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ORIGINAL RESEARCH
published: 07 January 2021
doi: 10.3389/fcell.2020.602493
Frontiers in Cell and Developmental Biology | www.frontiersin.org 1January 2021 | Volume 8 | Article 602493
Edited by:
George Calin,
University of Texas MD Anderson
Cancer Center, United States
Reviewed by:
Eddie Luidy Imada,
Johns Hopkins Medicine,
United States
Fuming Li,
University of Pennsylvania,
United States
*Correspondence:
Yong-Jie Lu
y.j.lu@qmul.ac.uk
Specialty section:
This article was submitted to
Molecular and Cellular Oncology,
a section of the journal
Frontiers in Cell and Developmental
Biology
Received: 03 September 2020
Accepted: 30 November 2020
Published: 07 January 2021
Citation:
Guo T, Wang Y, Jia J, Mao X,
Stankiewicz E, Scandura G, Burke E,
Xu L, Marzec J, Davies CR, Lu JJ,
Rajan P, Grey A, Tipples K, Hines J,
Kudahetti S, Oliver T, Powles T,
Alifrangis C, Kohli M, Shaw G,
Wang W, Feng N, Shamash J,
Berney D, Wang L and Lu Y-J (2021)
The Identification of Plasma Exosomal
miR-423-3p as a Potential Predictive
Biomarker for Prostate Cancer
Castration-Resistance Development
by Plasma Exosomal miRNA
Sequencing.
Front. Cell Dev. Biol. 8:602493.
doi: 10.3389/fcell.2020.602493
The Identification of Plasma
Exosomal miR-423-3p as a Potential
Predictive Biomarker for Prostate
Cancer Castration-Resistance
Development by Plasma Exosomal
miRNA Sequencing
Tianyu Guo 1,2 , Yang Wang 1,3 , Jing Jia 4, Xueying Mao 1, Elzbieta Stankiewicz 1,
Glenda Scandura 1, Edwina Burke 1, Lei Xu 1,5 , Jacek Marzec 1, 6, Caitlin R. Davies 1,
Jiaying Jasmin Lu 1, Prabhakar Rajan 1,7,8, 9, Alistair Grey 7, 8, Karen Tipples 7, John Hines 7,9 ,
Sakunthala Kudahetti 1, Tim Oliver 1, Thomas Powles 1, Constantine Alifrangis 7,9 ,
Manish Kohli 10,11 , Greg Shaw 7,8, 9, Wen Wang 12 , Ninghan Feng 3, Jonathan Shamash 13,
Daniel Berney 1, Liang Wang 4and Yong-Jie Lu 1,3
*
1Centre for Cancer Biomarker and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, London,
United Kingdom, 2Department of Cell Biology, Zhejiang University School of Medicine, The Second Affiliated Hospital,
Hangzhou, China, 3Department of Urology, Affiliated Wuxi No. 2 Hospital of Nanjing Medical University, Wuxi, China,
4Department of Tumor Biology, H. Lee Moffitt Cancer Center, Tampa, FL, United States, 5Department of Urology, Zhongshan
Hospital, Fudan University, Shanghai, China, 6Centre for Cancer Research, University of Melbourne, Melbourne, VIC,
Australia, 7Department of Urology, Barts Health NHS, London, United Kingdom, 8Division of Surgery and Interventional
Sciences, University College London, London, United Kingdom, 9Department of Uro-oncology, University College London
NHS Foundation Trust, London, United Kingdom, 10 Department of Medicine, University of Utah, Huntsman Cancer Institute,
Salt Lake City, UT, United States, 11 Department of Oncology, Mayo Clinic, Rochester, MN, United States, 12 Division of
Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, London, United Kingdom,
13 Department of Medical Oncology, Barts Health NHS, London, United Kingdom
Castration-resistant prostate cancer (CRPC) is the major cause of death from prostate
cancer. Biomarkers to improve early detection and prediction of CRPC especially using
non-invasive liquid biopsies could improve outcomes. Therefore, we investigated the
plasma exosomal miRNAs associated with CRPC and their potential for development into
non-invasive early detection biomarkers for resistance to treatment. RNA-sequencing,
which generated approximately five million reads per patient, was performed to identify
differentially expressed plasma exosomal miRNAs in 24 treatment-naive prostate cancer
and 24 CRPC patients. RT-qPCR was used to confirm the differential expressions of
six exosomal miRNAs, miR-423-3p, miR-320a, miR-99a-5p, miR-320d, miR-320b, and
miR-150-5p (p=7.3 ×10−8, 0.0020, 0.018, 0.0028, 0.0013, and 0.0058, respectively)
firstly in a validation cohort of 108 treatment-naive prostate cancer and 42 CRPC
patients. The most significant differentially expressed miRNA, miR-423-3p, was shown
to be associated with CRPC with area under the ROC curve (AUC) =0.784. Combining
miR-423-3p with prostate-specific antigen (PSA) enhanced the prediction of CRPC
(AUC =0.908). A separate research center validation with 30 treatment-naive and 30
CRPC patients also confirmed the differential expression of miR-423-3p (p=0.016).
Finally, plasma exosomal miR-423-3p expression in CRPC patients was compared to
Guo et al. Plasma Exosomal miR-423-3p Predicts CRPC
36 non-CRPC patients under androgen depletion therapy, which showed significantly
higher expression in CRPC than treated non-CRPC patients (p<0.0001) with AUC =
0.879 to predict CRPC with no difference between treatment-naive and treated non-
CRPC patients. Therefore, our findings demonstrate that a number of plasma exosomal
miRNAs are associated with CRPC and miR-423-3p may serve as a biomarker for early
detection/prediction of castration-resistance.
