Circulating Biomarkers in Bladder Cancer

Evaluating for Correlations between Specific Metabolites in Patients Receiving First-Line or Second-Line Immunotherapy for Metastatic or Recurrent NSCLC: An Exploratory Study Based on Two Cohorts

Immune checkpoint inhibitors (ICI) have displayed impressive clinical efficacy in the context of non-small cell lung cancer (NSCLC). However, most patients do not achieve long-term survival. Minimally invasive collected samples are attracting significant interest as new fields of biomarker study, and metabolomics is one of these growing fields. We concentrated on the augmented value of the metabolomic profile in differentiating long-term survival from short-term survival in patients with NSCLC subjected to ICIs. We prospectively recruited 97 patients with stage IV NSCLC who were treated with anti-PD-1 inhibitor, including patients treated with monoimmunotherapy as second-line treatment (Cohort 1), and patients treated with combination immunotherapy as first-line treatment (Cohort 2). Each cohort was divided into long-term and short-term survival groups. All blood samples were collected before beginning immunotherapy. Serum metabolomic profiling was performed by UHPLC-Q-TOF MS analysis. Pareto-scaled principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis were performed. In Cohort 1, the mPFS and mOS of long-survival patients are 27.05 and NR months, respectively, and those of short-survival patients are 2.79 and 10.59 months. In Cohort 2, the mPFS and mOS of long-survival patients are 27.35 and NR months, respectively, and those of short-survival patients are 3.77 and 12.17 months. A total of 41 unique metabolites in Cohort 1 and 47 in Cohort 2 were screened. In Cohorts 1 and 2, there are 6 differential metabolites each that are significantly associated with both progression-free survival and overall survival. The AUC values for all groups ranged from 0.73 to 0.95. In cohort 1, the top 3 enriched KEGG pathways, as determined through significant different metabolic pathway analysis, were primary bile acid biosynthesis, African trypanosomiasis, and choline metabolism in cancer. In Cohort 2, the top 3 enriched KEGG pathways were the citrate cycle (TCA cycle), PPAR signaling pathway, and primary bile acid biosynthesis. The primary bile acid synthesis pathway had significant differences in the long-term and short-term survival groups in both Cohorts 1 and 2. Our study suggests that peripheral blood metabolomic analysis is critical for identifying metabolic biomarkers and pathways responsible for the patients with NSCLC treated with ICIs.

Fatty Acid Metabolism Reprogramming in Advanced Prostate Cancer

Prostate cancer (PCa) is a carcinoma in which fatty acids are abundant. Fatty acid metabolism is rewired during PCa development. Although PCa can be treated with hormone therapy, after prolonged treatment, castration-resistant prostate cancer can develop and can lead to increased mortality. Changes to fatty acid metabolism occur systemically and locally in prostate cancer patients, and understanding these changes may lead to individualized treatments, especially in advanced, castration-resistant prostate cancers. The fatty acid metabolic changes are not merely reflective of oncogenic activity, but in many cases, these represent a critical factor in cancer initiation and development. In this review, we analyzed the literature regarding systemic changes to fatty acid metabolism in PCa patients and how these changes relate to obesity, diet, circulating metabolites, and peri-prostatic adipose tissue. We also analyzed cellular fatty acid metabolism in prostate cancer, including fatty acid uptake, de novo lipogenesis, fatty acid elongation, and oxidation. This review broadens our view of fatty acid switches in PCa and presents potential candidates for PCa treatment and diagnosis.

Discovery of novel serum metabolic biomarkers in patients with polycystic ovarian syndrome and premature ovarian failure

