A Specific Blood Signature Reveals Higher Levels of S100A12: A Potential Bladder Cancer Diagnostic Biomarker Along With Urinary Engrailed-2 Protein Detection

Advances in Liquid Biopsy for Diagnosis of Bladder Cancer

Bladder cancer (BCa) is the most common malignancy of the urinary system. It has a high recurrence rate and requires longterm follow-up. Significant advances in BCa research have been made in recent years; however, the initial diagnosis and follow-up of BCa relies on cystoscopy, which is an invasive and expensive procedure. Over the past decade, liquid biopsies (e.g., blood and urine) have proven to be highly efficient methods for the discovery of BCa biomarkers. This noninvasive sampling method is used to analyze unique tumor components released into body fluids and enables serial sampling and longitudinal monitoring of tumor progression. Several liquid biopsy biomarkers have been studied extensively and have shown promising results in the clinical applications of BCa, including early detection, microscopic residual disease detection, recurrence prediction, and treatment response. Therefore, this review aims to provide an update on various new liquid biopsy markers and the advantages and current limitations of liquid biopsy in the diagnosis of BCa.

Homeobox Gene Expression Dysregulation as Potential Diagnostic and Prognostic Biomarkers in Bladder Cancer

Homeobox genes serve as master regulatory transcription factors that regulate gene expression during embryogenesis. A homeobox gene may have either tumor-promoting or tumor-suppressive properties depending on the specific organ or cell lineage where it is expressed. The dysregulation of homeobox genes has been reported in various human cancers, including bladder cancer. The dysregulated expression of homeobox genes has been associated with bladder cancer clinical outcomes. Although bladder cancer has high risk of tumor recurrence and progression, it is highly challenging for clinicians to accurately predict the risk of tumor recurrence and progression at the initial point of diagnosis. Cystoscopy is the routine surveillance method used to detect tumor recurrence. However, the procedure causes significant discomfort and pain that results in poor surveillance follow-up amongst patients. Therefore, the development of reliable non-invasive biomarkers for the early detection and monitoring of bladder cancer is crucial. This review provides a comprehensive overview of the diagnostic and prognostic potential of homeobox gene expression dysregulation in bladder cancer.

Aptamer-antibody hybrid ELONA that uses hybridization chain reaction to detect a urinary biomarker EN2 for bladder and prostate cancer

We report an EN2-specific (K_d = 8.26 nM) aptamer, and a sensitive and specific enzyme-linked oligonucleotide assay (ELONA) for rapid and sensitive colorimetric detection of bladder and prostate cancer biomarker EN2 in urine. The assay relies on an aptamer-mediated hybridization chain reaction (HCR) to generate DNA nanostructures that bind to EN2 and simultaneously amplify signals. The assay can be performed within 2.5 h, and has a limit of detection of 0.34 nM in buffer and 2.69 nM in artificial urine. Moreover, this assay showed high specificity as it did not detect other urinary proteins, including biomarkers of other cancers. The proposed ELONA is inexpensive, highly reproducible, and has great chemical stability, so it may enable development of a simple, sensitive and accurate diagnostic tool to detect bladder and prostate cancers early.

Identification of immune-related genes that predict prognosis and risk of bladder cancer: bioinformatics analysis of TCGA database

Background: Bladder cancer (BLCA) is the major tumor of the urinary system, and immune-related genes (IRGs) contribute significantly to its initiation and prognosis.

Results: A total of 51 prognostic IRGs significantly associated with overall survival were identified. Functional enrichment analysis revealed that these genes were actively involved in tumor-related functions and pathways. Using multivariate Cox regression analysis, we detected 11 optimal IRGs (ADIPOQ, PPY, NAMPT, TAP1, AHNAK, OLR1, PDGFRA, IL34, MMP9, RAC3, and SH3BP2). We validated the prognostic value of this signature in two validation cohorts: GSE13507 (n = 165) and GSE32894 (n = 224). Furthermore, we performed a western blot and found that the expression of these IRGs matched their mRNA expression in TCGA. Moreover, correlations between risk score and immune-cell infiltration indicated that the prognostic signature reflected infiltration by several types of immune cells.

Conclusion: We identified and validated an 11-IRG-based risk signature that may be a reliable tool to evaluate the prognosis of BLCA patients and help to devise individualized immunotherapies.

Methods: Bioinformatics analysis was performed using TCGA and ImmPort databases. Cox regression was used to identify prognostic signatures. Two external GEO cohorts and western blotting of samples were performed to validate the IRG signature.

