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REVIEW
The potential of using blood circular RNAas liquid biopsy biomarker for human diseases
Guoxia Wen1 , Tong Zhou2& , Wanjun Gu1&
1 State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University,Nanjing 210096, China
2 Department of Physiology and Cell Biology, Reno School of Medicine, University of Nevada, Reno, NV 89557, USA& Correspondence: tongz@med.unr.edu (T. Zhou), wanjungu@seu.edu.cn (W. Gu)Received September 21, 2020 Accepted October 9, 2020
ABSTRACT
Circular RNA (circRNA) is a novel class of single-stranded RNAs with a closed loop structure. Themajority of circRNAs are formed by a back-splicingprocess in pre-mRNA splicing. Their expression isdynamically regulated and shows spatiotemporal pat-terns among cell types, tissues and developmentalstages. CircRNAs have important biological functions inmany physiological processes, and their aberrantexpression is implicated in many human diseases. Dueto their high stability, circRNAs are becoming promisingbiomarkers in many human diseases, such as cardio-vascular diseases, autoimmune diseases and humancancers. In this review, we focus on the translationalpotential of using human blood circRNAs as liquidbiopsy biomarkers for human diseases. We highlighttheir abundant expression, essential biological func-tions and significant correlations to human diseases invarious components of peripheral blood, includingwhole blood, blood cells and extracellular vesicles. Inaddition, we summarize the current knowledge of bloodcircRNA biomarkers for disease diagnosis or prognosis.
KEYWORDS peripheral blood circular RNA, liquidbiopsy, human diseases, translational biomarkers
INTRODUCTION
Liquid biopsy is a biopsy that uses body liquids as thesample source to diagnose, predict the outcome of or mon-itor the development of human diseases (Rubis et al., 2019;Luo et al., 2020a). Compared to traditional tissue biopsy,liquid biopsy has the advantages of being noninvasive,performed in real-time and accurate (Rubis et al., 2019; Luo
et al., 2020a). It has been proven to be applicable to themanagement of many human diseases, including cancers(Heitzer et al., 2019; Mattox et al., 2019; Rubis et al., 2019),prenatal genetic disorders (Zhang et al., 2019a), heart dis-eases (Zemmour et al., 2018), schizophrenia (Chen et al.,2020b), transplant rejection (Bloom et al., 2017) and infec-tious diseases (Burnham et al., 2018; Hong et al., 2018;Blauwkamp et al., 2019; Han et al., 2020a). To date, mostliquid biopsy studies have focused on its clinical applicationin human cancers (reviewed in Siravegna et al., 2017;Heitzer et al., 2019; Mattox et al., 2019; Rubis et al., 2019).For example, a cell-free DNA (cfDNA)-based liquid biopsytest that determines the mutational status of the epidermalgrowth factor receptor (EGFR) gene was used to guide theresponse of EGFR tyrosine kinase inhibitors in non-small celllung cancer (NSCLC) patients, which was approved by theFDA in clinical practice (Kwapisz, 2017). Another FDA-ap-proved liquid biopsy test, Epi proColon, assessed themethylation status of the Septin9 gene in whole blood, whichwas used to screen colorectal cancer patients from healthycontrols (Lamb and Dhillon, 2017). In addition to cfDNA(Wan et al., 2017; Cescon et al., 2020), several other ana-lytes within circulating body fluids were investigated as liquidbiopsy biomarkers, such as circulating tumor cells (CTCs)(Yu et al., 2013), extracellular vesicles (EVs) (Torrano et al.,2016), cell-free RNA (cfRNA) (Zaporozhchenko et al., 2018),circulating proteins (Surinova et al., 2015), circulatingmetabolites (Crutchfield et al., 2016) and platelets (Joosseand Pantel, 2015; Best et al., 2017). Among them, RNA-based liquid biopsy biomarkers have gained much moreattention in recent years since they have dynamic expres-sions and are closely related to different disease conditions(Zaporozhchenko et al., 2018; Sole et al., 2019). Circulatingnoncoding RNAs, especially microRNAs (miRNAs), haveshown promising potential as stable blood-based biomarkers
© The Author(s) 2020
Protein Cellhttps://doi.org/10.1007/s13238-020-00799-3 Protein&Cell
Protein
&Cell
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in liquid biopsy (Mitchell et al., 2008; Anfossi et al., 2018;Pardini et al., 2019).
Circular RNAs (circRNAs) are a group of endogenousnoncoding RNA molecules (Chen, 2016, 2020; Li et al.,2018d). They were first found in plant viroids (Sanger et al.,1976) and eukaryotic cells (Hsu and Coca-Prados, 1979) in1970s, and were recently observed to be functional (Hansenet al., 2013; Memczak et al., 2013). They are joined head totail to generate a covalently closed loop structure throughback-splicing (Vicens and Westhof, 2014; Li et al., 2018d).They lack a 5-prime cap and 3-prime poly-A tail, which isquite different from canonical linear RNAs (Vicens andWesthof, 2014; Li et al., 2018d). CircRNAs have beenidentified in almost all organisms across the eukaryotic treeof life (Wang et al., 2014). Some circRNAs are the pre-dominant transcript isoform of their host genes expressed inspecific tissues or cell types (Salzman et al., 2012, 2013;Rybak-Wolf et al., 2015). Furthermore, they are expressed ina tissue- (Zhou et al., 2018a), cell-type- (Salzman et al.,2013) and developmental stage-specific manner (Zhouet al., 2018a). Accumulating studies have revealed manyregulatory roles and versatile cellular functions that cir-cRNAs can perform (Li et al., 2018d; Chen, 2020). They canact as miRNA decoys (Hansen et al., 2013), RNA bindingprotein (RBP) sponges (Ashwal-Fluss et al., 2014; Huanget al., 2020a) and protein scaffolds (Li et al., 2015c, 2019a).Moreover, a small percentage of circRNAs can be translatedinto proteins (Legnini et al., 2017; Pamudurti et al., 2017;Heesch et al., 2019). Aberrant expression of circRNAs hasbeen related to many human diseases, including cancers(Shang et al., 2019; Vo et al., 2019), neurodegenerativediseases, cardiovascular diseases (Aufiero et al., 2019) andimmune diseases (Chen et al., 2019c; Zhou et al., 2019b).Due to their high stability (Enuka et al., 2015), abundantexpression (Jeck et al., 2013) and high specificity (Zhouet al., 2018a), circRNAs are becoming promising biomarkersfor human diseases (Zhang et al., 2018j).
Due to the importance of liquid biopsy biomarkers inprecision medicine (Vargas and Harris, 2016) and thesuperior characteristics of circRNAs as disease biomarkers(Zhang et al., 2018j), recent studies have put great effortsinto researching the use of circRNAs as liquid biopsybiomarkers for human diseases. Here, we review currentprogress that has been made on this topic. We focus oncircRNAs in the peripheral blood, although circRNAs areabundantly expressed in other body fluids, such as saliva(Bahn et al., 2014; Ghods, 2018) and urine (Kölling et al.,2019; Lam and Lo, 2019; Vo et al., 2019). In this review, wefirst briefly introduce the biogenesis, function, and expres-sion patterns of circRNAs and their close correlations tohuman diseases. Then, we emphasize the functional rolesthey may play in different blood components, including bloodcells, serum, plasma, platelets and EVs in the blood. Wesummarize peripheral blood circRNA biomarkers that havebeen constructed for the management of human diseases.
Finally, we discuss future opportunities and challenges oftranslating blood circRNAs into clinical practice.
THE BIOGENESIS OF CIRCRNAS
CircRNAs are derived from pre-mRNAs and formed by back-splicing, in which a downstream 5-prime splice site (ss) iscovalently joined with an upstream 3-prime ss (Fig. 1) (Liet al., 2018d). There are three major types of circRNAs,exonic circRNAs (ecircRNAs), exon-intron circRNAs (EI-circRNAs) and circular intronic RNAs (ciRNAs) (Fig. 1).EcircRNAs only contain exons, and most ecircRNAs arelocated in the cytoplasm. Two models have been proposedto explain the production of ecircRNAs (Fig. 1A) (Chen,2016; Wu et al., 2017a; Kristensen et al., 2019; Xiao et al.,2020). The first model suggests that when the pre-mRNA ispartially folded during transcription, canonical splicing willcause an “exon jump” and generate a linear RNA containingskipped exons (Starke et al., 2014; Kelly et al., 2015). Asubsequent back-splicing event will turn the ‘lariat interme-diate’ into a closed circular transcript (Barrett et al., 2015; Liet al., 2018d). The second model proposes that when the5-prime ss is pulled closer to the 3-prime ss by base pairingof flanking intronic complementary sequences (Ivanov et al.,2014; Vicens and Westhof, 2014; Zhang et al., 2014; Wilusz,2015) or specific bindings of intronic sequences to RBPs(Conn et al., 2015), a back-splicing event may occur, and acircRNA may be generated (Jeck et al., 2013). Thesemodels are supported by accumulating evidence showingthat cis-acting elements (Jeck et al., 2013; Zhang et al.,2014; Yoshimoto et al., 2019) and trans-acting factors(Ashwal-Fluss et al., 2014; Conn et al., 2015; Agirre et al.,2019) play crucial regulatory roles in circRNA formation.EIcircRNAs are generated when an intron or several intronsare alternatively retained in splicing (Li et al., 2015c).Therefore, EIcircRNAs are mainly considered intermediatesof ecircRNAs (Li et al., 2015c). CiRNAs, which only containintrons, are produced through escape from debranching ofintron lariats (Zhang et al., 2013). This process depends on aGU-rich motif near the 5-prime ss and a C-rich motif close tothe branch-point site (Zhang et al., 2013). Unlike ecircRNAs,EIcircRNAs and ciRNAs are mainly located in the nucleus.
THE FUNCTION OF CIRCRNAS
With the rapid application of high-throughput RNA-se-quencing (RNA-seq) technology (Stark et al., 2019) andrelated bioinformatics tools that identify circRNAs from RNA-seq datasets (Jeck and Sharpless, 2014; Szabo and Salz-man, 2016; Li et al., 2017a; Gao and Zhao, 2018), more thanone million circRNAs have been annotated in model organ-isms (Glažar et al., 2014; Vo et al., 2019; Cai et al., 2020; Wuet al., 2020a). However, the functional characterization ofcircRNAs is still at its early stage (Chen, 2016, 2020; Li et al.,2018d). Their circular conformation and the large sequenceoverlap with their linear mRNA counterparts have posed
© The Author(s) 2020
Protein
&Cell
REVIEW Guoxia Wen et al.
