Identification of Key Biomarkers in Bladder Cancer: Evidence from a Bioinformatics Analysis

Correlation of endoplasmic reticulum stress patterns with the immune microenvironment in hepatocellular carcinoma: a prognostic signature analysis

Backgrounds

The extended duration of endoplasmic reticulum stress (ERS) can impact the progression of hepatocellular carcinoma (HCC) and the efficacy of immunotherapies by interacting with immune cells that have infiltrated the tumor microenvironment (TME).

Methods and results

The study utilized a training cohort of 364 HCC patients with complete information from The Cancer Genome Atlas Program (TCGA) database, and a validation cohort of 231 HCC patients from the International Cancer Genome Consortium (ICGC) database. The genes related to ERS exhibiting a strong correlation with overall survival (OS) were identified using univariate Cox regression analysis. A 13-gene predictive signature was then produced through the least absolute shrinkage and selection operator (LASSO) regression approach. The data revealed that the ERS-associated gene signature effectively stratified patients into high- or low-risk groups regarding OS in both the training and validation cohorts (P < 0.0001 and P = 0.00029, respectively). Using the multivariate method, it is still an independent prognostic factor in both the training and validation cohorts (P < 0.001 and P = 0.008, respectively). Moreover, several metabolic pathways were identified to be enriched among the 13 genes in the predictive signature. When the ERS-associated gene signature was combined with the tumor-node-metastasis (TNM) stage, the ERS nomogram performed better than either the gene signature or the TNM stage alone (C-index values: 0.731, 0.729, and 0.573, respectively). Further analysis revealed that patients in the high-risk group exhibited increased infiltration of immune cells. Additionally, GP6 was downregulated in HCC tissues among these signature genes (P < 0.05), which was related to poor OS.

Conclusions

The data suggest that this novel ERS-associated gene signature could contribute to personalized cancer management for HCC. Moreover, targeting GP6 inhibition might be a potential method for HCC therapy.

Screening of immunotherapy-related genes in bladder cancer based on GEO datasets

Background

As one of the most prevalent genitourinary cancers, bladder cancer (BLCA) is associated with high morbidity and mortality. Currently, limited indicators are available for early detection and diagnosis of bladder cancer, and there is a lack of specific biomarkers for evaluating the prognosis of BLCA patients. This study aims to identify critical genes that affect bladder cancer immunity to improve the diagnosis and prognosis of bladder cancer and to identify new biomarkers and targets for immunotherapy.

Methods

Two GEO datasets were used to screen differentially expressed genes (DEGs). The STRING database was used to construct a protein-protein interaction network of DEGs, and plug-in APP CytoHubba in Cytoscape was used to identify critical genes in the network. GO and KEGG analyses explored the functions and pathways of differential gene enrichment. We used GEPIA to validate the expression of differential genes, their impact on patient survival, and their relationship to clinicopathological parameters. Additionally, hub genes were verified using qRT-PCR and Western blotting. Immune infiltration analysis and multiple immunohistochemistry reveal the impact of Hub genes on the tumor microenvironment.

Result

We screened out 259 differential genes, and identified 10 key hub genes by the degree algorithm. Four genes (ACTA2, FLNA, TAGLN, and TPM1) were associated with overall or disease-free survival in BLCA patients and were significantly associated with clinical parameters. We experimentally confirmed that the mRNA and protein levels of these four genes were significantly decreased in bladder cancer cells. Immunoassays revealed that these four genes affect immune cell infiltration in the tumor microenvironment; they increased the polarization of M2 macrophages.

Conclusion

These four genes affect the tumor microenvironment of bladder cancer, provide a new direction for tumor immunotherapy, and have significant potential in the diagnosis and prognosis of bladder cancer.

The role of CRYAB in tumor prognosis and immune infiltration: A Pan-cancer analysis

Purpose

There is evidence that the Crystallin Alpha B (CRYAB) gene is involved in the regulation of the tumor microenvironment and influences tumor prognosis in some cancers. However, the role of CRYAB gene in prognosis and immunology in pan-cancer is still unclear.

