A novel combined miRNA and methylation marker panel (miMe) for prediction of prostate cancer outcome after radical prostatectomy

Association of Androgenic Regulation and MicroRNAs in Acinar Adenocarcinoma of Prostate

Background: Prostate cancer represents 3.8% of cancer deaths worldwide. For most prostate cancer cells to grow, androgens need to bind to a cellular protein called the androgen receptor (AR). This study aims to demonstrate the expression of five microRNAs (miRs) and its influence on the AR formation in patients from the northern region of Brazil. Material and Methods: Eighty-four tissue samples were investigated, including nodular prostatic hyperplasia (NPH) and acinar prostatic adenocarcinoma (CaP). Five miRs (27a-3p, 124, 130a, 488-3p, and 506) were quantified using the TaqMan^® Real Time PCR method and AR was measured using Western blotting. Results: Levels of miRs 124, 130a, 488-3p, and 506 were higher in NPH samples. Conversely, in the CaP cases, higher levels of miR 27a-3p and AR were observed. Conclusion: In the future, these microRNAs may be tested as markers of CaP at the serum level. The relative expression of AR was 20% higher in patients with prostate cancer, which suggests its potential as a biomarker for prostate malignancy.

microRNA-based diagnostic and therapeutic applications in cancer medicine

It has been almost two decades since the first link between microRNAs and cancer was established. In the ensuing years, this abundant class of short noncoding regulatory RNAs has been studied in virtually all cancer types. This tremendously large body of research has generated innovative technological advances for detection of microRNAs in tissue and bodily fluids, identified the diagnostic, prognostic, and/or predictive value of individual microRNAs or microRNA signatures as potential biomarkers for patient management, shed light on regulatory mechanisms of RNA-RNA interactions that modulate gene expression, uncovered cell-autonomous and cell-to-cell communication roles of specific microRNAs, and developed a battery of viral and nonviral delivery approaches for therapeutic intervention. Despite these intense and prolific research efforts in preclinical and clinical settings, there are a limited number of microRNA-based applications that have been incorporated into clinical practice. We review recent literature and ongoing clinical trials that highlight most promising approaches and standing challenges to translate these findings into viable microRNA-based clinical tools for cancer medicine. This article is categorized under: RNA in Disease and Development > RNA in Disease.

A combined microRNA and target protein-based panel for predicting the probability and severity of uraemic vascular calcification: a translational study

AIMS

Vascular calcification (VC) increases the future risk of cardiovascular events in uraemic patients, but effective therapies are still unavailable. Accurate identification of those at risk of developing VC using pathogenesis-based biomarkers is of particular interest and may facilitate individualized risk stratification. We aimed to uncover microRNA (miRNA)-target protein-based biomarker panels for evaluating uraemic VC probability and severity.

METHODS AND RESULTS

We created a three-tiered in vitro VC model and an in vivo uraemic rat model receiving high phosphate diet to mimic uraemic VC. RNAs from the three-tiered in vitro and in vivo uraemic VC models underwent miRNA and mRNA microarray, with results screened for differentially expressed miRNAs and their target genes as biomarkers. Findings were validated in original models and additionally in an ex vivo VC model and human cells, followed by functional assays of identified miRNAs and target proteins, and tests of sera from end-stage renal disease (ESRD) and non-dialysis-dependent chronic kidney disease (CKD) patients without and with VC. Totally 122 down-regulated and 119 up-regulated miRNAs during calcification progression were identified initially; further list narrowing based on miRNA-mRNA pairing, anti-correlation, and functional enrichment left 16 and 14 differentially expressed miRNAs and mRNAs. Levels of four miRNAs (miR-10b-5p, miR-195, miR-125b-2-3p, and miR-378a-3p) were shown to decrease throughout all models tested, while one mRNA (SULF1, a potential target of miR-378a-3p) exhibited the opposite trend concurrently. Among 96 ESRD (70.8% with VC) and 59 CKD patients (61% with VC), serum miR-125b2-3p and miR-378a-3p decreased with greater VC severity, while serum SULF1 levels increased. Adding serum miR-125b-2-3p, miR-378a-3p, and SULF1 into regression models for VC substantially improved performance compared to using clinical variables alone.

