DNA microarray expression profiling of bladder cancer allows identification of noninvasive diagnostic markers

Identifying stage-associated hub genes in bladder cancer via weighted gene co-expression network and robust rank aggregation analyses

BACKGROUND

Bladder cancer (BC) is among the most frequent cancers globally. Although substantial efforts have been put to understand its pathogenesis, its underlying molecular mechanisms have not been fully elucidated.

METHODS

The robust rank aggregation approach was adopted to integrate 4 eligible bladder urothelial carcinoma microarray datasets from the Gene Expression Omnibus. Differentially expressed gene sets were identified between tumor samples and equivalent healthy samples. We constructed gene co-expression networks using weighted gene co-expression network to explore the alleged relationship between BC clinical characteristics and gene sets, as well as to identify hub genes. We also incorporated the weighted gene co-expression network and robust rank aggregation to screen differentially expressed genes.

RESULTS

CDH11, COL6A3, EDNRA, and SERPINF1 were selected from the key module and validated. Based on the results, significant downregulation of the hub genes occurred during the early stages of BC. Moreover, receiver operating characteristics curves and Kaplan-Meier plots showed that the genes exhibited favorable diagnostic and prognostic value for BC. Based on gene set enrichment analysis for single hub gene, all the genes were closely linked to BC cell proliferation.

CONCLUSIONS

These results offer unique insight into the pathogenesis of BC and recognize CDH11, COL6A3, EDNRA, and SERPINF1 as potential biomarkers with diagnostic and prognostic roles in BC.

A novel prognostic model based on three integrin subunit genes-related signature for bladder cancer

Background

Presently, a comprehensive analysis of integrin subunit genes (ITGs) in bladder cancer (BLCA) is absent. This study endeavored to thoroughly analyze the utility of ITGs in BLCA through computer algorithm-based bioinformatics.

Methods

BLCA-related materials were sourced from reputable databases, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). R software-based bioinformatics analyses included limma-differential expression analysis, survival-Cox analysis, glmnet-Least absolute shrinkage and selection operator (LASSO), clusterProfiler-functional annotation, and gsva-estimate-immune landscape analysis. The expression difference of key genes was verified by quantitative real-time polymerase chain reaction (qRT-PCR).

Results

Among the 11 ITGs that were abnormally expressed in BLCA, ITGA7, ITGA5, and ITGB6 were categorized as the optimal variables for structuring the risk model. The high-risk subcategories were typified by brief survival, abysmal prognosis, prominent immune and stromal markers, and depressed tumor purity. The risk model was also an isolated indicator of the impact of clinical outcomes in BLCA patients. Moreover, the risk model, specifically the high-risk subcategory with inferior prognosis, became heavily interlinked with the immune-inflammatory response and smooth muscle contraction and relaxation.

Conclusion

This study determined three ITGs with prognostic values (ITGA7, ITGA5, and ITGB6), composed a novel (ITG-associated) prognostic gene signature, and preliminarily probed the latent molecular mechanisms of the model.

CDH1 overexpression predicts bladder cancer from early stage and inversely correlates with immune infiltration

Background

Bladder cancer (BC) seriously endangers public health, but effective biomarkers for BC diagnosis, particularly in the early stage, are still lacking. Identification of reliable biomarkers associated with early-stage BC is of great importance to early treatment and an improved outcome.

Methods

Differentially expressed genes (DEGs) were identified using four publicly available early-stage BC gene-expression profiles. Protein–protein interaction (PPI) and survival analysis for hub genes was evaluated. The correlation between methylation of genes and prognosis was evaluated using the MethSurv database. Co-expressed genes were explored using Cancer Cell Line Encyclopedia database and the corresponding expression were assessed in vitro. The competing endogenous RNA network and the immune cell infiltration in BC were generated using data of The Cancer Genome Atlas.

Results

Ten hub genes of the 213 integrated DEGs were identified, including CDH1, IGFBP3, PPARG, SDC1, EPCAM, ACTA2, COL3A1, TPM1, ACTC1, and ACTN1. CDH1 appeared to increase from tumor initiation stage and negatively correlated with methylation. Six methylated sites in CDH1 indicated a good prognosis and one site indicated an aberrant prognosis. High CDH1 expression was negatively correlated with infiltrations by most immune cells, such as plasmacytoid dendritic cells (pDCs), regulatory T cells, macrophages, neutrophils, DCs, and natural killer cells. CDH1 was highly positively correlated with EPCAM and appeared to be directly regulated by miR-383.

Conclusions

The identified oncogenic alterations provide theoretical support for the development of novel biomarkers to advance early-stage BC diagnosis and personalized therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-022-01103-7.

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.

Downregulation of topoisomerase 1 and 2 with acriflavine sensitizes bladder cancer cells to cisplatin-based chemotherapy

BACKGROUND

Resistance to cisplatin is a major obstacle to effective treatment of bladder cancer (BC). The present study aimed to determine whether a combination of acriflavine (ACF) with cisplatin could potentiate the antitumor property of cisplatin against the BC cells. Furthermore, the molecular mechanism behind the anticancer action of ACF was considered.

METHODS AND RESULTS

Two human BC cells (5637 and EJ138) contain mutated form of p53 was culture in standard condition. Cotreatment protocol (simultaneous combination of IC₃₀ value of ACF + various dose of cisplatin for 72 h) and pretreatment protocol (pretreatment with IC₁₅ value of ACF for 24 h + various dose of cisplatin for 48 h) was used to determine the effect of ACF on the cells' sensitivity to main drug cisplatin. To assess the mechanism of action of ACF, real-time PCR was used to evaluate mRNA levels of hypoxia-inducible factor-1α (HIF-1α), Bax, Bcl-2, topoisomerase1 (TOP1) and topoisomerase 2 (TOP2A). Combination of ACF with cisplatin either as cotreatment or opretreatment protocol could significantly reduce the IC₅₀ values of cisplatin as compared to the IC₅₀ of cisplatin when use as a single drug. In addition, ACF could markedly decrease mRNA expression of TOP1 and TOP2A without changing the expression of HIF-1ɑ, Bax and Bcl-2.

CONCLUSIONS

Our findings indicate that combination of cisplatin with ACF was able to significantly enhance the sensitivity of BC cells to cisplatin. The antitumor activity of ACF is exerted through the downregulation of TOP1 and TOP2A genes expression. ACF may serve as an adjuvant to boost cisplatin-based chemotherapy.

Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin

Integrative Bioinformatics analysis helps to explore various mechanisms of Nitroglycerin activity in different types of cancers and help predict target genes through which Nitroglycerin affect cancers. Many publicly available databases and tools were used for our study. First step in this study is identification of Interconnected Genes. Using Pubchem and SwissTargetPrediction Direct Target Genes (activator, inhibitor, agonist and suppressor) of Nitroglycerin were identified. PPI network was constructed to identify different types of cancers that the 12 direct target genes affected and the Closeness Coefficient of the direct target genes so identified. Pathway analysis was performed to ascertain biomolecules functions for the direct target genes using CluePedia App. Mutation Analysis revealed Mutated Genes and types of cancers that are affected by the mutated genes. While the PPI network construction revealed the types of cancer that are affected by 12 target genes this step reveals the types of cancers affected by mutated cancers only. Only mutated genes were chosen for further study. These mutated genes were input into STRING to perform NW Analysis. NW Analysis revealed Interconnected Genes within the mutated genes as identified above. Second Step in this study is to predict and identify Upregulated and Downregulated genes. Data Sets for the identified cancers from the above procedure were obtained from GEO Database. DEG Analysis on the above Data sets was performed to predict Upregulated and Downregulated genes. A comparison of interconnected genes identified in step 1 with Upregulated and Downregulated genes obtained in step 2 revealed Co-Expressed Genes among Interconnected Genes. NW Analysis using STRING was performed on Co-Expressed Genes to ascertain Closeness Coefficient of Co-Expressed genes. Gene Ontology was performed on Co-Expressed Genes to ascertain their Functions. Pathway Analysis was performed on Co-Expressed Genes to identify the Types of Cancers that are influenced by co-expressed genes. The four types of cancers identified in Mutation analysis in step 1 were the same as the ones that were identified in this pathway analysis. This further corroborates the 4 types of cancers identified in Mutation analysis. Survival Analysis was done on the co-expressed genes as identified above using Survexpress. BIOMARKERS for Nitroglycerin were identified for four types of cancers through Survival Analysis. The four types of cancers are Bladder cancer, Endometrial cancer, Melanoma and Non-small cell lung cancer.

