Using gene expression from urine sediment to diagnose prostate cancer: development of a new multiplex mRNA urine test and validation of current biomarkers

Urinary SPP1 has potential as a non-invasive diagnostic marker for focal segmental glomerulosclerosis

Focal segmental glomerulosclerosis (FSGS) is a type of chronic glomerular nephropathy showing characteristic glomerular sclerosis, diagnosed by kidney biopsy. However, it is difficult and expensive to monitor disease progression with repeated renal biopsy in clinical practice, and thus here we explored the feasibility of urine biomarkers as non-invasive diagnostic tools. We downloaded scRNA-seq datasets of 20 urine cell samples and 3 kidney tissues and obtained two gene lists encoding extracellular proteins for bioinformatic analysis; in addition, we identified key EP-Genes by immunohistochemical staining and performed bulk RNA sequencing with 12 urine samples. We report that urine cells and kidney cells were correlated. A total of 64 EP-Genes were acquired by intersecting genes of distal tubular cluster with extracellular proteins. Function enrichment analysis showed that EP-Genes might be involved in the immune response and extracellular components. Six key EP-Genes were identified and correlated with renal function. IMC showed that key EP-Genes were located mainly in tubules. Cross verification and examination of a urine RNAseq dataset showed that SPP1 had diagnostic potential for FSGS. The presence of urine SPP1 was primarily associated with macrophage infiltration in kidney, and the pathogenesis of FSGS may be related to innate immunity. Urinary cells seemed to be strongly similar to kidney cells. In summary, SPP1 levels reflect renal function and may have potential as a biomarker for non-invasive diagnosis of FSGS.

Construction of a Diagnostic Model for Distinguishing Benign or Malignant Bone Cancer by Mining miRNA Expression Data

Bone tumor is a kind of rare cancer, the location of which is mainly in bone tissue as well as cartilage tissue. Bone tumor is mainly classified into benign and malignant types. The survival rate of patients with bone tumors can be considerably improved by early detection, and the danger of amputation caused by bone tumors can be greatly reduced. In this study, we first screened the top 25% serum miRNAs with the greatest variance in patients with malignant and benign bone tumor and healthy individuals. The expression of serum miRNAs in patients with bone tumor was then examined using unsupervised clustering and PCA, and the results revealed that the overall expression of serum miRNAs was ineffective in distinguishing patients with benign/malignant bone tumors. Subsequently, we screened 19 miRNA biomarkers that could be used to determine the benign/malignant bone tumor of patients by LASSO logistic regression. These genes were validated using ROC curves. Results showed that there were 11 miRNAs that could accurately distinguish benign/malignant bone tumor alone. These 11 miRNAs were, namely, hsa-miR-192-5p, hsa-miR-137, hsa-miR-142-3p, hsa-miR-155-3p, hsa-miR-1205, hsa-miR-1273a, hsa-miR-3187-3p, hsa-miR-1255b-2-3p, hsa-miR-1288-5p, hsa-miR-6836-5p, and hsa-miR-6862-5p. Next, we established a diagnostic model using logistic regression and validated the diagnostic model using ROC curves; the result of which showed that the model had good diagnostic efficacy. Then, we also verified that the diagnostic model established by these 11 miRNAs could distinguish patients with benign/malignant bone tumor using unsupervised clustering as well as PCA. Finally, by using qPCR, we validated the expression of 11 miRNAs in the serum of patients with malignant and benign bone tumors, as well as healthy volunteers. The results were consistent with the trend of miRNAs expression in public databases. In summary, we examined the differential expression of serum miRNAs in individuals with benign and malignant bone tumors and discovered 11 miRNA biomarkers that could be utilized to discriminate between the two.

Analysis of aberrant miRNA-mRNA interaction networks in prostate cancer to conjecture its molecular mechanisms

BACKGROUND

MicroRNAs (miRNAs) capable of post-transcriptionally regulating mRNA expression are essential to tumor occurrence and progression.

OBJECTIVE

This study aims to find negatively regulatory miRNA-mRNA pairs in prostate adenocarcinoma (PRAD).