Figure
1.Thebiogen
esis
andfunctionofcircR
NAs.(A)Circ
RNAsare
form
edbyback
-splicingofpre-m
RNAsbydifferentmech
anisms,
includ
ing:laria
tdriv
encirculariz
atio
n,
intronpairingdriv
encirculatio
n,RBPdriv
encirculariz
atio
n,a
nddebranc
hingescapeofintronlaria
ts.(B)Circ
RNAsca
nperform
diversebiologicalfunctions.
Firs
t,EIcirc
RNAsca
n
interact
with
RNAPolIIa
ndU1sn
RNPto
regulate
genetranscrip
tionin
thenuc
leus(i).S
eco
nd,e
circRNAsca
naccumulate
inthecy
toplasm
and
act
asmiRNAdeco
ys(ii),protein
regulators
(iii),
andtranslatio
ntemplates(iv
).Third
,circ
RNAsca
nbese
cretedinto
EVsbymanyce
lltypesandtran
sportedto
recipientc
ells
byEVs.
EVcircRNAsca
nalsoact
as
importantgeneregulators.
Translational potential of blood circular RNAs REVIEW
© The Author(s) 2020
Protein
&Cell
Table
1.Cell-freecircRNA
biomarkersin
circulatingperipheralblood.
Disease
CircRNA
biomarker
Source
Expression
chan
ge
Cohort
size
Clin
icalsignificance
AUC
Method
Reference
Breas
tca
nce
rHsa
_circ_000
1785
Plasm
aUp
57breast
canc
er/17HC
Ass
ociatedwith
histological
grade,TNM
stageand
distantmetastasis;
significantexp
ress
ion
difference
between
pre-
trea
tmentand
post-
trea
tment.
0.784
Microarray
RT-qPCR
Yin
etal.
(2018)
Bladder
cance
rHsa
_circ_000
0285
Serum
Down
97Bladder
canc
er/97HC
Ass
ociatedwith
tumorsize
,differentia
tion,LNM,distant
metastasis,
TNM
stage
and
cisp
latin
resp
onse
.
NA
RT-qPCR
Chie
tal.
(2019)
CA
Hsa
_circ_000
3204
Plasm
aderived
EV
Up
35CA/32HC
Associatedwith
proliferatio
n,
migratio
nandtube
form
atio
nofend
othelial
cells.
0.770
RT-qPCR
Zhangetal.
(2020b
)
CAD
Hsa
_circ_000
5540
Plasm
aderived
exo
some
Up
108CAD/89HC
Ass
ociatedwith
the
Framingham
HeartStudy
riskfactors.
0.853
RNA-seq
RT-qPCR
Wuetal.
(2020b
)
CHB
Circ
MTO1
Serum
Down
360CHB/360
HC
Ass
ociatedwith
liverfib
rosis
progress
ionandprognosis.
0.914
RT-qPCR
Wangetal.
(2019c
)
CHD
Hsa
_circ_004
183
Hsa
_circ_07926
5Hsa
_circ_10503
9
Plasm
aDown
40CHD/40HC
NA
0.965
Microarray
RT-qPCR
Wuetal.
(2019a
)
CLL
Circ
-RPL15
Plasm
aUp
150CLL/65HC
Associatedwith
progression
andoutcom
e.
0.840
Microarray
RT-qPCR
Wuetal.
(2020c
)
CRC
Hsa
_circ_000
6990
Plasm
aUp
60CRC/43HC
Associatedwith
TNM
stage.
0.724
RT-qPCR
Lie
tal.
(2019c
)
CRC
Circ
-CCDC66
Circ
-ABCC1
Circ
-STIL
Plasm
aDown
45CRC/61HC
Circ
-ABCC1wasassociated
with
tumorgrowth
and
progress
ion;significa
ntexp
ress
iondifference
of
circ-C
CDC66betweenpre-
trea
tmentand
post-
trea
tment.
0.780
RT-qPCR
Lin
etal.
(2019)
CRC
Hsa
_circ_000
4771
Serum
derived
exo
some
Up
135CRC/45HC
Ass
ociatedwith
TNM
stage
anddistantmetastasis;
significantexp
ress
ion
difference
between
pre-
trea
tmentand
post-
trea
tment.
0.880
RT-qPCR
Panetal.
(2019)
REVIEW Guoxia Wen et al.
© The Author(s) 2020
Protein
&Cell
Table
1co
ntinued
Disease
CircRNA
biomarker
Source
Expression
chan
ge
Cohort
size
Clin
icalsignificance
AUC
Method
Reference
CRC
Hsa
_circ_000
0826
Serum
Up
100CRC/100
HC
Ass
ociatedwith
liver
metastasis.
0.778
RT-qPCR
Shie
tal.
(2020)
CRC
Hsa
_circ_010
1802
Serum
derived
exo
some
Up
221CRC/221
HC
NA
0.854
RNA-seq
RT-qPCR
Xie
etal.
(2020c
)
CRC
Hsa
_circ_008
2182
Hsa
_circ_000
0370
Hsa
_circ_00354
45
Plasm
aUp
Up
Down
156CRC/66HC
Thefirst
twocircRNAswere
associatedwith
LNM
and
hadsignificantexp
ress
ion
difference
between
pre-
trea
tmentand
post-
trea
tment;thethird
was
associatedwith
TNM
stage.
0.835
Microarray
RT-qPCR
Yeeta
l.(201
9)
CRC
Hsa
_circ_000
7534
Plasm
aUp
112CRC/46HC
Associatedwith
progression
ofclinicalc
lass
ifica
tion,
metastatic
pheno
type,and
differentia
tion.
0.780
RT-qPCR
Zhangetal.
(2018f)
EC
Hsa
_circ_010
9046
Hsa
_circ_000
2577
Serum
derived
EV
Up
10EC/10HC
NA
NA
RNA-seq
RT-qPCR
Xuetal.
(2018a
)
EOC
Circ
BNC2
Plasm
aDown
83EOC/83
ben
ignova
rian
cyst/83HC
Ass
ociatedwith
histological
grade,se
rioussu
btyp
e,
LNM
anddistant
metastasis.
0.923
RT-qPCR
Huetal.
(2019b
)
ESCC
Hsa
_circ_000
1946
Hsa
_circ_00436
03
Plasm
aDown
50ESCC/50HC
Ass
ociatedwith
recu
rrence
,ove
rallsu
rvival
and
disea
se-freesu
rvival.
0.894
0.836
Microarray
RT-qPCR
Fanetal.
(2019)
ESCC
Circ
GSK3ß
Plasm
aUp
86ESCC/11
ben
ignlesion
/43HC
Ass
ociatedwith
recu
rrence
,metastasis,
clinicals
tage
andoutcom
e;significa
nt
exp
ress
iondifference
betweenpre-treatm
entand
post-treatm
ent.
0.793
Microarray
ddPCR
RT-qPCR
Huetal.
(2019a
)
ESCC
Circ
-TTC17
Plasm
aUp
30ESCC/25HC
Ass
ociatedwith
TNM
stage,
LNM
andsu
rvival
time.
0.820
RT-qPCR
Wangetal.
(2019b
)
ESCC
Circ
-SLC7A5
Plasm
aUp
87ESCC/53HC
Associatedwith
TNM
stage
andsu
rvivaltim
e.
0.772
Microarray
RT-qPCR
Wangetal.
(2020c
)
Translational potential of blood circular RNAs REVIEW
© The Author(s) 2020
Protein
&Cell
Table
1co
ntinued
Disease
CircRNA
biomarker
Source
Expression
chan
ge
Cohort
size
Clin
icalsignificance
AUC
Method
Reference
GBM
Circ
FOXO3
Hsa
_circ_002
9426
Circ
-SHPRH
Plasm
aDown
100GBM/100
HC
NA
0.906-
0.980
RT-qPCR
Chenetal.
(2020a
)
GC
Hsa
_circ_000
0190
Plasm
aDown
104GC/104HC
Associatedwith
blood
carcinoembryo
nic
antig
en
leve
l.
0.600
RT-qPCR
Chenetal.
(2017c
)
GC
Hsa
_circ_002
1087
Hsa
_circ_000
5051
Plasm
aDown
70GC/70HC
Ass
ociatedwith
tumorsize
andTNM
stage;significa
nt
exp
ress
iondifference
of
hsa
_circ
_002
1087between
pre-treatm
entandpost-
trea
tment.
0.773
RT-qPCR
Hanetal.
(2020c
)
GC
Hsa
_circ_000
0745
Plasm
aDown
60GC/60HC
Associatedwith
TNM
stage.
0.683
RNA-seq
RT-qPCR
Huangetal.
(2017a
)
GC
Hsa
_circ_000
01649
Serum
Down
20GC
(pre-
ope
ratio
nvs.
pos
t-op
eration)
Ass
ociatedwith
pathological
differentia
tion;significa
nt
exp
ress
iondifference
betweenpre-treatm
entand
post-treatm
ent.
0.834
RT-qPCR
Lie
tal.
(2017d
)
GC
Hsa
_circ_000
1017
Hsa
_circ_006
1276
Plasm
aDown
121GC/121HC
Ass
ociatedwith
distal
metastasis,
ove
rallsu
rvival,
prognosisand
outcome.
0.912
Microarray
RT-qPCR
RT- ddPCR
Lie
tal.
(2018c
)
GC
Hsa
_circ_000
0467
Plasm
aUp
40GC/20HC
Associatedwith
TNM
stage;
significantexp
ress
ion
difference
between
pre-
trea
tmentand
post-
trea
tment.
0.790
RT-qPCR
Luetal.
(2019a
)
GC
Hsa
_circ_000
6848
Plasm
aDown
30GC/30HC
Associatedwith
differentia
tionandtumor
size
;significantexp
ress
ion
difference
between
pre-
trea
tmentand
post-
trea
tment.
0.733
RT-qPCR
Luetal.
(2019b
)
GC
Hsa
_circ_001
0882
Plasm
aUp
66GC/66HC
Associatedwith
tumorsize
andhistologicalg
rade,
ove
rallsu
rviveand
prognosis.
NA
RT-qPCR
Pengetal.
(2020)
REVIEW Guoxia Wen et al.
© The Author(s) 2020
Protein
&Cell
Table
1co
ntinued
Disease
CircRNA
biomarker
Source
Expression
chan
ge
Cohort
size
Clin
icalsignificance
AUC
Method
Reference
GC
Circ
PSMC3
Plasm
aDown
106GC/21HC
Ass
ociatedwith
TNM
stage,
LNM
andove
rallsu
rvival.
0.933
Microarray
RT-qPCR
Rongetal.
(2019)
GC
Hsa
_circ_006
5149
Plasm
aderived
exo
some
Down
39GC/41HC
NA
0.640
RT-qPCR
Shaoetal.
