Prostate Health Index (Phi) and Prostate Cancer Antigen 3 (PCA3) Significantly Improve Prostate Cancer Detection at Initial Biopsy in a Total PSA Range of 2–10 ng/ml

Many efforts to reduce prostate specific antigen (PSA) overdiagnosis and overtreatment have been made. To this aim, Prostate Health Index (Phi) and Prostate Cancer Antigen 3 (PCA3) have been proposed as new more specific biomarkers. We evaluated the ability of phi and PCA3 to identify prostate cancer (PCa) at initial prostate biopsy in men with total PSA range of 2–10 ng/ml. The performance of phi and PCA3 were evaluated in 300 patients undergoing first prostate biopsy. ROC curve analyses tested the accuracy (AUC) of phi and PCA3 in predicting PCa. Decision curve analyses (DCA) were used to compare the clinical benefit of the two biomarkers. We found that the AUC value of phi (0.77) was comparable to those of %p2PSA (0.76) and PCA3 (0.73) with no significant differences in pairwise comparison (%p2PSA vs phi p = 0.673, %p2PSA vs. PCA3 p = 0.417 and phi vs. PCA3 p = 0.247). These three biomarkers significantly outperformed fPSA (AUC = 0.60), % fPSA (AUC = 0.62) and p2PSA (AUC = 0.63). At DCA, phi and PCA3 exhibited a very close net benefit profile until the threshold probability of 25%, then phi index showed higher net benefit than PCA3. Multivariable analysis showed that the addition of phi and PCA3 to the base multivariable model (age, PSA, %fPSA, DRE, prostate volume) increased predictive accuracy, whereas no model improved single biomarker performance. Finally we showed that subjects with active surveillance (AS) compatible cancer had significantly lower phi and PCA3 values (p<0.001 and p = 0.01, respectively). In conclusion, both phi and PCA3 comparably increase the accuracy in predicting the presence of PCa in total PSA range 2–10 ng/ml at initial biopsy, outperforming currently used %fPSA.     

Age-Specific Cutoff Value for the Application of Percent Free Prostate-Specific Antigen (PSA) in Chinese Men with Serum PSA Levels of 4.0–10.0 ng/ml

Objective

The influence of age on the performance of percent free prostate-specific antigen (%fPSA) in diagnosing prostate cancer (PCa) in East Asians is controversial. We tested the diagnostic performance of %fPSA in a multi-center biopsy cohort in China and identified the proper age-specific cutoff values to avoid unnecessary biopsies.

Methods

Consecutive patients with a prostate-specific antigen (PSA) level of 4.0–10.0 ng/ml or 10.1–20.0 ng/ml who underwent transrectal ultrasound-guided or transperineal prostate biopsy were enrolled from 22 Chinese medical centers from Jan 1, 2010 to Dec 31, 2013. The diagnostic accuracy of PSA and %fPSA was determined using the area under the receiver operating characteristic (ROC) curve (AUC). Age-specific cutoff values were calculated using ROC curve analysis.

Results

The median %fPSA was much lower in younger patients compared with older patients with a PSA level of 4.0–10.0 ng/ml or 10.1–20.0 ng/ml. The AUC of %fPSA was higher than PSA only in older patients. In patients aged 50 to 59 years, %fPSA failed to improve the diagnosis compared with PSA in these two PSA ranges. Age-specific cutoff values were 24%, 27% and 32% for patients aged 60–69, 70–79 and ≥80 years, respectively, to reduce unnecessary biopsies in men with PSA levels of 4.0–10.0 ng/ml to detect 90% of all PCa.

Conclusions

The effectiveness of %fPSA is correlated with age in the Chinese population. Age-specific cutoff values would help avoid unnecessary biopsies in the Chinese population.     