Several widely recognized metabolites play a role in regulating the pathophysiological processes of various disorders. Nonetheless, the lack of effective biomarkers for the early diagnosis of polycystic ovarian syndrome (PCOS) and premature ovarian failure (POF) has led to the discovery of serum-based metabolic biomarkers for these disorders. We aimed to identify various differentially expressed metabolites (DEMs) through serum-based metabolic profiling in patients with PCOS and POF and in healthy individuals by using liquid chromatography–mass spectrometry analysis. Furthermore, heatmap clustering, correlation, and Z-score analyses were performed to identify the top DEMs. Kyoto Encyclopedia of Genes and Genomes enriched pathways of DEMs were determined using metabolite-based databases. Moreover, the clinical significance of these DEMs was evaluated on the basis of area under the receiver operating characteristic curve. Significantly dysregulated expressions of several metabolites were observed in the intergroup comparisons of the PCOS, POF, and healthy control groups. Furthermore, 6 DEMs were most frequently observed among the three groups. The expressions of these DEMs were not only directly correlated but also exhibited potential significance in patients with PCOS and POF. Novel metabolites with up/downregulated expressions can be discovered in patients with PCOS and POF using serum-based metabolomics; these metabolites show good diagnostic performance and can act as effective biomarkers for the early detection of PCOS and POF. Furthermore, these metabolites might be involved in the pathophysiological mechanisms of PCOS and POF via interplay with corresponding genes.

Serum Metabolic Profiling Identifies a Biomarker Panel for Improvement of Prostate Cancer Diagnosis

Objectives

To identify and validate a biomarker panel by serum metabolic profiling for improvement of PCa diagnosis.

Materials and Methods

Totally, 134 individuals were included in this study. Among them, 39 PCa patients and 45 control patients (negative prostate biopsy) were involved in the discovery phase and 50 healthy controls were enrolled for validation phase of metabolomics study. LC-MS Analysis was used for the identification of the serum metabolites of patients.

Results

Logistics regression analysis shows that 5 metabolites [dMePE(18:0/18:2), PC(16:0/20:2), PS(15:0/18:2), SM(d16:0/24:1], Carnitine C14:0) were significantly changed in PCa patients compared with control patients. A metabolic panel (MET) was calculated, showing a significantly higher diagnostic performance than PSA in differentiating PCa from control patients [AUC (MET vs. PSA): 0.823 ± 0.046 vs. 0.712 ± 0.057, p<0.001]. Moreover, this panel was superior to PSA in distinguishing PCa from negative prostate biopsies when PSA levels were less than 20 ng/ml [AUC (MET vs. PSA]: 0.836 ± 0.050 vs. 0.656 ± 0.067, p<0.001]. In the validation set, the MET panel yielded an AUC of 0.823 in distinguishing PCa patients from healthy controls, showing a significant improvement of PCa detection.

Conclusions

The metabolite biomarker panel discovered in this study presents a good diagnostic performance for the detection of PCa.

PUM1 and RNase P genes as potential cell-free DNA markers in breast cancer

BACKGROUND

Cell-free DNA (cfDNA) is used in clinical research to identify biomarkers for diagnosis of and follow-up on cancer. Here, we propose a fast and innovative approach using traditional housekeeping genes as cfDNA targets in a copy number analysis. We focus on the application of highly sensitive technology such as digital PCR (dPCR) to differentiate breast cancer (BC) patients and controls by quantifying regions of PUM1 and RPPH1 (RNase P) in plasma samples.

METHODS

We conducted a case-control study with 82 BC patients and 82 healthy women. cfDNA was isolated from plasma using magnetic beads and quantified by spectrophotometry to estimate total cfDNA. Then, both PUM1 and RPPH1 genes were specifically quantified by dPCR. Data analysis was calibrated using a reference genomic DNA in different concentrations.

RESULTS

We found RNase P and PUM1 values were correlated in the patient group (intraclass correlation coefficient [ICC] = 0.842), but they did not have any correlation in healthy women (ICC = 0.519). In dPCR quantification, PUM1 showed the capacity to distinguish early-stage patients and controls with good specificity (98.67%) and sensitivity (100%). Conversely, RNase P had lower cfDNA levels in triple-negative BC patients than luminal subtypes (p < 0.025 for both), confirming their utility for patient classification.

CONCLUSION

We propose the PUM1 gene as a cfDNA marker for early diagnosis of BC and RNase P as a cfDNA marker related to hormonal status and subtype classification in BC. Further studies with larger sample sizes are warranted.