METHODS

Combining The Cancer Genome Atlas (TCGA) RNA-Seq data with Gene Expression Omnibus (GEO) mRNA/miRNA expression profiles, differently expressed miRNA/mRNA (DE-miRNAs/DE-mRNAs) were identified. MiRNA-mRNA pairs were screened by miRTarBase and TarBase, databases collecting experimentally confirmed miRNA-mRNA pairs, and verified in 30 paired prostate specimens by real-time reverse transcription polymerase chain reaction (RT-qPCR). The diagnostic values of miRNA-mRNA pairs were measured by receiver operation characteristic (ROC) curve and Decision Curve Analysis (DCA). DAVID-mirPath database and Connectivity Map were employed in GO/KEGG analysis and compounds research. Interactions between miRNA-mRNA pairs and phenotypic features were analyzed with correlation heatmap in hiplot.

RESULTS

Based on TCGA RNA-Seq data, 22 miRNA and 14 mRNA GEO datasets, 67 (20 down and 47 up) miRNAs and 351 (139 up and 212 down) mRNAs were selected. After screening from 2 databases, 8 miRNA (up)-mRNA (down) and 7 miRNA (down)-mRNA (up) pairs were identified with Pearson's correlation in TCGA. By external validation, miR-221-3p (down)/GALNT3 (up) and miR-20a-5p (up)/FRMD6 (down) were chosen. The model combing 4 signatures possessed better diagnostic value. These two miRNA-mRNA pairs were significantly connected with immune cells fraction and tumor immune microenvironment.

CONCLUSIONS

The diagnostic model containing 2 negatively regulatory miRNA-mRNA pairs was established to distinguish PRADs from normal controls.

Selective Microfluidic Capture and Detection of Prostate Cancer Cells from Urine without Digital Rectal Examination

Simple Summary

Prostate cancer is the second most common cancer and the fifth leading cause of cancer death in men worldwide. The current diagnosis methods for prostate cancer are invasive and costly. In particular, digital rectal examination (DRE) or prostate massage adds considerable discomfort to patients, reduces compliance to cancer screening schedules, and raises the cost of the diagnostic procedure. New technologies are urgently needed for the effective and yet noninvasive detection of these conditions. This manuscript describes streamlined biotechnology for the noninvasive detection of prostate cancer from malignant cells shed in urine. For the first time, a whole-cell immunocapture approach combined with photodynamic diagnostic principles is used in a device to detect whole cancer cells from unprocessed patient urine samples collected without prior DRE.

Abstract

Urine-based biomarkers have shown suitable diagnostic potential for prostate cancer (PCa) detection. Yet, until now, prostatic massage remains required prior to urine sampling. Here, we test a potential diagnostic approach using voided urine collected without prior digital rectal examination (DRE). In this study, we evaluated the diagnostic performance of a microfluidic-based platform that combines the principle of photodynamic diagnostic with immunocapture for the detection of PCa cells. The functionality and sensitivity of this platform were validated using both cultured cells and PCa patient urine samples. Quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) demonstrated this platform had a detection limit of fewer than 10 cells per 60 µL and successfully validated the presence of a PCa biomarker in the urine of cancer patients without prior DRE. This biosensing platform exhibits a sensitivity of 72.4% and a specificity of 71.4%, in suitable agreement with qRT-PCR data. The results of this study constitute a stepping stone in the future development of noninvasive prostate cancer diagnostic technologies that do not require DRE.

Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review

BACKGROUND

Machine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance.

OBJECTIVE

This study assesses the source of heterogeneity and the performance of machine learning applied to radiomic, genomic, and clinical biomarkers for the diagnosis of prostate cancer. One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 816 titles were identified from the PubMed, Scopus, and OvidSP databases. Studies that used machine learning to detect prostate cancer and provided performance measures were included in our analysis. The quality of the eligible studies was assessed using the QUADAS-2 (quality assessment of diagnostic accuracy studies-version 2) tool. The hierarchical multivariate model was applied to the pooled data in a meta-analysis. To investigate the heterogeneity among studies, I2 statistics were performed along with visual evaluation of coupled forest plots. Due to the internal heterogeneity among machine learning algorithms, subgroup analysis was carried out to investigate the diagnostic capability of machine learning systems in clinical practice.