High S100A7 expression is associated with early muscle invasion and poor survival in bladder carcinoma

Muscle-invasive bladder carcinoma (MIBC) accounts for 25% of newly diagnosed bladder carcinomas (BCs) and presents a high risk of progression and metastasis. This study aimed to identify reliable biomarkers associated with muscle invasion and prognosis to identify potential therapeutic targets for MIBC. Four gene datasets were downloaded from the Gene Expression Omnibus, and the integrated differentially expressed genes (DEGs) were then subjected to gene ontology (GO) terms and pathway enrichment analyses. Correlation analysis between the expression of the top-ranking DEGs and pathological T stages was performed to identify the genes associated with early muscle invasion. The corresponding prognostic values were evaluated, and co-expressed genes mined in the cBioPortal database were loaded into ClueGo in Cytoscape for pathway enrichment analysis. Using data mining from the STRING and TCGA databases, protein-protein interaction and competitive endogenous RNA networks were constructed. In total, 645 integrated DEGs were identified and these were mainly enriched in 26 pathways, including cell cycle, bladder cancer, DNA replication, and PPAR signaling pathway. S100A7 expression was significantly increased from the T2 stage and showed significantly worse overall survival and disease-specific survival in patients with BC. In total, 144 genes co-expressed with S100A7 in BC were significantly enriched in the IL-17 pathway. S100A7 was predicted to directly interact with LYZ, which potentially shows competitive binding with hsa-mir-140 to affect the expression of six lncRNAs in MIBC. In conclusion, high S100A7 expression was predicted to be associated with early muscle invasion and poor survival in patients with BC.

Bioinformatic Identification of Potential Hub Genes in Muscle-Invasive Bladder Urothelial Carcinoma

Despite aggressive treatment approaches, muscle-invasive bladder urothelial carcinoma (MIBC) patients still have a 50% chance of developing general incurable metastases. Therefore, there is an urgent need for candidate markers to enhance diagnosis and generate effective treatments for this disease. We evaluated four mRNA microarray datasets to find differences between non-MIBC (NMIBC) and MIBC tissues. Through a gene expression profile analysis via the Gene Expression Omnibus database, we identified 56 differentially expressed genes (DEGs). Enrichment analysis of gene ontology, Kyoto Encyclopedia of Genes and Genomes, and Reactome pathways revealed the interactions between these DEGs. Next, we established a protein-protein interaction network to determine the interrelationship between the DEGs and selected 10 hub genes accordingly. Bladder urothelial carcinoma (BLCA) patients with COL1A2, COL5A1, and COL5A2 alterations showed poor disease-free survival rates, while BLCA patients with COL1A1 and LUM alterations showed poor overall survival rates. Oncomine analysis of MIBC versus NMIBC tissues showed that COL1A1, COL5A2, COL1A2, and COL3A1 were consistently among the top 20 overexpressed genes in different studies. Using the TCGAportal, we noted that the high expression of each of the four genes led to shorter BLCA patient overall survival. It was evident that BLCA patients with an elevated high combined gene expression had significantly shorter overall survival and relapse-free survival than those with low combined gene expression using PROGgeneV2. Using Gene Expression Profiling Interactive Analysis, we noted that COL1A1, COL1A2, COL3A1, and COL5A2 were positively correlated with each other in BLCA. These genes are considered as clinically relevant genes, suggesting that they may play an important role in the carcinogenesis, development, invasion, and metastasis of MIBC. However, considering we adopted a bioinformatic approach, more research is crucial to confirm our results. Nonetheless, our findings may have important prospective clinical implementations.

Clinical impact of copy number variation changes in bladder cancer samples

The aim of the present study was to detect copy number variations (CNVs) related to tumour progression and metastasis of urothelial carcinoma through whole-genome scanning. A total of 30 bladder cancer samples staged from pTa to pT4 were included in the study. DNA was extracted from freshly frozen tissue via standard phenol-chloroform extraction and CNV analysis was performed on two alternative platforms (CytoChip Oligo aCGH, 4x44K and Infinium OncoArray-500K BeadChip; Illumina, Inc.). Data were analysed with BlueFuse Multi software and Karyostudio, respectively. The results highlight the role of genomic imbalances in regions containing genes with metastatic and proliferative potential for tumour invasion. A high level of genomic instability in uroepithelial tumours was observed and a total of 524 aberrations, including 175 losses and 349 gains, were identified. The most prevalent genetic imbalances affected the following regions: 1p, 1q, 2q, 4p, 4q, 5p, 5q, 6p, 6q, 7q, 8q, 9p, 9q, 10p, 10q, 11q, 13q and 17q. High-grade tumours more frequently harboured genomic imbalances (n=227) than low-grade tumours (n=103). A total of 36 CNVs in high-grade bladder tumours were detected in chromosomes 1-5, 8-11, 14, 17, 19 and 20. Furthermore, five loss of heterozygosity variants containing 176 genes were observed in high-grade bladder cancer and may be used as potential targets for precision therapy. Revealing specific chromosomal regions related to the metastatic potential of uroepithelial tumours may lay a foundation for implementing molecular CNV profiling of bladder tumours as part of a routine progression risk estimation strategy, thus expanding the personalized therapeutic approach.

Overexpression of SHMT2 Predicts a Poor Prognosis and Promotes Tumor Cell Growth in Bladder Cancer

SHMT2 was overexpressed in many tumors, however, the role of SHMT2 in bladder cancer (BLCA) remains unclear. We first analyzed the expression pattern of SHMT2 in BLCA using the TNMplot, Oncomine, the Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO) databases. Next, the association between SHMT2 expression and overall survival (OS)/disease-free survival (DFS) in BLCA patients were analyzed using TCGA and PrognoScan database. The correlation between SHMT2 expression and clinicopathology was determined using TCGA database. Furthermore, the genes co-expressed with SHMT2 and their underlying molecular function in BLCA were explored based on the Oncomine database, Metascape and gene set enrichment analysis (GSEA). Finally, the effects of SHMT2 on cell proliferation, cell cycle, and apoptosis were assessed using in vitro experiments. As a results, SHMT2 was significantly overexpressed in BLCA tissues and cells compared to normal bladder tissues and cells. A high SHMT2 expression predicts a poor OS of BLCA patients. In addition, SHMT2 expression was higher in patients with a high tumor grade and in those who were older than 60 years. However, the expression of SHMT2 was not correlated with gender, tumor stage, lymph node stage, and distant metastasis stage. Finally, overexpression of SHMT2 promoted BLCA cell proliferation and suppressed apoptosis, the silencing of SHMT2 significantly inhibited BLCA cell proliferation by impairing the cell cycle, and promoting apoptosis. SHMT2 mediates BLCA cells growth by regulating STAT3 signaling. In summary, SHMT2 regulates the proliferation, cell cycle and apoptosis of BLCA cells, and may act as a candidate therapeutic target for BLCA.

Trends in urine biomarker discovery for urothelial bladder cancer: DNA, RNA, or protein?

Urothelial bladder cancer is a complex disease displaying a landscape of heterogenous molecular subtypes, mutation profiles and clinical presentations. Diagnosis and surveillance rely on flexible cystoscopy which has high accuracy, albeit accompanied by a high-cost burden for healthcare providers and discomfort for patients. Advances in "omic" technologies and computational biology have provided insights into the molecular pathogenesis of bladder cancer and provided powerful tools to identify markers for disease detection, risk stratification, and predicting responses to therapy. To date, numerous attempts have been made to discover and validate diagnostic biomarkers that could be deployed as an adjunct to the cystoscopic diagnosis and long-term surveillance of bladder cancer. We report a comprehensive literature analysis using PubMed to assess the changing trends in investigating DNA, RNA, or proteins as diagnostic urinary biomarkers over a period of 5 decades: 1970-2020. A gradual shift has been observed in research away from protein biomarkers to nucleic acids including different classes of RNA, and DNA methylation and mutation markers. Until 2000, publications involving protein biomarker discovery constituted 87% of the total number of research articles with DNA comprising 6% and RNA 7%. Since 2000 the proportion of protein biomarker articles has fallen to 40%, and DNA and RNA studies increased to 32% and 28%, respectively. Clearly research focus, perhaps driven by technological innovation, has shifted from proteins to nucleic acids. We optimistically hypothesise that, following thorough validation, a clinically useful detection test for bladder cancer based on a panel of DNA or RNA markers could become reality within 5-10 years.