Incorporating Known Genetic Variants Does Not Improve the Accuracy of PSA Testing to Identify High Risk Prostate Cancer on Biopsy

Introduction

Prostate-specific antigen (PSA) testing is a widely accepted screening method for prostate cancer, but with low specificity at thresholds giving good sensitivity. Previous research identified four single nucleotide polymorphisms (SNPs) principally associated with circulating PSA levels rather than with prostate cancer risk (TERT rs2736098, FGFR2 rs10788160, TBX3 rs11067228, KLK3 rs17632542). Removing the genetic contribution to PSA levels may improve the ability of the remaining biologically-determined variation in PSA to discriminate between high and low risk of progression within men with identified prostate cancer. We investigate whether incorporating information on the PSA-SNPs improves the discrimination achieved by a single PSA threshold in men with raised PSA levels.

Materials and Methods

Men with PSA between 3-10ng/mL and histologically-confirmed prostate cancer were categorised as high or low risk of progression (Low risk: Gleason score≤6 and stage T1-T2a; High risk: Gleason score 7–10 or stage T2C). We used the combined genetic effect of the four PSA-SNPs to calculate a genetically corrected PSA risk score. We calculated the Area under the Curve (AUC) to determine how well genetically corrected PSA risk scores distinguished men at high risk of progression from low risk men.

Results

The analysis includes 868 men with prostate cancer (Low risk: 684 (78.8%); High risk: 184 (21.2%)). Receiver operating characteristic (ROC) curves indicate that including the 4 PSA-SNPs does not improve the performance of measured PSA as a screening tool for high/low risk prostate cancer (measured PSA level AU C = 59.5% (95% CI: 54.7,64.2) vs additionally including information from the 4 PSA-SNPs AUC = 59.8% (95% CI: 55.2,64.5) (p-value = 0.40)).

Conclusion

We demonstrate that genetically correcting PSA for the combined genetic effect of four PSA-SNPs, did not improve discrimination between high and low risk prostate cancer in men with raised PSA levels (3-10ng/mL). Replication and gaining more accurate estimates of the effects of the 4 PSA-SNPs and additional variants associated with PSA levels and not prostate cancer could be obtained from subsequent GWAS from larger prospective studies.     

经直肠剪切波弹性成像技术联合血清CEA、PSA、FPSA对前列腺良恶性病变的鉴别诊断价值研究

摘要 目的：研究经直肠剪切波弹性成像技术（TRSWE）联合血清癌胚抗原（CEA）、前列腺特异性抗原（PSA）、游离前列腺特异性抗原（FPSA）对前列腺良恶性病变的鉴别诊断价值。方法：选取合肥市第二人民医院2019年1月～2022年6月收治的90例前列腺病变患者,根据病理检查分为前列腺癌组（47例）和前列腺良性病变组（43例）。对所有前列腺病变患者均行TRSWE检查,分析前列腺良恶性病变的图像差异以及弹性模量最大值（Emax）、弹性模量平均值（Emean）。检测所有前列腺病变患者的血清CEA、PSA、FPSA水平并进行对比。采用受试者工作特征（ROC）曲线分析明确Emax值、Emean值以及血清CEA、PSA、FPSA水平联合诊断前列腺良恶性病变的效能。结果：前列腺癌组Emax值、Emean值均高于前列腺良性病变组（均P＜0.05）。前列腺癌组血清CEA、PSA及FPSA水平均高于前列腺良性病变组（均P＜0.05）。经ROC曲线分析发现,Emax值、Emean值以及血清CEA、PSA、FPSA水平联合检测诊断前列腺良恶性病变的效能优于上述5项指标单独检测。结论：TRSWE联合血清CEA、PSA、FPSA对前列腺良恶性病变的鉴别诊断价值较高,可有效提升前列腺癌的检出率,可能值得临床推广应用。     

Le test urinaire PCA3 : quelles perspectives ?