Keywords: prostate cancer, castration-resistance development, biomarker, plasma exosome miRNA, miR-423-3p
INTRODUCTION
Prostate cancer (PCa) is the most frequently diagnosed male
cancer and the second-leading cause of oncological mortality in
the USA, with estimated 174,650 new cases and 31,620 deaths
in 2019 (Siegel et al., 2019). Androgen deprivation therapy
(ADT) has been the standard of care for initial management
of locally advanced and metastatic PCa. However, patients
inevitably progress to castration-resistant PCa (CRPC) within
1–3 years from the start of primary ADT, despite the initial
benefits (Chandrasekar et al., 2015). The prognosis of CRPC
patients is historically poor, with median overall survival after
ADT failure being 2–3 years (West et al., 2014; Ryan et al.,
2015). Prognostic biomarkers for patients with CRPC have been
investigated in many studies (Armstrong et al., 2012; Olmos
et al., 2012; Ross et al., 2012; Huang et al., 2015; Pantel et al.,
2019) and circulating tumor cell analysis has been approved by
the FDA as a prognostic biomarker for patients with metastatic
CRPC (Pantel et al., 2019). In contrast, biomarkers to predict or
monitor CRPC development are rarely investigated (Varenhorst
et al., 2016), despite the fact that prediction and early detection
of CRPC and earlier modifications to treatment may be more
effective in controlling the disease than changing therapy after
clinically apparent CRPC has developed.
Currently, CRPC is usually diagnosed based on biochemical
and radiographic progression (Cornford et al., 2017).
Biochemical progression relies on measuring the serum
prostatic-specific antigen (PSA) level. However, the serum
PSA level does not always correlate with the clinical status of
CRPC (Mizokami et al., 2017). Radiographic progression reflects
well-developed CRPC and necessitates frequent bone and CT
scans. These issues may lead to delays in treatment changes,
missing the opportunity to eradicate small subclones of CRPC
cells when it is easier to cure. Thus, it is important to further
investigate the molecular mechanisms of CRPC development
and identify additional liquid biopsy biomarkers, which can be
more easily utilized to efficiently detect/predict early CRPC to
promptly change treatment into one of the effective therapies
developed in recent years for CRPC patients (Afshar et al., 2015).
Exosomes are small cell secreted vesicles (30–150 nm), which
contain numerous molecular constituents, including lipids,
proteins, RNA and DNA, and mediate cell-cell communication
by transferring these exosomal components between cells (Rana
et al., 2013; Matei et al., 2017; Chen et al., 2018). MicroRNAs
(miRNAs) are enriched in exosomes (Valadi et al., 2007) and
can be transferred between cells via exosomes to regulate various
biological processes associated with cancer development and
progression (Rana et al., 2013; Sánchez et al., 2016). MiRNAs
in exosomes are protected from degradation in the circulation
(Ge et al., 2014) and studies have demonstrated the potential
of exploiting exosomal miRNAs as non-invasive and dynamic
biomarkers in cancer diagnosis and prognosis (Hu et al., 2012).
Plasma exosomal miRNAs have been previously reported as
valuable prognostic biomarkers in patients who have already
developed CRPC (Huang et al., 2013, 2015; Yuan et al., 2016).
However, no studies on the association of plasma exosomal
miRNA with CRPC development have been reported.
Identification of plasma exosomal miRNAs associated with
CRPC would not only improve our understanding of CRPC
development mechanisms, but more importantly help the
development of biomarkers to predict/detect the early occurrence
of CRPC, enabling prompt alteration of therapeutic regimens
before CRPC is fully developed. We therefore investigated plasma
exosomal miRNAs associated with CRPC and evaluated their
potential as predictive biomarkers for CRPC occurrence.
MATERIALS AND METHODS
Patients
Blood samples from 24 treatment-naive PCa patients and 24
CRPC patients for RNA next-generation sequencing (RNA-seq)
and 108 treatment-naive PCa and 42 CRPC patients (including
the 24 CRPC patients used for RNA-seq) in the validation
cohort I were collected with patients’ informed consent from
St Bartholomew’s Hospital, BartsHealth NHS, London, UK,
between 2015 and 2018. Samples from an additional cohort
of treated non-CRPC patients included 36 PCa patients who
had started initial hormone-therapy <3 months before blood
collection was obtained also from St Bartholomew’s Hospital.
Blood samples from 30 treatment-naive PCa patients and 30
CRPC patients in the independent validation cohort II were
collected from Mayo Clinic, Rochester, US between 2009 and
2012 with patients’ informed consent. All CRPC patients had
been treated with ADT and had evidence of disease progression
with rising PSA and/or imaging-based progression. Patients’
clinical data is summarized in Table 1. Use of patient blood
samples and clinical data in this study was approved by London
City & East Research Ethics Committee (09/H0704/4+5) and
Institutional Review Board of Medical College of Wisconsin
(PRO00017780). RNA-seq and validation workflow is presented
in Figure 1.
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Guo et al. Plasma Exosomal miR-423-3p Predicts CRPC
TABLE 1 | Clinical characteristics of patients in the RNA-sequencing and RT-qPCR validation cohorts.
RNA-sequencing cohort Validation cohort I Treated non-CRPC Validation cohort II
Treatment-naive CRPC Treatment-naive CRPCaTreatment-naive CRPC
Case number 24 24 108 42 36 30 30
Age, yr, median (IQR) 65.15
(56.13-70.73)
75.55
(68.75-81.89)
65.90
(57.89–72.10)
72.65
(68.38–80.88)
65.90
(61.10–73.55)
67.30
(63.38–80.55)
71.70
(65.85–76.58)
Gleason score at diagnosis
≤6 8 1 36 1 1 2 4
7 16 2 58 7 11 14 9
8 0 5 6 8 5 3 4
≥9 0 9 8 16 14 8 10
unknown 0 7 0 10 5 3 3
Median PSA at sample
collection, ng/ml (IQR)
9.9
(6.9–12.75)
51
(17.75–241.8)
8.9
(5.75–13)
57
(21.5–192.3)
15.5
(6.255–112.7)
7
(1.75–28.55)b
47.7
(9.35–119)
Yr, year; IQR, interquartile range; CRPC, castration-resistant prostate cancer; PSA, prostate-specific antigen. aincluding 24 patients used for RNA-sequencing; bone patient’s data
is missing.