(2020)
GC
Circ
KIAA1244
Plasm
aDown
62GC/25HC
Ass
ociatedwith
TNM
stage,
LNM
andove
rallsu
rvival.
0.748
Microarray
RT-qPCR
Tangetal.
(2018)
GC
Hsa
_circ_000
0419
Plasm
aDown
44GC/43HC
Associatedwith
tumorstage,
lymph
atic
and
distal
metastasis,
venou
sand
perin
euralinva
sion.
0.840
RT-qPCR
Taoetal.
(2020)
GC
Hsa
_circ_000
0936
Serum
derived
exo
some
Up
32GC/20HC
Ass
ociatedwith
TNM
stage
andprognosis;
significa
nt
exp
ress
iondifference
before
andafte
rgastrectomy.
NA
RT-qPCR
Xie
etal.
(2020a
)
GC
Hsa
_circ_000
0181
Plasm
aDown
102GC/105HC
Associatedwith
differentia
tionand
carcinoembryo
nic
antig
en.
0.756
RT-qPCR
Zhaoetal.
(2018b
)
Glioma
Circ
NF1X
Serum
derived
exo
some
Up
69glioma/10
HC
Ass
ociatedwith
temozo
lomideresp
ons
eandprognosis.
0.885
RT-qPCR
Dingetal.
(2020)
HCC
Hsa
_circ_005
1443
Plasm
aderived
exo
some
Down
60HCC/60HC
Ass
ociatedwith
progress
ion.
0.809
Microarray
RT-qPCR
Chenetal.
(2020c
)
HCC
Circ
-ZEB1.33
Serum
Up
64HCC/30HC
Ass
ociatedwith
TNM
stage
andprognosis.
NA
RT-qPCR
Gongetal.
(2018)
HCC
Hsa
_circ_100
338
Serum
derived
exo
some
Up
39HCC
(pre-
ope
ratio
nvs.
pos
t-op
eration)
Associatedwith
metastasis,
proliferatio
n,angiogen
esis
andprognosis.
NA
RT-qPCR
Huangetal.
(2020b
)
HCC
Circ
SMARCA5
Plasm
aDown
135HCC/143
cirrho
sis/117
hep
atitis/103
HC
NA
0.938
RT-qPCR
Lie
tal.
(2019e
)
Translational potential of blood circular RNAs REVIEW
© The Author(s) 2020
Protein
&Cell
Table
1co
ntinued
Disease
CircRNA
biomarker
Source
Expression
chan
ge
Cohort
size
Clin
icalsignificance
AUC
Method
Reference
HCC
Hsa
_circ_000
3998
Plasm
aUp
100HCC/50
hep
atitis/50
HC
Significa
ntexp
ress
ion
difference
between
pre-
trea
tmentand
post-
trea
tment.
0.892
RT-qPCR
Qiaoetal.
(2019)
HCC
Hsa
_circ_000
4001
Hsa
_circ_00041
23
Hsa
_circ_007
5792
Serum
Up
21HCC/32HC
Ass
ociatedwith
TNM
stage
andtumorsize
.0.890
RT-qPCR
Sunetal.
(2020b
)
HCC
Circ
_FOXP1
Serum
Up
30HCC/16HC
Ass
ociatedwith
tumorsize
,microva
scularinva
sionand
adva
nce
dTNM
stageand
survivaltim
e.
0.932
RT-qPCR
Wangetal.
(2020d
)
HCC
Hsa
_circ_104
075
Serum
Up
101HCC/60
HC/23
hep
atitis
B/26
hep
atitis
C/23
cirrhosis/20
LC/19GC/30
colonca
nce
r/21breast
canc
er
Ass
ociatedwith
TNM
stage;
significantexp
ress
ion
difference
between
pre-
trea
tmentand
post-
trea
tment.
0.973
RT-qPCR
Zhangetal.
(2018g
)
HCC
Hsa
_circ_000
1445
Plasm
aDown
104HCC/57
cirrhosis/44
hep
atitis
B/52
HC
Ass
ociatedwith
serum
alpha-
fetoprotein
leve
l.0.862
RT-qPCR
Zhangetal.
(2018h
)
HCM
Circ
DNAJC
6Circ
TMEM56
Circ
MBOAT2
Serum
Down
64HCM/53HC
Ass
ociatedwith
left
ventricularoutflow
tract
gradientandthickn
essof
interventricularse
ptum
inpatie
nts
with
obs
tructive
HCM.
0.819
0.756
0.738
RT-qPCR
Sonnen
schein
etal.(2019)
Hyp
ertension
Hsa
_circ_000
5870
Plasm
aDown
54 Hyp
ertens
ion/
54HC
NA
NA
Microarray
RT-qPCR
Wuetal.
(2017b
)
Heartfailure
Hsa
_circ_006
2960
Plasm
aUp
30Heartfailure/
30HC
Ass
ociatedwith
B-typ
enatriuretic
peptid
ese
rum
leve
ls.
0.838
Microarray
RT-qPCR
Sunetal.
(2020c
)
REVIEW Guoxia Wen et al.
© The Author(s) 2020
Protein
&Cell
Table
1co
ntinued
Disease
CircRNA
biomarker
Source
Expression
chan
ge
Cohort
size
Clin
icalsignificance
AUC
Method
Reference
IPAH
Hsa
_circ_006
8481
Serum
Up
82IPAH/82HC
Associatedwith
heart
function,6-m
inwalk
distance
,se
rum
N-term
inal
pro-B-typ
enatriu
retic
peptid
e,se
rum
H2S,ris
kstratifica
tion,rig
htheart
failure,anddeath.
0.895
RT-qPCR
Zhangetal.
(2019b
)
KD
Circ
ANRIL
Hsa
_circ_012
3996
Serum
Down
Up
56KD/56HC
Ass
ociatedwith
multip
leclinicallaboratory
factors;
significantexp
ress
ion
difference
ofcircANRIL
betweenpre-treatm
entand
post-treatm
ent.
0.624
0.747
RT-qPCR
Wuetal.
(2019b
)
LAC
Hsa
_circ_005
6616
Plasm
aderived
exo
some
Up
42LAC
with
LNM/48LAC
with
outLNM
Ass
ociatedwith
theleve
lof
CXCR4protein,Tstage,
Mstage,
andTNM
grade.
0.812
RT-qPCR
Heetal.
(2020)
LAC
Hsa
_circ_001
3958
Plasm
aUp
30LAC/30HC
Associatedwith
TNM
stage
andLNM.
0.794
Microarray
RT-qPCR
Zhuetal.
(2017)
LC
Hsa
_circ_000
0190
Plasm
aUp
231LC/41HC
Associatedwith
tumorsize
,histologicaltyp
e,stage,
distantmetastasis,
extrathoracicmetastasis,
survival,prognos
is,PD-L1
leve
landtherapyresp
onse
.
0.950
RNA-seq
RT-qPCR
RT- ddPCR
Luoetal.
(2020c
)
LN
Hsa
_circ_002
453
Plasm
aUp
59SLE(30with
LN
and
29
with
outLN)/26
RA/32HC
Ass
ociatedwith
seve
rityof
renalinvo
lvementand
24-hourproteinuria
.
0.906
Microarray
RT-qPCR
Ouya
ngetal.
(2018)
LUAD
Hsa
_circ_000
5962
Hsa
_circ_008
6414
Plasm
aUp
Down
153LU
AD/54
HC
Hsa
_circ_008
6414was
associatedwith
EGFR
mutatio
ns;
significa
nt
exp
ress
iondifference
of
hsa
_circ
_000
5962between
pre-treatm
entandpost-
trea
tment.
0.810
RT-qPCR
Liu
etal.
(2019b
)
LUAD
Hsa
_circ_002
178
Serum
derived
exo
some
Up
120LUAD/30
HC
Ass
ociatedwith
programmed
celldeath
protein
1(PD1)
exp
ress
ion.
0.997
RT-qPCR
Wangetal.
(2020b
)
Translational potential of blood circular RNAs REVIEW
© The Author(s) 2020
Protein
&Cell
Table
1co
ntinued
Disease
CircRNA
biomarker
Source
Expression
chan
ge
Cohort
size
Clin
icalsignificance
AUC
Method
Reference
MCL
Circ
CDYL
Plasm
aUp
18MCL/17HC
Associatedwith
cell
proliferatio
n.
0.856
RT-qPCR
Meie
tal.
(2019)
MDD
Circ
DYM
Plasm
aDown
60MDD/32HC
Ass
ociatedwith
thesc
oresof
the24-item
Hamilton
Depress
ionRatin
gSca
le,
retardatio
nsu
bscale
and
trea
tmentresp
onse
.
0.643
RT-qPCR
Songetal.
(2020)
NPC
Hsa
_circ_000
0285
Serum
Up
150NPC/100
HC
Ass
ociatedwith
tumorsize
,differentia
tion,LNM,distant
metastasis,
TNM
stage
,su
rvivalrate
and
radiotherapyresp
onse
.
NA
RT-qPCR
Shuaie
tal.
(2018)
NPC
Hsa
_circ_006
6755
Plasm
aUp
86NPC/86HC
Associatedwith
clinical
stage.
0.904
RT-qPCR
Wangetal.
(2020a
)
NSCLC
Circ
FARSA
Plasm
aUp
50NSCLC
/50
HC
NA
0.710
RNA-seq
RT-qPCR
Hangetal.
(2018)
NSCLC
Hsa
_circ_010
9320
Plasm
aUp
90NSCLC
Associatedwith
progression-
free
survivala
ndgefi
tinib
resp
onse
.
0.805
Microarray
RT-qPCR
Liu
etal.
(2019c
)
NSCLC
Hsa
_circ_000
2130
Serum
derived
exo
some
Up
28drug-
resistan
ceNSCLC
/32
drug-sens
itive
NSCLC
Ass
ociatedwith
osimertinib
resp
onse
.0.792
RT-qPCR
Maetal.
(2020)
Osteo
sarcoma
Hsa
_circ_000
0885
Serum
Up
55 osteo
sarcoma/
27benign
bon
etumor/25
HC
Ass
ociatedwith
clinical
prognosis;
significa
nt
exp
ress
iondifference
betweenpre-treatm
entand
post-treatm
ent.
0.783
RNA-seq
RT-qPCR
Zhuetal.
(2019a
)
PBC
Hsa
_circ_402
458
Plasm
aUp
35PBC/36HC
NA
0.710
Microarray
RT-qPCR
Zhengetal.
(2016)
PC
Circ
-LDLRAD3
Plasm
aUp
31PC/31HC
Ass
ociatedwith
CA19-9,N
classifica
tion,ve
nous
inva
sion,lymphatic
inva
sion,stage,
metastasis.
0.670
RT-qPCR
Yangetal.
(2017a
)
REVIEW Guoxia Wen et al.