RESULTS

In the final analysis, 37 studies were included, of which 29 entered the meta-analysis pooling. The analysis of machine learning methods to detect prostate cancer reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications.

CONCLUSIONS

The performance of machine learning for diagnosis of prostate cancer was considered satisfactory for several studies investigating the multiparametric magnetic resonance imaging and urine biomarkers; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings. Recommendations on the use of machine learning techniques were also provided to help researchers to design robust studies to facilitate evidence generation from the use of radiomic and genomic biomarkers.

Improving hexaminolevulinate enabled cancer cell detection in liquid biopsy immunosensors

Hexaminolevulinate (HAL) induced Protoporphyrin IX (PpIX) fluorescence is commonly used to differentiate cancer cells from normal cells in vivo, as for instance in blue light cystoscopy for bladder cancer diagnosis. A detailed approach is here provided to use this diagnostic principle ex vivo in an immunosensor device, towards enabling non-invasive cancer diagnostic from body fluids, such as urine. Several factors susceptible to affect the applicability of HAL-assisted diagnosis in body fluids were tested. These included the cell viability and its impact on PpIX fluorescence, the storage condition and shelf life of HAL premix reagent, light exposure (360–450 nm wavelengths) and its corresponding effect on both intensity and bleaching of the PpIX fluorescence as a function of the microscopy imaging conditions. There was no significant decrease in the viability of bladder cancer cells after 6 h at 4 °C (student’s t-test: p > 0.05). The cellular PpIX fluorescence decreased in a time-dependent manner when cancer cells were kept at 4 °C for extended period of time, though this didn’t significantly reduce the fluorescence intensity contrast between cancer and non-cancer cells kept in the same condition for 6 h. HAL premix reagent kept in long term storage at 4 °C induced stronger PpIX fluorescence than reagent kept in the − 20 °C freezer. The PpIX fluorescence was negatively affected by repeated light exposure but increased with illumination intensity and exposure time. Though this applied to both healthy and cancer cell lines, and therefore did not statistically improved the differentiation between cell types. This study revealed important experimental settings that need to be carefully considered to benefit from the analytical potential of HAL induced fluorescence when used in technologies for the diagnosis of cancer from body fluids.

Exosomal mRNAs and lncRNAs involved in multiple myeloma resistance to bortezomib

The bone marrow microenvironment plays an essential role in multiple myeloma (MM) progression. We aimed to explore the alterations of levels of long noncoding RNAs and messenger RNAs (mRNAs), derived from exosomes in peripheral blood, in resistance to bortezomib (Btz) of MM patients. Peripheral blood samples were collected from five Btz‐resistant and five Btz‐sensitive MM patients. Exosomes in patients' peripheral blood were enriched, and the profiles of long noncoding RNAs (lncRNAs) and mRNAs in exosomes were determined using deep sequencing. Bioinformatics analysis was performed to explore biological function. MTS was employed to determine the viability of Roswell Park Memorial Institute (RPMI) 8226 and LP‐1 cells incubated with exosomes derived from Btz‐resistant patients. Quantitative polymerase chain reaction (qPCR) was used to evaluate the levels of exosomal FFAR1, SP9, HIST1H2BG, and ITIH2. Incubation with Btz‐resistant patient‐derived exosomes significantly increased the viability of Btz‐treated RPMI 8226 and LP‐1 cells in a dose‐dependent manner. We identified 482 lncRNAs and 2099 mRNAs deregulated in exosomes of the Btz‐resistance group; and 78 mRNAs were enriched in DR‐related pathways, including mammalian target of rapamycin, platinum drug resistance, and the cAMP and phosphoinositide 3‐kinase–Akt signaling pathways. qPCR results verified the increases in FFAR1 and SP9 and decreases in HIST1H2BG and ITIH2 in Btz‐resistant patient‐derived exosomes. Moreover, exosomal FFAR1 and SP9 exhibited potential as independent prognostic indicators of survival of MM patients. Our study reveals significant dysregulation of exosomal RNA components in the Btz‐resistant group of MM patients as well as several mRNAs that may be used as biomarkers of prognosis of MM patients that are resistant to Btz.