Negative Correlation Between Circular RNA SMARC5 and MicroRNA 432, and Their Clinical Implications in Bladder Cancer Patients

Objective: Our study aimed to evaluate the correlation of circular RNA SMARCA5 (circ-SMARCA5) and microRNA 432 (miR-432) with clinical characteristics and survival in bladder cancer patients. Methods: Preoperative clinicopathologic features and survival data of 156 bladder cancer patients were retrospectively reviewed. A total of 156 cases of tumor tissues, whereas 71 cases out of 156 available adjacent tissues were obtained from the Pathology Department for circ-SMARCA5 and miR-432 detections using real-time quantitative polymerase chain reaction. Results: Circ-SMARCA5 was upregulated but miR-432 was downregulated in tumor tissues compared with adjacent tissues; meanwhile, circ-SMARCA5 expression was negatively correlated with miR-432 in bladder cancer tissues. Circ-SMARCA5 high expression was correlated with larger tumor size, higher tumor stage, and lymph node (LYN) metastasis. However, miR-432 high expression was correlated with single multiplicity, smaller tumor size, lower tumor stage, less LYN metastasis in bladder cancer patients. Regarding survival, circ-SMARCA5 high expression was correlated with shorter disease-free survival (DFS) and overall survival (OS); whereas, miR-432 high expression was correlated with longer DFS and OS in bladder cancer patients. Further multivariate Cox's regression analysis displayed that circ-SMARCA5 high expression was an independent predictive factor for both worse DFS and OS in bladder cancer patients. Conclusion: Circ-SMARCA5 high expression but miR-432 low expression is correlated with advanced tumor features and poor survival of bladder cancer patients, which present as potential prognostic markers in bladder cancer.

Development and Validation of a Nine-Redox-Related Long Noncoding RNA Signature in Renal Clear Cell Carcinoma

Background

Redox plays an essential role in the pathogeneses and progression of tumors, which could be regulated by long noncoding RNA (lncRNA). We aimed to develop and verify a novel redox-related lncRNA-based prognostic signature for clear cell renal cell carcinoma (ccRCC).

Materials and Methods

A total of 530 ccRCC patients from The Cancer Genome Atlas (TCGA) were included in this study. All the samples were randomly split into training and test group at a 1 : 1 ratio. Then, we screened differentially expressed redox-related lncRNAs and constructed a novel prognostic signature from the training group using the least absolute shrinkage and selection operation (LASSO) and COX regression. Next, to verify the accuracy of the signature, we conducted risk and survival analysis, as well as the construction of ROC curve, nomogram, and calibration curves in the training group, test group, and all samples. Finally, the redox gene-redox-related lncRNA interaction network was constructed, and gene set enrichment analysis (GSEA) was performed to investigate the status of redox-related functions between high/low-risk groups.

Results

A nine-redox-related lncRNA signature consisted of AC025580.3, COLCA1, AC027601.2, DLEU2, AC004918.3, AP006621.2, AL031670.1, SPINT1-AS1, and LAMA5-AS1 was significantly associated with overall survival in ccRCC patients. The signature proved efficient, and thus, a nomogram was successfully assembled. In addition, the GSEA results demonstrated that two major redox-related functions were enhanced in the high-risk group ccRCC patients.

Conclusions

Our findings robustly demonstrate that the nine-redox-related lncRNA signature could serve as an efficient prognostic indicator for ccRCC.

Circulating Non-coding RNAs in Renal Cell Carcinoma-Pathogenesis and Potential Implications as Clinical Biomarkers

Liquid biopsy-the determination of circulating cells, proteins, DNA or RNA from biofluids through a "less invasive" approach-has emerged as a novel approach in all cancer entities. Circulating non-(protein) coding RNAs including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and YRNAs can be passively released by tissue or cell damage or actively secreted as cell-free circulating RNAs, bound to lipoproteins or carried by exosomes. In renal cell carcinoma (RCC), a growing body of evidence suggests circulating non-coding RNAs (ncRNAs) such as miRNAs, lncRNAs, and YRNAs as promising and easily accessible blood-based biomarkers for the early diagnosis of RCC as well as for the prediction of prognosis and treatment response. In addition, circulating ncRNAs could also play a role in RCC pathogenesis and progression. This review gives an overview over the current study landscape of circulating ncRNAs and their involvement in RCC pathogenesis as well as their potential utility as future biomarkers in RCC diagnosis and treatment.