Methods

In this study, we analyzed the transcriptional profiles and survival data of cancer patients from The Cancer Genome Atlas (TCGA) database. CRYAB gene and its relationships with pan-cancer were analyzed using R packages, TIMER2.0, GEPIA2, Sangerbox, UALCAN, cBioPortal, ESTIMATE algorithm, and STRING. Besides, real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was utilized to detect CRYAB expression in KIRC and a human KIRC cell line (Caki-1).

Results

We found that CRYAB expression was different in tumors and adjacent tumors in human cancers, affecting patients' prognosis in 15 cancer types. Additionally, CRYAB expression significantly correlated with tumor microenvironment (TME), immune checkpoints (ICP), tumor mutational burden (TMB), and microsatellite instability (MSI) in human cancers. Besides, CRYAB expression was positively associated with the immune infiltration of cancer-associated fibroblasts (CAFs) and endothelial cells in most human cancers. Based on enrichment analysis, the most prevalent CRYAB gene mechanism in malignant tumors may be through anti-apoptotic activity. Moreover, some FDA-approved drugs were found to be associated with CRYAB and might be potential cancer therapeutic candidates.

Conclusions

CRYAB is a crucial component of the TME and influences immune cell infiltration, making it a promising biomarker to assess immune infiltration and prognosis in many malignancies.

Identify Potential Urine Biomarkers for Bladder Cancer Prognosis Using NGS Data Analysis and Experimental Validation

Bladder cancer (BC) is one of the most often reported malignancies globally, with a high recurrence rate and associated morbidity and mortality, especially in advanced BC. There has been a surge in the number of molecular targets revealed for BC prognosis and treatment. However, there is still a great need to discover novel biomarkers. Consequently, the current study investigated biomarkers that might indicate the progression of bladder cancer. In this study, bioinformatics analysis was done on a single GEO dataset, and TCGA-BLCA information was connected with differentially expressed genes (DEGs). The levels of mRNA and protein expression were validated using qRT-PCR. According to our findings, CRYAB, ECM1, ALDOB, AOC, GPX3, IGFBP7, AQP2, LASS2, TMEM176A, GALNT1, and LASS2 were highly enriched in cell division, identical protein binding, and developmental process in bladder cancer patients. In addition, among the highly differentiated genes, ECM1, GALNT1, LASS2, and GPX3 showed significant molecular alterations in BC, which are crucial for marker identification. Moreover, the mRNA, CNVs, and protein levels of ECM1, GALNT1, LASS2, and GPX3 were significantly increased in BC patients. Our predictions and analysis studies stated that these four genes act as urine biomarkers and played a crucial role in disease prognosis and the therapeutic process of bladder cancer. Our outcomes showed that these four novel urine biomarkers have the potential to provide innovative diagnostics, early predictions, and disease targets, ultimately improving the BC patient's prognosis.

Polygenic risk modeling of tumor stage and survival in bladder cancer

Introduction

Bladder cancer assessment with non-invasive gene expression signatures facilitates the detection of patients at risk and surveillance of their status, bypassing the discomforts given by cystoscopy. To achieve accurate cancer estimation, analysis pipelines for gene expression data (GED) may integrate a sequence of several machine learning and bio-statistical techniques to model complex characteristics of pathological patterns.

Methods

Numerical experiments tested the combination of GED preprocessing by discretization with tree ensemble embeddings and nonlinear dimensionality reductions to categorize oncological patients comprehensively. Modeling aimed to identify tumor stage and distinguish survival outcomes in two situations: complete and partial data embedding. This latter experimental condition simulates the addition of new patients to an existing model for rapid monitoring of disease progression. Machine learning procedures were employed to identify the most relevant genes involved in patient prognosis and test the performance of preprocessed GED compared to untransformed data in predicting patient conditions.