Kidney Diseases: The Age of Molecular Markers

Kidney diseases are conditions that increase the morbidity and mortality of those afflicted. Diagnosis of these conditions is based on parameters such as the glomerular filtration rate (GFR), measurement of serum and urinary creatinine levels and equations derived from these measurements (Wasung, Chawla, Madero. Clin Chim Acta 438:350-357, 2015). However, serum creatinine as a marker for measuring renal dysfunction has its limitations since it is altered in several other physiological situations, such as in patients with muscle loss, after intense physical exercise or in people on a high protein diet (Riley, Powers, Welch. Res Q Exerc Sport 52(3):339-347, 1981; Juraschek, Appel, Anderson, Miller. Am J Kidney Dis 61(4):547-554, 2013). Besides the fact that serum creatinine is a marker that indicates glomerular damage, it is necessary the discovery of new biomarkers that reflect not only glomerular damage but also tubular impairment. Recent advances in Molecular Biology have led to the generation or identification of new biomarkers for kidney diseases such as: Acute Kidney Failure (AKI), chronic kidney disease (CKD), nephritis or nephrotic syndrome. There are recent markers that have been used to aid in diagnosis and have been shown to be more sensitive and specific than classical markers, such as neutrophil gelatinase associated lipocalin (NGAL) or kidney injury molecule-1 (KIM-1) (Wasung, Chawla, Madero. Clin Chim Acta 438:350-357, 2015; George, Gounden. Adv Clin Chem 88:91-119, 2019; Han, Bailly, Abichandani, Thadhani, Bonventre. Kidney Int 62(1):237-244, 2002; Fontanilla, Han. Expert Opin Med Diagn 5(2):161-173, 2011). However, early diagnostic biomarkers are still necessary to assist the intervention and monitor of the progression of these conditions.

Discovery and Validation of Urinary Molecular Signature of Early Sepsis

Supplemental Digital Content is available in the text.

Objectives:

Identify alterations in gene expression unique to systemic and kidney-specific pathophysiologic processes using whole-genome analyses of RNA isolated from the urinary cells of sepsis patients.

Design:

Prospective cohort study.

Setting:

Quaternary care academic hospital.

Patients:

A total of 266 sepsis and 82 control patients enrolled between January 2015 and February 2018.

Interventions:

Whole-genome transcriptomic analysis of messenger RNA isolated from the urinary cells of sepsis patients within 12 hours of sepsis onset and from control subjects.

Measurements and Main Results:

The differentially expressed probes that map to known genes were subjected to feature selection using multiple machine learning techniques to find the best subset of probes that differentiates sepsis from control subjects. Using differential expression augmented with machine learning ensembles, we identified a set of 239 genes in urine, which show excellent effectiveness in classifying septic patients from those with chronic systemic disease in both internal and independent external validation cohorts. Functional analysis indexes disrupted biological pathways in early sepsis and reveal key molecular networks driving its pathogenesis.

Conclusions:

We identified unique urinary gene expression profile in early sepsis. Future studies need to confirm whether this approach can complement blood transcriptomic approaches for sepsis diagnosis and prognostication.

Urine biopsy technologies: Cancer and beyond

Since the discovery of circulating tumor cells in 1869, technological advances in the study of biomarkers from liquid biopsy have made it possible to diagnose disease in a less invasive way. Although blood-based liquid biopsy has been used extensively for the detection of solid tumors and immune diseases, the potential of urine-based liquid biopsy has not been fully explored. Advancements in technologies for the harvesting and analysis of biomarkers are providing new opportunities for the characterization of other disease types. Liquid biopsy markers such as exfoliated bladder cancer cells, cell-free DNA (cfDNA), and exosomes have the potential to change the nature of disease management and care, as they allow a cost-effective and convenient mode of patient monitoring throughout treatment. In this review, we addressed the advancement of research in the field of disease detection for the key liquid biopsy markers such as cancer cells, cfDNA, and exosomes, with an emphasis on urine-based liquid biopsy. First, we highlighted key technologies that were widely available and used extensively for clinical urine sample analysis. Next, we presented recent technological developments in cell and genetic research, with implications for the detection of other types of diseases, besides cancer. We then concluded with some discussions on these areas, emphasizing the role of microfluidics and artificial intelligence in advancing point-of-care applications. We believe that the benefits of urine biopsy provide diagnostic development potential, which will pave opportunities for new ways to guide treatment selections and facilitate precision disease therapies.