Exosome Isolation
Plasma was isolated within 2 h of blood draw by centrifugation
of whole blood at 1,200 g for 10 min, followed by another
centrifugation of the supernatant at 2,000 g for 10min. The
supernatant was taken as plasma and stored at −80◦C for future
use. Two hundred microliter of plasma from each case was used.
Prior to exosome precipitation, plasma samples were treated with
RNase A to remove free circulating RNAs and then RNase A was
inactivated with RNase inhibitor. Exosomes were isolated using
the Total Exosome Isolation Kit (from plasma) (InvitrogenTM)
following manufacturer’s instructions. For the validation cohort
at Mayo Clinic, US, exosomes were isolated using ExoQuick
Plasma prep and Exosome precipitation kit (System Biosciences)
without RNase pre-treatment.
RNA Extraction
The exosome pellet was re-suspended in Buffer RLT (Qiagen)
with 0.25 µg/µl Proteinase K and incubated at 50◦C for 30 min.
Exosomal RNA was then extracted using miRNeasy Micro Kit
(Qiagen) and QIAzol Lysis Reagent (Qiagen) according to the
manufacturer’s protocol. PC3 cell miRNAs were extracted using
AllPrep R
DNA/RNA/Protein Mini kit (Qiagen) and miReasy
micro kit (Qiagen) according to the recommended protocol.
RNA-Seq and Data Analysis
Indexed libraries were prepared from the exosomal RNA as
instructed by the NEBNext Multiplex Small RNA Library Prep
Set for Illumina (NEB) without size selection. RNA-seq was
performed on Illumina NextSeq 500 platform. The data was
trimmed with trimgalore (end minimum quality level 30 and
minimum read length 15) and aligned to human reference
genome build hg19 with Bowtie 0.12.8 (seed mismatch limit 1
and seed length 10) implemented in BaseSpace (Illumina, CA).
BAM files were uploaded into Partek Genomic Suite (Partek
Inc) and annotated against mirBase 20 mature miRNA. Samples
were sequenced twice and read counts from two runs were
combined for data analysis. The raw data was deposited to
Gene Expression Omnibus (accession number: GSE136321).
MiRNAs expressed in <25% of samples were removed from
differential expression analysis. The miRNA read counts were
analyzed using four pipelines to identify differentially expressed
miRNAs (Figure 1): (1) Counts per million (CPM), (2) The
trimmed mean of M-value normalization (TMM) (Robinson
and Oshlack, 2010), (3) Limma (Ritchie et al., 2015), and (4)
DESeq2 (Love et al., 2014). For CPM analysis, filtered read
counts were transformed to CPM using the cpm() function
implemented in edgeR package (version 2.4.0) and then Welch’s
t-test was performed in SPSS 24. TMM was performed using
functions implemented in the edgeR. Filtered read counts were
input as a DGEList in R (v4.0.2). TMM normalization was
performed using calcNormFactors() function implemented in
edgeR() package. EstimateCommonDisp() was performed to
estimate the common dispersion parameter and the ExactTest()
functions in edgeR were used to detect differentially expressed
miRNAs. The results were then written out as a csv file. Pipeline
combining functions in Limma and edgeR were performed using
functions implemented in both software packages in R. Filtered
read counts were input as a DGEList and normalized with
calcNormFactors() function. The normalized data was voom
transformed with voom() function. Then a linear model was
fitted using lmfit() and the empirical Bayes statistics was applied
with eBayes() function to smooth the standard errors. The results
were then written out as a csv file. DESeq2 differential expression
analysis was performed using functions in the DESeq2 R package
(v4.0.2). A DESeq data set was generated with filtered read
counts using DESeqDataSetFromMatrix() function implemented
in DESeq2() package. Normalization and differentially expressed
gene analysis was done using DESeq() function. The results were
then written out as a csv file.
Reverse Transcription Quantitative
Real-Time Polymerase Chain Reaction
(RT-qPCR)
The reverse transcription was performed using miScript II
RT kit (Qiagen). RT-qPCR was performed with miScript
primer assays (Qiagen) (MS00004179—Hs_miR-423_1 miScript
Primer, MS00008932—Hs_miR-193a-5p_1 miScript Primer,
Frontiers in Cell and Developmental Biology | www.frontiersin.org 3January 2021 | Volume 8 | Article 602493
Guo et al. Plasma Exosomal miR-423-3p Predicts CRPC
FIGURE 1 | Overview of the plasma exosomal miRNA analysis workflow. CRPC, castration-resistant prostate cancer; CPM, counts per million transformation; Limma,
linear models for microarray and RNA-sequencing data; TMM, the trimmed mean of M-value normalization.
MS00003738—Hs_miR-200a_1 miScript Primer, MS00032158—
Hs_miR-99a_2 miScript Primer, MS00014707—Hs_miR-320a_1
miScript Primer, MS00031710—Hs_miR-320d_2 miScript
Primer, MS00031703—Hs_miR-320b_2 miScript Primer,
MS00006552—Hs_miR-24_1 miScript Primer, MS00010752—
Hs_miR-9_1 miScript Primer, MS00007350—Hs_miR-30a-5p_1
miScript Primer, MS00003129—Hs_let-7c_1 miScript Primer,
MS00008372—Hs_miR-101_3 miScript Primer, MS00003556—
Hs_miR-148a_1 miScript Primer, MS00003577—Hs_miR-150_1
miScript Primer, MS00031829—Hs_miR-375_2 miScript Primer,
MS00004242—Hs_miR-451_1 miScript Primer) and miScript
SYBR R
Green PCR Kit (Qiagen) on QuantstudioTM real-time
PCR system (ThermoFisher Scientific). Let-7c-5p and miR-30a-
5p were selected as endogenous reference genes based on their
small variation across all samples in our sequencing data and
previous publication (Huang et al., 2015). 1Ct =(CtmiRNA –
average Ctreferencegenes) and where PC3 cell line miRNA was used
to normalize multiple RT-qPCR plates, 1Ct was calculated as
sample (CtmiRNA – average Ctreferencegenes) – PC3 (CtmiRNA –
average Ctreferencegenes). The relative expression level of miRNAs
in exosomes was calculated as 2−1Ct. All reactions were run
in triplicate.