Amplification-free profiling of microRNA-122 biomarker in DILI patient serums, using the luminex MAGPIX system

Dynamic chemical labelling is a single-base specific method to enable detection and quantification of micro-Ribonucleic Acids in biological fluids without extraction and pre-amplification. In this study, dynamic chemical labelling was combined with the Luminex MAGPIX system to profile levels of microRNA-122 biomarker in serum from patients with Drug-Induced Liver Injury.

Proteomic research and diagnosis in bladder cancer: state of the art review

Purpose:

Proteomic biomarkers have been emerging as alternative methods to the gold standard procedures of cystoscopy and urine cytology in the diagnosis and surveillance of bladder cancer (BC). This review aims to update the state of the art of proteomics research and diagnosis in BC.

Materials and Methods:

We reviewed the current literature related to BC research on urinary, tissue, blood and cell line proteomics, using the Pubmed database.

Findings:

Two urinary protein biomarkers are FDA-approved (NMP22® and BTA® tests), only if performed along with cystoscopy for surveillance after initial diagnosis, but not in the primary diagnostic setting due to high false-positive rates in case of infections, stones and hematuria. There are a great number of non-FDA approved proteins being studied, with good preliminary results; panels of proteins seem valuable tools to be refined in ongoing trials. Blood proteins are a bigger challenge, because of the complexity of the serum protein profile and the scarcity of blood proteomic studies in BC. Previous studies with the BC tissue proteome do not correlate well with the urinary proteome, likely due to the tumor heterogeneity. Cell line proteomic research helps in the understanding of basic mechanisms that drive BC development and progression; the main difficulty is culturing low-grade tumors in vitro, which represents the majority of BC tumors in clinical practice.

Conclusion:

Protein biomarkers have promising value in the diagnosis, surveillance and prognostic of BC. Urine is the most appropriate body fluid for biomarker research in BC due to its easiness of sampling, stability and enrichment of shed and secreted tumor-specific proteins. Panels of biomarkers may exhibit higher sensitivity than single proteins in the diagnosis of BC at larger populations due to clinical and tumor heterogeneity. Prospective clinical trials are warranted to validate the relevance of proteomic data in the clinical management of BC.

Limited utility of qPCR-based detection of tumor-specific circulating mRNAs in whole blood from clear cell renal cell carcinoma patients

Background

RNA sequencing data is providing abundant information about the levels of dysregulation of genes in various tumors. These data, as well as data based on older microarray technologies have enabled the identification of many genes which are upregulated in clear cell renal cell carcinoma (ccRCC) compared to matched normal tissue. Here we use RNA sequencing data in order to construct a panel of highly overexpressed genes in ccRCC so as to evaluate their RNA levels in whole blood and determine any diagnostic potential of these levels for renal cell carcinoma patients.

Methods

A bioinformatics analysis with Python was performed using TCGA, GEO and other databases to identify genes which are upregulated in ccRCC while being absent in the blood of healthy individuals. Quantitative Real Time PCR (RT-qPCR) was subsequently used to measure the levels of candidate genes in whole blood (PAX gene) of 16 ccRCC patients versus 11 healthy individuals. PCR results were processed in qBase and GraphPadPrism and statistics was done with Mann-Whitney U test.

Results

While most analyzed genes were either undetectable or did not show any dysregulated expression, two genes, CDK18 and CCND1, were paradoxically downregulated in the blood of ccRCC patients compared to healthy controls. Furthermore, LOX showed a tendency towards upregulation in metastatic ccRCC samples compared to non-metastatic.

Conclusions

This analysis illustrates the difficulty of detecting tumor regulated genes in blood and the possible influence of interference from expression in blood cells even for genes conditionally absent in normal blood. Testing in plasma samples indicated that tumor specific mRNAs were not detectable. While CDK18, CCND1 and LOX mRNAs might carry biomarker potential, this would require validation in an independent, larger patient cohort.