© The Author(s) 2020
Protein
&Cell
Table
1co
ntinued
Disease
CircRNA
biomarker
Source
Expression
chan
ge
Cohort
size
Clin
icalsignificance
AUC
Method
Reference
PDAC
Hsa
_circ_003
6627
Plasm
aderived
exo
some
Up
93PDAC/20HC
Ass
ociatedwith
duode
nal
inva
sion,va
scularinva
sion,
Tfactor,TNM
stage
and
survivalrate.
NA
Microarray
RT-qPCR
Lie
tal.
(2018e
)
PDAC
Circ
-IARS
Plasm
aderived
exo
some
Up
20PDAC
with
metastasis/20
PDAC
with
out
metastasis
Ass
ociatedwith
tumorve
ssel
inva
sion,liver
metastasis,
TNM
stage,andprognosis.
NA
Microarray
RT-qPCR
Lie
tal.
(2018a
)
PoAF
Hsa
_circ_025
016
Plasm
aUp
769underw
ent
off-pump
coronary
artery
byp
ass
grafting/15HC
Ass
ociatedwith
fastingblood
gluco
se.
0.802
Microarray
RT-qPCR
Zhangetal.
(2018c
)
SAI
Circ
FUNDC1
Plasm
aUp
26AIS
with
SAI/
42AIS
with
out
SAI
Ass
ociatedwith
neutrophils
counts,
white
bloodce
lland
neutrophilratio
s.
0.661
RT-qPCR
Zuoetal.
(2020)
SCC
Circ
FoxO
3a
Serum
Down
103SCC/30HC
Associatedwith
stromal
inva
sion,LNM
and
prognosis.
NA
RT-qPCR
Tangetal.
(2020)
SCLC
Circ
FLI1
Serum
derived
exo
some
Up
61SCLC
/55HC
Ass
ociatedwith
tumor
survivala
ndch
emotherapy
resp
onse
.
NA
RT-qPCR
Lie
tal.
(2018b
)
SLE
Hsa
_circ_407
176
Hsa
_circ_001
308
Plasm
aDown
126SLE/102
HC
NA
0.599
0.662
Microarray
RT-qPCR
Zhangetal.
(2018e
)
SOC
Circ
SETDBI
Serum
Up
60SOC/60HC
Ass
ociatedwith
clinical
stage,
LNM,ch
emotherapy
resp
onse
andprogress
ion-
free
survival.
0.830
RT-qPCR
Wangetal.
(2019d
)
TB
Hsa
_circ_000
1204
Hsa
_circ_00017
47
Plasm
aDown
195TB/50
pne
umonia/50
LC/50COPD/
170
HC
Ass
ociatedwith
the
radiologicals
eve
rityscores;
significantexp
ress
ion
difference
between
pre-
trea
tmentand
post-
trea
tment.
0.928
RT-qPCR
Huangetal.
(2018b
)
TB
Hsa
_circ_000
1953
Hsa
_circ_000
9024
Plasm
aUp
123TB/103HC
Ass
ociatedwith
TBse
verity;
significantexp
ress
ion
difference
between
pre-
trea
tmentand
post-
trea
tment.
0.915
Microarray
RT-qPCR
Huangetal.
(2018a
)
Translational potential of blood circular RNAs REVIEW
© The Author(s) 2020
Protein
&Cell
Table
1co
ntinued
Disease
CircRNA
biomarker
Source
Expression
chan
ge
Cohort
size
Clin
icalsignificance
AUC
Method
Reference
TB
Hsa
_circ_051
239
Hsa
_circ_02996
5Hsa
_circ_404
022
Serum
Up
131TB/50
pne
umonia/53
HC
Hsa
_circRNA_05123
9was
associatedwith
TBdrug
resp
onse
.
0.992
Microarray
RT-qPCR
Liu
etal.
(2020)
TB
Hsa
_circ_103
571
Plasm
aDown
35TB/32HC
NA
0.838
Microarray
RT-qPCR
Yietal.(2018)
UCB
Circ
PRMT5
Serum
derived
exo
some
Up
71UCB/50HC
Ass
ociatedwith
LNM
and
tumorprogress
ion.
NA
RT-qPCR
Chenetal.
(2018b
)
Abbreviation:AIS:a
cute
isch
emicstroke
;CA:c
erebrala
therosclerosis;
CAD:c
oronaryartery
disea
se;C
HB:c
hronichep
atitisB;C
HD:c
onge
nita
lheartdisea
ses;
CLL:
chroniclymphocytic
leuke
mia;COPD:ch
ronicobstructivepulm
onary
disea
se;C
RC:co
lorectalcance
r;ddPCR:dropletdigita
lPCR;E
C:e
ndometrialc
ance
r;EOC:epith
elialo
varia
nca
nce
r;ESSC:eso
phagea
l
squamou
sce
llca
nce
r;EV:extrace
llularve
sicle;GBM:glioblastoma;GC:gastric
cance
r;HC:health
yco
ntrol;HCC:hep
atoce
llularca
rcinoma;HCM:hyp
ertrophic
cardiomyo
pathy;
IPAH:
idiopathic
pulm
onaryarteria
lhyp
ertension
;KD:Kawasa
kidisea
se;LAC:lungadenoc
arcinoma;LC:lungca
nce
r;LN:lupusnephritis;LNM:lymphnodemetastasis;
LUAD:lungadeno-
carcinoma;MCL:
man
tlece
lllymphoma;MDD:majordep
ress
ivedisorder;NA:notapplicab
le;RT-qPCR:reve
rsetranscrip
tionandquantitativePCR;NPC:naso
pharyng
ealca
rcinoma;
NSCLC:non-smallce
lllungca
nce
r;PBC:prim
ary
biliary
cholangitis;PC:pan
creatic
cance
r;PDAC:pancreatic
ductal
adenoc
arcinom
a;PoAF:postop
erativeatrialfib
rillatio
n;SAI:stroke
ass
ociatedinfection;SCC:sq
uam
ousce
rvicalc
ance
r;SCLC:sm
allce
lllungca
nce
r;SLE:sy
stemic
lupuserythematosu
s;SOC:se
rousova
rianca
nce
r;TB:tuberculosis;
TNM:tumornode
metastasis;
UCB:urothelialc
arcinomaofthebladde
r..
REVIEW Guoxia Wen et al.
© The Author(s) 2020
Protein
&Cell
substantial challenges in investigating the functions of cir-cRNAs (Li et al., 2018d). Based on our current knowledge,circRNAs have diverse functions as miRNA decoys, protein
regulators and translation templates (Fig. 1B, see (Chen,2016, 2020; Li et al., 2018d) for excellent reviews).
Figure 2. Peripheral blood circRNAs are implicated in human diseases and can be used as potential disease biomarkers in
liquid biopsy. (A) Peripheral blood circRNAs are abundantly expressed and can be reliably detected in cell-free circulating blood
components (such as exosomes, EVs, plasma, and serum) and blood cells (including PBMCs, macrophages, RBCs, and platelets).
(B) Some exosomal circRNAs, such as circ-DB (i), circSHKBP1 (ii), and circUHRF1 (iii), are important regulators in oncogenic
pathways, while some circRNAs in exosomes, such as circHIPK3 (iv), play key roles in the release of inflammatory cytokines.
(C) Peripheral blood circRNAs, both cell-free circRNAs and intracellular circRNAs in blood cells, have potential clinical applications as
liquid biopsy biomarkers in many human diseases, such as the diagnosis, prognosis and treatment guidance of many human
diseases, including autoimmune diseases, cancers, cardiovascular diseases, immuno-deficiency diseases, infectious diseases, and
neurodegenerative diseases.
Translational potential of blood circular RNAs REVIEW
© The Author(s) 2020
Protein
&Cell
MiRNA decoys
The most well-known function of circRNAs is that they canact as a sponge to inhibit the function of miRNAs and indi-rectly regulate the expression of miRNA target genes at theposttranscriptional level (Fig. 1B) (Hansen et al., 2013;Memczak et al., 2013; Weng et al., 2017). For example,Cdr1as can compete with mRNAs by binding miR-7 throughmiRNA response elements (MREs) (Hansen et al., 2013). Afollow-up in vivo experiment that knocked down the Cdr1aslocus in the mouse genome confirmed the importance of theCdr1as and miR-7 interaction in normal brain function (Pi-wecka et al., 2017). Although it may not be a common
phenomenon for most circRNAs (Guo et al., 2014), severalabundant circRNAs also function as miRNA sponges,including circASAP1 (Hu et al., 2019c), circBIRC6 (Yu et al.,2017), circHIPK2 (Huang et al., 2017b), circHIPK3 (Zhenget al., 2016) and circSry (Hansen et al., 2013).
Protein regulators
In addition to miRNAs, circRNAs can interact with proteins orprotein complexes to regulate protein expression and func-tion (Fig. 1B) (Huang et al., 2020a). First, circRNAs can bindto some RBPs and act as RBP sponges. CircMbl, a circRNAoriginating from the second exon of the splicing factor
Figure 3. CircRNAs are actively involved in host immune responses to exogenous pathogens. (A) CircRasGEF1B, a circRNA
induced by LPS, can protect cells from bacterial infection by regulating the expression of ICAM-1 mRNA in the TLR4/LPS pathway.
(B) Exogenous circRNAs released by viruses can be recognized by RIG-I, thus activating the host innate immunity to viruses (i).
Moreover, NF90/NF110 not only promotes circRNA production in the nucleus but also interacts with mature host circRNAs to form
circRNP complexes in the cytoplasm. Upon viral infection, NF90/NF110 can be released from circRNP complexes and bind viral
mRNAs to inhibit viral replication (ii). In addition, circRNAs can form RNA duplexes and act as inhibitors of PKR under normal
conditions. When a virus invades the cells of its host, RNase L is activated to efficiently degrade circRNAs, and PKR is released and
activated to initiate the early cellular innate immune response (iii).
REVIEW Guoxia Wen et al.
© The Author(s) 2020
Protein
&Cell
Table
2.CircRNA
biomarkers
inbloodcells
orwhole
blood.
Disease
CircRNA
biomarker
Source
Expression
change
Cohort
size
Clin
ical
significan
ce
AUC
Method
Reference
AIS
Circ
-DLGAP4
PBMC
Down
170AIS
/170
HC
Associatedwith
Health
Stroke
Sca
lesc
ore
andleve
lsof
C-reactiveprotein,TNF-α,IL-
6,IL-8,IL-22.
0.816
RT-qPCR
Zhuetal.
(2019b
)
ALS
Hsa
_circ_00239
19
Hsa
_circ_00634
11Hsa
_circ_0088
036
Leuko
cyte
Down
Up
Up
60ALS
/15HC
Hsa
_circ
_0063
411
was
ass
ociatedwith
thedisease
duratio
nandsu
rvival
time.
