC proliferation, migration, invasion, and drug sensitivity.

Results: We identified three m7G clusters. The expression of m7G regulators was higher in cluster C than in other clusters. m7G cluster C was related to immune activation, low tumor purity, good prognosis, and low m7G score. Cluster B was related to drug metabolism, high tumor purity, poor survival, and high m7G score. Cluster A was related to purine metabolism. The m7G score can well-predict the prognosis of patients with KIRC, and its prediction accuracy based on the m7G score nomogram was very high. Patients with high m7G scores were more sensitive to rapamycin, gefitinib, sunitinib, and vinblastine than other patients. Knocking down WDR4 can inhibit the proliferation, migration, and invasion of 786-0 and Caki-1 cells and increase sensitivity to sorafenib and sunitinib.

Conclusion: We proposed a novel molecular subtype related to m7G modification and revealed the immune cell infiltration characteristics of different subtypes. The developed m7G score can well-predict the prognosis of patients with KIRC, and our research provides a basis for personalized treatment of patients with KIRC.

Rac1 as a therapeutic anticancer target: Promises and limitations

Small molecule inhibitors of GTPases are increasingly considered for the treatment of multiple human pathologies. The GTPase Rac1 (Ras-related C3 botulinum toxin substrate 1) plays major roles in vital cellular processes, notably in the control cell motility and dynamic, the regulation of oxidative stress, and in inflammatory and immune surveillance. As such, Rac1 is viewed as a potential target to combat cancers but also diverse inflammatory, metabolic, neurodegenerative, respiratory, cardiovascular, viral, and parasitic diseases. Potent and selective Rac1 inhibitors have been identified and designed, such as compounds GYS32661 and MBQ-167 both in preclinical development for the treatment of advanced solid tumors. The pleiotropic roles and ubiquitous expression of the protein can be viewed as limitations for anticancer approaches. However, the frequent overexpression and/or hyperactivation of the Rac1 in difficult-to-treat chemoresistant cancers, make Rac1 an attractive target in oncology. The key roles of Rac1 in multiple cellular pathways, together with its major implications in carcinogenesis, tumor proliferation and metastasis, support the development of small molecule inhibitors. The challenge is high and the difficulty shall not be underestimated, but the target is innovative and promising in combination with chemo- and/or immuno-therapy. Opportunities and challenges associated with the targeting of Rac1 are discussed.

Computational Analysis Identifies Novel Biomarkers for High-Risk Bladder Cancer Patients

In the case of bladder cancer, carcinoma in situ (CIS) is known to have poor diagnosis. However, there are not enough studies that examine the biomarkers relevant to CIS development. Omics experiments generate data with tens of thousands of descriptive variables, e.g., gene expression levels. Often, many of these descriptive variables are identified as somehow relevant, resulting in hundreds or thousands of relevant variables for building models or for further data analysis. We analyze one such dataset describing patients with bladder cancer, mostly non-muscle-invasive (NMIBC), and propose a novel approach to feature selection. This approach returns high-quality features for prediction and yet allows interpretability as well as a certain level of insight into the analyzed data. As a result, we obtain a small set of seven of the most-useful biomarkers for diagnostics. They can also be used to build tests that avoid the costly and time-consuming existing methods. We summarize the current biological knowledge of the chosen biomarkers and contrast it with our findings.