Diagnostic and Prognostic Scoring System for Prostate Cancer Using Urine and Plasma Biomarkers

Ma, Wanlong; Diep, Kevin; Fritsche, Herbert A.; Shore, Neal D.; Albitar, Mäher

doi:10.1089/gtmb.2013.0424

Cited by 19 publications

(27 citation statements)

References 28 publications

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“…Thus, we included genes with an adjusted p value up to < 0.10 in our predictive modeling. Combined, our panel of five biomarkers had good accuracy for determining feeding success (AUROC =0.78), similar to the accuracy of newly developed scoring systems to detect prostate cancer 21 and far exceeding common genetic variants to predict breast cancer 22 . Further, predictive models combining salivary gene expression profiles, PCA and sex, not only assessed the probability of feeding success, but most importantly highlighted specific developmental pathways that were likely contributing to feeding immaturity.…”

Section: Discussionsupporting

confidence: 64%

Computational Gene Expression Modeling Identifies Salivary Biomarker Analysis that Predict Oral Feeding Readiness in the Newborn

Maron

Hwang

Pathak

et al. 2015

The Journal of Pediatrics

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Objective-To combine mathematical modeling of salivary gene expression microarray data and systems biology annotation with RT-qPCR amplification to identify (phase I) and validate (phase II) salivary biomarker analysis for the prediction of oral feeding readiness in preterm infants.Study design-Comparative whole transcriptome microarray analysis from 12 preterm newborns pre-and post-oral feeding success was used for computational modeling and systems biology analysis to identify potential salivary transcripts associated with oral feeding success (phase I). Selected gene expression biomarkers (15 from computational modeling; 6 evidencebased; and 3 reference) were evaluated by RT-qPCR amplification on 400 salivary samples from successful (n=200) and unsuccessful (n=200) oral feeders (phase II). Genes, alone and in combination, were evaluated by a multivariate analysis controlling for sex and post-conceptional age (PCA) to determine the probability that newborns achieved successful oral feeding.Results-Advancing post-conceptional age (p < 0.001) and female sex (p = 0.05) positively predicted an infant's ability to feed orally. A combination of five genes, NPY2R (hunger signaling), AMPK (energy homeostasis), PLXNA1 (olfactory neurogenesis), NPHP4 (visual behavior) and WNT3 (facial development), in addition to PCA and sex, demonstrated good accuracy for determining feeding success (AUROC = 0.78). Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.The authors declare no conflicts of interest. Conclusions-We have identified objective and biologically relevant salivary biomarkers that noninvasively assess a newborn's developing brain, sensory and facial development as they relate to oral feeding success. Understanding the mechanisms that underlie the development of oral feeding readiness through translational and computational methods may improve clinical decision making while decreasing morbidities and health care costs. NIH Public AccessPreterm births affect an estimated 11.5% of all pregnancies in the United States resulting in medical costs exceeding $26 billion annually 1 . Prior to discharge, each infant must demonstrate mature oral feeding skills in accordance to the American Academy of Pediatrics' guidelines 2 . The determination of oral feeding readiness in the preterm newborn remains a significant clinical challenge 3 . Oral feeding is a complex developmental task requiring maturation and integration of the nervous, gastrointestinal, sensory, skeletal muscular and hypothalamic systems 4 . Disruption or delayed maturation in one or several of these developmental syst...

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Section: Discussionsupporting

confidence: 64%

Computational Gene Expression Modeling Identifies Salivary Biomarker Analysis that Predict Oral Feeding Readiness in the Newborn

Maron

Hwang

Pathak

et al. 2015

The Journal of Pediatrics

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“…The urine sample was concentrated and total nucleic acid was extracted as previously described. Similarly, total nucleic acid was extracted from plasma as previously described . Briefly, urine samples were concentrated by centrifugation using Amicon Ultra‐15 centrifugal filter units with 3 kDa membrane (Millipore, Billerica, MA).…”