Update on urine as a biomarker in cancer: a necessary review of an old story

Introduction: Cancer causes thousands of deaths worldwide each year. Therefore, monitoring of health status and the early diagnosis of cancer using noninvasive assays, such as the analysis of molecular biomarkers in urine, is essential. However, effective biomarkers for early diagnosis of cancer have not been established in many types of cancer.Areas covered: In this review, we discuss recent findings with regard to the use of urine composition as a biomarker in eleven types of cancer. We also highlight the use of urine biomarkers for improving early diagnosis.Expert opinion: Urinary biomarkers have been applied for clinical application of early diagnosis. The main limitation is a lack of integrated approaches for identification of new biomarkers in most cancer. The utilization of urinary biomarker detection will be promoted by improved detection methods and new data from different types of cancers. With the development of precision medicine, urinary biomarkers will play an increasingly important clinical role. Future early diagnosis would benefit from changes in the utilization of urinary biomarkers.

Characterization of Matricellular Protein Expression Signatures in Mechanistically Diverse Mouse Models of Kidney Injury

Fibrosis is the most common pathophysiological manifestation of Chronic Kidney Disease (CKD). It is defined as excessive deposition of extracellular matrix (ECM) proteins. Embedded within the ECM are a family of proteins called Matricellular Proteins (MCPs), which are typically expressed during chronic pathologies for ECM processing. As such, identifying potential MCPs in the pathological secretome of a damaged kidney could serve as diagnostic/therapeutic targets of fibrosis. Using published RNA-Seq data from two kidney injury mouse models of different etiologies, Folic Acid (FA) and Unilateral Ureteral Obstruction (UUO), we compared and contrasted the expression profile of various members from well-known MCP families during the Acute and Fibrotic injury phases. As a result, we identified common and distinct MCP expression signatures between both injury models. Bioinformatic analysis of their differentially expressed MCP genes revealed similar top annotation clusters from Molecular Function and Biological Process networks, which are those commonly involved in fibrosis. Using kidney lysates from FA- and UUO-injured mice, we selected MCP genes from our candidate list to confirm mRNA expression by Western Blot, which correlated with injury progression. Understanding the expressions of MCPs will provide important insight into the processes of kidney repair, and may validate MCPs as biomarkers and/or therapeutic targets of CKD.

Bioinformatics Identification of Drug Gene Modules: Application to Clear Cell Carcinoma of the Ovary

Targeted therapy is a treatment that targets the cancer's specific genes, proteins, or the tissue environment that contributes to cancer growth and survival. Identification of therapeutics targets is a very challenging problem in bioinformatics. An integrative and iterative approach for the identification of drug-gene modules (i.e., groups of genes and drugs such that genes in the same module may regulate each other and are targets of some of the drugs in the same module) is developed. Application to clear cell carcinoma of the ovary data reveals several drug-gene modules and a target network that may play important roles in treating this disease.

Urinary RNA-based biomarkers for prostate cancer detection

Prostate cancer (PCa) is the commonest malignancy in the male population worldwide. Serum prostate specific antigen (PSA) test is the most important biomarker for the detection, follow-up and therapeutic monitoring of PCa. Defects in PSA specificity have elicited research for new biomarkers to improve early diagnosis and avoid false-positive results. This review evaluates urinary RNA-based biomarkers. Urine is a versatile body fluid for non-invasive biomarker detection in case of urological malignancies. The importance of RNA-based biomarkers has been demonstrated by the current use of PCA3, a long non coding RNA biomarker already approved by the Food and Drugs Administration. Through the years, other urinary RNA biomarkers have been evaluated, including the well-known TMPRSS2:ERG transcript, as well as many messenger RNAs, long non coding RNAs and micro-RNA. Validation of a specific urinary RNA-based marker or an algorithm of different biomarkers levels as diagnostic markers for PCa could be useful to avoid unnecessary prostate biopsies.