CONCLUSION

Using a translational approach, we discovered a novel panel of biomarkers for gauging the probability/severity of uraemic VC based on miRNAs/target proteins, which improved the diagnostic accuracy.

Study on effects of miR-141-3p in proliferation, migration, invasion and apoptosis of colon cancer cells by inhibiting Bcl2

PURPOSE

This study aimed to investigate the relationship between miR-141-3p and B lymphocyte-2 gene (Bcl2) gene and its biological behavior on colon cancer cell line SW480.

METHODS

qRT-PCR was used to detect the expression level of miR-141-3p in colon cancer tissues and adjacent tissues, as well as in colon cancer cell line and normal human colonic epithelial cell line FHC. MTT assay, wound assay, and Transwell demonstrated the effects of miR-141-3p on colon cancer proliferation, migration and invasion. Targetscan7.1 predictive software and dual luciferase reporter assays were used to detect the targeted regulation of miR-141-3p on the apoptosis-related gene Bcl2. MTT assay, wound assay, Transwell and flow cytometry were used to detect the effect of Bcl2 on miR-141-3p on colon cancer proliferation, migration, invasion and apoptosis.

RESULTS

Compared with adjacent tissues, the expression of miR-141-3p in colon cancer tissues was significantly down-regulated. Colon cancer patients with low expression of miR-141-3p had poorer prognosis. Compared with normal colonic epithelial cells, miR-141-3p expression was significantly down-regulated in colon cancer cell lines, and overexpression of miR-141-3p significantly attenuated the proliferation, migration and invasion of colon cancer cells. Knockdown of miR-141-3p significantly promoted the proliferation, migration and invasion of colon cancer cells. miR-141-3p targets the negative regulation of Bcl2. Knockdown of Bcl2 significantly attenuated the promotion of miR-141-3p inhibitor on proliferation, migration and invasion of colon cancer cells and inhibition of apoptosis. Knockdown of Bcl2 significantly enhanced the inhibition effect of miR-141-3p inhibitor on proliferation, migration and invasion of colon cancer cells.

CONCLUSIONS

In conclusion, miR-141-3p can inhibit the cancer by regulating Bcl2, and miR-141-3p has the potential to become a potential therapeutic target for colon cancer.

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.

Identification of a Prognostic Signature Model with Tumor Microenvironment for predicting Disease-free Survival after Radical Prostatectomy

Background: The tumor microenvironment (TME) and immune checkpoint inhibitors have been shown to promote active immune responses through different mechanisms. We attempted to identify the important prognostic genes and prognostic characteristics related to TME in prostate cancer (PCa).

Methods: The gene transcriptome profiles and clinical information of PCa patients were obtained from The Cancer Genome Atlas (TCGA) database, and the immune and stromal scores were calculated by the ESTIMATE algorithm. We evaluated the prognostic value of the risk score (RS) model based on univariate Cox analysis and least absolute shrinkage and selection operation (LASSO) Cox regression analysis and established a nomogram to predict disease-free survival (DFS) in PCa patients. The GSE70768 dataset was utilized for external validation. Twenty-two subsets of tumor-infiltrating immune cells were analyzed using the CIBERSORT algorithm.