Section: Methodsmentioning

confidence: 99%

“…The RNA levels of PCa‐specific gene, PDLIM5 , HSPD1 , PSA , IMPDH2 , PCA3 , TMPRSS2 , ERG , UAP1 , PTEN , AR , and the housekeeping B2M and GAPDH genes were quantified in urine and plasma using quantitative reverse transcription and real time polymerase chain reaction (qRT‐PCR) as previously detailed . Briefly, reverse transcription qRT‐PCR was performed using the RNA Ultrasense One‐Step Quantitative RT‐PCR System (Applied Biosystems, Foster City, CA) using a ViiA™ 7 Real‐Time PCR System (Applied Biosystems) with the following thermocycler conditions: hold stage of 50°C for 15 min, 95°C for 2 min, followed by 45 cycles of 95°C for 15 s and 60°C for 30 s. The primers and probes were purchased as TaqMan Gene Expression.…”

Section: Methodsmentioning

confidence: 99%

“…In this algorithm, urine/plasma biomarkers are combined with prostate size, history of prior Bx, clinical history, and sPSA to generate a score used to determine the risk of having PCa GS ≥ 3 + 4. Three cut‐off points (standard, high, and low) were used in this study …”

Section: Methodsmentioning

confidence: 99%

“…We recently reported the development of a combined urine/plasma assay that has been shown to predict PCa GS ≥ 3 + 4 as compared with Bx‐derived GS. This test measures the expression levels of 10 different genes in urine and plasma and incorporates these measurements in an algorithm that includes serum PSA (sPSA), prostate size, history of prior biopsy, ethnic background, and age . The PCa specific‐genes included in this test are UAP1 , PDLIM5 , IMPDH2 , HSPD1 , PSA , PCA3 , TMPRSS2 , ERG , AR , and PTEN , which have been previously associated with progression, recurrence, and metastasis in PCa .…”

Section: Introductionmentioning

confidence: 99%

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Prostatectomy‐based validation of combined urine and plasma test for predicting high grade prostate cancer

Albitar

Lund

et al. 2018

The Prostate

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Genetic susceptibility to prostate cancer in Taiwan: A genome‐wide association study

Bau,

Tsai,

Chang

et al. 2024

Molecular Carcinogenesis

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We conducted the first genome‐wide association study (GWAS) of prostate cancer (PCa) in Taiwan with 1844 cases and 80,709 controls. Thirteen independent single‐nucleotide polymorphisms (SNPs) reached genome‐wide significance (p < 5 × 10−8). Among these, three were distinct from previously identified loci: rs76072851 in CORO2B gene (15q23), odds ratio (OR) = 1.54, 95% confidence interval (CI), 1.36–1.76, p = 5.30 × 10−11; rs7837051, near two long noncoding RNA (lncRNA) genes, PRNCR1 and PCAT2 (8q24.21), OR = 1.41 (95% CI, 1.31–1.51), p = 8.77 × 10−21; and rs56339048, near an lncRNA gene, CASC8 (8q24.21), OR = 1.25 (95% CI, 1.16–1.35), p = 2.14 × 10−8. We refined the lead SNPs for two previously identified SNPs in Taiwanese: rs13255059 (near CASC8), p = 9.02 × 10−43, and rs1456315 (inside PRNCR1), p = 4.33 × 10−42. We confirmed 35 out of 49 GWAS‐identified East Asian PCa susceptibility SNPs. In addition, we identified two SNPs more specific to Taiwanese than East Asians: rs34295433 in LAMC1 (1q25.3) and rs6853490 in PDLIM5 (4q22.3). A weighted genetic risk score (GRS) was developed using the 40 validated SNPs and the area under the receiver‐operating characteristic curve for the GRS to predict PCa was 0.67 (95% CI, 0.63–0.71). These identified SNPs provide valuable insights into the molecular mechanisms of prostate carcinogenesis in Taiwan and underscore the significant role of genetic susceptibility in regional differences in PCa incidence.

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Diagnostic and Prognostic Scoring System for Prostate Cancer Using Urine and Plasma Biomarkers

Abstract: A scoring system incorporating two algorithms using urine and plasma biomarkers highly predicts the presence of GS ≥7 PCa in 75% of patients. Our algorithms may assist with both biopsy indication and patient prognosis.

Cited by 19 publications

References 28 publications

Computational Gene Expression Modeling Identifies Salivary Biomarker Analysis that Predict Oral Feeding Readiness in the Newborn

Computational Gene Expression Modeling Identifies Salivary Biomarker Analysis that Predict Oral Feeding Readiness in the Newborn

Prostatectomy‐based validation of combined urine and plasma test for predicting high grade prostate cancer

Genetic susceptibility to prostate cancer in Taiwan: A genome‐wide association study

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