Results

Data embedding paired with dimensionality reduction produced prognostic maps with well-defined clusters of patients, suitable for medical decision support. A second experiment simulated the addition of new patients to an existing model (partial data embedding): Uniform Manifold Approximation and Projection (UMAP) methodology with uniform data discretization led to better outcomes than other analyzed pipelines. Further exploration of parameter space for UMAP and t-distributed stochastic neighbor embedding (t-SNE) underlined the importance of tuning a higher number of parameters for UMAP rather than t-SNE. Moreover, two different machine learning experiments identified a group of genes valuable for partitioning patients (gene relevance analysis) and showed the higher precision obtained by preprocessed data in predicting tumor outcomes for cancer stage and survival rate (six classes prediction).

Conclusions

The present investigation proposed new analysis pipelines for disease outcome modeling from bladder cancer-related biomarkers. Complete and partial data embedding experiments suggested that pipelines employing UMAP had a more accurate predictive ability, supporting the recent literature trends on this methodology. However, it was also found that several UMAP parameters influence experimental results, therefore deriving a recommendation for researchers to pay attention to this aspect of the UMAP technique. Machine learning procedures further demonstrated the effectiveness of the proposed preprocessing in predicting patients’ conditions and determined a sub-group of biomarkers significant for forecasting bladder cancer prognosis.

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Simple Summary

Bladder cancer is evidently a challenge as far as its prognosis and treatment are concerned. The investigation of potential biomarkers and therapeutic targets is indispensable and still in progress. Most studies attempt to identify differential signatures between distinct molecular tumor subtypes. Therefore, keeping in mind the heterogeneity of urinary bladder tumors, we attempted to identify a consensus gene-related signature between the common expression profile of bladder cancer and control samples. In the quest for substantive features, we were able to identify key hub genes, whose signatures could hold diagnostic, prognostic, or therapeutic significance, but, primarily, could contribute to a better understanding of urinary bladder cancer biology.

Abstract

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

CAR-T Cells in the Treatment of Urologic Neoplasms: Present and Future

In recent years, with the breakthrough of CAR-T cells in the treatment of hematological tumors, they are increasingly being used to treat solid tumors, including urologic neoplasms. There are many relatively specific targets for urologic neoplasms, especially prostate cancer. Besides, urologic neoplasms tend to progress more slowly than tumors in other organs of the body, providing ample time for CAR-T cell application. Therefore, CAR-T cells technology has inherent advantages in urologic neoplasms. CAR-T cells in the treatment of urologic neoplasms have been extensively studied and preliminary achievements have been made. However, no breakthrough has been made due to the problems of targeting extra-tumor cytotoxicity and poor anti-tumor activity. we systematacially summarized the research actuality of CAR-T cells in urologic neoplasms, discussed the potential value and difficulties of the research. The application of CAR-T cells in the treatment of urologic neoplasms requires improvement of function through screening for better targets, modification of CAR structures, or in combination with other antitumor approaches.

The comprehensive and systematic identification of BLCA-specific SF-regulated, survival-related AS events

Bladder urothelial carcinoma (BLCA) is a complex disease with high morbidity and mortality. Changes in alternative splicing (AS) and splicing factor (SF) can affect gene expression, thus playing an essential role in tumorigenesis. This study downloaded 412 patients' clinical information and 433 samples of transcriptome profiling data from TCGA. And we collected 48 AS signatures from SpliceSeq. LASSO and Cox analyses were used for identifying survival-related AS events in BLCA. Finally, 1,645 OS-related AS events in 1,129 genes were validated by Kaplan-Meier (KM) survival analysis, ROC analysis, risk curve analysis, and independent prognostic analysis. Finally, our survey provides an AS-SF regulation network consisting of five SFs and 46 AS events. In the end, we profiled genes that AS occurred in pan-cancer and five SFs' expression in tumor and normal samples in BLCA. We selected CLIP-seq data for validation the interaction regulated by RBP. Our study paves the way for potential therapeutic targets of BLCA.