0.950
Microarray
RT-qPCR
Dolinar
etal.
(2019)
AML
Hsa
_circ_00042
77
Mononu
clear
cell
Down
115AML/12HC
Associatedwith
progress
ive
stage.
0.957
Microarray
RT-qPCR
Lie
tal.
(2017c
)
CAD
Circ
ZNF609
Leuko
cyte
Down
330CAD/209
HC
Associatedwith
infla
mmatory
proce
sses.
0.761
RT-qPCR
Liangetal.
(2020)
CAD
Hsa
_circ_00018
79
Hsa
_circ_0004
104
PBM
Up
436CAD/297
HC
Hsa
_circ
_0001
879was
ass
ociatedwith
bod
ymas
sindex(BMI),systolic
blood
press
ure,diastolic
blood
press
ure
andGensini
score;
hsa
_circ_0004
104was
ass
ociatedwith
high-density
lipoprotein
cholesterol.
0.703
0.700
Microarray
RT-qPCR
Wangetal.
(2019a
)
CAD
Hsa
_circ_01246
44
Whole
blood
Up
179CAD/157
HC
Associatedwith
seve
rity.
0.769
Microarray
RT-qPCR
Zhaoetal.
(2017a
)
CAP
Hsa
_circ_00184
29
Hsa
_circ_00265
79
Hsa
_circ_00991
88
Hsa
_circ_00125
35
Whole
blood
Up
36CAP/36HC
NA
0.878
Microarray
RT-qPCR
Zhaoetal.
(2019)
CML
Hsa
_circ_10005
3PBMC
Up
150CML/100
HC
Associatedwith
clinical
stage,
BCR/ABLmutantstatus,
imatin
ibresp
onse
and
prognos
is.
NA
RNA-seq
RT-qPCR
Pingetal.
(2019)
CSCC
Hsa
_circ_01019
96
Hsa
_circ_0101119
Whole
blood
Up
87CSCC/55HC
NA
0.964
RT-qPCR
Wangetal.
(2017a
)
EH
Hsa
_circ_00379
11Whole
blood
Up
100EH/100HC
Associatedwith
gender,
smoking
,drin
kingand
serum
creatin
ine.
0.627
Microarray
RT-qPCR
Baoetal.
(2018)
EH
Hsa
_circ_00379
09
Whole
blood
Up
48EH/48HC
Associatedwith
serum
creatin
ineandlow‐density
lipoprotein.
0.682
RT-qPCR
Baoetal.
(2019)
EH
Hsa
_circ_00142
43
Whole
blood
Up
89EH/89HC
Associatedwith
age,high-
density
lipoprotein
leve
land
gluco
seleve
l.
0.732
RT-qPCR
Zhengetal.
(2019)
Translational potential of blood circular RNAs REVIEW
© The Author(s) 2020
Protein
&Cell
Table
2co
ntin
ued
Disease
CircRNA
biomarker
Source
Expression
change
Cohort
size
Clin
ical
significan
ce
AUC
Method
Reference
EH
Hsa
_circ_91025
Whole
blood
Up
96EH/96HC
Associatedwith
high‐density
lipoprotein,BMI,diastolic
bloodpress
ure
andsy
stolic
bloodpress
ure.
0.620
RT-qPCR
Zhengetal.
(2020)
GC
Hsa
_circ_00018
21
Whole
blood
Down
30GC/30HC
NA
0.872
RT-qPCR
Kongetal.
(2019)
HCC
Hsa
_circ_00007
98
PBMC
Up
72HCC/30HC
Associatedwith
tumorsize
,cirrhosisandove
rallsu
rvival.
0.703
RNA-seq
RT-qPCR
Leie
tal.
(2019)
HT
Hsa
_circ_00891
72
PBMC
Up
35HT/35HC
Associatedwith
these
rum
leve
lofthethyroid
peroxidase
antibody.
0.715
RNA-seq
RT-qPCR
Xiongetal.
(2019)
IAHsa
_circ_00210
01
Whole
blood
Down
223IA/131
HC
Associatedwith
aneurysm
rupture,Hunt,Hess
leve
l,tim
ingofsu
rgery,
disease
-freesu
rvival
and
ove
rall
survival.
0.870
RT-qPCR
Tengetal.
(2017)
MI
MICRA
Whole
blood
Down
642Acu
teMI/8
6HC
Associatedwith
theris
kofleft
ventriculardysfunction.
NA
Microarray
RT-qPCR
Vauso
rtetal.
(2016)
MS
Hsa
_circ_00054
02
Hsa
_circ_0035
560
Leuco
cyte
Down
45MS/26HC
NA
0.899
0.706
Microarray
RT-qPCR
Iparraguirre
etal.
(2017)
NSCLC
Hsa
_circ_01025
33
Whole
blood
Up
41NSCLC/26
HC
Associatedwith
tumortype,
TNM
stage,
LNM
anddistant
metastasis.
0.774
Microarray
RT-qPCR
Zhouetal.
(2018b
)
OA
Hsa
_circ_00321
31
Whole
blood
Up
25OA/25HC
Associatedwith
the
pathologicalp
roce
ss.
0.846
Microarray
RT-qPCR
Wangetal.
(2019f)
KBD
/OA
Hsa
_circ_00200
14
Whole
blood
Down
25KBD/25OA
Associatedwith
early
diagnosisofOAandKBD.
0.642
Microarray
RT-qPCR
Wangetal.
(2020e
)
PE
Hsa
_circ_00049
04
Hsa
_circ_0001
855
Whole
blood
Up
35PE/35HC
Associatedwith
serum
pregnan
cy-ass
ociated
plasm
aprotein
Aleve
l.
0.611
0.621
Microarray
RT-qPCR
Jiangetal.
(2018)
PE
Hsa
_circ_10122
2Blood
corpus
cle
Up
41PE/41HC
NA
0.706
Microarray
RT-qPCR
Zhangetal.
(2016)
PMOP
Hsa
_circ_00012
75
PBMC
Up
58PMOP/41HC
Associatedwith
T-sco
re.
0.759
Microarray
RT-qPCR
Zhaoetal.
(2018a
)
RA
Hsa
_circ_00442
35
Whole
blood
Down
77RA/31SLE/
50HC
NA
0.779
RT-qPCR
Luoetal.
(2018)
REVIEW Guoxia Wen et al.
© The Author(s) 2020
Protein
&Cell
Table
2co
ntin
ued
Disease
CircRNA
biomarker
Source
Expression
change
Cohort
size
Clin
ical
significan
ce
AUC
Method
Reference
RA
Hsa
_circ_10487
1Hsa
_circ_00352
4Hsa
_circ_10187
3Hsa
_circ_10304
7
PBMC
Up
35RA/30HC
NA
0.833
0.683
0.676
0.671
Microarray
RT-qPCR
Ouya
ngetal.
(2017)
RA
Hsa
_circ_00003
96
Hsa
_circ_01304
38
PBMC
Down
32RA/20HC
NA
0.809
0.774
RNA-seq
RT-qPCR
Yangetal.
(2019)
SLE
Hsa
_circ_00004
79
PBMC
Up
97SLE
/50RA/
89HC
Associatedwith
albumin
leve
l,urin
eprotein,IgG,
leuko
cytes,
hem
oglobin
and
ESR.
0.731
RNA-seq
RT-qPCR
Guoetal.
(2019)
SLE
Hsa
_circ_00577
62
Hsa
_circ_00030
90
Whole
blood
Up
24SLE
/24HC
Hsa
_circ
_0057
762was
ass
ociatedwith
theSLEDAI-
2Ksc
ore.
0.804
0.848
Microarray
RT-qPCR
Lie
tal.
(2019b
)
SLE
Hsa
_circ_00442
35
Hsa
_circ_0068
367
PBMC
Down
79SLE
/30RA/
62HC
Hsa
_circ
_0044
235was
ass
ociatedwith
thenumbers
ofmonocytesand
autoantibodies.
0.873
0.768
Microarray
RT-qPCR
Luoetal.
(2019)
SLE
Circ
PTPN22
PBMC
Down
53SLE
/40HC
Associatedwith
SLEactivity
indexsc
ores.
0.918
RNA-seq
RT-qPCR
Miaoetal.
(2019)
SLE
Hsa
_circ_40717
6Hsa
_circ_00130
8PBMC
Down
126SLE/102
HC
NA
0.806
0.722
Microarray
RT-qPCR
Zhangetal.
(2018e
)
SLE
Hsa
_circ_00129
19
CD4+
Tce
llUp
28SLE
/18HC
Associatedwith
clinical
andlab
features
.–
Microarray
RT-qPCR
Zhangetal.
(2018a
)
Sch
izop
hrenia
Hsa
_circ_10459
7PBMC
Down
102 Sch
izop
hrenia/
103HC
Significa
ntexp
ression
difference
betweenpre-
treatm
entandpost-
treatm
ent.
0.885
Microarray
RT-qPCR
Yaoetal.
(2019)
T2DM
Circ
ANKRD36
Leuco
cyte
Up
43T2DM/45HC
Associatedwith
gluco
se,
glyco
sylatedhemoglobin
and
interle
ukin.
NA
RNA-seq
RT-qPCR
Fangetal.
(2018)
T2DM
Hsa
_circ_00546
33
Whole
blood
Up
90T2DM/83
Pre-diabetes/
86HC
NA
0.751
Microarray
RT-qPCR
Zhaoetal.
(2017b
)
TB
Hsa
_circ_10301
7Hsa
_circ_05991
4Hsa
_circ_1011
28
PBMC
Up
31TB/30HC
Hsa
_circ
_1011
28was
ass
ociatedwith
theleve
lof
let‐7a.
0.870
0.821
0.817
Microarray
RT-qPCR
Fuetal.
(2019)
Translational potential of blood circular RNAs REVIEW
© The Author(s) 2020
Protein
&Cell
Table
2co
ntin
ued
Disease
CircRNA
biomarker
Source
Expression
change
Cohort
size
Clin
ical
significan
ce
AUC
Method
Reference
TB
Hsa
_circ_00434
97
Hsa
_circ_00012
04
Monocyte
Up
101TB/88HC
Significa
ntexp
ression
difference
betweenpre-
treatm
entandpost-
treatm
ent.
0.860
0.848
Microarray
RT-qPCR
Huang
etal.,
(2017c
)
TB
Hsa
_circ_00193
7PBMC
Up
178TB/40PE/
40COPD/40
LC/133HC
Associatedwith
radiological
scores;
significa
nte
xpress
ion
difference
betweenpre-
treatm
entandpost-
treatm
ent.