Characterization of Protein Content Present in Exosomes Isolated from Conditioned Media and Urine

Cells secrete biomolecules into the extracellular space as a way of intercellular communication. Secreted proteins can act as ligands that engage specific receptors-on the same cell, nearby cells, or distant cells-and induce defined signaling pathways. Proteins and other biomolecules can also be packaged as cargo molecules within vesicles that are released to the extracellular space (termed extracellular vesicles or EVs). A subclass of such EVs, exosomes have been shown to horizontally transfer information. In recent years, exosomes have sparked tremendous interest in biological research, both for the discovery of novel biomarkers and for the identification of signaling molecules, as part of their cargo. Although multiple methods have been described for the isolation of exosomes, described here is a simple differential centrifugation approach that is well suited for the isolation of exosomes from conditioned cell culture media and urine. Mass spectrometry provides an ideal method to comprehensively analyze the protein cargo of exosomes. © 2017 by John Wiley & Sons, Inc.

Toward Precision Medicine: A Cancer Molecular Subtyping Nano-Strategy for RNA Biomarkers in Tumor and Urine

Cancer is a heterogeneous disease which manifests as different molecular subtypes due to the complex nature of tumor initiation, progression, and metastasis. The concept of precision medicine aims to exploit this cancer heterogeneity by incorporating diagnostic technology to characterize each cancer patient's molecular subtype for tailored treatments. To characterize cancer molecular subtypes accurately, a suite of multiplexed bioassays have currently been developed to detect multiple oncogenic biomarkers. Despite the reliability of current multiplexed detection techniques, novel strategies are still needed to resolve limitations such as long assay time, complex protocols, and difficulty in interpreting broad overlapping spectral peaks of conventional fluorescence readouts. Herein a rapid (80 min) multiplexed platform strategy for subtyping prostate cancer tumor and urine samples based on their RNA biomarker profiles is presented. This is achieved by combining rapid multiplexed isothermal reverse transcription-recombinase polymerase amplification (RT-RPA) of target RNA biomarkers with surface-enhanced Raman spectroscopy (SERS) nanotags for "one-pot" readout. This is the first translational application of a RT-RPA/SERS-based platform for multiplexed cancer biomarker detection to address a clinical need. With excellent sensitivity of 200 zmol (100 copies) and specificity, we believed that this platform methodology could be a useful tool for rapid multiplexed subtyping of cancers.

Overexpression of myosin VI regulates gastric cancer cell progression

Myosin VI (MYO6) is a unique member of the myosin superfamily. Although it has been reported to participate in human cancer progression, the role of MYO6 in gastric cancer remains unclear. In this study, we found the expression of MYO6 gene was higher in gastric cancer tissues than in the normal tissues by Oncomine database mining and affects patient overall survival using the Kaplan-Meier plotter online analysis. Additionally, the expression levels of MYO6 were widely expressed in gastric cancer cells by quantitative real-time Polymerase Chain Reaction (qRT-PCR) and western blot assay. Then knockdown of MYO6 significantly suppressed the proliferation and colony formation abilities of AGS and MGC80-3 cells. Moreover, cell cycle analysis showed that inhibition of MYO6 induced cell cycle arrested in G0/G1 phase in AGS and MGC80-3 cells. Further analysis showed knockdown of MYO6 downregulated cell-cycle activators cyclin A and cyclin D1 and upregulated cell-cycle inhibitor p21, as determined by qRT-PCR and western blot analysis in MGC80-3 cells. Meanwhile, MYO6 inhibition significantly induced apoptosis in AGS and MGC80-3 cells. Also, knockdown of MYO6 increased the expression of apoptosis-related proteins Bax and cleaved Caspase-3, and decreased Bcl-2 expression by western blot analysis in MGC80-3 cells. In addition, MYO6 knockdown also inhibited cell migration ability in MGC80-3 cells. Taken together, our study indicates that MYO6 may play an important role in gastric cancer tumorigenesis and may serve as a potential therapeutic target in human gastric cancer.