An EMT-related gene signature for the prognosis of human bladder cancer

The transition from non-muscle-invasive bladder cancer (NMIBC) to muscle-invasive bladder cancer (MIBC) is detrimental to bladder cancer (BLCA) patients. Here, we aimed to study the underlying mechanism of the subtype transition. Gene set variation analysis (GSVA) revealed the epithelial-mesenchymal transition (EMT) signalling pathway with the most positive correlation in this transition. Then, we built a LASSO Cox regression model of an EMT-related gene signature in BLCA. The patients with high risk scores had significantly worse overall survival (OS) and disease-free survival (DFS) than those with low risk scores. The EMT-related gene signature also performed favourably in the accuracy of prognosis and in the subtype survival analysis. Univariate and multivariate Cox regression analyses demonstrated that the EMT-related gene signature, pathological N stage and age were independent prognostic factors for predicting survival in BLCA patients. Furthermore, the predictive nomogram model was able to effectively predict the outcome of BLCA patients by appropriately stratifying the risk score. In conclusion, we developed a novel EMT-related gene signature that has tumour-promoting effects, acts as a negative independent prognostic factor and might facilitate personalized counselling and treatment in BLCA.

Improving the diagnosis of high grade and stage bladder cancer by detecting increased urinary calprotectin expression in tumor tissue and tumor-associated inflammatory response

PURPOSE

To investigate whether measurement of urinary calprotectin can serve as a biomarker in the diagnosis of primary bladder cancer and to confirm its diagnostic role in determining high grade and stage disease.

MATERIALS AND METHODS

Urinary calprotectin was measured in spot urine samples from patients with primary bladder cancer and control subjects. To confirm levels in urine, tissue samples were also obtained from bladder tumor and healthy trigone of bladder by transurethral resection in both groups. Finally, calprotectin levels in tissue and urine of the patients and control subjects were compared and their diagnostic potential was investigated in high grade and stage bladder cancers.

RESULTS

Of 82 participants, 52 were patients with bladder cancer and 30 were control subjects. The two groups were comparable in terms of age, smoking status, and comorbidities. Tissue and urinary calprotectin levels were significantly higher in the bladder cancer group. In subgroup analyses, urinary calprotectin levels were significantly higher in patients with high-grade, muscle-invasive tumors. After receiver operating characteristic analyses, the sensitivity and specificity of urinary calprotectin was 100% and 96.7%, respectively, in the diagnosis of primary bladder cancer. High grade and stage bladder cancers were detected with sensitivity and specificity of 70% and 74.2%, and 80% and 84.8%, respectively.

CONCLUSIONS

Urinary calprotectin may be a valuable parameter in the diagnosis of primary bladder cancer with high sensitivity and specificity. Furthermore, it may be useful in the prediction of high grade and stage disease. However, more investigations are needed.

The current role of circulating biomarkers in non-muscle invasive bladder cancer

Non-muscle invasive bladder cancer (NMIBC) is characterized by its high rate of disease recurrence and relevant disease progression rates. Up to today clinical models are insufficiently predicting outcomes for reliable patient counseling and treatment decision-making. This particularly is a serious problem in patients with high-risk NMIBC who are at high risk for failure of local treatment and thus candidates for early radical cystectomy or even systemic (neoadjuvant) chemotherapy. Next to its clinical variability, bladder cancer is genetically a highly heterogeneous disease. There is an essential need of biomarkers for improving clinical staging, real-time monitoring of disease with or without active treatment, as well as improved outcome prognostication. Liquid biopsies of circulating biomarkers in the blood and urine are promising non-invasive diagnostics that hold the potential facilitating these needs. In this review we report the latest data and evidence on cell-free circulating tumor desoxyribonucleic acid (ctDNA) and circulating tumor cells (CTC) in NMIBC. We summarize their current status in clinical diagnostics, discuss limitations and address future needs.