Since its introduction 10 years ago, the urinary PCA3 test was evaluated in several studies that allowed demonstration of its diagnostic value when predicting prostate biopsy outcome. Its good specificity, notably when compared to seric PSA, offers to the urologist the opportunity to better evaluate whether prostate biopsies are warranted, especially in patients with at least one set of previous negative biopsies. Several points remain to be determined such as its true influence on prostate decision making in clinical practice and the resulting medico-economic benefit. The place of the urinary PCA3 test among other developing prostate cancer biomarkers, notably PHI index and urinary TMPRSS2:ERG fusions, has also to be defined.     

Evaluation of Multiparametric Magnetic Resonance Imaging in Detection and Prediction of Prostate Cancer

Background

Although European Society of Urogenital Radiology proposed the potential of multiparametric magnetic resonance imaging (MP-MRI) as a tool in the diagnostic pathway for prostate cancer (PCa) and published a unified scoring system named Prostate Imaging Reporting and Data System (PI-RADS version 1), these still need to be validated by real-life studies.

Objective

To evaluate the role of MP-MRI in detection and prediction of PCa.

Methods

Patients with clinical suspicion of PCa who underwent prebiopsy MP-MRI from 2002 to 2009 were recruited. MP-MRI results were retrospectively assigned as overall scores using PI-RADS by two radiologists. Patients were followed and the end point was the diagnosis of PCa. Receiver operating characteristics (ROC) curve was performed to test diagnostic efficacy of MP-MRI, under results of biopsy within three months. The cox proportional hazards model was used to identify independent variables for the detection of PCa.

Results

Finally, 1113 of the 1806 enrolled patients were included for analysis. The median follow-up was 56.0 months (1–137 mo). For 582 patients biopsied within three months, area under the curve for the detection of PCa with MP-MRI was 0.88 (95% confidence interval [CI], 0.75–1.00) in group of baseline prostate specific antigen (PSA) 0.01–4.00 ng/ml (n = 31), 0.90 (95% CI, 0.84–0.95) in PSA 4.01–10.00 ng/ml (n = 142), and 0.91 (95% CI, 0.87–0.94) in PSA >10.00 ng/ml (n = 409), respectively. In the cox model adjusted for age and baseline PSA level, for the detection rate of PCa, compared with PI-RADS 1–2 (reference), the hazard ratio was 6.43 (95% CI, 4.29–9.65) for PI-RADS 3, 18.58 (95% CI, 13.36–25.84) for PI-RADS 4–5 (p < 0.001).

Conclusions

Prebiopsy MP-MRI with PI-RADS is demonstrated as a valuable diagnostic and predictive tool for PCa.     

Household rat infestation in urban slum populations: Development and validation of a predictive score for leptospirosis

Domestic rats are the principal reservoir for urban leptospirosis. However, few studies have identified infestation markers in slums and evaluated their predictivity for leptospirosis risk. We compared households with leptospirosis cases in Salvador, Brazil between 2007 and 2009 and their neighbors using a case control design, surveying for rodent infestation signs and environmental characteristics. With the 2007–2008 data, a conditional logistic regression modeling identified the peridomiciliar presence of rodent burrows (OR, 3.30; 95% CI, 1.50–7.26), rat feces (2.86; 1.24–6.59), runs (2.57; 1.06–6.22), households bordering abandoned houses (2.48; 1.04–6.02), and unplastered walls (2.22; 1.02–6.02) as risk factors and developed a predictive score for leptospirosis. With an independent data set from 2009, a receiver operating characteristic (ROC) curve analysis evaluated the prediction score performance, with the area under the curve being 0.70 (95% CI, 0.64–0.76) for score development and 0.71 (0.65–0.79) for validation. Results indicate that high proportions of urban slum households are infested with R. norvegicus. The score performed well when identifying high-risk households within slums. These findings need confirmation in other urban centers, but suggest that community-based screening for rodent infestation can allow to target rodent and environmental control measures in populations at highest risk for leptospirosis.     