Integration of Urinary EN2 Protein & Cell-Free RNA Data in the Development of a Multivariable Risk Model for the Detection of Prostate Cancer Prior to Biopsy

Simple Summary

Prostate cancer is a disease responsible for a large proportion of all male cancer deaths but there is a high chance that a patient will die with the disease rather than from. Therefore, there is a desperate need for improvements in diagnosing and predicting outcomes for prostate cancer patients to minimise overdiagnosis and overtreatment whilst appropriately treating men with aggressive disease, especially if this can be done without taking an invasive biopsy. In this work we develop a test that predicts whether a patient has prostate cancer and how aggressive the disease is from a urine sample. This model combines the measurement of a protein-marker called EN2 and the levels of 10 genes measured in urine and proves that integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy.

Abstract

The objective is to develop a multivariable risk model for the non-invasive detection of prostate cancer prior to biopsy by integrating information from clinically available parameters, Engrailed-2 (EN2) whole-urine protein levels and data from urinary cell-free RNA. Post-digital-rectal examination urine samples collected as part of the Movember Global Action Plan 1 study which has been analysed for both cell-free-RNA and EN2 protein levels were chosen to be integrated with clinical parameters (n = 207). A previously described robust feature selection framework incorporating bootstrap resampling and permutation was applied to the data to generate an optimal feature set for use in Random Forest models for prediction. The fully integrated model was named ExoGrail, and the out-of-bag predictions were used to evaluate the diagnostic potential of the risk model. ExoGrail risk (range 0–1) was able to determine the outcome of an initial trans-rectal ultrasound guided (TRUS) biopsy more accurately than clinical standards of care, predicting the presence of any cancer with an area under the receiver operator curve (AUC) = 0.89 (95% confidence interval(CI): 0.85–0.94), and discriminating more aggressive Gleason ≥ 3 + 4 disease returning an AUC = 0.84 (95% CI: 0.78–0.89). The likelihood of more aggressive disease being detected significantly increased as ExoGrail risk score increased (Odds Ratio (OR) = 2.21 per 0.1 ExoGrail increase, 95% CI: 1.91–2.59). Decision curve analysis of the net benefit of ExoGrail showed the potential to reduce the numbers of unnecessary biopsies by 35% when compared to current standards of care. Integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy.

lncRNA-UCA1 in the diagnosis of bladder cancer: A meta-analysis

BACKGROUND

The main purpose of this study is to systematically evaluate the diagnostic value of long-chain non-coding RNA urothelial carcinoembryonic antigen 1 (lncRNA-UCA1) for bladder cancer, and to provide a scientific basis for the diagnosis of bladder cancer.

METHODS

By searching PubMed, Web of Science, EMBASE, CNKI, Wanfang, Weipu and other databases, in order to collect relevant literature of lncRNA-UCA1 for diagnosis of bladder cancer. The starting and ending time of the search is from the establishment of the database to December 31, 2019. Screen documents and extract data according to inclusion and exclusion criteria. QUADAS entry tool was used to evaluate the quality of literature. Meta-Disc 1.4 and Stata 12.0 software were used for statistical analysis, and UCA1 was combined for the statistics of bladder cancer diagnosis.

RESULTS

A total of 7 articles were included in this study, including 954 cases of bladder cancer patients and 482 cases of non-bladder cancer patients. The receiver operating characteristic curve (ROC) curve AUC of lncRNA-UCA1 used to diagnose bladder cancer was 0.86. The sensitivity was 0.83 (95% CI: 0.80-0.85), and the specificity was 0.86 (95% CI: 0.82-0.89). The positive likelihood ratio is 6.38 (95% CI: 3.01-13.55), and the negative likelihood ratio is 0.20 (95% CI: 0.13-0.31). The diagnostic odds ratio is 33.13 (95% CI: 11.16-98.33).

CONCLUSION

lncRNA-UCA1 has a high value of clinical auxiliary diagnosis for bladder cancer, and it can be further promoted and applied clinically.

Identification of a novel immune microenvironment signature predicting survival and therapeutic options for bladder cancer

Few studies have investigated the potential of tumor immune microenvironment genes as indicators of urinary bladder cancer. Here, we sought to establish an immune-related gene signature for determining prognosis and treatment options. We developed a ten-gene tumor immune microenvironment signature and evaluated its prognostic capacity on internal and external cohorts. Multivariate Cox regression and nomogram analyses revealed the prognostic risk model as an independent and effective indicator of prognosis. We observed lower proportions of CD8+ T cells, dendritic cells, regulatory T cells, higher proportions of macrophages and neutrophils in high UBC risk group. UBC tissues with high-risk score tend to exhibit high TP53 and RB1 mutation rates, high PD1/PD-L1 expression and poor-survival basal squamous subtypes, while those with low-risk score tend to have high FGFR3 mutation rates and luminal papillary subtypes. Unexpectedly, we found a highly significant positive correlation between glycolytic genes and risk score, highlighting metabolic competition in tumor ecosystem and potential therapeutic avenues. Our study thus revealed a tumor immune microenvironment signature for predicting prognostic and response to immune checkpoint inhibitors against bladder cancer. Prospective studies are required to further test the predictive capacity of this model.