A risk score staging system based on the expression of seven genes predicts the outcome of bladder cancer

Bladder cancer (BLCA) is among the most malignant types of cancer. At present, the prognostic tools available for this disease are insufficient. In the present study, the transcriptome of 1,049 BLCA samples from four datasets from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA) were analyzed. By utilizing the RNA-seq data provided by TCGA, a risk score staging system model was built to predict the outcome of patients with BLCA using random forest variable hunting and Cox multivariate regression. A total of 7 genes, including zinc finger protein 230, Bcl2-like 14, AHNAK, transmembrane protein 109, apolipoprotein L2, advanced glycation end-product specific receptor and amine oxidase, copper containing 2 were identified as predicting the survival time of patients with BLCA. The patients with a low risk score had a significantly higher survival rate than those with a high-risk score both in the training and validation datasets. Association analyses between risk score and other clinical information were additionally performed; it was demonstrated that the risk score was significantly associated with pathological stage. A nomogram was plotted to compare risk score and other clinical information. The risk score spanned the greatest range of points, indicating the relative accuracy of risk score. In summary, the risk staging model based on the expression of 7 genes is robust and performs more effectively than other clinical information in predicting a prognosis.

Developments in Point-of-Care Diagnostic Technology for Cancer Detection

Cancer is the cause of death for one in seven individuals worldwide. It is widely acknowledged that screening and early diagnosis are of vital importance for improving the likelihood of recovery. However, given the costly, time-consuming, and invasive nature of the many methods currently in use, patients often do not take advantage of the services available to them. Consequently, many researchers are exploring the possibility of developing fast, reliable, and non-invasive diagnostic tools that can be used directly or by local physicians at the point-of-care. Herein, we look at the use of established biomarkers in cancer therapy and investigate emerging biomarkers exhibiting future potential. The incorporation of these biomarkers into point-of-care devices could potentially reduce the strain currently experienced by screening programs in hospitals and healthcare systems. Results derived from point-of-care tests should be accurate, sensitive, and generated rapidly to assist in the selection of the best course of treatment for optimal patient care. Essentially, point-of-care diagnostics should enhance the well-being of patients and lead to a reduction in cancer-related deaths.

A Large-scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the third most lethal cancer worldwide. The lack of effective biomarkers for the early detection of HCC results in unsatisfactory curative treatments. Here, metabolite biomarkers were identified and validated for HCC diagnosis. A total of 1,448 subjects, including healthy controls and patients with chronic hepatitis B virus infection, liver cirrhosis, and HCC, were recruited from multiple centers in China. Liquid chromatography-mass spectrometry-based metabolomics methods were used to characterize the subjects' serum metabolic profiles and to screen and validate the HCC biomarkers. A serum metabolite biomarker panel including phenylalanyl-tryptophan and glycocholate was defined. This panel had a higher diagnostic performance than did α-fetoprotein (AFP) in differentiating HCC from a high-risk population of cirrhosis, such as an area under the receiver-operating characteristic curve of 0.930, 0.892, and 0.807 for the panel versus 0.657, 0.725, and 0.650 for AFP in the discovery set, test set, and cohort 1 of the validation set, respectively. In the nested case-control study, this panel had high sensitivity (range 80.0%-70.3%) to detect preclinical HCC, and its combination with AFP provided better risk prediction of preclinical HCC before clinical diagnosis. Besides, this panel showed a larger area under the receiver-operating characteristic curve than did AFP (0.866 versus 0.682) to distinguish small HCC, and 80.6% of the AFP false-negative patients with HCC were correctly diagnosed using this panel in the test set, which was corroborated by the validation set. The specificity and biological relevance of the identified biomarkers were further evaluated using sera from another two cancers and HCC tissue specimens, respectively. Conclusion: The discovered and validated serum metabolite biomarker panel exhibits good diagnostic performance for the early detection of HCC from at-risk populations. (Hepatology 2018;67:662-675).