Pathway Over-Representation Analysis
Targets of miRNAs were retrieved from TarBase v7.0 and
pathway overrepresentation analysis was performed using
KEGG pathways via online tool DIANA-miRPath v3.0
(http://snf-515788.vm.okeanos.grnet.gr/) (Vlachos et al.,
Frontiers in Cell and Developmental Biology | www.frontiersin.org 4January 2021 | Volume 8 | Article 602493
Guo et al. Plasma Exosomal miR-423-3p Predicts CRPC
FIGURE 2 | Scatter plots of plasma exosomal miRNA expression levels evaluated by RT-qPCR in validation cohort I of 108 treatment-naive prostate cancer and 42
castration resistant prostate cancer (CRPC) patients. (A) The five significant differentially expressed miRNAs from RNA-seq identified by four RNA-seq analysis
methods; (B) The six significant differentially expressed miRNAs from RNA-seq identified by three RNA-seq analysis methods; (C) The differentially expressed miRNAs
from RNA-seq identified by two RNA-seq analysis methods. The y-axis shows the relative expression level of miRNAs calculated by 2−1Ct. The error bars show the
mean ±standard error of mean (SEM). *p<0.05, **p<0.01, ***p<0.001, ns, not significant.
2015). The CRPC associated miRNAs identified in this
study were input and analyzed using TarBase to retrieve
target genes of the miRNAs. Pathways union mode was
used to identify all the pathways significantly targeted by
the selected miRNAs. False Discovery Rate correction was
applied. P-value threshold was set at 0.05 and microT
threshold was defaulted at 0.8. Fisher’s exact test was
selected as the enrichment analysis method. To generate
heatmap for each miRNA, significance clusters/heatmap option
was selected.
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Guo et al. Plasma Exosomal miR-423-3p Predicts CRPC
Statistical Analysis
Analysis of the RNA-seq data was described in previous section.
Unpaired Mann-Whitney U tests were performed on RT-qPCR
relative expression levels to identify differences in exosomal
miRNA expression levels between treatment-naive PCa, treated
non-CRPC and CRPC and were performed in GraphPad Prism
7. Receiver operating characteristic (ROC) curves were generated
and the area under the curve (AUC) was used to evaluate the
prediction values of parameters for CRPC. ROC curve and AUC
was generated in GraphPad Prism 7. Binomial logistic regression
was performed with miR-423-3p relative expression level and
PSA level as predictors for CRPC (yes, no) using the binomial
logistic regression function in SPSS 24. A combination model
(CM) was computed as the linear predictor of the fitted bivariate
logistic model with PSA and 423-3p relative expression level
as only predictors as a∗PSA+b∗miR-423-3p, where the values
of “a” and “b” are the covariance factors of PSA and miR-
423-3p relative expression level, respectively. Univariate logistic
regression analyses were performed separately for miR-423-3p
relative expression level and PSA level to evaluate and compare
their association with CRPC. The miR-423-3p relative expression
and PSA level were treated as continuous variables, and CRPC
status was considered as a categorical variable. To examine
the independent association of miR-423-3p expression with
CRPC status and adjust it for PSA level a multivariate logistic
regression analysis was performed. All statistical tests were
two sided. We did not apply multiple event testing correction
to exosomal RNA-seq data analysis, since there are only a
small number of miRNAs for the analysis and the differential
miRNA expression was identified by different analysis tools
to select candidates (more than selected based on adjusted p-
value) for experimental validation. Bonferroni correction test
was performed to modify p-values for RT-qPCR result multiple
tests through dividing the critical p-value by the number of
comparisons being made.
RESULTS
The Plasma Exosomal miRNA Expression
Profiles in Patients With Treatment-Naive
PCa and CRPC
To identify candidate plasma exosomal miRNAs associated with
CRPC development, RNA-seq was performed in a screening
cohort of 24 treatment-naive PCa patients and 24 CRPC
patients. Combing two runs of data, the RNA-seq produced
an average of five million reads per sample. This data enabled
us to detect 612 plasma exosomal miRNAs in total, with 483
detected in treatment-naive PCa and 499 in CRPC patients
(Supplementary Table 1). There were 37 miRNAs which were
consistently detected in all individual samples, while 45 miRNAs
detected in all CRPC samples and 45 miRNAs detected
in all treatment-naive PCa samples (Supplementary Table 2).
On average, miR-451a was the most abundant miRNA in
both treatment-naive PCa and CRPC. The top ten most
abundant miRNAs detected in these two groups are listed in
Supplementary Table 3.
Identification of Differential Expression of
miRNAs by RNA-Seq Comparing Patients
With Treatment-Naive PCa and CRPC
To identify differentially expressed plasma exosomal miRNAs
between the treatment-naive PCa and CRPC patients, we
employed four methods (CPM, TMM, Limma, and DESeq2)
to analyze the RNA-seq data. MiRNAs expressed in <25%
of samples were filtered out from the analysis. From the
remaining 185 miRNAs, we identified 13, 13, 11, and 14
differentially expressed miRNAs by Limma, TMM, CPM, and
DESeq2 methods, respectively, at p<0.01 (Table 2 and
Supplementary Table 4). Among them, five miRNAs (miR-423-
3p, miR-99a-5p, miR-320a, miR-200a-3p, and miR-193a-5p) were
identified by all four methods and they were all present at
higher levels in CRPC than treatment naive PCa patient samples.
Six miRNAs (miR-375, miR-451a, miR-320b, miR-148a-3p, miR-
150-5p, miR-320d) were detected by three of the methods and
three miRNAs (miR-101-3p, miR-9-5p, miR-24-3p) by two of
the methods. Of note, all of the miRNAs with p<0.01 from
DESeq2 were also detected by one of the other methods. Except
for DESeq2, the other three methods presented some unique
miRNAs which were not detected by others.