Differential gene expression profile between progressive and de novo muscle invasive bladder cancer and its prognostic implication

This study aimed to ascertain gene expression profile differences between progressive muscle-invasive bladder cancer (MIBC) and de novo MIBC, and to identify prognostic biomarkers to improve patients’ treatment. Retrospective multicenter study in which 212 MIBC patients who underwent radical cystectomy between 2000 and 2019 were included. Gene expression profiles were determined in 26 samples using Illumina microarrays. The expression levels of 94 genes were studied by quantitative PCR in an independent set of 186 MIBC patients. In a median follow-up of 16 months, 46.7% patients developed tumor progression after cystectomy. In our series, progressive MIBC patients show a worse tumor progression (p = 0.024) and cancer-specific survival (CSS) (p = 0.049) than the de novo group. A total of 480 genes were found to be differently expressed between both groups. Differential expression of 24 out of the 94 selected genes was found in an independent cohort. RBPMC2 and DSC3 were found as independent prognostic biomarkers of tumor progression and CALD1 and LCOR were identified as prognostic biomarkers of CSS between both groups. In conclusion, progressive and de novo MIBC patients show different clinical outcome and gene expression profiles. Gene expression patterns may contribute to predict high-risk of progression to distant metastasis or CSS.

Rac Family Small GTPase 3 Correlates with Progression and Poor Prognosis in Bladder Cancer

Bladder cancer (BC) is a common genitourinary malignancy worldwide. However, the molecular pathogenesis of BC remains unclear. The current study conducted bioinformatic analyses to discover key genes involved in BC progression. A total of 375 differentially expressed genes (DEGs) were screened in the GEO database and The Cancer Genome Atlas (TCGA) database, which were further evaluated by the core level in the protein-protein interaction network. RAC3 (Rac family small GTPase 3), one of the top hub genes, was focused on for its gene expression and prognostic value in BC. Immunohistochemical assays indicated elevated RAC3 levels in BC tissues compared with normal tissues. Overexpression of RAC3 expression was closely associated with poor differentiation (p = 0.035), advanced TNM stage (p = 0.014), lymph metastasis (p = 0.033), and recurrence (p < 0.001). Kaplan-Meier and Cox proportional hazards analyses demonstrated that high RAC3 expression indicated poor survival of BC patients, which could serve as an independent prognostic factor for overall survival (HR = 3.159, p = 0.023) and disease-free survival (HR = 4.633, p = 0.002). Moreover, bioinformatic analyses indicated that RAC3 might be correlated with malignant phenotypes and immune infiltration of BC. Taken together, RAC3 could be a novel prognostic biomarker for BC.

Deletion of inositol polyphosphate 4-phosphatase type-II B affects spermatogenesis in mice

Inositol polyphosphate-4-phosphatase type II (INPP4B) is a dual-specificity phosphatase that acts as a tumor suppressor in multiple cancers. INPP4B dephosphorylates phospholipids at the 4th position of the inositol ring and inhibits AKT and PKC signaling by hydrolyzing of PI(3,4)P2 and PI(4,5)P2, respectively. INPP4B protein phosphatase targets include phospho-tyrosines on Akt and phospho-serine and phospho-threonine on PTEN. INPP4B is highly expressed in testes, suggesting its role in testes development and physiology. The objective of this study was to determine whether Inpp4b deletion impacts testicular function in mice. In testis, Inpp4b expression was the highest in postmeiotic germ cells in both mice and men. The testes of Inpp4b knockout male mice were significantly smaller compared to the testes of wild-type (WT) males. Inpp4b^-/- males produced fewer mature sperm cells compared to WT, and this difference increased with age and high fat diet (HFD). Reduction in early steroidogenic enzymes and luteinizing hormone (LH) receptor gene expression was detected, although androgen receptor (AR) protein level was similar in WT and Inpp4b^-/- testes. Germ cell apoptosis was significantly increased in the knockout mice, while expression of meiotic marker γH2A.X was decreased. Our data demonstrate that INPP4B plays a role in maintenance of male germ cell differentiation and protects testis functions against deleterious effects of aging and high fat diet.

Knockdown of CDCA8 inhibits the proliferation and enhances the apoptosis of bladder cancer cells

Bladder cancer is a tumour of the urinary system with high mortality, and there is also a great lack of therapeutic targets in the clinic. Cell division cycle associated 8 (CDCA8), an important component of the vertebrate chromosomal passenger complex, is highly expressed in various tumours and promotes tumour development. However, the role of CDCA8 in bladder cancer is not fully understood. This study aimed to reveal the function of CDCA8 in bladder cancer by determining the relationship between CDCA8 expression and proliferation, metastasis and apoptosis of bladder cancer cells. Firstly, we studied the mRNA expression of CDCA8 through the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases and analysed the correlation between CDCA8 expression and prognosis of patients with bladder cancer. We also verified CDCA8 expression in bladder cancer tissues by immunohistochemistry. In addition, CDCA8 expression was inhibited in bladder cancer T24 and 5637 cells, and the effects of CDCA8 on the proliferation, migration and invasion of bladder cancer cell lines were investigated using cell counting kit-8, colony formation, cell cycle, apoptosis, wound healing and Transwell invasion assays. Results showed that CDCA8 was highly expressed in bladder cancer compared with normal tissues, and the high CDCA8 expression was significantly correlated with the poor prognosis of patients. Inhibiting CDCA8 expression inhibited the proliferation, migration and invasion of T24 and 5637 cells and induced the apoptosis of bladder cancer cells. CDCA8 was involved in the regulation of the growth cycle of bladder cancer cells. Bioinformatics-based mechanism analysis revealed that high CDCA8 expression may affect the cell cycle and P53 signalling pathways. In conclusion, our results suggest that CDCA8 is highly expressed in bladder cancer and can promote tumour development. Hence, CDCA8 may serve as an effective therapeutic target for treatment of bladder cancer.

Identification of Prognostic Immune Genes in Bladder Urothelial Carcinoma

Background

The aim of this study is to identify possible prognostic-related immune genes in bladder urothelial carcinoma and to try to predict the prognosis of bladder urothelial carcinoma based on these genes.

Methods

The Cancer Genome Atlas (TCGA) expression profile data and corresponding clinical traits were obtained. Differential gene analysis was performed using R software. Reactome was used to analyze the pathway of immune gene participation. The differentially expressed transcription factors and differentially expressed immune-related genes were extracted from the obtained list of differentially expressed genes, and the transcription factor-immune gene network was constructed. To analyze the relationship between immune genes and clinical traits of bladder urothelial carcinoma, a multifactor Cox proportional hazards regression model based on the expression of immune genes was established and validated.

Results

Fifty-eight immune genes were identified to be associated with the prognosis of bladder urothelial carcinoma. These genes were enriched in Cytokine Signaling in Immune System, Signaling by Receptor Tyrosine Kinases, Interferon alpha/beta signaling, and other immune related pathways. Transcription factor-immune gene regulatory network was established, and EBF1, IRF4, SOX17, MEF2C, NFATC1, STAT1, ANXA6, SLIT2, and IGF1 were screened as hub genes in the network. The model calculated by the expression of 16 immune genes showed a good survival prediction ability (p < 0.05 and AUC = 0.778).

Conclusion

A transcription factor-immune gene regulatory network related to the prognosis of bladder urothelial carcinoma was established. EBF1, IRF4, SOX17, MEF2C, NFATC1, STAT1, ANXA6, SLIT2, and IGF1 were identified as hub genes in the network. The proportional hazards regression model constructed by 16 immune genes shows a good predictive ability for the prognosis of bladder urothelial carcinoma.

The “Gene Cube”: A Novel Approach to Three-dimensional Clustering of Gene Expression Data

Background:

A very popular technique for isolating significant genes from cancerous tissues is the application of various clustering algorithms on data obtained by DNA microarray experiments.

Aim:

The objective of the present work is to take into consideration the chromosomal identity of every gene before the clustering, by creating a three-dimensional structure of the form Chromosomes×Genes×Samples. Further on, the k-Means algorithm and a triclustering technique called δ- TRIMAX, are applied independently on the structure.

Materials and Methods:

The present algorithm was developed using the Python programming language (v. 3.5.1). For this work, we used two distinct public datasets containing healthy control samples and tissue samples from bladder cancer patients. Background correction was performed by subtracting the median global background from the median local Background from the signal intensity. The quantile normalization method has been applied for sample normalization. Three known algorithms have been applied for testing the “gene cube”, a classical k-means, a transformed 3D k-means and the δ-TRIMAX.

Results:

Our proposed data structure consists of a 3D matrix of the form Chromosomes×Genes×Samples. Clustering analysis of that structure manifested very good results as we were able to identify gene expression patterns among samples, genes and chromosomes. Discussion: to the best of our knowledge, this is the first time that such a structure is reported and it consists of a useful tool towards gene classification from high-throughput gene expression experiments.

Conclusion:

Such approaches could prove useful towards the understanding of disease mechanics and tumors in particular.