Results: In this study, the patients with higher immune/stromal scores were associated with a worse DFS, higher Gleason score, and higher pathological T stage. Based on the immune and stromal scores, 515 differentially expressed genes (DEGs) were identified. The univariate Cox and LASSO Cox regression models were employed to select 18 DEGs from 515 DEGs and construct an RS model. The DFS of the high-RS group was significantly lower than that of the low-RS group (P<0.001). The AUCs for the 1-year, 3-year and 5-year DFS rates in the RS model were 0.890, 0.877 and 0.841, respectively. A nomogram of DFS was established based on the RS and Gleason score, and the AUCs for the 1-year, 3-year and 5-year DFS rates in the nomogram were 0.907, 0.893, and 0.872, respectively. These results were further validated in the GSE70768 dataset. In addition, the proportion of Tregs was determined to be higher in high-RS patients (P<0.05), and the expression levels of five immune checkpoints (CTLA-4, PD-1, LAG-3, TIM-3 and TIGIT) were observed to be higher in high-RS patients (P<0.05).

Conclusions: Our study established and validated an 18-gene prognostic signature model associated with TME, which might serve as a prognosis stratification tool to predict DFS in PCa patients after radical prostatectomy.

Epigenetic methylation in Eosinophilic Esophagitis: Molecular ageing and novel biomarkers for treatment response

BACKGROUND

Treatment failure in eosinophilic esophagitis (EoE) is common. We hypothesize that DNA methylation differs between patients by treatment response to topical steroids (oral viscous budesonide), thus offering the potential to inform targeting therapies.

OBJECTIVE

We sought to identify differentially methylated sites and affiliated genes in pre-treatment oesophageal cells between responders and non-responders and test for accelerated epigenetic ageing in oesophageal cells of EoE patients.

METHODS

DNA was extracted from prospectively collected and biobanked oesophageal biopsies from 36 Caucasian treatment naïve EoE patients at diagnosis. Methylation assays were completed using the Infinium HumanMethylation450 BeadChip. Normalized β values for each CpG site were tested (t test) for differential methylation. Further, 353 CpG probes were used to estimate epigenetic age for each patient and a linear regression model tested whether chronologic age and epigenetic age differed. Epigenetic age results were confirmed in an independent cohort of healthy controls.

RESULTS

Eighteen CpG sites were differentially methylated by treatment response (P < .00001). The mean epigenetic age and chronological age were 56.1 ± 11.1 and 36.7 ± 12.3 years, a mean age difference of 19.3 ± 5.2 years (P < .0001); accelerated ageing was not observed in the oesophageal cells of healthy controls.

CONCLUSIONS AND CLINICAL RELEVANCE

EoE patients that respond versus do not respond to treatment have differences in their methylation profile, including enrichment of genes in pathways consistent with cellular injury and repair due to environmental stress and cell adhesion and barrier integrity. EoE also appears to accelerate cellular ageing. Whether treatment can arrest or reverse accelerated epigenetic ageing and the implications for long-term disease progression is important areas for future research.

Validation of the four-miRNA biomarker panel MiCaP for prediction of long-term prostate cancer outcome

Improved prostate cancer prognostic biomarkers are urgently needed. We previously identified the four-miRNA prognostic biomarker panel MiCaP ((miR-23a-3p × miR-10b-5p)/(miR-133a-3p × miR-374b-5p)) for prediction of biochemical recurrence (BCR) after radical prostatectomy (RP). Here, we identified an optimal numerical cut-off for MiCaP dichotomisation using a training cohort of 475 RP patients and tested this in an independent cohort of 281 RP patients (PCA281). Kaplan–Meier, uni- and multivariate Cox regression analyses were conducted for multiple endpoints: BCR, metastatic-(mPC) and castration-resistant prostate cancer (CRPC), prostate cancer-specific (PCSS) and overall survival (OS). Functional effects of the four MiCaP miRNAs were assessed by overexpression and inhibition experiments in prostate cancer cell lines. We found the numerical value 5.709 optimal for MiCaP dichotomisation. This was independently validated in PCA281, where a high MiCaP score significantly [and independent of the Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) score] predicted BCR, progression to mPC and CRPC, and PCSS, but not OS. Harrell’s C-index increased upon addition of MiCaP to CAPRA-S for all endpoints. Inhibition of miR-23a-3p and miR-10b-5p, and overexpression of miR-133a-3p and miR-374b-5p significantly reduced cell survival. Our results may promote future implementation of a MiCaP-based test for improved prostate cancer risk stratification.