Validation of Plasma Exosomal miRNAs as
Potential Biomarkers for CRPC by
RT-qPCR
To validate candidate plasma exosomal miRNAs as biomarkers
for CRPC development, we performed RT-qPCR and tested
all the five miRNAs with p<0.01 in all four RNA-seq
analysis methods, the six miRNAs with p<0.01 in three
methods, and three miRNAs with p<0.01 in two methods in
validation cohort I, consisting of 108 treatment-naive PCa and
42 CRPC patients collected in St Bartholomew’s Hospital. Of
the five miRNAs identified by all four sequencing data analysis
methods, the expression of miR-200a-3p was too low to be
detected by RT-qPCR. Out of the remaining four miRNAs,
three miRNAs, miR-423-3p, miR-320a, and miR-99a-5p were
significantly differentially expressed between treatment-naive
PCa patients and CRPC (p=7.3 ×10−8, 0.002 and 0.0186,
respectively) (Figure 2A), while miR-193a-5p showed a trend
without a statistically significant difference (p=0.0547). After
multiple testing correction, miR-423-3p and miR-320a remained
statistically significant. Consistent with the RNA-seq data, these
miRNAs were expressed at higher levels in CRPC compared to
treatment-naive PCa patients.
Among the six miRNAs which showed significant difference in
three of the four RNA-seq analysis methods, miR-375 could not
be detected efficiently by our RT-qPCR method. Of the remaining
five miRNAs, three miRNAs, miR-320d, miR-320b, and miR-
150-5p were significantly differentially expressed (p=0.0028,
0.0013, and 0.0058, respectively) (Figure 2B) and all remained
statistically significant after multiple testing correction. miR-
148a-3p and miR-451a showed no significant difference between
treatment-naive PCa and CRPC (p=0.95 and 0.98, respectively)
(Figure 2B).
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Guo et al. Plasma Exosomal miR-423-3p Predicts CRPC
TABLE 2 | Differentially expressed plasma exosomal miRNAs at p<0.01 between treatment-naive prostate cancer and CRPC by RNA-sequencing analyses.
Limma TMM CPM DESeq2
MiRNA p-value MiRNA p-value MiRNA p-value MiRNA p-value
miR-375 3.73 ×10−04 miR-375 5.71 ×10−11 miR-423-3p 4.20 ×10−04 miR-375 7.79 ×10−07
miR-193a-5p 3.93 ×10−04 miR-193a-5p 1.32 ×10−05 miR-21-5p 1.06 ×10−03 miR-193a-5p 2.36 ×10−05
miR-101-3p 9.07 ×10−04 miR-9-5p 1.56 ×10−05 miR-320a 1.10 ×10−03 miR-320b 1.53 ×10−04
miR-451a 1.70 ×10−03 miR-184 9.39 ×10−05 miR-193a-5p 2.07 ×10−03 miR-200a-3p 2.73 ×10−04
miR-320b 1.73 ×10−03 miR-200a-3p 4.00 ×10−04 miR-451a 2.61 ×10−03 miR-320a 2.83 ×10−04
miR-320a 3.07 ×10−03 miR-320b 4.90 ×10−04 miR-24-3p 3.25 ×10−03 miR-99a-5p 5.40 ×10−04
miR-423-3p 4.79 ×10−03 miR-148a-3p 1.04 ×10−03 miR-99a-5p 4.74 ×10−03 miR-423-3p 6.32 ×10−04
miR-148a-3p 5.33 ×10−03 miR-99a-5p 1.70 ×10−03 miR-146a-5p 7.78 ×10−03 miR-9-5p 6.39 ×10−04
miR-150-5p 5.87 ×10−03 miR-150-5p 2.63 ×10−03 miR-200a-3p 8.33 ×10−03 miR-150-5p 8.47 ×10−04
miR-342-5p 6.39 ×10−03 miR-320d 2.90 ×10−03 miR-22-5p 9.21 ×10−03 miR-148a-3p 1.09 ×10−03
miR-200a-3p 6.55 ×10−03 miR-320a 3.26 ×10−03 miR-320d 9.82 ×10−03 miR-320d 2.86 ×10−03
miR-99a-5p 8.05 ×10−03 miR-423-3p 3.27 ×10−03 miR-101-3p 5.31 ×10−03
miR-30c-5p 8.84 ×10−03 miR-122-5p 7.07 ×10−03 miR-451a 5.62 ×10−03
miR-24-3p 6.09 ×10−03
CRPC, castration-resistant prostate cancer; CPM, counts per million transformation; TMM, the trimmed mean of M-value normalization. MiRNAs in bold are these differentially expression
by all four analysis methods.
Among the three miRNAs which showed significant difference
in only two of the four RNA-seq analysis methods, miR-9 had
too low an expression to be detected. MiR-101-3p and miR-
24-3p showed no significant difference (p=0.086 and 0.075,
respectively) (Figure 2C).
As treatment-naive PCa is mostly of low Gleason score (6
and 7) and more than half of the CRPC cases have a high
Gleason score (8 or higher), we interrogated Gleason score
associations in the treatment-naive PCa cohort, where we have
sufficient number of patients for subgroup analysis. We found no
significant association of Gleason score with the expression of any
of the six miRNAs (Supplementary Figure 1).
To evaluate the predictive value of the plasma exosomal
miRNAs for CRPC, we performed ROC curves analysis based
on the RT-qPCR results. Comparing the treatment-naive PCa
and CRPC, the AUC of miR-423-3p, miR-320a, miR-99a-5p,
miR-320d, miR-320b, miR-150-5p was 0.784 (95% confidence
interval (CI): 0.707–0.860, p<0.0001), 0.663 (95% CI: 0.562–
0.764, p=0.0020), 0.624 (95% CI: 0.514–0.734, p=0.019),
0.657 (95% CI: 0.549–0.766, p=0.0028), 0.670 (95% CI: 0.560–
0.779, p=0.0013), and 0.645 (95% CI: 0.539–0.751, p=0.0058),
respectively (Figure 3A). When miR-423-3p, which showed
the best predictive value, was combined with PSA (AUC =
0.837, 95% CI: 0.740–0.934) with a logistic binominal regression
model (combination model =0.026∗PSA+0.033∗miR-423-3p),
the performance improved to AUC =0.908 (95% CI: 0.861–
0.955, p<0.0001) with 80.95% (95% CI: 65.88–91.4%) sensitivity
and 82.41% (73.9–89.06%) specificity (Figure 3B). Using a
multivariate logistic regression analysis, including miR-423-3p
expression and PSA level as variables, we found that both
exosomal miR-423-3p expression (p=7.29 ×10−05) and
PSA level (p=0.000557) were independently associated with
CRPC status.