Systematic screening of protein-coding gene expression identified HMMR as a potential independent indicator of unfavorable survival in patients with papillary muscle-invasive bladder cancer

Papillary and non-papillary are two histological patterns of bladder carcinogenesis and are considered as dual-track oncogenic pathways, which have different genetic alterations. The TCGA-bladder cancer (BLCA) database contains clinicopathological, genomic and survival data from over 400 muscle-invasive bladder cancer patients. In this study, using data from this database, we performed a systematic screening of gene expression to identify the protein-coding gene that might have prognostic value in papillary and non-papillary muscle-invasive bladder cancer (MIBC). The data of patients with primary MIBC in TCGA-BLCA was acquired from the UCSC Xena project (http://xena.ucsc.edu) for re-analysis. By setting |log2 fold change|≥2 and adjusted p value <0.01 as the screening criteria, we found 751 significantly dysregulated genes, including 183 overexpressed and 568 downregulated genes. HMMR was identified as a potential prognostic marker with unique expression. Multivariate analysis showed that its expression was an independent prognostic indicator of shorter progression-free survival (PFS) (HR: 1.400, 95%CI: 1.021-1.920, p = 0.037) in the papillary subtype. ENST00000393915.8 and ENST00000358715.3, two transcripts that contain all 18 exons and encode the full length of HMMR, were significantly upregulated in cancer tissues compared with normal bladder tissues. None of the 17 CpG sites in its DNA locus was relevant to HMMR expression. 26/403 (6.5%) MIBC cases had HMMR gene-level amplification, which was associated with upregulated HMMR expression compared with the copy-neutral and deletion groups. Gene set enrichment analysis (GSEA) in papillary MIBC found that the high HMMR expression group was associated with upregulated genes enriched in multiple gene sets with well-established role in BC development, including G2M checkpoint, E2 F Targets, Myc Targets V1, Myc Targets V2 and Glycolysis. Based on these findings, we infer that HMMR expression might be a specific prognostic marker in terms of PFS in papillary MIBC. DNA amplification might be an important mechanism of its elevation.

Bioinformatics-based discovery of the urinary BBOX1 mRNA as a potential biomarker of diabetic kidney disease

Background

Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD) in the world. Emerging evidence has shown that urinary mRNAs may serve as early diagnostic and prognostic biomarkers of DKD. In this article, we aimed to first establish a novel bioinformatics-based methodology for analyzing the “urinary kidney-specific mRNAs” and verify their potential clinical utility in DKD.

Methods

To select candidate mRNAs, a total of 127 Affymetrix microarray datasets of diabetic kidney tissues and other tissues from humans were compiled and analyzed using an integrative bioinformatics approach. Then, the urinary expression of candidate mRNAs in stage 1 study (n = 82) was verified, and the one with best performance moved on to stage 2 study (n = 80) for validation. To avoid potential detection bias, a one-step Taqman PCR assay was developed for quantification of the interested mRNA in stage 2 study. Lastly, the in situ expression of the selected mRNA was further confirmed using fluorescent in situ hybridization (FISH) assay and bioinformatics analysis.

Results

Our bioinformatics analysis identified sixteen mRNAs as candidates, of which urinary BBOX1 (uBBOX1) levels were significantly upregulated in the urine of patients with DKD. The expression of uBBOX1 was also increased in normoalbuminuric diabetes subjects, while remained unchanged in patients with urinary tract infection or bladder cancer. Besides, uBBOX1 levels correlated with glycemic control, albuminuria and urinary tubular injury marker levels. Similar results were obtained in stage 2 study. FISH assay further demonstrated that BBOX1 mRNA was predominantly located in renal tubular epithelial cells, while its expression in podocytes and urothelium was weak. Further bioinformatics analysis also suggested that tubular BBOX1 mRNA expression was quite stable in various types of kidney diseases.

Conclusions

Our study provided a novel methodology to identify and analyze urinary kidney-specific mRNAs. uBBOX1 might serve as a promising biomarker of DKD. The performance of the selected urinary mRNAs in monitoring disease progression needs further validation.

Electronic supplementary material

The online version of this article (10.1186/s12967-019-1818-2) contains supplementary material, which is available to authorized users.

Identification of several cell cycle relevant genes highly correlated with the progression and prognosis of human bladder urothelial tumor

The incidence of bladder cancer (BCa) in China is the highest among genitourinary system tumors, and its progression is affected by multitudinous pathways, of which cell cycle progress plays an important role. This study screened and enriched differentially expressed genes (DEGs) from four gene expression profiles using bioinformatics analysis methods. The enrichment and analysis of gene function showed that these genes were highly correlated with cell cycle regulation. Identification of candidate small molecules was conducted to evaluate the application of clinical transformation in these DEGs. Prognostic and stage-related expression analysis further sorted five highly expressed genes associated with worse prognosis and higher stages in patients with BCa. Further analysis revealed their interaction in cell cycle regulation and genetical alteration. Meanwhile, we validated the elevated expression of these genes through transcription and translation levels. Taking the results together, we could infer that these five genes are valuable in diagnosis, prediction, and providing candidate therapeutic targets for patients with BCa in different stages.

Identification of key candidate genes and biological pathways in bladder cancer

Background

Bladder cancer is a malignant tumor in the urinary system with high mortality and recurrence rates. However, the causes and recurrence mechanism of bladder cancer are not fully understood. In this study, we used integrated bioinformatics to screen for key genes associated with the development of bladder cancer and reveal their potential molecular mechanisms.

Methods

The GSE7476, GSE13507, GSE37815 and GSE65635 expression profiles were downloaded from the Gene Expression Omnibus database, and these datasets contain 304 tissue samples, including 81 normal bladder tissue samples and 223 bladder cancer samples. The RobustRankAggreg (RRA) method was utilized to integrate and analyze the four datasets to obtain integrated differentially expressed genes (DEGs), and the gene ontology (GO) functional annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. The OncoLnc online tool was utilized to analyze the relationship between the expression of hub genes and the prognosis of bladder cancer.

Results

In total, 343 DEGs, including 111 upregulated and 232 downregulated genes, were identified from the four datasets. GO analysis showed that the upregulated genes were mainly involved in mitotic nuclear division, the spindle and protein binding. The downregulated genes were mainly involved in cell adhesion, extracellular exosomes and calcium ion binding. The top five enriched pathways obtained in the KEGG pathway analysis were focal adhesion (FA), PI3K-Akt signaling pathway, proteoglycans in cancer, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. The top 10 hub genes identified from the PPI network were vascular endothelial growth factor A (VEGFA), TOP2A, CCNB1, Cell division cycle 20 (CDC20), aurora kinase B, ACTA2, Aurora kinase A, UBE2C, CEP55 and CCNB2. Survival analysis revealed that the expression levels of ACTA2, CCNB1, CDC20 and VEGFA were related to the prognosis of patients with bladder cancer. In addition, a KEGG pathway analysis of the top 2 modules identified from the PPI network revealed that Module 1 mainly involved the cell cycle and oocyte meiosis, while the analysis in Module 2 mainly involved the complement and coagulation cascades, vascular smooth muscle contraction and FA.

Conclusions

This study identified key genes and pathways in bladder cancer, which will improve our understanding of the molecular mechanisms underlying the development and progression of bladder cancer. These key genes might be potential therapeutic targets and biomarkers for the treatment of bladder cancer.

Melittin Constrains the Expression of Identified Key Genes Associated with Bladder Cancer

This work is aimed at investigating the effect of melittin on identified key genes in bladder cancer (BC) and further providing a theoretical basis for BC treatment. GSE35014 downloaded from the Gene Expression Omnibus (GEO) database was used to screen differentially expressed genes (DEGs) in BC cells and control. Results showed that a total of 389 upregulated and 169 downregulated genes were identified. Subsequently, GO analysis, KEGG pathway enrichment analysis, and PPI network analysis were employed to disclose the crucial genes and signaling pathways involved in BC. Fifteen module-related DEGs and their associated signaling pathways were obtained according to the PPI network and modular analyses. Based on the analysis of articles retrieved in the PubMed database, we found that melittin could induce apoptosis and constrain the progression of tumor cells as a result of regulating critical cancer-related signaling pathways, such as PI3K-Akt and TNF signaling pathways. Furthermore, PI3K-Akt and TNF signaling pathways were also found to be associated with module-related DEGs according to biological analyses. At last, qRT-PCR analysis demonstrated that melittin could constrain the expression of module-related DEGs (LPAR1, COL5A1, COL6A2, CXCL1, CXCL2, and CXCL3) associated with PI3K-Akt and TNF signaling pathways in BC cells. Functional assays revealed that melittin could constrain the proliferative and migrated abilities of BC cells. Conjointly, these findings provide a theoretical basis for these six genes as drug-sensitive markers of melittin in BC treatment.