Combining biomarkers to detect disease with application to prostate cancer

In early detection of disease, combinations of biomarkers promise improved discrimination over diagnostic tests based on single markers. An example of this is in prostate cancer screening, where additional markers have been sought to improve the specificity of the conventional Prostate-Specific Antigen (PSA) test. A marker of particular interest is the percent free PSA. Studies evaluating the benefits of percent free PSA reflect the need for a methodological approach that is statistically valid and useful in the clinical setting. This article presents methods that address this need. We focus on and-or combinations of biomarker results that we call logic rules and present novel definitions for the ROC curve and the area under the curve (AUC) that are applicable to this class of combination tests. Our estimates of the ROC and AUC are amenable to statistical inference including comparisons of tests and regression analysis. The methods are applied to data on free and total PSA levels among prostate cancer cases and matched controls enrolled in the Physicians' Health Study.     

Urinary proteomics in chronic kidney disease: diagnosis and risk of progression beyond albuminuria

Background

The contrast between a high prevalence of chronic kidney disease (CKD) and the low incidence of end-stage renal disease highlights the need for new biomarkers of progression beyond albuminuria testing. Urinary proteomics is a promising method, but more studies focusing on progression rate and patients with hypertensive nephropathy are needed.

Results

We analyzed urine samples with capillary electrophoresis coupled to a mass-spectrometer from 18 well characterized patients with CKD stage 4–5 (of whom six with hypertensive nephropathy) and 17 healthy controls. Classification scores based on a previously developed panel of 273 urinary peptides were calculated and compared to urine albumin dipstick results. Urinary proteomics classified CKD with a sensitivity of 0.95 and specificity of 1.00. Overall diagnostic accuracy (area under ROC curve) was 0.98, which was better than for albuminuria (0.85, p = 0.02). Results for hypertensive nephropathy were similar to other CKD diagnoses. Adding the proteomic score to an albuminuria model improved detection of rapid kidney function decline (>4 ml/min/1.73 m² per year) substantially: area under ROC curve increased from 0.762 to 0.909 (p = 0.042), and 38% of rapid progressors were correctly reclassified to a higher risk and 55% of slow progressors were correctly reclassified to a lower risk category. Reduced excretion of collagen types I–III, uromodulin, and other indicators of interstitial inflammation, fibrosis and tubular dysfunction were associated with CKD diagnosis and rapid progression. Patients with hypertensive nephropathy displayed the same findings as other types of CKD.

Conclusions

Urinary proteomic analyses had a high diagnostic accuracy for CKD, including hypertensive nephropathy, and strongly improved identification of patients with rapid kidney function decline beyond albuminuria testing.     

Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma

Background: The present study investigated the independent prognostic value of glycolysis-related long noncoding (lnc)RNAs in clear cell renal cell carcinoma (ccRCC).Methods: A coexpression analysis of glycolysis-related mRNAs–long noncoding RNAs (lncRNAs) in ccRCC from The Cancer Genome Atlas (TCGA) was carried out. Clinical samples were randomly divided into training and validation sets. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to establish a glycolysis risk model with prognostic value for ccRCC, which was validated in the training and validation sets and in the whole cohort by Kaplan–Meier, univariate and multivariate Cox regression, and receiver operating characteristic (ROC) curve analyses. Principal component analysis (PCA) and functional annotation by gene set enrichment analysis (GSEA) were performed to evaluate the risk model.Results: We identified 297 glycolysis-associated lncRNAs in ccRCC; of these, 7 were found to have prognostic value in ccRCC patients by Kaplan–Meier, univariate and multivariate Cox regression, and ROC curve analyses. The results of the GSEA suggested a close association between the 7-lncRNA signature and glycolysis-related biological processes and pathways.Conclusion: The seven identified glycolysis-related lncRNAs constitute an lncRNA signature with prognostic value for ccRCC and provide potential therapeutic targets for the treatment of ccRCC patients.     