Pathway Analysis of Differentially
Expressed Exosomal miRNAs
After identified plasma exosomal miRNAs associated with CRPC,
we sought to explore the potential pathways in which these
miRNAs are involved. We performed functional pathway analysis
of targets for the six validated CRPC associated miRNAs,
miR-423-3p, miR-320a, miR-99a-5p, miR-320d, miR-320b, and
miR-150-5p. Targets of miRNAs were retrieved from TarBase
v7.0 and pathway overrepresentation analysis was performed
using KEGG pathways via DIANA-miRPath v3.0. Most of
the significantly enriched pathways were cancer-associated.
Hippo signaling pathway, TGF-beta signaling pathway and
Adherens junction were the three most significant pathways as
shown in the collected target analysis of the six differentially
expressed miRNAs in Table 3. Additionally, we also explored the
significantly enriched pathways (corrected p<0.05) for each
miRNA (Figure 4).
Additional Sample Cohort Validation of
Plasma Exosomal miR-423-3p as a CRPC
Biomarker
To further validate the CRPC association of exosomal miR-
423-3p, which showed the most significant difference between
treatment-naive PCa and CRPC, its expression was investigated
in an independent validation cohort II. This cohort consisted
of 30 treatment-naive PCa and 30 CRPC patients from Mayo
Clinic, where patients in the untreated PCa group were selectively
enriched for metastatic disease (16/30). Plasma exosomal
miRNAs were extracted by a different protocol to validation
cohort I as described previously in materials and methods.
Comparing the 30 treatment-naive PCa to the 30 CRPC patients,
miR-423-3p was again expressed at a significantly (p=0.016)
Frontiers in Cell and Developmental Biology | www.frontiersin.org 7January 2021 | Volume 8 | Article 602493
Guo et al. Plasma Exosomal miR-423-3p Predicts CRPC
FIGURE 3 | Receiver operating characteristic (ROC) analysis of the efficiencies of classifiers in discriminating castration-resistant prostate cancer (CRPC) from
treatment-naive prostate cancer. (A) ROC analysis of the six confirmed plasma exosomal miRNAs significantly different between treatment-naive and CRPC patients in
validation cohort I; (B) ROC analysis of miR-423-3p, serum prostate-specific antigen (PSA) and the combination model (CM) of miR-423-3p and PSA in predicting
CRPC in validation cohort I.
TABLE 3 | KEGG pathway analysis of target genes of six plasma exosomal miRNAs.
KEGG pathway p-value Number of target genes miRNAs
Hippo signaling pathway 2.20 ×10−09 20 3
TGF-beta signaling pathway 1.71 ×10−08 15 2
Adherens junction 3.02 ×10−08 16 4
Transcriptional misregulation in cancer 8.04 ×10−06 32 2
Pathways in cancer 2.58 ×10−05 72 3
Viral carcinogenesis 5.26 ×10−05 43 3
Proteoglycans in cancer 6.67 ×10−05 25 2
Glioma 1.06 ×10−04 17 2
Glycosphingolipid biosynthesis—lacto and neolacto
series
6.23 ×10−04 2 1
Colorectal cancer 1.05 ×10−03 16 2
Renal cell carcinoma 4.98 ×10−03 5 1
Central carbon metabolism in cancer 6.43 ×10−03 4 1
Chronic myeloid leukemia 0.012 19 2
Endometrial cancer 0.024 5 1
Pancreatic cancer 0.035 11 1
higher level in plasma exosomes from CRPC than treatment-
naive PCa patients (Figure 5A), which confirmed the association
of plasma exosomal miR-423-3p with CRPC regardless of
different cohorts and different detection methods.
Increase of Plasma Exosomal miR-423-3p
Expression Is Correlated With CRPC
Development but Not in Response to ADT
To exclude the possibility that the increase of plasma exosomal
miR-423-3p in CRPC as compared to treatment naive PCa is the
result of ADT rather than castration resistance development, we
further performed RT-qPCR analysis of plasma exosomal miR-
423-3p in a group of 36 early stage treatment non-CRPC patients,
who had been receiving ADT for <3 months. We found that the
expression of plasma exosomal miR-423-3p was not significantly
different between treatment-naive PCa and ADT-treated non-
CRPC patients (p=0.3353), indicating that ADT does not affect
plasma exosomal miR-423-3p levels (Figure 5B). However, miR-
423-3p expression was consistently and significantly (p<0.0001)
higher in CRPC than treated non-CRPC patients (Figure 5B).
Based on the data from this cohort, plasma exosomal miR-423-
3p alone had an excellent CRPC prediction value of AUC =0.879
(95% CI: 0.7981–0.9599, p<0.0001) (Figure 5C).
Frontiers in Cell and Developmental Biology | www.frontiersin.org 8January 2021 | Volume 8 | Article 602493
Guo et al. Plasma Exosomal miR-423-3p Predicts CRPC
FIGURE 4 | Heatmap of significant pathways predicted by DIANA-miRPath (v.3.0) for six differentially expressed plasma exosomal miRNAs between treatment-naive
prostate cancer and castration-resistant prostate cancer patients. Pathways are depicted on the x-axis and miRNAs on the y-axis. The color code represents the log
(p-value), with the most significant predicted miRNA-pathway interactions in red.