Identification of differentially expressed genes and biological pathways in bladder cancer

The purpose of the present study was to identify key genes and investigate the related molecular mechanisms of bladder cancer (BC) progression. From the Gene Expression Omnibus database, the gene expression dataset GSE7476 was downloaded, which contained 43 BC samples and 12 normal bladder tissues. GSE7476 was analyzed to screen the differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for the DEGs using the DAVID database, and a protein-protein interaction (PPI) network was then constructed using Cytoscape software. The results of the GO analysis showed that the upregulated DEGs were significantly enriched in cell division, nucleoplasm and protein binding, while the downregulated DEGs were significantly enriched in ‘extracellular matrix organization’, ‘proteinaceous extracellular matrix’ and ‘heparin binding’. The results of the KEGG pathway analysis showed that the upregulated DEGs were significantly enriched in the ‘cell cycle’, whereas the downregulated DEGs were significantly enriched in ‘complement and coagulation cascades’. JUN, cyclin-dependent kinase 1, FOS, PCNA, TOP2A, CCND1 and CDH1 were found to be hub genes in the PPI network. Sub-networks revealed that these gene were enriched in significant pathways, including the ‘cell cycle’ signaling pathway and ‘PI3K-Akt signaling pathway’. In summary, the present study identified DEGs and key target genes in the progression of BC, providing potential molecular targets and diagnostic biomarkers for the treatment of BC.

Preoperative hemoglobin-platelet ratio can significantly predict progression and mortality outcomes in patients with T1G3 bladder cancer undergoing transurethral resection of bladder tumor

Objective

To investigate the prognostic role of hematological biomarkers, especially hemoglobin-platelet ratio (HPR) in the oncological outcomes in stage 1 and grade 3 (T1G3) bladder cancer.

Materials and Methods

We identified 457 T1G3 bladder cancer patients who underwent transurethral resection of the bladder (TURB) between 2009 and 2014. Based on hematological parameters (hemoglobin-platelet ratio (HPR), hemoglobin, and platelet counts), recurrence-free survival (RFS), progression-free survival (PFS), and overall survival (OS) and cancer-specific survival (CSS) were analyzed by using Kaplan-Meier analysis. Multivariate Cox regression model was adopted to identify the predictors of oncological outcomes.

Results

Kaplan-Meier survival analysis showed that low HPR (< 0.615), low hemoglobin (< 125g/l) and elevated platelet counts (> 240 × 10³/μl) were correlated with poor OS. Low HPR, but not low hemoglobin and high platelet counts, is associated with worse PFS. Low HPR and low hemoglobin, but not elevated platelet counts, are associated with worse CSS. However, no significant difference was observed in RFS according to any of these hematological markers. On multivariate analysis, low HPR (HR = 1.27, 95% CI = 0.81–1.75, P = 0.030), low hemoglobin (HR = 1.20, 95% CI = 0.79–1.84, P = 0.028) and elevated platelet counts (HR = 1.07, 95% CI = 0.72–1.32, P = 0.038) were significantly associated with OS. Low hemoglobin (HR = 1.08, 95% CI = 0.68–1.82, P = 0.041) was significantly linked with CSS. Particularly, low HPR was identified as an independent predictor of PFS (HR = 1.16, 95% CI = 0.97–1.49, P = 0.033) and CSS (HR = 1.14, 95% CI = 0.87–1.78, P = 0.029).

Conclusions

Preoperative HPR can be taken into account as a factor predictive of oncological outcomes for T1G3 bladder cancer, particularly disease progression and mortality outcomes.

A five-gene expression signature to predict progression in T1G3 bladder cancer

OBJECTIVE

The aim of this study was to analyze tumour gene expression profiles of progressive and non-progressive T1G3 bladder cancer (BC) patients to develop a gene expression signature to predict tumour progression.

METHODS

Retrospective, multicenter study of 96 T1G3 BC patients without carcinoma in situ (CIS) who underwent a transurethral resection. Formalin-fixed paraffin-embedded tissue samples were collected. Global gene expression patterns were analyzed in 21 selected samples from progressive and non-progressive T1G3 BC patients using Illumina microarrays. Expression levels of 94 genes selected based on microarray data and based on literature were studied by quantitative polymerase chain reaction (qPCR) in an independent series of 75 progressive and non-progressive T1G3 BC patients. Univariate logistic regression was used to identify individual predictors. A variable selection method was used to develop a multiplex biomarker model. Discrimination of the model was measured by area under the receiver-operating characteristic curve. Interaction networks between the genes of the model were built by GeneMANIA Cytoscape plugin.

RESULTS

A total of 1294 genes were found differentially expressed between progressive and non-progressive patients. Differential expression of 15 genes was validated by qPCR in an additional set of samples. A five-gene expression signature (ANXA10, DAB2, HYAL2, SPOCD1, and MAP4K1) discriminated progressive from non-progressive T1G3 BC patients with a sensitivity of 79% and a specificity of 86% (AUC = 0.83). Direct interactions between the five genes of the model were not found.

CONCLUSIONS

Progressive and non-progressive T1G3 bladder tumours have shown different gene expression patterns. To identify T1G3 BC patients with a high risk of progression, a five-gene expression signature has been developed.

Exploring the role of sphingolipid machinery during the epithelial to mesenchymal transition program using an integrative approach

The epithelial to mesenchymal transition (EMT) program is activated in epithelial cancer cells and facilitates their ability to metastasize based on enhanced migratory, proliferative, anti-apoptotic, and pluripotent capacities. Given the fundamental impact of sphingolipid machinery to each individual process, the sphingolipid-related mechanisms might be considered among the most prominent drivers/players of EMT; yet, there is still limited knowledge. Given the complexity of the interconnected sphingolipid system, which includes distinct sphingolipid mediators, their synthesizing enzymes, receptors and transporters, we herein apply an integrative approach for assessment of the sphingolipid-associated mechanisms underlying EMT program. We created the sphingolipid-/EMT-relevant 41-gene/23-gene signatures which were applied to denote transcriptional events in a lung cancer cell-based EMT model. Based on defined 35-gene sphingolipid/EMT-attributed signature of regulated genes, we show close associations between EMT markers, genes comprising the sphingolipid network at multiple levels and encoding sphingosine 1-phosphate (S1P)-/ceramide-metabolizing enzymes, S1P and lysophosphatidic acid (LPA) receptors and S1P transporters, pluripotency genes and inflammation-related molecules, and demonstrate the underlying biological pathways and regulators. Mass spectrometry-based sphingolipid analysis revealed an EMT-attributed shift towards increased S1P and LPA accompanied by reduced ceramide levels. Notably, using transcriptomics data across various cell-based perturbations and neoplastic tissues (24193 arrays), we identified the sphingolipid/EMT signature primarily in lung adenocarcinoma tissues; besides, bladder, colorectal and prostate cancers were among the top-ranked. The findings also highlight novel regulatory associations between influenza virus and the sphingolipid/EMT-associated mechanisms. In sum, data propose the multidimensional contribution of sphingolipid machinery to pathological EMT and may yield new biomarkers and therapeutic targets.

Cytoplasmatic and Nuclear YAP1 and pYAP1 Staining in Urothelial Bladder Cancer

INTRODUCTION

Yes-associated protein 1 (YAP1), the nuclear effector of the Hippo pathway, plays an important role in many tumor entities. We evaluated staining and clinical significance of YAP1 and phosphorylated YAP1 (pYAP1) in urothelial bladder cancer (BCA).

MATERIALS AND METHODS

We used a tissue micorarray with samples of patients with muscle-invasive bladder cancer (MIBC, n = 192), non-muscle-invasive bladder cancer (NMIBC, n = 192) and normal urothelial bladder tissue (CTRL, n = 38) to determine the immunhistochemical staining of YAP1 and pYAP1. Cytoplasmatic and nuclear levels were evaluated. The t test was used for comparative analysis. Overall survival and progression-free survival were evaluated by Kaplan-Meier estimates and the Cox proportional hazard regression model.

RESULTS

Nuclear YAP1 as well as cytoplasmatic pYAP1 levels were higher in CTRL than in BCA, whereby both--NMIBC and MIBC--had lower levels than CTRL. Among patients with MIBC, cytoplasmatic YAP1 and pYAP1 staining decreased with advanced stage. YAP1 and pYAP1 staining did not correlate with the recurrence rate, progression-free, cancer-specific or overall survival.

CONCLUSIONS

Immunhistochemical staining and subcellular localization of YAP1 and pYAP1 are different for BCA, NMIBC, MIBC and CTRL, indicating that the Hippo pathway is involved in urothelial carcinogenesis.