Development of a multivariable risk model integrating urinary cell DNA methylation and cell-free RNA data for the detection of significant prostate cancer

BACKGROUND

Prostate cancer exhibits severe clinical heterogeneity and there is a critical need for clinically implementable tools able to precisely and noninvasively identify patients that can either be safely removed from treatment pathways or those requiring further follow up. Our objectives were to develop a multivariable risk prediction model through the integration of clinical, urine-derived cell-free messenger RNA (cf-RNA) and urine cell DNA methylation data capable of noninvasively detecting significant prostate cancer in biopsy naïve patients.

METHODS

Post-digital rectal examination urine samples previously analyzed separately for both cellular methylation and cf-RNA expression within the Movember GAP1 urine biomarker cohort were selected for a fully integrated analysis (n = 207). A robust feature selection framework, based on bootstrap resampling and permutation, was utilized to find the optimal combination of clinical and urinary markers in a random forest model, deemed ExoMeth. Out-of-bag predictions from ExoMeth were used for diagnostic evaluation in men with a clinical suspicion of prostate cancer (PSA ≥ 4 ng/mL, adverse digital rectal examination, age, or lower urinary tract symptoms).

RESULTS

As ExoMeth risk score (range, 0-1) increased, the likelihood of high-grade disease being detected on biopsy was significantly greater (odds ratio = 2.04 per 0.1 ExoMeth increase, 95% confidence interval [CI]: 1.78-2.35). On an initial TRUS biopsy, ExoMeth accurately predicted the presence of Gleason score ≥3 + 4, area under the receiver-operator characteristic curve (AUC) = 0.89 (95% CI: 0.84-0.93) and was additionally capable of detecting any cancer on biopsy, AUC = 0.91 (95% CI: 0.87-0.95). Application of ExoMeth provided a net benefit over current standards of care and has the potential to reduce unnecessary biopsies by 66% when a risk threshold of 0.25 is accepted.

CONCLUSION

Integration of urinary biomarkers across multiple assay methods has greater diagnostic ability than either method in isolation, providing superior predictive ability of biopsy outcomes. ExoMeth represents a more holistic view of urinary biomarkers and has the potential to result in substantial changes to how patients suspected of harboring prostate cancer are diagnosed.

A Novel Predictor Tool of Biochemical Recurrence after Radical Prostatectomy Based on a Five-MicroRNA Tissue Signature

Within five to ten years after radical prostatectomy (RP), approximately 15–34% of prostate cancer (PCa) patients experience biochemical recurrence (BCR), which is defined as recurrence of serum levels of prostate-specific antigen >0.2 µg/L, indicating probable cancer recurrence. Models using clinicopathological variables for predicting this risk for patients lack accuracy. There is hope that new molecular biomarkers, like microRNAs (miRNAs), could be potential candidates to improve risk prediction. Therefore, we evaluated the BCR prognostic capability of 20 miRNAs, which were selected by a systematic literature review. MiRNA expressions were measured in formalin-fixed, paraffin-embedded (FFPE) tissue RP samples of 206 PCa patients by RT-qPCR. Univariate and multivariate Cox regression analyses were performed, to assess the independent prognostic potential of miRNAs. Internal validation was performed, using bootstrapping and the split-sample method. Five miRNAs (miR-30c-5p/31-5p/141-3p/148a-3p/miR-221-3p) were finally validated as independent prognostic biomarkers. Their prognostic ability and accuracy were evaluated using C-statistics of the obtained prognostic indices in the Cox regression, time-dependent receiver-operating characteristics, and decision curve analyses. Models of miRNAs, combined with relevant clinicopathological factors, were built. The five-miRNA-panel outperformed clinically established BCR scoring systems, while their combination significantly improved predictive power, based on clinicopathological factors alone. We conclude that this miRNA-based-predictor panel will be worth to be including in future studies.