0.873
Microarray
RT-qPCR
Huang
etal.,
(2018c
)
TB
Hsa
_circ_00013
80
PBMC
Down
32TB/31HC
NA
0.950
RT-qPCR
Luoetal.
(2020b
)
TB
Hsa
_circ_00004
14
Hsa
_circ_00006
81
Hsa
_circ_00021
13
Hsa
_circ_00023
62
Hsa
_circ_00029
08
Hsa
_circ_00087
97
Hsa
_circ_00631
79
PBMC
Up
12TB/13HC
NA
0.946
RNA-seq
Microarray
RT-qPCR
Qianetal.
(2018)
TB
Hsa
_circ_00058
36
PBMC
Down
49TB/45HC
NA
NA
RNA-seq
RT-qPCR
(Zhuan
getal.,
201
7)
Abbreviation:AIS:acu
teisch
emic
stroke
;ALS
:amyo
trophiclaterals
clerosis;
AML:
acu
temye
loid
leuke
mia;CAD:co
ronary
artery
disea
se;CAP:co
mmunity‐acq
uire
dpneumonia;CML:
chronic
mye
loid
leuke
mia;COPD:ch
ronic
obs
tructivepulm
onarydisea
se;CSCC:ce
rvical
squam
ousce
llca
rcinoma;EH:essentia
lhyp
ertension
;GC:gastric
cance
r;HC:hea
lthyco
ntrol;
HCC:hepatoce
llularca
rcinoma;HT:
Hash
imoto’s
thyroiditis;
IA:intracranialane
urysm
;KBD:Kash
in-Bec
kdisea
se;MI:myo
cardialinfarctio
n;MS:multiple
sclerosis;
NA:notapp
licable;
NSCLC:n
on-smallce
lllungca
nce
r;OA:osteoa
rthritis;P
BMC:perip
heralb
loodmonon
uclearce
ll;PE:pre-eclam
psia;P
MOP:postmenopau
salo
steop
orosis;
RA:rheumatoid
arthritis;SLE:
systemic
lupuserythem
atosu
s;T2DM:type2diabetesmellitus;
TB:tuberculosis..
REVIEW Guoxia Wen et al.
© The Author(s) 2020
Protein
&Cell
muscleblind (MBL), is the first circRNA that was discoveredto function as an RBP sponge (Ashwal-Fluss et al., 2014). Itwas observed that both the circular exon and the flankingintrons of circMbl contain many MBL binding sites that canbe specifically bound by MBL (Ashwal-Fluss et al., 2014).Therefore, increased expression of MBL can promote back-splicing of circMbl and lead to decreased expression ofcanonical linear MBL transcripts. At the same time, circMblcan sequester MBL in the cytoplasm and prevent it fromperforming splicing functions (Ashwal-Fluss et al., 2014).Several other circRNAs, such as circAmotl1 (Yang et al.,2017b), circANRIL (Holdt et al., 2016), circPABPN1 (Abdel-mohsen et al., 2017), circPOLR2A (Li et al., 2017e) andcircDHX34 (Li et al., 2017e), can function as protein decoysas well. Second, circRNAs can form a complex with otherRNAs and proteins to perform functions. For example, someintron-containing circRNAs, such as circEIF3J and circBPTF,can interact with U1 snRNP to form an RNA-protein com-plex, which further interacts with RNA polymerase II (Pol II)at the promoter region of their parental genes and enhancestheir transcription (Zhang et al., 2013; Li et al., 2015c).Similarly, some ciRNAs, such as ci-ankrd52, can alsoassociate with Pol II as well (Zhang et al., 2013; Li et al.,2015c). They can also regulate the expression of their par-ental gene by modulating the elongation of Pol II (Zhanget al., 2013; Li et al., 2015c). Many circRNAs, rather than aspecific circRNA, can interact with NF90/NF110 as a groupto form circRNP complexes in the cytoplasm, which mayfunction as a reservoir of NF90/NF110 (Li et al., 2017e).Upon viral invasion, NF90/NF110 could be released fromcircRNP complexes and bind to viral mRNAs to perform itsantiviral functions (Li et al., 2017e). Third, the interaction ofcircRNAs with proteins can facilitate or block the function oftheir binding proteins. For instance, increased expression ofcircFoxo3 in the cytoplasm may arrest the anti-senescentprotein ID1, transcription factor E2F1, anti-stress proteinsFAK and HIF1α and prevent the nuclear translocation ofthese transcription factors and their function, thus leading toincreased cellular senescence (Du et al., 2017). CircFoxo3can also form a tertiary circRNA-protein complex with cyclin-dependent kinase 2 (CDK2) and cyclin-dependent kinaseinhibitor 1 (p21), which facilitates the inhibition of CDK2 byp21, thus blocking cell cycle progression (Du et al., 2016a).Moreover, circFoxo3 can bind to Mdm2 and p53 in a breastcancer cell line, which leads to tumor cell apoptosis (Duet al., 2016b).
Translation
Some circRNAs with an internal ribosome entry site (IRES),such as circMbl (Pamudurti et al., 2017) and circZNF609(Legnini et al., 2017), can share the start codon of their hostgenes and be translated in a cap-independent manner. Thetranslation initiation of circRNAs may be promoted by N6-methyladenosine (m6A) modification of circRNAs (Yanget al., 2017c). In the human heart, 40 circRNAs were
observed to be associated with ribosomes and thus may betranslated (Heesch et al., 2019). Some of them, includingcircSLC8A1, circMYBPC3 and circRYR2, are heart-specificcircRNAs (Heesch et al., 2019). The translation of thesecircRNAs may be related to the physiological roles of thehuman heart. Further functional analysis observed that somecircRNA-encoded proteins can exert agonist or antagonisteffects on cancer progression. For example, PINT87aa(Zhang et al., 2018d) and AKT3-A74aa (Xia et al., 2019), twopeptides encoded by circRNAs, can interact with signalfactors and inhibit glioblastoma tumorigenicity in glioblas-toma. However, circGprc5a-secreted peptides can promotebladder oncogenesis and metastasis through its binding toGprc5a (Gu et al., 2018).
CIRCRNA EXPRESSION AND ITS IMPLICATION INDISEASES
Given the important functions that circRNAs can perform,their expression may provide significant clues in under-standing the underlying mechanisms of biological processesand disease states. Previous studies have observed thatcircRNAs are widely expressed in all tissues (Zhou et al.,2018a; Ji et al., 2019; Wu et al., 2020a) and cell types(Salzman et al., 2013) of nearly all species across theeukaryotic tree of life (Wang et al., 2014). Specifically, cir-cRNAs are enriched in brain samples (Westholm et al.,2014; Rybak-Wolf et al., 2015; Szabo et al., 2015; Venøet al., 2015) and human blood samples (Memczak et al.,2015), including peripheral blood mononuclear cells(PBMCs) (Qian et al., 2018), erythrocytes (Alhasan et al.,2016), platelets (Alhasan et al., 2016) and exosomes (Liet al., 2015b). Furthermore, circRNAs are expressed in tis-sue- (Guo et al., 2014; Szabo et al., 2015; Zhou et al.,2018a) or cell-type- (Salzman et al., 2013) specific and age-dependent manner (Rybak-Wolf et al., 2015; Szabo et al.,2015; You et al., 2015; Zhou et al., 2018a). Using the ratBodyMap dataset, we observed tissue-specific circRNAexpression in all 11 rat tissues, which may be closely relatedto the physiological functions of those tissues (Zhou et al.,2018a). The dynamic expression of circRNAs across timehas also been correlated with neuronal differentiation (Ry-bak-Wolf et al., 2015), neural development (Szabo et al.,2015; You et al., 2015), human terminal B cell differentiation(Agirre et al., 2019) and spermatogenesis (Zhou et al.,2018a).
The spatiotemporal expression of circRNAs is mediatedby the balance between circRNA generation and circRNAturnover, which can be regulated at both the transcriptionaland posttranscriptional levels (Li et al., 2018d; Chen, 2020).On the one hand, the production of circRNAs can be regu-lated by intronic complement sequences (ICSs) (Jeck et al.,2013; Liang and Wilusz, 2014; Zhang et al., 2014), cis-splicing elements (Ashwal-Fluss et al., 2014; Starke et al.,2014; Wang and Wang, 2015) and trans-acting splicing
Translational potential of blood circular RNAs REVIEW
© The Author(s) 2020
Protein
&Cell
factors (Ashwal-Fluss et al., 2014; Conn et al., 2015; Krameret al., 2015; Khan et al., 2016; Aktaş et al., 2017; Errichelliet al., 2017; Fei et al., 2017; Li et al., 2017e). On the otherhand, the degradation of circRNAs can be affiliated bymiRNA-initiated AGO2 cleavage (Kleaveland et al., 2018)and nuclease-mediated cleavage, including RNase Lendonuclease (Liu et al., 2019a), RNase P/MRP endonu-clease complex (Park et al., 2019) and G3BP1 endonucle-ase (Fischer et al., 2020). Dysregulation of circRNAgeneration or turnover may lead to aberrant circRNAexpression in cells or tissues, which may be related to manyhuman diseases (Chen et al., 2016). Cdr1as, one of the moststudied circRNAs, harbors more than 70 binding sites formiR-7, and its overexpression strongly inhibits the activity ofthe tumor suppressor miR-7 (Hansen et al., 2013). Accu-mulating evidence has demonstrated its significant increasein colorectal cancer samples, which can inhibit miR-7 func-tion and activate EGFR and RAF1 oncogenes (Weng et al.,2017). Moreover, the dysregulation of the miR-7/Cdr1as axisis involved in some other diseases, such as Alzheimer’sdiseases (Lukiw, 2013), diabetes (Xu et al., 2015) and car-diometabolic diseases (Geng et al., 2016). Furthermore, theloss of the Cdr1as locus causes miRNA deregulation andaffects synaptic transmission in knockout mice (Piweckaet al., 2017). In addition to Cdr1as, other circRNAs are alsoimplicated in many different human diseases, includingcancers (Zhang et al., 2018j; Chen et al., 2019b; Smid et al.,2019; Su et al., 2019; Vo et al., 2019), cardiovascular dis-eases (Zhang et al., 2018j; Aufiero et al., 2019), neurode-velopment and neurodegenerative diseases (Kumar et al.,2017; Zhang et al., 2018j; Dube et al., 2019; Chen et al.,2020d; Hanan et al., 2020) and immune diseases (Chenet al., 2019c; Zhou et al., 2019b).