Impact of reduced levels of APE1 transcripts on the survival of patients with urothelial carcinoma of the bladder

Molecular evidence indicates that alterations in genes involved in the maintenance of genome stability may be related to susceptibility to bladder carcinoma. Our goal was to evaluate the prognostic role of base excision repair (BER) genes in a cohort of patients diagnosed with primary urothelial carcinoma of the bladder (UCB). The levels of all APE1, XRCC1 and POLB transcripts were detected by quantitative real-time PCR (qPCR) technique in tumor samples from 52 patients undergoing transurethral resection (TUR) for primary UCB at the Department of Urology, Brazilian National Cancer Institute, Rio de Janeiro. Increased levels of APE1, XRCC1 and POLB transcripts were significantly associated with high-grade tumors when compared to these levels in low-grade tumors (p<0.01) and could be attributed to different mechanisms of transcriptional regulation as a response to tumorigenesis and oxidative stress. By analyzing the collected data in the present study, regardless of pathological grade or stage, univariate analysis revealed that the reduced levels of APE1 transcripts were significantly associated with cancer-specific mortality (p=0.032). Furthermore, the variant genotype (TG/GG) of the APE1 T1349G polymorphism was observed in 75% of a subset of patients who concomitantly experienced reduced levels of the APE1 transcript and death and/or recurrence events. Taken together, our data reinforce the idea that human DNA repair mechanisms must be finely regulated in order to avoid instability leading to tumorigenesis and poor clinical outcomes in UCB patients.

Developing proteomic biomarkers for bladder cancer: towards clinical application

Clinical use of proteomic biomarkers has the potential to substantially improve the outcomes of patients with bladder cancer. An unmet clinical need evidently exists for noninvasive biomarkers, which might enable improvements in both the diagnosis and prognosis of patients with bladder cancer, as well as improved monitoring of patients for the presence of recurrence. Urine is considered the optimal noninvasive source of proteomic biomarkers in patients with bladder cancer. Currently, a number of single-protein biomarkers have been detected in urine and tissue using a variety of proteomic techniques, each having specific conceptual considerations and technical implications. Promising preclinical data are available for several of these proteins; however, the combination of single urinary proteins into multimarker panels might better encompass the molecular heterogeneity of bladder cancer within this patient population, and prove more effective in clinical use.

Gene expression profiles in prostate cancer: identification of candidate non-invasive diagnostic markers

OBJECTIVE

To analyze gene expression profiles of prostate cancer (PCa) with the aim of determining the relevant differentially expressed genes and subsequently ascertain whether this differential expression is maintained in post-prostatic massage (PPM) urine samples.

MATERIAL AND METHODS

Forty-six tissue specimens (36 from PCa patients and 10 controls) and 158 urine PPM-urines (113 from PCa patients and 45 controls) were collected between December 2003 and May 2007. DNA microarrays were used to identify genes differentially expressed between tumour and control samples. Ten genes were technically validated in the same tissue samples by quantitative RT-PCR (RT-qPCR). Forty two selected differentially expressed genes were validated in an independent set of PPM-urines by qRT-PCR.

RESULTS

Multidimensional scaling plot according to the expression of all the microarray genes showed a clear distinction between control and tumour samples. A total of 1047 differentially expressed genes (FDR≤.1) were indentified between both groups of samples. We found a high correlation in the comparison of microarray and RT-qPCR gene expression levels (r=.928, P<.001). Thirteen genes maintained the same fold change direction when analyzed in PPM-urine samples and in four of them (HOXC6, PCA3, PDK4 and TMPRSS2-ERG), these differences were statistically significant (P<.05).

CONCLUSION

The analysis of PCa by DNA microarrays provides new putative mRNA markers for PCa diagnosis that, with caution, can be extrapolated to PPM-urines.

Expression of histone deacetylases 1, 2 and 3 in urothelial bladder cancer

BACKGROUND

Histone deacetylases (HDACs) are known to be associated with an overexpression in different types of cancer such as colon and prostate cancer. In this study we aimed to evaluate the protein expression of class I HDACs in urothelial carcinoma of the bladder.

METHODS

A tissue microarray containing 348 tissuesamples from 174 patients with a primary urothelial carcinoma of the bladder was immunohistochemically stained for HDAC 1, 2 and 3. Intensity of staining was evaluated and the association with clinico-pathological features and prognosis was assessed.

RESULTS

High HDAC expression levels were found in 40 to 60% of all investigated urothelial carcinomas (HDAC-1: 40%, HDAC-2: 42%, HDAC-3: 59%).HDAC-1 and HDAC-2 were significantly associated with higher tumour grades.Although all three markers could not predict progression in univariate analyses, high HDAC-1 expression was associated with a trend toward poorer prognosis. Patients with high-grade tumours and high expression levels of HDAC-1 were more likely to progress compared to all other patients (p < 0.05).

CONCLUSIONS

High-grade noninvasive papillary bladder tumours are associated with high expression levels of HDAC-1 and HDAC-2. High grade tumours in combination with high expression of HDAC-1 showed a worse prognosis than the other tumours. The high expression levels of HDACs observed particularly in high grade urothelial bladder cancer clearly warrant subsequent studies on the potential use of HDAC inhibitors as a novel therapeutic approach.

Limited efficacy of specific HDAC6 inhibition in urothelial cancer cells

Epigenetic modifiers such as histone deacetylases (HDACs) have come into focus as novel drug targets for cancer therapy due to their functional role in tumor progression. Since common pan-HDAC inhibitors have adverse side effects and minor anti-cancer activity against solid tumors, enzyme-specific inhibitors were developed. HDAC6 is especially well-suited for specific inhibition due to its unique domain structure and mode of action and has been suggested to provide an exceptionally suitable target for cancer therapy. However, expression and function of HDACs have been insufficiently studied in urothelial cancers (UC), a disease urgently requiring new therapeutic approaches. The present study sought to evaluate HDAC6 as a target for treatment of urothelial cancers with enzyme-specific inhibitors. We observed moderate HDAC6 overexpression in urothelial cancer tissues and a broad range of expression in urothelial cancer cell lines. In the cell lines Tubacin was the most potent inhibitor, compared with Tubastatin and ST-80, but still active only at high micromolar concentrations. HDAC6 expression levels correlated poorly with sensitivity to enzyme inhibition. Combined treatments with heat shock, HSP90 inhibition by 17-AAG, proteasome inhibition by bortezomib, or DNA-damaging agents did not result in significant synergistic effects. Experiments with siRNA-mediated knockdown further underlined that urothelial cancer cells do not critically depend on HDAC6 expression for survival.

Serum total hCGβ level is an independent prognostic factor in transitional cell carcinoma of the urothelial tract

Background:

Serum total human chorionic gonadotrophin β subunit (hCGβ) level might have prognostic value in urothelial transitional cell carcinoma (TCC) but has not been investigated for independence from other prognostic variables.

Methods:

We utilised a clinical database of patients receiving chemotherapy between 2005 and 2011 for urothelial TCC and an independent cohort of radical cystectomy patients for validation purposes. Prognostic variables were tested by univariate Kaplan–Meier analyses and log-rank tests. Statistically significant variables were then assessed by multivariate Cox regression. Total hCGβ level was dichotomised at < vs ⩾2 IU l⁻¹.

Results:

A total of 235 chemotherapy patients were eligible. For neoadjuvant chemotherapy, established prognostic factors including low ECOG performance status, normal haemoglobin, lower T stage and suitability for cisplatin-based chemotherapy were associated with favourable survival in univariate analyses. In addition, low hCGβ level was favourable when assessed either before (median survival not reached vs 1.86 years, P=0.001) or on completion of chemotherapy (4.27 vs 0.42 years, P=0.000002). This was confirmed in multivariate analyses and in patients receiving first- and second-line palliative chemotherapy, and in a radical cystectomy validation set.

Conclusions:

Serum total hCGβ level is an independent prognostic factor in patients receiving chemotherapy for urothelial TCC in both curative and palliative settings.