Prevention and Treatment of Side Effects of Immunotherapy for Bladder Cancer

Bladder cancer (BC) is one of the most important tumors of the genitourinary system, associated with high morbidity and mortality rates. Over the years, various antitumor treatments have been developed, and immunotherapy is one of the most effective methods. Immunotherapy aims to activate the body’s immune system to kill cancer cells. It has been established that immunotherapy drugs can be classified into “non-targeted” and “targeted” drugs depending on their site of action. Immunotherapy is reportedly effective for BC. Even though it can attack cancer cells, it can also cause the immune system to attack healthy cells, which can occur at any time during treatment and sometimes even after immunotherapy is stopped. Importantly, different types of immunotherapies can cause different side effects. Side effects may manifest themselves as signs or as symptoms. The prevention and treatment of side effects caused by immunotherapy is an important part of cancer patient management.

A New Approach to Drug Repurposing with Two-Stage Prediction, Machine Learning, and Unsupervised Clustering of Gene Expression

Drug repurposing has broad importance in planetary health for therapeutics innovation in infectious diseases as well as common or rare chronic human diseases. Drug repurposing has also proved important to develop interventions against the COVID-19 pandemic. We propose a new approach for drug repurposing involving two-stage prediction and machine learning. First, diseases are clustered by gene expression on the premise that similar patterns of altered gene expression imply critical pathways shared in different disease conditions. Next, drug efficacy is assessed by the reversibility of abnormal gene expression, and results are clustered to identify repurposing targets. To cluster similar diseases, gene expression data from 262 cases of 31 diseases and 268 controls were analyzed by Uniform Manifold Approximation and Projection for Dimension Reduction followed by k-means to optimize the number of clusters. For evaluation, we examined disease-specific gene expression data for inclusion, body myositis, polymyositis, and dermatomyositis (DM), and used LINCS L1000 characteristic direction signatures search engine (L1000CDS2) to obtain lists of small-molecule compounds that reversed the expression patterns of these specifically altered genes as candidates for drug repurposing. Finally, the functions of affected genes were analyzed by Gene Set Enrichment Analysis to examine consistency with expected drug efficacy. Consequently, we found disease-specific gene expression, and importantly, identified 20 drugs such as BMS-387032, phorbol-12-myristate-13-acetate, mitoxantrone, alvocidib, and vorinostat as candidates for repurposing. These were previously noted to be effective against two of the three diseases, and have a high probability of being effective against the other. That is, inclusion body myositis and DM. The two-stage prediction approach to drug repurposing presented here offers innovation to inform future drug discovery and clinical trials in a variety of human diseases.

Construction of a Co-Expression Network for lncRNAs and mRNAs Related to Urothelial Carcinoma of the Bladder Progression

Carcinoma of urinary bladder is the most familiar cancer of the urinary tract, with the highest incidence in men. However, its prognosis and treatment have not improved significantly in the last 30 years. The main reason for this may be related to the alteration and regulation of genes. These alterations in genes that play a crucial role in cell cycle regulation may result in high-grade tumors and may alter drug sensitivity. Notably, the role of lncRNA in bladder cancer, especially the lncRNA-mRNA regulatory network, has not been fully elucidated. In this manuscript, we compared RNA sequencing (RNA-seq) data from 19 normal bladder tissues and 411 primary bladder tumor tissues using The Cancer Genome Atlas (TCGA) data bank, subjected differentially expressed mRNAs and lncRNAs to weighted gene co-expression network analysis, and screened out modules highly correlated with tumor progression. Subsequently, a lncRNA-mRNA co-expression network was built, and two key mRNAs were identified via COX regression analysis. Kaplan-Meier curve analysis revealed that the overall survival of sick people in the high-risk section was significantly shorter than those in the low-risk section. Therefore, this lncRNA-mRNA-based co-expression pattern may be used clinically to predict the prognosis of carcinoma of urinary bladder people. Our study not only provides a genetic target for carcinoma of urinary bladder therapy but also provides new ideas for people in the medical profession to discover the treatment of various tumors.

Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways

BACKGROUND

The bladder cancer (BC) pathology is caused by both exogenous environmental and endogenous molecular factors. Several genes have been implicated, but the molecular pathogenesis of BC and its subtypes remains debatable. The bioinformatic analysis evaluates high numbers of proteins in a single study, increasing the opportunity to identify possible biomarkers for disorders.

METHODS

The aim of this study is to identify biomarkers for the identification of BC using several bioinformatic analytical tools and methods. BC and normal samples were compared for each probeset with T test in GSE13507 and GSE37817 datasets, and statistical probesets were verified with GSE52519 and E-MTAB-1940 datasets. Differential gene expression, hierarchical clustering, gene ontology enrichment analysis, and heuristic online phenotype prediction algorithm methods were utilized. Statistically significant proteins were assessed in the Human Protein Atlas database. GSE13507 (6271 probesets) and GSE37817 (3267 probesets) data were significant after the extraction of probesets without gene annotation information. Common probesets in both datasets (2888) were further narrowed by analyzing the first 100 upregulated and downregulated probesets in BC samples.

RESULTS

Among the total 400 probesets, 68 were significant for both datasets with similar fold-change values (Pearson r: 0.995). Protein-protein interaction networks demonstrated strong interactions between CCNB1, BUB1B, and AURKB. The HPA database revealed similar protein expression levels for CKAP2L, AURKB, APIP, and LGALS3 both for BC and control samples.

CONCLUSION

This study disclosed six candidate biomarkers for the early diagnosis of BC. It is suggested that these candidate proteins be investigated in a wet lab to identify their functions in BC pathology and possible treatment approaches.

Identification of hub genes in bladder cancer based on weighted gene co-expression network analysis from TCGA database

Background

Muscular invasive bladder cancer (MIBC) is a common malignant tumor in the world. Because of their heterogeneity in prognosis and response to treatment, biomarkers that can predict survival or help make treatment decisions in patients with MIBC are essential for individualized treatment.

Aim

We aimed to integrate bioinformatics research methods to identify a set of effective biomarkers capable of predicting, diagnosing, and treating MIBC. To provide a new theoretical basis for the diagnosis and treatment of bladder cancer.

Methods and results

Gene expression profiles and clinical data of MIBC were obtained by downloading from the Cancer Genome Atlas database. A dataset of 129 MIBC cases and controls was included. 2084 up‐regulated genes and 2961 down‐regulated genes were identified by differentially expressed gene (DEG) analysis. Then, gene ontology analysis was performed to explore the biological functions of DEGs, respectively. The up‐regulated DEGs are mainly enriched in epidermal cell differentiation, mitotic nuclear division, and so forth. They are also involved in the cell cycle, p53 signaling pathway, PPAR signaling pathway, and so forth. The weighted gene co‐expression network analysis yielded five modules related to pathological stages and grading, of which blue and turquoise were the most relevant modules for MIBC. Next, Using Kaplan–Meier survival analysis to identify further hub genes, the screening criteria at p ≤ .05, we found CNKSR1, HIP1R, CFL2, TPM1, CSRP1, SYNM, POPDC2, PJA2, and RBBP8NL genes associated with the progression and prognosis of MIBC patients. Finally, immunohistochemistry experiments further confirmed that CNKSR1 plays a vital role in the tumorigenic context of MIBC.

Conclusion

The research suggests that CNKSR1, POPDC2, and PJA2 may be novel biomarkers as therapeutic targets for MIBC, especially we used immunohistochemical further to validate CNKSR1 as a therapeutic target for MIBC which may help to improve the prognosis for MIBC.