DISCUSSION
The development of CRPC is a major clinical problem in the
management of advanced PCa. While a number of studies
have investigated biomarkers for CRPC prognosis (Armstrong
et al., 2012; Olmos et al., 2012; Ross et al., 2012; Huang
et al., 2015; Pantel et al., 2019), there are limited investigations
into biomarkers predicting or monitoring CRPC development
(Varenhorst et al., 2016). In this study, we generated plasma
exosomal miRNA profiles in treatment-naive PCa and CRPC
by RNA-seq and identified plasma exosomal miRNAs associated
with CRPC. We validated six differentially expressed miRNAs,
miR-423-3p, miR-320a, miR-99a-5p, miR-320d, miR-320b, and
miR-150-5p in a larger cohort of treatment-naive PCa and
CRPC samples by RT-qPCR, with multicenter validation of miR-
423-3p differential expression, which is the most significantly
associated miRNA with CRPC. Furthermore, we showed that
increased plasma exosomal miR-423-3p expression in CRPC is
not associated with the response to ADT treatment, confirming
that its expression increase is a cause or result of CRPC
development. Our results show the potential of using plasma
exosomal miRNAs as biomarkers to predict/monitor CRPC
development, which might enable an early treatment change
before CRPC is well-established.
Blood-based test has a number of advantages for clinical
utility. It is non-invasive and can provide real-time information
on patient status. MiRNAs in plasma exosomes serve as good
candidates for blood biomarkers as they are protected from
degradation, allowing for their stable and easy detection by
methods such as RT-qPCR assays. In a previous study, Huang
et al. generated extracellular vesicle RNA profiles from CRPC and
a small number of hormone-sensitive PCa patients (Yuan et al.,
Frontiers in Cell and Developmental Biology | www.frontiersin.org 9January 2021 | Volume 8 | Article 602493
Guo et al. Plasma Exosomal miR-423-3p Predicts CRPC
FIGURE 5 | Plasma exosomal miR-423-3p expression levels evaluated by RT-qPCR in validation cohort II and treated non-CRPC. (A) Scatter plot of plasma
exosomal miR-423-3p expression levels evaluated by RT-qPCR in validation cohort II of 30 treatment-naive prostate cancer patients and 30 castration-resistant
prostate cancer (CRPC) patients; (B) Scatter plot of plasma exosomal miR-423-3p expression levels evaluated by RT-qPCR in treated non-CRPC patients in
comparison to patients in validation cohort I; (C) Receiver operating characteristic (ROC) analysis of the plasma exosomal miR-423-3p in discriminating CRPC in
cohort I from treated non-CRPC. In scatter plots, the y-axis show the relative expression level of miR-423-3p calculated by 2−1Ct and the error bars are showing the
mean ±standard error of mean (SEM). *p<0.05, ***p<0.001, ns, not significant.
2016) without exosomal miRNA analysis in association with
CRPC. Using a modified plasma exosome isolation and RNA-
seq method, we identified in a large patient cohort several plasma
exosomal miRNAs significantly correlated to CRPC, which were
validated by RT-qPCR method in a larger cohort of patients. Our
data, showing that there was no significant expression difference
of the six miRNAs between different Gleason grade groups, ruled
out the influence of Gleason grade on the correlation of these
miRNA expression with CRPC. The association of miR-423-3p
with CRPC development was further supported by its differential
expression in the comparison between ADT treated and CRPC
cohorts and the independent validation cohort II, where the
Gleason grade distribution was similar between the groups. We
identified miR-423-3p as the most significantly differentially
expressed miRNA between treatment-naive PCa and CRPC and
the correlation of its increase with CRPC was also validated in
a separate sample cohort from an independent research center.
In a previous study by Watahiki et al. with a small patient
cohort, higher levels of plasma miR-423-3p has been reported in
patients with metastatic CRPC (n=25) compared to treatment-
naive localized PCa (n=25) (Watahiki et al., 2013). However,
while their results are consistent with ours regarding the potential
of using miR-423-3p as a circulating biomarker for metastatic
CRPC prediction, we are the first to demonstrate that miR-423-3p
exists in the plasma exosomes and presents at different expression
levels during PCa development. This is important, as it means
that plasma miR-423-3p is not just released from dying cells, but
is actively secreted by cells, which can be involved in cell-cell
communication to promote CRPC development. Furthermore,
the result from the study by Watahiki et al. (2013) cannot rule out
whether the increase of plasma miR-423-3p was the consequence
of ADT treatment or CRPC development. We demonstrated
that plasma exosomal miR-423-3p was associated with CRPC
development instead of ADT treatment by comparing newly
treated PCa patients who were at the responsive stage with
those who had developed CRPC. Our study suggests that plasma
exosomal miR-423-3p has strong potential to be developed in a
biomarker for monitoring CRPC development and its predictive
value for CRPC development should be further validated in a
longitudinal study of pre-hormone therapy patients with CRPC
development follow-up data.
The limited studies published to date, using patient samples
to investigate genes or biomarkers associated with CRPC
development, mainly compared treatment-naive PCa and CRPC
samples (Nguyen et al., 2013; Watahiki et al., 2013; Goto et al.,
2015). A potential issue affecting the reliability of the CRPC
association is that one group of patients is untreated and the other
group of patients is treated. Therefore, the genetic/molecular
changes could be induced by the treatment. In this study, we
have confirmed the CRPC specific association of a potential
biomarker by comparing androgen depletion treated non-CRPC
patients to treatment-naive PCa and CRPC patients. Therefore,
we have developed a robust approach for the investigation of
CRPC associated genetic changes or biomarkers using clinical
samples and identified potential clinically valuable biomarkers
for the early detection/prediction of CRPC occurrence.
Several normalization methods for RNA-seq data have
been proposed, but no standard method has currently been
established. We employed four most commonly used methods to
analyze our sequencing data, CPM, Limma, TMM, and DESeq2,
each with their advantages (Dillies et al., 2013; Tam et al.,
2015). In our study, the miRNAs identified as differentially
expressed by four and three RNA-seq analysis methods gave more
reproducible results in RT-qPCR validation than those identified
only by two of these methods. As there is no gold-standard
method for miRNA sequencing data analysis, our results indicate
that the combination of different analysis methods should be used
to identify candidate miRNAs for further validation.