Identifying combinations of cancer markers for further study as triggers of early intervention

In many long-term clinical trials or cohort studies, investigators repeatedly collect and store tissue or serum specimens and later test specimens from cancer cases and a random sample of controls for potential markers for cancer. An important question is what combination, if any, of the molecular markers should be studied in a future trial as a trigger for early intervention. To answer this question, we summarized the performance of various combinations using Receiver Operating Characteristic (ROC) curves, which plot true versus false positive rates. To construct the ROC curves, we proposed a new class of nonparametric algorithms which extends the ROC paradigm to multiple tests. We fit various combinations of markers to a training sample and evaluated the performance in a test sample using a target region based on a utility function. We applied the methodology to the following markers for prostate cancer, the last value of total prostate-specific antigen (PSA), the last ratio of total to free PSA, the last slope of total PSA, and the last slope of the ratio. In the test sample, the ROC curve for last total PSA was slightly closer to the target region than the ROC curve for a combination of four markers. In a separate validation sample, the ROC curve for last total PSA intersected the target region in 77% of bootstrap replications, indicating some promise for further study. We also discussed sample size calculations.     

Discovery and Validation of a Prostate Cancer Genomic Classifier that Predicts Early Metastasis Following Radical Prostatectomy

Purpose

Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis.

Methods

A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 who experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set.

Results

Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67–0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression.

Conclusion

A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.     

Newer potential biomarkers in prostate cancer

Prostate-specific antigen (PSA) screening has led to a significant rise in the number of men diagnosed with prostate cancer and an associated increase in biopsies performed. Despite its limitations, including a positive predictive value of only 25%-40%, PSA remains the only generally accepted biomarker for prostate cancer. There is a need for better tools to not only identify men with prostate cancer, but also to recognize those with potentially lethal disease who will benefit from intervention. A great deal of work has been done worldwide to improve our knowledge of the genetics behind prostate cancer and the specificity of PSA by developing assays for different PSA isoforms. Common genetic alterations in prostate cancer patients have been identified, including CpG hypermethylation of GSPT1 and TMPRSS2:ERG gene fusion. Serum and urine detection of RNA biomarkers (eg, PCA3) and prostate cancer tissue protein antibodies (eg, EPCA) are being evaluated for detection and prognostic tools. This article reviews some of the promising developments in biomarkers.     

A predictive model for respiratory syncytial virus (RSV) hospitalisation of premature infants born at 33–35 weeks of gestational age,based on data from the Spanish FLIP study

Background

The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature infants born 33–35 weeks'' gestational age (GA).

Methods

The predictive model was developed using risk factors captured in the Spanish FLIP dataset, a case-control study of 183 premature infants born between 33–35 weeks'' GA who were hospitalised with RSV, and 371 age-matched controls. The model was validated internally by 100-fold bootstrapping. Discriminant function analysis was used to analyse combinations of risk factors to predict RSV hospitalisation. Successive models were chosen that had the highest probability for discriminating between hospitalised and non-hospitalised infants. Receiver operating characteristic (ROC) curves were plotted.

Results

An initial 15 variable model was produced with a discriminant function of 72% and an area under the ROC curve of 0.795. A step-wise reduction exercise, alongside recalculations of some variables, produced a final model consisting of 7 variables: birth ± 10 weeks of start of season, birth weight, breast feeding for ≤ 2 months, siblings ≥ 2 years, family members with atopy, family members with wheeze, and gender. The discrimination of this model was 71% and the area under the ROC curve was 0.791. At the 0.75 sensitivity intercept, the false positive fraction was 0.33. The 100-fold bootstrapping resulted in a mean discriminant function of 72% (standard deviation: 2.18) and a median area under the ROC curve of 0.785 (range: 0.768–0.790), indicating a good internal validation. The calculated NNT for intervention to treat all at risk patients with a 75% level of protection was 11.7 (95% confidence interval: 9.5–13.6).