Tackling tumor microenvironment through epigenetic tools to improve cancer immunotherapy

BACKGROUND

Epigenetic alterations are known contributors to cancer development and aggressiveness. Additional to alterations in cancer cells, aberrant epigenetic marks are present in cells of the tumor microenvironment, including lymphocytes and tumor-associated macrophages, which are often overlooked but known to be a contributing factor to a favorable environment for tumor growth. Therefore, the main aim of this review is to give an overview of the epigenetic alterations affecting immune cells in the tumor microenvironment to provoke an immunosuppressive function and contribute to cancer development. Moreover, immunotherapy is briefly discussed in the context of epigenetics, describing both its combination with epigenetic drugs and the need for epigenetic biomarkers to predict response to immune checkpoint blockage.

MAIN BODY

Combining both topics, epigenetic machinery plays a central role in generating an immunosuppressive environment for cancer growth, which creates a barrier for immunotherapy to be successful. Furthermore, epigenetic-directed compounds may not only affect cancer cells but also immune cells in the tumor microenvironment, which could be beneficial for the clinical response to immunotherapy.

CONCLUSION

Thus, modulating epigenetics in combination with immunotherapy might be a promising therapeutic option to improve the success of this therapy. Further studies are necessary to (1) understand in depth the impact of the epigenetic machinery in the tumor microenvironment; (2) how the epigenetic machinery can be modulated according to tumor type to increase response to immunotherapy and (3) find reliable biomarkers for a better selection of patients eligible to immunotherapy.

Identification of a 5-Gene-Based Scoring System by WGCNA and LASSO to Predict Prognosis for Rectal Cancer Patients

Background

Although accumulating evidence suggested that a molecular signature panel may be more effective for the prognosis prediction than routine clinical characteristics, current studies mainly focused on colorectal or colon cancers. No reports specifically focused on the signature panel for rectal cancers (RC). Our present study was aimed at developing a novel prognostic signature panel for RC.

Methods

Sequencing (or microarray) data and clinicopathological details of patients with RC were retrieved from The Cancer Genome Atlas (TCGA-READ) or the Gene Expression Omnibus (GSE123390, GSE56699) database. A weighted gene coexpression network was used to identify RC-related modules. The least absolute shrinkage and selection operator analysis was performed to screen the prognostic signature panel. The prognostic performance of the risk score was evaluated by survival curve analyses. Functions of prognostic genes were predicted based on the interaction proteins and the correlation with tumor-infiltrating immune cells. The Human Protein Atlas (HPA) tool was utilized to validate the protein expression levels.

Results

A total of 247 differentially expressed genes (DEGs) were commonly identified using TCGA and GSE123390 datasets. Brown and yellow modules (including 77 DEGs) were identified to be preserved for RC. Five DEGs (ASB2, GPR15, PRPH, RNASE7, and TCL1A) in these two modules constituted the optimal prognosis signature panel. Kaplan-Meier curve analysis showed that patients in the high-risk group had a poorer prognosis than those in the low-risk group. Receiver operating characteristic (ROC) curve analysis demonstrated that this risk score had high predictive accuracy for unfavorable prognosis, with the area under the ROC curve of 0.915 and 0.827 for TCGA and GSE56699 datasets, respectively. This five-mRNA classifier was an independent prognostic factor. Its predictive accuracy was also higher than all clinical factor models. A prognostic nomogram was developed by integrating the risk score and clinical factors, which showed the highest prognostic power. ASB2, PRPH, and GPR15/TCL1A were predicted to function by interacting with CASQ2/PDK4/EPHA67, PTN, and CXCL12, respectively. TCL1A and GPR15 influenced the infiltration levels of B cells and dendritic cells, while the expression of PRPH was positively associated with the abundance of macrophages. HPA analysis supported the downregulation of PRPH, RNASE7, CASQ2, EPHA6, and PDK4 in RC compared with normal controls.