In addition to biomarker potential, the identified plasma
exosomal miRNAs may have important functions in CRPC
development. Emerging evidence shows that exosomes play a
key role in cell-cell crosstalk, which may impact tumor cell
Frontiers in Cell and Developmental Biology | www.frontiersin.org 10 January 2021 | Volume 8 | Article 602493
Guo et al. Plasma Exosomal miR-423-3p Predicts CRPC
growth, metastasis, angiogenesis and cancer microenvironment
(Maia et al., 2018). The involvement of these six miRNAs in
CRPC development has been previously reported. Consistent to
our findings of miR-150-5p under-expression in CRPC patients,
previous studies showed low miR-150-5p expression in CRPC
tissue compared to PCa and non-PCa tissue (Okato et al., 2017)
and its role as a tumor suppressor (Okato et al., 2017; Osako
et al., 2017). Paradoxically, while we detected the overexpression
of miR-320a, miR-320b, miR-320d, and miR-99a-5p in CRPC
patient plasma exosomes, miR-320 and miR-99a-5p have been
reported to suppress prostate carcinogenesis (Hsieh et al., 2013;
Arai et al., 2018), and lower expression levels of miR-320a and
miR-99a-5p in CRPC tissues have been reported by comparing a
small number of CRPC with untreated PCa cases (Okato et al.,
2016; Arai et al., 2018). The tumor suppressor role of these
miRNAs does not necessarily conflict with our observations in
plasma exosomes. Regarding the deregulation of these miRNAs
in the metastatic CRPC tissues, these observations have to
be validated in larger cohorts. The opposite results for the
expression levels of miRNAs in plasma and tissue samples are
not uncommon as previously found in the studies of miR-320
in glioblastoma (Roth et al., 2011; Dong et al., 2014; Manterola
et al., 2014) and of miR-99a in endometrioid endometrial
carcinoma (Torres et al., 2012). The pathway overrepresentation
analysis identified the Hippo signaling pathway as the most
significantly enriched pathway. Hippo signaling pathway has
been intensively studied in cancer development which showed
an important regulation role of PCa development (Salem and
Hansen, 2019). Therefore, it would be interesting to further
investigate if multiple miRNAs regulate Hippo signaling and cell-
cell communication and play a potential role in the development
of CRPC. Thus, the functional roles of these plasma exosomal
miRNAs warrant further investigation.
MiR-423-3p is the most significantly dysregulated miRNA
in our study. Functional studies have demonstrated the cancer
promoting role of miR-423-3p in other cancer types (Guan et al.,
2014; Li et al., 2015; Kong et al., 2017; Xu et al., 2018). miR-423-
3p overexpression has been reported in many human cancers
including lung, breast, gastric and colorectal cancers, where it
acts in an oncogenic manner via enhancing cell proliferation,
cell cycle progression, cell migration and invasion (Murria Estal
et al., 2013; Li et al., 2015;Zhao et al., 2015; Kong et al.,
2017; Sun et al., 2019; Wang et al., 2019). A study investigating
brain-metastasis related miRNAs in lung adenocarcinoma found
that miR-423-3p over expression directly contributed to brain
metastasis by increasing cancer cell proliferation, migration and
invasion (Sun et al., 2019). In Colorectal cancer, it was found that
miR-423-3p was overexpressed in cancer compared to normal
tissues and as a result increased cell proliferation, migration and
invasion through inhibiting P21 (Li et al., 2015). However, its
functional role in PCa has not yet been reported. Further studies
to investigate miR-423-3p, in particular exosomal miR-423-3p, in
CRPC development are warranted.
In conclusion, by analyzing the exosomal miRNA signature
of multiple cohorts of treatment-naive PCa and CRPC patients
with RNA-seq and RT-qPCR validation, we identified plasma
exosomal miRNAs potentially associated with CRPC. Our
multicenter validation as well as inclusion of androgen depletion
treated non-CRPC patients confirmed the association of plasma
exosomal miR-423-3p specifically with CRPC development. We
demonstrated that plasma exosomal miRNAs may serve as
biomarkers for non-invasive real-time monitoring of PCa status
and the prediction of CRPC occurrence, which would have great
potential to improve PCa treatment.
DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and
accession number(s) can be found at: https://www.ncbi.nlm.nih.
gov/geo/query/acc.cgi?acc=GSE136321.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by London City & East Research Ethics Committee;
Institutional Review Board of Medical College of Wisconsin. The
patients/participants provided their written informed consent to
participate in this study.
AUTHOR CONTRIBUTIONS
Y-JL and TG conceived the study. TG and YW conducted the
experiments at Barts Cancer Institute. JJ conducted experiment
at MCW Cancer Center. TG, JM, and Y-JL analyzed the data. XM,
GSc, EB, LX, ES, SK, PR, AG, KT, JH, TO, TP, CA, GSh, WW, NF,
JS, DB, MK, JL, and CD helped with sample and data collection.
PR, AG, KT, JH, TO, TP, CA, GSh, WW, NF, JS, DB, LW, and Y-JL
were involved in data collection and interpretation. TG, Y-JL, JM,
XM, EB, LX, ES, GSh, JS, LW, and CD were involved in drafting
the manuscript. Y-JL supervised the study. All authors read and
approved the final manuscript.
FUNDING
This work was supported by Orchid, Cancer Research
UK (C16420/A18066), Chinese Scholarship Council, and
partially supported by the National Institutes of Health to
LW (R01CA212097).
ACKNOWLEDGMENTS
We thank Eva Wozniak, Anna Terry, and Charles Meins at
QMUL Genome Center for the technical assistance with RNA
sequencing. We also thank all patients and healthy donors
participating in this study.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fcell.2020.
602493/full#supplementary-material
Frontiers in Cell and Developmental Biology | www.frontiersin.org 11 January 2021 | Volume 8 | Article 602493
Guo et al. Plasma Exosomal miR-423-3p Predicts CRPC
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Conflict of Interest: The authors declare that the research was conducted in the
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