CIRCRNAS ARE PROMISING BIOMARKERS FORHUMAN DISEASES
In general, biomarkers are physiological indexes or biologi-cal molecules that can be objectively measured and canindicate either a normal or a pathogenic state (Lesko andAtkinson, 2001). Technological advances in genomics,transcriptomics, proteomics and metabolomics have led tothe discovery and validation of many biomarkers that arepushing personalized medicine forward (Chen et al., 2012;Karczewski and Snyder, 2018; Ahadi et al., 2020). A goodbiomarker with clinical significance must meet the criteria ofanalytical validity, clinical validity and clinical utility (Byronet al., 2016). As a novel type of noncoding RNA, circRNAhas several distinct advantages over canonical linear RNAsas a disease biomarker (Zhang et al., 2018j). First, circRNAis more stable than linear RNAs because it has a closed loopstructure without 5-prime and 3-prime ends (Enuka et al.,2015; Li et al., 2015b). In the MCF10A human mammaryepithelial cell line, Enuka et al. observed at least 2.5 timeslonger half-lives of circRNAs compared to linear RNAs,
including mRNAs and miRNAs (Enuka et al., 2015). Second,circRNAs are abundantly expressed in many tissue sam-ples. For example, brain samples express more than 10,000circRNAs in the rat BodyMap dataset (Zhou et al., 2018a). Insome cases, the expression values of circRNAs are muchhigher than their linear counterparts (Westholm et al., 2014;Rybak-Wolf et al., 2015; You et al., 2015; Liang et al., 2017).Third, circRNAs are expressed in a tissue-specific (Salzmanet al., 2013; Guo et al., 2014; Szabo et al., 2015; Zhou et al.,2018a) and developmental stage-specific manner (Rybak-Wolf et al., 2015; Szabo et al., 2015; You et al., 2015; Zhouet al., 2018a). Importantly, the tissue specificity of circRNA ishigher than that of mRNA of its host gene (Guo et al., 2014;Zhou et al., 2018a). These features suggest that circRNAsmay have better analytical validity (Zhang et al., 2018j),including analytical specificity, robustness, reproducibilityand repeatability (Byron et al., 2016), when used as bio-marker molecules. In a recent study, Maass et al. investi-gated circRNA expression in 20 clinically relevant tissuesamples, which underscored the feasibility of using cir-cRNAs as potential disease biomarkers (Maass et al., 2017).
To date, many circRNAs have been identified asbiomarkers for human diseases (Zhang et al., 2018j),especially human cancers (Li et al., 2015a; Meng et al.,2017; Su et al., 2019; Sheng et al., 2020). For example,Cdr1as has been revealed as a prognostic biomarker incolorectal cancer patients (Weng et al., 2017). In a trainingcohort comprising 153 primary colorectal cancer tissues and44 matched normal mucosae, significantly increased Cdr1asexpression was observed in colorectal cancer tissues, andits overexpression was associated with poor patient survival.The prognostic power of Cdr1as was further validated in anindependent validation cohort comprising 165 colorectalcancer patients (Weng et al., 2017). A four-circRNA signa-ture, consisting of hsa_circ_101308, hsa_circ_104423,hsa_circ_104916 and hsa_circ_100269, has been con-structed that can predict the early recurrence of stage IIIgastric cancer (GC) patients after surgery (Zhang et al.,2017). Moreover, Vo et al. constructed a comprehensivecatalog of circRNAs in human cancer tissues in a systematicanalysis of more than 2,000 cancer samples (Vo et al.,2019). They identified two circRNAs, circ-AURKA and circ-AMACR, as potential diagnostic biomarkers for neuroen-docrine prostate cancer (Vo et al., 2019). Although thesecircRNA biomarkers are potentially useful in cancer man-agement, most of them are derived from tissue samples (Liet al., 2015a; Meng et al., 2017; Su et al., 2019; Sheng et al.,2020). To improve biomarker accessibility, especially forbiomarkers of cancer screening and diagnosis, circRNAbiomarkers in body fluids are ideal for clinical application(Anfossi et al., 2018). Hence, the authors further investigatedand validated the reliability of detecting circRNA biomarkersin urine samples of prostate cancer patients (Vo et al., 2019),which suggests that circRNA in urine samples is a promisingstrategy for prostate cancer screening.
REVIEW Guoxia Wen et al.
© The Author(s) 2020
Protein
&Cell
BLOOD CIRCRNAS AND LIQUID BIPOSYBIOMARKERS
Liquid biopsy has been a revolutionary tool in diseasemanagement, supporting the diagnosis, prognosis andtreatment guidance of human diseases (Heitzer et al., 2019;Mattox et al., 2019; Rubis et al., 2019; Luo et al., 2020a). Incomparison to urine, saliva or cerebrospinal fluid, peripheralblood has been used as the major body fluid in liquid biopsy(Rubis et al., 2019; Luo et al., 2020a). In the circulatingblood, aberrantly expressed RNAs or fusion transcripts indifferent blood components have been associated withhuman cancers (Byron et al., 2016; Zaporozhchenko et al.,2018; Sole et al., 2019) and infectious diseases (Byron et al.,2016; Correia et al., 2017). These RNA biomarkers includecfRNAs in plasma or serum (Mitchell et al., 2008; Fehlmannet al., 2020), exosome-derived RNAs (Maas et al., 2017),EV-incorporated cfRNAs (Maas et al., 2017) and RNA tran-scripts in tumor-educated platelets (Best et al., 2017, 2018).These cell-free or EV-incorporated RNA biomarkers repre-sent the changes in expression that occur in abnormal cells,such as dysregulated genes in cancer cells (Byron et al.,2016; Rubis et al., 2019; Luo et al., 2020a). Other thancfRNAs, gene expression profiles of PBMCs or whole bloodhave been proven to be good indicators of many humandiseases (Chaussabel and Baldwin, 2014; Chaussabel,2015), since they can assess the immune status (Chauss-abel, 2015). Unlike the expression of cfRNAs in serum,plasma or EVs, RNA expression levels in PBMCs or wholeblood are measures of the host response to exogenouspathogens or autoantigens (Schnell et al., 2018; Shaked,2019). Several whole blood or PBMC gene expression sig-natures have been developed for cancer management,including the early diagnosis of colorectal cancer (Marshallet al., 2010; Ciarloni et al., 2015, 2016) and lung cancer(Showe et al., 2009; Kossenkov et al., 2018), the prognosisof adult acute myeloid leukemia (Bullinger et al., 2004; Valket al., 2004) and prostate cancer patients (Ross et al., 2012),and the monitoring of renal cell carcinoma relapse (Giraldoet al., 2017). Moreover, whole blood or PBMC RNA signa-tures have been widely investigated as liquid biopsy diag-nostic tools for infectious diseases (Ramilo and Mejias,2017), such as discriminating influenza from other respira-tory viral infections (Zaas et al., 2009), differentiating viraland bacterial infections (Tsalik et al., 2016), diagnosingseptic patients (Sweeney et al., 2018; Gunsolus et al., 2019;Mayhew et al., 2020), and discriminating active tuberculosis(TB) patients from patients with latent TB or other lung dis-eases (Bloom et al., 2013; Anderson et al., 2014; Qian et al.,2016; Zak et al., 2016; Sambarey et al., 2017; MacLeanet al., 2019; Warsinske et al., 2019; Esmail et al., 2020;Gupta et al., 2020). In addition, the transcriptome of bloodcells has been implicated in assessing the likelihood ofdeveloping obstructive coronary artery disease in symp-tomatic nondiabetic patients (Vargas et al., 2013) and
predicting antibody-mediated kidney allograft rejection inkidney transplant patients (Loon et al., 2019).
With regard to RNA molecules, both protein-codingmRNAs (Byron et al., 2016; Sole et al., 2019) and severalclasses of noncoding RNAs (Byron et al., 2016; Anfossiet al., 2018; Pardini et al., 2019; Sole et al., 2019) have beenused as blood disease biomarkers. In comparison to mRNAsor long noncoding RNAs, small noncoding RNAs, such asmiRNAs (Mitchell et al., 2008; Max et al., 2018; Fehlmannet al., 2020) and noncanonical small RNAs (Fritz et al., 2016;Pardini et al., 2019), have the advantage of high stability inthe circulating blood (Mitchell et al., 2008; Anfossi et al.,2018). This superiority is especially important when trans-lating RNA biomarkers into clinical practice (Byron et al.,2016; Anfossi et al., 2018), since fast mRNA degradation inblood sample processing may affect the performance ofmRNA biomarkers (Dvinge et al., 2014). Given the highstability of circRNAs, great efforts and substantial progresshave been made to investigate the possibility of using cir-cRNA biomarkers in liquid biopsy in recent years.
Next, we summarize circRNAs in the peripheral blood,their correlations with human diseases and their potentialapplication in liquid biopsy as disease biomarkers (Fig. 2).We classified blood circRNAs into blood cell-free circRNAsand circRNAs in blood cells since they have distinct biolog-ical meanings in the context of human diseases. Blood cell-free circRNAs, including circulating cell-free circRNAs inplasma, serum and blood EVs, are secreted from differenttissue cells into the blood. Therefore, cell-free circRNAshave corresponding tissue origins, and cell-free circRNAbiomarkers represent their clinical significance in the originaltissue (Li et al., 2019d, 2020). On the other hand, blood cellcircRNAs consist of circRNAs in various blood cells, such asmonocytes, erythrocytes, neutrophils and platelets. Cir-cRNAs in mixtures of different blood cells, such as circRNAsin lymphocytes, PBMCs and whole blood, are classified inthis group as well. The circRNA expression profiles ofperipheral blood cells or a mixture of blood cells are impor-tant indicators of the host’s immune status (Chaussabel,2015), which may undergo dynamic changes during acuteevents such as viral infections (Chen et al., 2018a; Roseet al., 2019; Zhou et al., 2019a). Hence, circRNA biomarkersin blood cells, PBMCs or whole blood represent the specificimmune response of an individual to different physiologicalaspects.