Conclusion

Our immune-related signature panel may be a promising prognostic indicator for RC.

Implications of androgen receptor and FUS expression on tumor progression in urothelial carcinoma

Androgen receptor (AR) interact with many pathways involved in bladder cancer development and progression. FUS (fused in liposarcoma), a multifunctional protein essential for different cellular processes, has been demonstrated as a key link between androgen receptor signaling and cell-cycle progression in prostate cancer but has not been examined in urothelial carcinoma (UC) despite an intimate association between prostate and bladder carcinogenesis.

AIM

To examine the immunohistochemical expression of AR and FUS in urothelial carcinoma in relation to prognostic parameters and to extrapolate any possible link between the expression of both markers and tumor progression.

STUDY DESIGN

Retrospective study using immunohistochemical staining for AR and FUS on (88) cases of urothelial carcinoma.

RESULTS

AR shows statistically significant relations with late tumor stage, high tumor grade, and non-papillary tumor pattern. On the other hand, FUS expression correlates with early tumor stage, low tumor grade and papillary pattern. An inverse relation is found between AR and FUS expression (p=0.001). Cases with high AR IHC expression show statistically significant shorter OS, RFS and PFS compared to cases with low AR expression. Cases with high FUS IHC expression reveal statistically significant longer OS, RFS and PFS compared to cases with low FUS expression.

CONCLUSION

FUS expression is associated with favorable prognostic parameters of UC. A possible interaction is suggested between FUS and AR pathways involved in urothelial cancer progression. Manipulating FUS levels and androgen deprivation therapy can provide new promising targets for treatment trials.

Focus on Biochemical and Clinical Predictors of Response to Immune Checkpoint Inhibitors in Metastatic Urothelial Carcinoma: Where Do We Stand?

Urothelial bladder cancer is one of the most lethal cancers worldwide with barely 5% five-year survival in patients with metastatic disease. Intravesical immunotherapy with Bacillus Calmette-Guérin and platinum-based chemotherapy are currently the standard of care for non-muscle invasive and advanced or metastatic urothelial cancer (mUC), respectively. Recently, a subset of patients with locally advanced or mUC has shown to be responsive to immune checkpoint inhibitors (ICIs), e.g., the anti-cytotoxic T-lymphocyte-associated protein 4 and programmed cell death -1/programmed death-ligand1 (PD-1/PD-L1) antibodies. Due to the relevant clinical benefit of immunotherapy for mUC, in 2016, the United States Food and Drug Administration (FDA) approved five immunotherapeutic agents as second-line or first-line treatments for patients with advanced bladder cancer who did not profit from or were ineligible for standard therapy. In this review, we discuss the role of immunotherapy in bladder cancer and recent clinical applications of PD-1/PD-L1 blockade in mUC. Furthermore, we evaluate a variable response rate to ICIs treatment and outline potential biomarkers predictive of immunotherapy response.

A Methylation-Based Reclassification of Bladder Cancer Based on Immune Cell Genes

Simple Summary

Bladder cancer (BC) development is highly related to immune cell infiltration. In this study, we aimed to construct a new classification of bladder cancer molecular subtypes based on immune-cell-associated CpG(Methylation) sites. The classification was accurate and stable. BC patients were successfully divided into three subtypes based on the immune-cell-associated CpG sites. The clinicopathologic features, distribution of immune cells, level of expression of checkpoints, stromal score, immune score, ESTIMATEScore, tumor purity, APC co_inhibition, APC co_stimulation, HLA, MHC class_I, Type I IFN_respons, Type II IFN response, and DNA stemness score (DNAss) presented significant differences among the three subgroups. The specific genomic alteration was also different across subgroups. High-level immune infiltration showed a correlation with high-level methylation. A lower RNA stemness score (RNAss) was associated with higher immune infiltration. Cluster 2 demonstrated a better response to chemotherapy. The anti-cancer targeted drug therapy results are different among the three subgroups.