Conclusion

A robust model based on seven risk factors was developed, which is able to predict which premature infants born between 33–35 weeks'' GA are at highest risk of hospitalisation from RSV. The model could be used to optimise prophylaxis with palivizumab across Europe.     

PSMA PET/CT to evaluate response to SBRT for prostate cancer bone metastases

BackgroundIn the current study we evaluated ⁶⁸Ga PSMA PET/ CT to measure local control of bone metastasis in oligometastatic prostate cancer patients treated with SBRT.Materials and methodsAfter the institutional review board approval, a retrospective review of medical records of consecutive prostate cancer patients treated between 2014 and 2018 was conducted. Only medical records of patients that were treated with SBRT for bone metastasis and had pre-and post-SBRT ⁶⁸Ga PSMA PET/CT scans were included in our study. Data extracted from the medical files included patient-related (age), disease-related (Gleason score, site of metastasis), and treatment-related factors and outcomes.ResultsDuring the study period, a total of 12 patients (15 lesions) were included, with a median age of 73 years. The median follow-up was 26.5 months (range 13–45 months). Median time of ⁶⁸Ga PSMA PET/ CT follow up was 17.0 months (range 3–39 months). The median pre-treatment PSA was 2 ng/mL (range 0.56–44 ng/mL) vs. post treatment PSA nadir of 0.01 ng/mL (0.01–4.32) with a median time to nadir of 7 months (range, 2–12). Local control was 93% during the follow up period and there was correlation with PS MA avidity on PE T. None patients developed recurrences in the treated bone. None of the patients had grade 3 or more toxicities during follow-up.ConclusionsSBRT is a highly effective and safe method for treatment of prostate cancer bone metastases. More studies are required to determine if SBRT provides greater clinical benefit than standard fractionation for oligometastatic prostate cancer patients. ⁶⁸Ga PSMA PET/CT should be further investigated for delineation and follow-up.     

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.     

Plasma MicroRNAs as Potential Noninvasive Biomarkers for In-Stent Restenosis

Objective

To investigate whether microRNAs (miRs) can serve as novel biomarkers for in-stent restenosis (ISR).

Methods

This retrospective, observational single-centre study was conducted at the cardiovascular department of a tertiary hospital centre in the north of China. Follow-up coronary angiography at 6 to 12 months was performed in 181 consecutive patients implanted with drug-eluting stents. Fifty-two healthy volunteers served as the control group. The plasma miRs levels were analyzed by quantitative real-time PCR. Receiver-operating characteristic curve (ROC) analysis was performed to investigate the characters of these miRs as potential biomarkers of ISR.

Results

MiR-21 levels in ISR patients were significantly higher than those in non-ISR patients and healthy controls (P<0.05), while miR-100 (P<0.05), miR-143 (P<0.001) and miR-145 (P<0.0001) levels were significantly decreased in ISR patients. Further analysis showed that miR-21 levels were remarkably increased (P = 0.045), while miR-100 (P = 0.041), miR-143 (P = 0.029) and miR-145 (P<0.01) levels were dramatically decreased in patients with diffuse ISR compared to those with focal ISR. ROC analysis demonstrated that the area under curve of miR-145, miR-143, miR-100 and miR-21 were 0.880 (95% confidence interval; CI = 0.791–0.987, P<0.001), 0.818 (95% confidence interval; CI = 0.755–0.963, P<0.001), 0.608 (95% confidence interval; CI = 0.372–0.757, P<0.05) and 0.568 (95% confidence interval; CI = 0.372–0.757, P<0.05), with specificity of 83.1%, 80.1%, 68.9% and 68.6%, and sensitivity of 88.7%, 82.1%, 60.2% and 50.1%, respectively.

Conclusions

Circulating miR-143 and miR-145 levels are associated with the occurrence of ISR and can serve as novel noninvasive biomarkers for ISR.