Advanced urothelial carcinoma: next-generation sequencing reveals diverse genomic alterations and targets of therapy

Although urothelial carcinoma (UC) of the urinary bladder generally portends a favorable prognosis, metastatic tumors often follow an aggressive clinical course. DNA was extracted from 40 μm of formalin-fixed, paraffin-embedded (FFPE) sections from 35 stage IV UCs that had relapsed and progressed after primary surgery and conventional chemotherapy. Next-generation sequencing (NGS) was performed on hybridization-captured, adaptor ligation-based libraries for 3320 exons of 182 cancer-related genes plus 37 introns from 14 genes frequently rearranged in cancer to at an average sequencing depth of 1164 × and evaluated for all classes of genomic alterations (GAs). Actionable GAs were defined as those impacting the selection of targeted anticancer therapies on the market or in registered clinical trials. A total of 139 GAs were identified, with an average of 4.0 GAs per tumor (range 0-10), of which 78 (56%) were considered actionable, with an average of 2.2 per tumor (range 0-7). Twenty-nine (83%) cases harbored at least one actionable GA including: PIK3CA (9 cases; 26%); CDKN2A/B (8 cases; 23%); CCND1 (5 cases; 14%); FGFR1 (5 cases; 14%); CCND3 (4 cases; 11%); FGFR3 (4 cases; 11%); MCL1 (4 cases; 11%); MDM2 (4 cases; 11%); EGFR (2 cases, 6%); ERBB2 (HER2/neu) (2 cases, 6%); NF1 (2 cases, 6%) and TSC1 (2 cases, 6%). Notable additional alterations included TP53 (19 cases, 54%) and RB1 (6 cases; 17%). Genes involved in chromatin modification were altered by nonsense mutation, splice site mutation or frameshift indel in a mutually exclusive manner in nearly half of all cases including KDM6A (10 cases; 29%) and ARID1A (7 cases; 20%). Comprehensive NGS of 35 UCs of the bladder revealed a diverse spectrum of actionable GAs in 83% of cases, which has the potential to inform treatment decisions for patients with relapsed and metastatic disease.

Microarray gene expression profiling and analysis of bladder cancer supports the sub-classification of T1 tumours into T1a and T1b stages

OBJECTIVE

To try and identify a molecular signature for pathological staging and/or grading. through microarray analysis.

PATIENTS AND METHODS

We performed a prospective multicentre study between September 2007 and May 2008 that included 108 bladder tumours (45 pTa, 35 pT1 and 28>pT1). Microarray analysis was performed using Agilent Technologies Human Whole Genome 4 × 44K oligonucleotide microarrays (Agilent, Santa Clara, CA, USA). A 'dual colour' method was used vs a reference pool of tumours. From the lists of genes provided by the Biometric Research Branch class comparison analyses, we validated the microarray results of 38 selected differentially expressed genes using reverse transcriptase quantitative PCR in another bladder tumour cohort (n = 95).

RESULTS

The cluster 'superficial vs invasive stage' correctly classified 92.9% of invasive stages and 66.3% of superficial stages. Among the superficial tumours, the cluster analysis showed that pT1b tumours were closer to invasive stages than pT1a tumours. We also found molecular differences between low and high grade superficial tumours, but these differences were less well defined than the difference observed for staging.

CONCLUSIONS

We confirmed that the histopathological classification into subgroups pTa, pT1a and pT1b can be translated into a molecular signature with a continuous progression of deregulation (overexpression or repression of these genes) from superficial (pTa) to more invasive (pT1a then b) stages.

Using microRNA profiling in urine samples to develop a non-invasive test for bladder cancer

Current standard methods used to detect and monitor bladder urothelial cell carcinoma (UCC) are invasive or have low sensitivity. The incorporation into clinical practice of a non-invasive tool for UCC assessment would enormously improve patients' quality of life and outcome. This study aimed to examine the microRNA (miRNA) expression profiles in urines of UCC patients in order to develop a non-invasive accurate and reliable tool to diagnose and provide information on the aggressiveness of the tumor. We performed a global miRNA expression profiling analysis of the urinary cells from 40 UCC patients and controls using TaqMan Human MicroRNA Array followed by validation of 22 selected potentially diagnostic and prognostic miRNAs in a separate cohort of 277 samples using a miRCURY LNA qPCR system. miRNA-based signatures were developed by multivariate logistic regression analysis and internally cross-validated. In the initial cohort of patients, we identified 40 and 30 aberrantly expressed miRNA in UCC compared with control urines and in high compared with low grade tumors, respectively. Quantification of 22 key miRNAs in an independent cohort resulted in the identification of a six miRNA diagnostic signature with a sensitivity of 84.8% and specificity of 86.5% (AUC = 0.92) and a two miRNA prognostic model with a sensitivity of 84.95% and a specificity of 74.14% (AUC = 0.83). Internal cross-validation analysis confirmed the accuracy rates of both models, reinforcing the strength of our findings. Although the data needs to be externally validated, miRNA analysis in urine appears to be a valuable tool for the non-invasive assessment of UCC.

Gene expression is highly correlated on the chromosome level in urinary bladder cancer

OBJECTIVE

Chromosome correlation maps display correlations between gene expression patterns on the same chromosome. Our goal was to map the genes on chromosome regions and to identify correlations through their location on chromosome regions.

MATERIALS AND METHODS

Following microarray analysis we used Ingenuity Pathway Analysis (IPA) to construct gene networks of the co-deregulated genes in bladder cancer. Chromosome mapping, mathematical modeling and data simulations were performed using the WebGestalt and Matlab(®) softwares.

RESULTS

The top deregulated molecules among 129 bladder cancer samples were implicated in the PI3K/AKT signaling, cell cycle, Myc-mediated apoptosis signaling and ERK5 signaling pathways. Their most prominent molecular and cellular functions were related to cell cycle, cell death, gene expression, molecular transport and cellular growth and proliferation. Chromosome correlation maps allowed us to detect significantly co-expressed genes along the chromosomes. We identified strong correlations among tumors of Tα-grade 1, as well as for those of Tα-grade 2, in chromosomes 1, 2, 3, 7, 12 and 19. Chromosomal domains of gene co-expression were revealed for the normal tissues, as well. The expression data were further simulated, exhibiting an excellent fit (0.7 < R(2) < 0.9). The simulations revealed that along the different samples, genes on same chromosomes are expressed in a similar manner.

CONCLUSIONS

Gene expression is highly correlated on the chromosome level. Chromosome correlation maps of gene expression signatures can provide further information on gene regulatory mechanisms. Gene expression data can be simulated using polynomial functions.

Combined proteome and transcriptome analyses for the discovery of urinary biomarkers for urothelial carcinoma

Background:

Proteomic discovery of cancer biomarkers in body fluids is challenging because of their low abundance in a complex background. Altered gene expression in tumours may not reflect protein levels in body fluids. We have tested combining gene expression profiling of tumours with proteomic analysis of cancer cell line secretomes as a strategy to discover urinary biomarkers for bladder cancer.

Methods:

We used shotgun proteomics to identify proteins secreted by three bladder cancer cell lines. Secreted proteins with high mRNA levels in bladder tumours relative to normal urothelium were assayed by ELISA in urine samples from 642 patients.

Results:

Midkine and HAI-1 were significantly increased in bladder cancer patients, with the highest levels in invasive disease (area under the receiver operating characteristic curve 0.89 vs non-cancer). The urinary concentration of both proteins was too high to be explained by bladder cancer associated haematuria and most likely arises by direct tumour secretion.

Conclusions:

This ‘dual-omic' strategy identified tumour secreted proteins whose urine concentrations are increased significantly by bladder cancer. Combined secretome-transcriptome analysis may be more useful than direct proteomic analysis of body fluids for biomarker discovery in both bladder cancer and other tumour types.

A candidate molecular biomarker panel for the detection of bladder cancer

BACKGROUND

Bladder cancer is among the five most common malignancies worldwide, and due to high rates of recurrence, one of the most prevalent. Improvements in noninvasive urine-based assays to detect bladder cancer would benefit both patients and health care systems. In this study, the goal was to identify urothelial cell transcriptomic signatures associated with bladder cancer.

METHODS

Gene expression profiling (Affymetrix U133 Plus 2.0 arrays) was applied to exfoliated urothelia obtained from a cohort of 92 subjects with known bladder disease status. Computational analyses identified candidate biomarkers of bladder cancer and an optimal predictive model was derived. Selected targets from the profiling analyses were monitored in an independent cohort of 81 subjects using quantitative real-time PCR (RT-PCR).

RESULTS

Transcriptome profiling data analysis identified 52 genes associated with bladder cancer (P ≤ 0.001) and gene models that optimally predicted class label were derived. RT-PCR analysis of 48 selected targets in an independent cohort identified a 14-gene diagnostic signature that predicted the presence of bladder cancer with high accuracy.

CONCLUSIONS

Exfoliated urothelia sampling provides a robust analyte for the evaluation of patients with suspected bladder cancer. The refinement and validation of the multigene urothelial cell signatures identified in this preliminary study may lead to accurate, noninvasive assays for the detection of bladder cancer.

IMPACT

The development of an accurate, noninvasive bladder cancer detection assay would benefit both the patient and health care systems through better detection, monitoring, and control of disease.