Identification of Biomarkers Correlated with the TNM Staging and Overall Survival of Patients with Bladder Cancer

Objective: To identify candidate biomarkers correlated with clinical prognosis of patients with bladder cancer (BC). Methods: Weighted gene co-expression network analysis was applied to build a co-expression network to identify hub genes correlated with tumor node metastasis (TNM) staging of BC patients. Functional enrichment analysis was conducted to functionally annotate the hub genes. Protein-protein interaction network analysis of hub genes was performed to identify the interactions among the hub genes. Survival analyses were conducted to characterize the role of hub genes on the survival of BC patients. Gene set enrichment analyses were conducted to find the potential mechanisms involved in the tumor proliferation promoted by hub genes. Results: Based on the results of topological overlap measure based clustering and the inclusion criteria, top 50 hub genes were identified. Hub genes were enriched in cell proliferation associated gene ontology terms (mitotic sister chromatid segregation, mitotic cell cycle and, cell cycle, etc.) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (cell cycle, Oocyte meiosis, etc.). 17 hub genes were found to interact with ≥5 of the hub genes. Survival analysis of hub genes suggested that lower expression of MMP11, COL5A2, CDC25B, TOP2A, CENPF, CDCA3, TK1, TPX2, CDCA8, AEBP1, and FOXM1were associated with better overall survival of BC patients. BC samples with higher expression of hub genes were enriched in gene sets associated with P53 pathway, apical junction, mitotic spindle, G2M checkpoint, and myogenesis, etc. Conclusions: We identified several candidate biomarkers correlated with the TNM staging and overall survival of BC patients. Accordingly, they might be used as potential diagnostic biomarkers and therapeutic targets with clinical utility.

MiR-26a inhibits cell proliferation and induces apoptosis in human bladder cancer through regulating EZH2 bioactivity

Bladder cancer is the second most common malignant tumor of the urinary tract worldwide and is associated with significant morbidity and mortality. EZH2, the enzymatic subunit of Polycomb repressive complex 2 (PRC2), is frequently overexpressed in multiple tumor types including Bladder cancer and plays multiple roles in tumor cell proliferation and apoptosis. Previous study showed that miR-26a has different roles in different tumors and the expression of EZH2 is identified as a potential target of miR-26a which miR-26a has been found to decrease in bladder cancer. But the mechanism between EZH2 and miR-26a is not completely clear in bladder cancer. Western blot and Real-time PCR were involved to detect both expression of mRNA and protein of EZH2. And we used mimics-miR26a to elaborate the relationship between EZH2 and miR-26a in cell proliferation and apoptosis process through lots of specific assays. The results showed that EZH2 express mainly in bladder tumor tissues than para-carcinoma tissues. Meanwhile, miR26a can down-regulate the expression of EZH2 through suppressing EZH2 activity. Both miR26a and downregulated EZH2 can induce bladder cancer cell apoptosis and increase cell at G1 stage as well as suppress cell proliferation. The further assays reveal that miR-26a can suppress cell proliferation and enhance cell apoptosis through EZH2. In this study, we found that EZH2 was overexpressed in bladder tumor tissue and miR-26a could downregulate the expression of EZH2 to inhibit proliferation and enhance apoptosis in bladder cancer.

Risk score based on three mRNA expression predicts the survival of bladder cancer

Bladder cancer (BLCA) is one of the most malignant cancers worldwide, and its prognosis varies. 1214 BLCA samples in five different datasets and 2 platforms were enrolled in this study. By utilizing the gene expression in The Cancer Genome Atlas (TCGA) dataset, and another two datasets, in GSE13507 and GSE31684, we constructed a risk score staging system with Cox multivariate regression to evaluate predict the outcome of BLCA patients. Three genes consist of RCOR1, ST3GAL5, and COL10A1 were used to predict the survival of BLCA patients. The patients with low risk score have a better survival rate than those with high risk score, significantly. The survival profiles of another two datasets (GSE13507 and GSE31684), which were used for candidate gene selection, were similar as the training dataset (TCGA). Furthermore, survival prediction effect of risk score staging system in another 2 independent datasets, GSE40875 and E-TABM-4321, were also validated. Compared with other clinical observations, and the risk score performs better in evaluating the survival of BLCA patients. Moreover, the correlation between radiation were also evaluated, and we found that patients have a poor survival in high risk group, regardless of radiation. Gene Set Enrichment Analysis was also implemented to find the difference between high-risk and low-risk groups on biological pathways, and focal adhesion and JAK signaling pathway were significantly enriched. In summary, we developed a risk staging model for BLCA patients with three gene expression. The model is independent from and performs better than other clinical information.