Cell-free circRNAs in peripheral blood
Mounting evidence has demonstrated abundant circRNAexpression in blood plasma (Maass et al., 2017; Yi et al.,2018; Smid et al., 2019) and serum (Gu et al., 2017; Maasset al., 2017; Sonnenschein et al., 2019; Sun et al., 2020b)(Fig. 2A). In the circulating blood, EVs, including exosomesand microvesicles, are important blood components thathave great diagnostic and prognostic value (Revenfeld et al.,2014). Li et al. investigated circRNA expression in MHCC-
Translational potential of blood circular RNAs REVIEW
© The Author(s) 2020
Protein
&Cell
LM3 liver cancer cells and cell-derived exosomes and foundat least 2-fold circRNA enrichment in exosomes compared toproducer cells (Li et al., 2015b). They further found that theexpression level of exosomal circRNAs is largely the sameafter serum incubation at room temperature for up to 24 h (Liet al., 2015b). Using the extracellular vesicle long RNA(exLR) sequencing method, the same group explored theexpression profiles of circRNAs in 352 plasma EV samples(Li et al., 2019d). They found 137,196 circRNA candidatesthat were expressed in normal plasma EV samples, and thecircular to linear ratio was significantly higher in EVs than inPBMCs (Li et al., 2019d). A recent study also isolated pla-telet-derived EVs and demonstrated the selective release ofplatelet-specific circRNAs in exosomes and microvesicles(Preußer et al., 2018). Based on these data, a database ofblood exosomal circRNAs, exoRBase, has been developedto facilitate the development of circRNA signatures in bloodexosomes (Li et al., 2017b). In addition, two previous studieshave observed that the majority of circulating cell-free miR-NAs were associated with AGO2 protein complexes ratherthan with vesicles in blood plasma (Arroyo et al., 2011;Turchinovich et al., 2011). Like cell-free miRNAs, blood cell-free circRNAs may bind to several RNA-binding proteins orprotein complexes as well (Huang et al., 2020a). Therefore,some cell-free circRNAs may not be associated with bloodEVs. In a pilot study of circRNA expression profiles in 348primary breast cancer tissue samples, Smid et al. found thatcircCNOT2 is a prognostic biomarker of aromatase inhibitortherapy in advanced breast cancer patients (Smid et al.,2019). To investigate the possibility of using circCNOT2 as anoninvasive biomarker, they further amplified circCNOT2 inplasma samples of four breast cancer patients and foundvariable circCNOT2 expression in all plasma samples (Smidet al., 2019). All these results suggest that cell-free circRNAsin peripheral blood are stable, enriched and detectable.
Blood EVs can be secreted from cells of many differentbiological systems and can be circulated to recipient cellsthrough the bloodstream (Revenfeld et al., 2014; Lasda andParker, 2016). Therefore, blood cell-free circRNAs can per-form vital roles in many biological processes, such as cancercell proliferation, cancer metastasis, drug resistance,hemostasis and inflammation (Fig. 2B) (Wang et al., 2019e;Cui et al., 2020; Li et al., 2020; Shang et al., 2020; Xu et al.,2020b). For example, circ-deubiquitination (circ-DB) in adi-pose-secreted exosomes was found to regulate deubiquiti-nation via the suppression of miR-34a and the activation ofdeubiquitination-related USP7 in plasma samples of hepa-tocellular carcinoma (HCC) patients, which could reduceDNA damage and promote HCC cell growth (Zhang et al.,2018b). Exosomal circSHKBP1 was found to promote theprogression of GC by regulating the miR-582-3p/HUR/VEGFpathway and suppressing HSP90 degradation (Xie et al.,2020a). CircUHRF1 in plasma exosomes can inhibit thefunctions of natural killer cells by upregulating the expressionof TIM-3 via miR-449c-5p degradation and drive resistanceto anti-PD1 immunotherapy in HCC patients (Zhang et al.,
2020a). Moreover, exosomal circHIPK3 can preventischemic muscle injury by downregulating miR-421 expres-sion, increasing FOXO3a expression, inhibiting pyroptosisand releasing IL-1β and IL-18 (Yan et al., 2020).
Given the biological functions that blood cell-free cir-cRNAs can perform (Wang et al., 2019e; Cui et al., 2020; Liet al., 2020) and their implications in many human diseases,cell-free circRNAs in the peripheral blood have greatpotential as liquid biopsy biomarkers of human diseases(Fig. 2C) (Anfossi et al., 2018; Zaporozhchenko et al., 2018;Zhang et al., 2018j). To date, many blood cell-free circRNAshave been introduced for cancer management, includingearly cancer diagnosis, cancer prognosis and prediction ofcancer treatment (Preußer et al., 2018; Aufiero et al., 2019;Fraipont et al., 2019; Lu et al., 2019c; Pardini et al., 2019;Sole et al., 2019; Su et al., 2019; Wang et al., 2019e; Bel-trán-García et al., 2020; Cui et al., 2020; Li et al., 2020). In arecent study, Luo et al. measured the expression levels oftwo circRNAs in plasma samples of 231 lung cancer patientsand 41 healthy controls using reverse transcription dropletdigital PCR (RT-ddPCR) (Luo et al., 2020c). They identifiedhsa_circ_0000190 as a circRNA biomarker in human bloodplasma that can predict the survival outcomes of lung cancerpatients (Luo et al., 2020c). Furthermore, the increasedexpression of plasma hsa_circ_0000190 was also correlatedwith poor response to systemic therapy and immunotherapyin lung cancer patients (Luo et al., 2020c). Similarly, Li et al.investigated the clinical relevance of serum exosomal cir-cFLI1 in lung cancer patients in a cohort of 61 small cell lungcancer (SCLC) patients and 55 normal subjects. They foundthat serum exosomal circFLI1 levels were significantly higherin SCLC patients, especially in SCLC patients with distantmetastasis (Li et al., 2018b). Notably, they observed thatSCLC patients with lower exosomal circFLI1 expressionlevels experienced longer disease remissions, indicating itsprognostic power in SCLC. The authors also suggested thatserum exosomal circFLI1 may be used as a biomarker thatcan monitor the clinical response to chemotherapy in SCLCpatients (Li et al., 2018b). By analyzing blood plasma sam-ples of 62 GC patients and 25 healthy controls, Tang et al.proposed a novel circulating diagnostic biomarker of GC,plasma exosomal circKIAA124, that was correlated withclinical TNM stage, lymphatic metastasis and overall survivaltime of GC patients (Tang et al., 2018). In addition to theaberrant expression of blood cell-free circRNAs, the pres-ence of some fusion circRNAs (f-circRNAs) in the peripheralblood has also been used as a liquid biopsy biomarker.Guarnerio et al. found that f-circRNAs could be producedfrom cancer-associated chromosomal translocations incancer cells (Fig. 1), and f-circRNAs could promote cellulartransformation, cell viability and resistance upon therapy(Guarnerio et al., 2016). In a systematic analysis of f-cir-cRNAs in localized prostate cancer tissues, Chen et al.observed more f-circRNAs in tumors with worse prognosis(Chen et al., 2019b). Due to the lack of recurrence of thesef-circRNAs, the authors suggested that f-circRNAs are good
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biomarker candidates (Chen et al., 2019b). In NSCLCpatients, Tan et al. found that F-circEA, an f-circRNA origi-nating from the EML4-ALK fusion gene, was exclusivelyexpressed in the plasma of patients with the EML4-ALKfusion (Tan et al., 2018). Therefore, plasma F-circEA mayserve as a liquid biopsy biomarker to diagnose NSCLCpatients with EML4-ALK translocation and guide targetedtherapy for NSCLC patients in this subgroup (Tan et al.,2018). More cell-free circRNA biomarkers in the blood aresummarized in Table 1.
CircRNAs in blood cells and whole blood
CircRNA expression in blood cells and whole blood, a majorsource of liquid biopsy samples, has been extensivelyinvestigated (Fig. 2A). In a pilot study, Memczak et al.detected thousands of circRNAs in peripheral whole bloodsamples using RNA-seq (Memczak et al., 2015). They foundthat the expression levels of these blood circRNAs werecomparable to those in circRNA-rich cerebellar tissue(Memczak et al., 2015). In separate blood cell populations,circRNAs were observed to be enriched approximately100-fold in platelets and anucleate erythrocytes relative tonucleated tissues, such as lung, brain and colon (Alhasanet al., 2016). Gaffo et al. investigated circRNA expression inT cells, B cells and monocytes of healthy subjects and foundabundant circRNA expression in these mature blood cells(Gaffo et al., 2019). We also explored the expression land-scape of circRNAs in PBMCs and found that the expressionlevel of circRNAs in PBMCs, together with that in platelets,red blood cells (RBCs) and whole blood, is high enough tobe detected (Qian et al., 2018). All these results suggest thatwhole blood (Memczak et al., 2015; Qian et al., 2018),PBMCs (Qian et al., 2018), and several blood cells, includingneutrophils (Maass et al., 2017), T cells (Gaffo et al., 2019),B cells (Gaffo et al., 2019), monocytes (Gaffo et al., 2019),RBCs (Alhasan et al., 2016; Qian et al., 2018) and platelets(Maass et al., 2017; Qian et al., 2018; Gaffo et al., 2019), arereliable clinical samples for circRNA profiling in liquid biopsy.
Accumulating evidence has suggested that circRNAsplay crucial roles in the immune response and its regulation(Fig. 3) (Chen et al., 2017b, 2019c; Xu et al., 2018b, 2020a;Yang et al., 2018; Zhou et al., 2019b; Awan et al., 2020).First, circRNAs are important regulators of blood cell bio-genesis, differentiation and activation (Chen et al., 2019c;Zhou et al., 2019b; Xu et al., 2020a). In a comprehensivestudy of circRNA expression in hematopoietic progenitors,differentiated lymphoid and myeloid cells, Nicolet et al.observed a cell-type specific pattern of circRNA expressionprofiles in blood cells, and the type and number of circRNAsincreased upon hematopoietic maturation (Nicolet et al.,2018). Moreover, Holdt et al. found that circANRIL can bindto PES1 to impede the generation of pre-rRNA and ribo-somes, resulting in the biogenesis of macrophages by p53activation (Holdt et al., 2016). Second, circRNAs are activelyinvolved in antiviral immune responses (Fig. 3B) (Cadena
and Hur, 2017; Awan et al., 2020). For example, Chen et al.showed that exogenous circRNAs released by viruses canbe recognized by the pattern recognition receptor RIG-I ofhost cells, which activates host innate immunity (Chen et al.,2017a). In their subsequent work, the authors showed thatm6A RNA modification of human circRNAs inhibits innateimmunity, while unmodified circRNAs and K63-polyubiquitincan activate RIG-I and innate immune response (Chen et al.,2019a). Li et al. found that two immune factors, NF90/NF110,not only promote circRNA production in the nucleus but alsobind to mature host circRNAs to form circRNP complexes inthe cytoplasm. Upon viral infection, circRNP complexes inthe cytoplasm can release NF90/NF110, which binds viralmRNAs to inhibit viral replication (Li et al., 2017e). Moreover,Liu et al. presented that circRNAs can form RNA duplexesand act as inhibitors of innate immunity-related proteinkinase (PKR) under normal conditions (Liu et al., 2019a).Upon poly(I:C) treatment or viral infection, RNase L is acti-vated to efficiently degrade circRNAs, and PKR is thusreleased from circRNA inhibition to initiate the early cellularinnate immune response (Liu et al., 2019a). Third, circRNAsare closely associated with the antibacterial immuneresponse as well. Ng et al. characterized circRNAs inducedby lipopolysaccharide (LPS) and identified circRasGEF1B asa conserved positive regulator of the LPS r
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