Predictive and prognostic molecular markers for cancer medicine

Over the last 10 years there has been an explosion of information about the molecular biology of cancer. A challenge in oncology is to translate this information into advances in patient care. While there are well-formed routes for translating new molecular information into drug therapy, the routes for translating new information into sensitive and specific diagnostic, prognostic and predictive tests are still being developed. Similarly, the science of using tumor molecular profiles to select clinical trial participants or to optimize therapy for individual patients is still in its infancy. This review will summarize the current technologies for predicting treatment response and prognosis in cancer medicine, and outline what the future may hold. It will also highlight the potential importance of methods that can integrate molecular, histopathological and clinical information into a synergistic understanding of tumor progression. While these possibilities are without doubt exciting, significant challenges remain if we are to implement them with a strong evidence base in a widely available and cost-effective manner.

Spotlight on differentially expressed genes in urinary bladder cancer

INTRODUCTION

We previously identified common differentially expressed (DE) genes in bladder cancer (BC). In the present study we analyzed in depth, the expression of several groups of these DE genes.

MATERIALS AND METHODS

Samples from 30 human BCs and their adjacent normal tissues were analyzed by whole genome cDNA microarrays, qRT-PCR and Western blotting. Our attention was focused on cell-cycle control and DNA damage repair genes, genes related to apoptosis, signal transduction, angiogenesis, as well as cellular proliferation, invasion and metastasis. Four publicly available GEO Datasets were further analyzed, and the expression data of the genes of interest (GOIs) were compared to those of the present study. The relationship among the GOI was also investigated. GO and KEGG molecular pathway analysis was performed to identify possible enrichment of genes with specific biological themes.

RESULTS

Unsupervised cluster analysis of DNA microarray data revealed a clear distinction in BC vs. control samples and low vs. high grade tumors. Genes with at least 2-fold differential expression in BC vs. controls, as well as in non-muscle invasive vs. muscle invasive tumors and in low vs. high grade tumors, were identified and ranked. Specific attention was paid to the changes in osteopontin (OPN, SPP1) expression, due to its multiple biological functions. Similarly, genes exhibiting equal or low expression in BC vs. the controls were scored. Significant pair-wise correlations in gene expression were scored. GO analysis revealed the multi-facet character of the GOIs, since they participate in a variety of mechanisms, including cell proliferation, cell death, metabolism, cell shape, and cytoskeletal re-organization. KEGG analysis revealed that the most significant pathway was that of Bladder Cancer (p = 1.5×10(-31)).

CONCLUSIONS

The present work adds to the current knowledge on molecular signature identification of BC. Such works should progress in order to gain more insight into disease molecular mechanisms.

Identification of common differentially expressed genes in urinary bladder cancer

Background

Current diagnosis and treatment of urinary bladder cancer (BC) has shown great progress with the utilization of microarrays.

Purpose

Our goal was to identify common differentially expressed (DE) genes among clinically relevant subclasses of BC using microarrays.

Methodology/Principal Findings

BC samples and controls, both experimental and publicly available datasets, were analyzed by whole genome microarrays. We grouped the samples according to their histology and defined the DE genes in each sample individually, as well as in each tumor group. A dual analysis strategy was followed. First, experimental samples were analyzed and conclusions were formulated; and second, experimental sets were combined with publicly available microarray datasets and were further analyzed in search of common DE genes. The experimental dataset identified 831 genes that were DE in all tumor samples, simultaneously. Moreover, 33 genes were up-regulated and 85 genes were down-regulated in all 10 BC samples compared to the 5 normal tissues, simultaneously. Hierarchical clustering partitioned tumor groups in accordance to their histology. K-means clustering of all genes and all samples, as well as clustering of tumor groups, presented 49 clusters. K-means clustering of common DE genes in all samples revealed 24 clusters. Genes manifested various differential patterns of expression, based on PCA. YY1 and NFκB were among the most common transcription factors that regulated the expression of the identified DE genes. Chromosome 1 contained 32 DE genes, followed by chromosomes 2 and 11, which contained 25 and 23 DE genes, respectively. Chromosome 21 had the least number of DE genes. GO analysis revealed the prevalence of transport and binding genes in the common down-regulated DE genes; the prevalence of RNA metabolism and processing genes in the up-regulated DE genes; as well as the prevalence of genes responsible for cell communication and signal transduction in the DE genes that were down-regulated in T1-Grade III tumors and up-regulated in T2/T3-Grade III tumors. Combination of samples from all microarray platforms revealed 17 common DE genes, (BMP4, CRYGD, DBH, GJB1, KRT83, MPZ, NHLH1, TACR3, ACTC1, MFAP4, SPARCL1, TAGLN, TPM2, CDC20, LHCGR, TM9SF1 and HCCS) 4 of which participate in numerous pathways.

Conclusions/Significance

The identification of the common DE genes among BC samples of different histology can provide further insight into the discovery of new putative markers.

Methods for the discovery of low-abundance biomarkers for urinary bladder cancer in biological fluids

For the study of bladder cancer and the identification of respective tumor markers, blood and, in particular, urine constitute suitable sources of biological material, while both harboring their specific challenges for analytics concerning low-abundance biomarkers. Dissolved proteins and nucleic acids as well as cells and cell-bound molecules can be the analytes. In urine, exfoliated bladder tumor cells have to be identified and in blood, circulating tumor cells have to be detected among huge amounts of other cells. For the detection of both low-abundance cells and molecules, their specific enrichment prior to analysis is advantageous or even necessary. Adapted methods for the analysis of proteomes and subproteomes by 2D-gel electrophoresis, multidimensional chromatography and antibody arrays are discussed. Analysis of nucleic acid-based markers exploits the high amplification power of PCR and modified PCR combined with previous (subtransciptomes) or subsequent (microarray) enrichment to sensitively and specifically detect markers. DNA mutations, DNA-methylation status and apoptotic DNA fragments, as well as levels of ribonucleic acids including microRNAs, can be analyzed by means of these methods. Finally, the challenge of identifying circulating tumor cells and assigning them to their original tissue is critically discussed.

Recombinant antigen microarrays for serum/plasma antibody detection

Recombinant antigen arrays represent a new frontier in parallel analysis of multiple immune response profiles requiring only minute blood samples. In this article, we review the benefits and pitfalls of recombinant antigen microarrays developed for multiplexed antibody quantification. In particular, we describe the development of antigen arrays presenting a set of Y chromosome-encoded antigens, called H-Y antigens. These H-Y antigens are immunologically recognized as minor histocompatibility antigens (mHA) following allogeneic blood and organ transplantation. Clinically relevant B-cell responses against H-Y antigens have been demonstrated in male patients receiving female hematopoietic stem cell grafts and are associated with chronic graft versus host development. This chapter discusses our recombinant antigen microarray methods to measure these clinically relevant allo-antibodies.

Bladder cancer: translating molecular genetic insights into clinical practice

Transitional cell (urothelial) carcinoma of the bladder is the second most common urologic malignancy and is one of the best understood neoplasms, with relatively well-defined pathogenetic pathways, natural history, and tumor biology. Conventional clinical and pathologic parameters are widely used to grade and stage tumors and to predict clinical outcome of transitional cell carcinoma; but the predictive ability of these parameters is limited, and there is a lack of indices that could allow prospective assessment of risk for individual patients. In the last decade, a wide range of candidate biomarkers representing key pathways in carcinogenesis have been reported to be clinically relevant and potentially useful as diagnostic and prognostic molecular markers, and as potential therapeutic targets. The use of molecular markers has facilitated the development of novel and more accurate diagnostic, prognostic, and therapeutic strategies. FGFR3 and TP53 mutations have been recognized as key genetic pathways in the carcinogenesis of transitional cell carcinoma. FGFR3 appears to be the most frequently mutated oncogene in transitional cell carcinoma; its mutation is strongly associated with low tumor grade, early stage, and low recurrence rate, which confer a better overall prognosis. In contrast, TP53 mutations are associated with higher tumor grade, more advanced stage, and more frequent tumor recurrences. These molecular markers offer the potential to characterize individual urothelial neoplasms more completely than is possible by histologic evaluation alone. Areas in which molecular markers may prove valuable include prediction of tumor recurrence, molecular staging of transitional cell carcinoma, detection of lymph node metastasis and circulating cancer cells, identification of therapeutic targets, and prediction of response to therapy. With accumulating molecular knowledge of transitional cell carcinoma, we are closer to the goal of bridging the gap between molecular findings and clinical outcomes. Assessment of key genetic pathways and expression profiles could ultimately establish a set of molecular markers to predict the biological nature of tumors and to establish new standards for molecular tumor grading, classification, and prognostication. The main focus of this review is to discuss clinically relevant biomarkers that might be useful in the management of transitional cell carcinoma and to provide approaches in the analysis of molecular pathways that influence the clinical course of bladder cancer.