Abstract

Background: Bladder cancer is highly related to immune cell infiltration. This study aimed to develop a new classification of BC molecular subtypes based on immune-cell-associated CpG sites. Methods: The genes of 28 types of immune cells were obtained from previous studies. Then, methylation sites corresponding to immune-cell-associated genes were acquired. Differentially methylated sites (DMSs) were identified between normal samples and bladder cancer samples. Unsupervised clustering analysis of differentially methylated sites was performed to divide the sites into several subtypes. Then, the potential mechanism of different subtypes was explored. Results: Bladder cancer patients were divided into three groups. The cluster 3 subtype had the best prognosis. Cluster 1 had the poorest prognosis. The distribution of immune cells, level of expression of checkpoints, stromal score, immune score, ESTIMATEScore, tumor purity, APC co_inhibition, APC co_stimulation, HLA, MHC class_I, Type I IFN Response, Type II IFN Response, and DNAss presented significant differences among the three subgroups. The distribution of genomic alterations was also different. Conclusions: The proposed classification was accurate and stable. BC patients could be divided into three subtypes based on the immune-cell-associated CpG sites. Specific biological signaling pathways, immune mechanisms, and genomic alterations were varied among the three subgroups. High-level immune infiltration was correlated with high-level methylation. The lower RNAss was associated with higher immune infiltration. The study of the intratumoral immune microenvironment may provide a new perspective for BC therapy.

Immunotherapy in Bladder Cancer: Current Methods and Future Perspectives

Bladder cancer is one of the most significant genitourinary cancer, causing high morbidity and mortality in a great number of patients. Over the years, various treatment methods for this type of cancer have been developed. The most common is the highly efficient method using Bacillus Calmette-Guerin, giving a successful effect in a high percentage of patients. However, due to the genetic instability of bladder cancer, together with individual needs of patients, the search for different therapy methods is ongoing. Immune checkpoints are cell surface molecules influencing the immune response and decreasing the strength of the immune response. Among those checkpoints, the PD-1 (programmed cell death protein-1)/PD-L1 (programmed cell death protein ligand 1) inhibitors aim at blocking those molecules, which results in T cell activation, and in bladder cancer the use of Atezolizumab, Avelumab, Durvalumab, Nivolumab, and Pembrolizumab has been described. The inhibition of another pivotal immune checkpoint, CTLA-4 (cytotoxic T cell antigen), may result in the mobilization of the immune system against bladder cancer and, among anti-CTLA-4 antibodies, the use of Ipilimumab and Tremelimumab has been discussed. Moreover, several different approaches to successful bladder cancer treatment exists, such as the use of ganciclovir and mTOR (mammalian target of rapamycin) kinase inhibitors, IL-12 (interleukin-12) and COX-2 (cyclooxygenase-2). The use of gene therapies and the disruption of different signaling pathways are currently being investigated. Research suggests that the combination of several methods increases treatment efficiency and the positive outcome in individual.

Bioinformatics Analysis Identifying Key Biomarkers in Bladder Cancer

Our goal was to find new diagnostic and prognostic biomarkers in bladder cancer (BCa), and to predict molecular mechanisms and processes involved in BCa development and progression. Notably, the data collection is an inevitable step and time-consuming work. Furthermore, identification of the complementary results and considerable literature retrieval were requested. Here, we provide detailed information of the used datasets, the study design, and on data mining. We analyzed differentially expressed genes (DEGs) in the different datasets and the most important hub genes were retrieved. We report on the meta-data information of the population, such as gender, race, tumor stage, and the expression levels of the hub genes. We include comprehensive information about the gene ontology (GO) enrichment analyses and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. We also retrieved information about the up- and down-regulation of genes. All in all, the presented datasets can be used to evaluate potential biomarkers and to predict the performance of different preclinical biomarkers in BCa.