PIIS0016508523002366.pdf

1. GI CANCER
A Liquid Biopsy Signature for the Early Detection of Gastric
Cancer in Patients
Xin Guo,1,2,3,
* Yunhua Peng,4,
* Qiying Song,3,
* Jiangpeng Wei,1,
* Xinxin Wang,3
Yi Ru,5
Shenhui Xu,6
Xin Cheng,7
Xiaohua Li,1
Di Wu,3
Lubin Chen,1,2
Bo Wei,3,§
Xiaohui Lv,8,§
and
Gang Ji1,§
1
Department of Digestive Surgery, Xijing Hospital, Fourth Military Medical University, Xi’an, China; 2
Department of Endoscopic
Surgery, Air Force 986th
Hospital, Fourth Military Medical University, Xi’an, China; 3
Department of General Surgery, Chinese
People’s Liberation Army General Hospital, Beijing, China; 4
Center for Mitochondrial Biology and Medicine, The Key Laboratory
of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong
University, Xi’an, China; 5
Department of Biochemistry and Molecular Biology, Fourth Military Medical University, Xi’an, China;
6
Department of Pathology, Xijing Hospital, Fourth Military Medical University, Xi’an, China; 7
Department of Hepatobiliary
Surgery, Xijing Hospital, Fourth Military Medical University, Xi’an, China; and 8
Department of Gynecology and Obstetrics, Xijing
Hospital, Fourth Military Medical University, Xi’an, China
BACKGROUND & AIMS: Diagnosing gastric cancer (GC) while
the disease remains eligible for surgical resection is chal-
lenging. In view of this clinical challenge, novel and robust
biomarkers for early detection thus improving prognosis of GC
are necessary. The present study is to develop a blood-based
long noncoding RNA (LR) signature for the early-detection of
GC. METHODS: The present 3-step study incorporated data
from 2141 patients, including 888 with GC, 158 with chronic
atrophic gastritis, 193 with intestinal metaplasia, 501 healthy
donors, and 401 with other gastrointestinal cancers. The LR
profile of stage I GC tissue samples were analyzed using tran-
scriptomic profiling in discovery phase. The extracellular
vesicle (EV)–derived LR signature was identified with a training
cohort (n ¼ 554) and validated with 2 external cohorts (n ¼
429 and n ¼ 504) and a supplemental cohort (n ¼ 69).
RESULTS: In discovery phase, one LR (GClnc1) was found to be
up-regulated in both tissue and circulating EV samples with an
area under the curve (AUC) of 0.9369 (95% confidence interval
[CI], 0.9073–0.9664) for early-stage GC (stage I/II). The diag-
nostic performance of this biomarker was further confirmed in 2
external validation cohorts (Xi’an cohort, AUC: 0.8839; 95% CI:
0.8336–0.9342; Beijing cohort, AUC: 0.9018; 95% CI: 0.8597–
0.9439). Moreover, EV-derived GClnc1 robustly distinguished
early-stage GC from precancerous lesions (chronic atrophic
gastritis and intestinal metaplasia) and GC with negative tradi-
tional gastrointestinal biomarkers (CEA, CA72-4, and CA19-9).
The low levels of this biomarker in postsurgery and other
gastrointestinal tumor plasma samples indicated its GC speci-
ficity. CONCLUSIONS: EV-derived GClnc1 serves as a circulating
biomarker for the early detection of GC, thus providing opportu-
nities for curative surgery and improved survival outcomes.
Keywords: Extracellular Vesicle; Early Detection; lncRNA
GClnc1; Diagnostic Biomarker; Gastric Cancer.
Gastric cancer (GC) is the fifth most common cause of
cancer and the third leading cause of cancer-
associated mortality in the world.1
These high mortality
rates are primarily attributable to the fact that most patients
with GC are diagnosed when the disease is already relatively
advanced.2,3
Indeed, the detection of early-stage GC (EGC)
while tumors are still eligible for surgical resection can
improve patient survival rates to up to 70%, as compared with
the relatively dismal 20% survival rates observed for in-
dividuals with advanced-stage GC.4,5
In China and other coun-
tries with high rates of GC, routine mass screening currently
relies on a combination of endoscopy and photofluorog-
raphy.6,7
However, this screening strategy is often criticized for
the poor patient compliance and the risk of potential compli-
cations, strongly emphasizing the necessity to develop alter-
native biomarkers for the early detection of this malignancy.
Liquid biopsy approaches hold great promise as a means
of diagnosing a variety of cancers and enabling the routine
monitoring of patients undergoing treatment.8–11
Currently,
the primary targets for liquid biopsy focus on extracellular
vesicles (EVs), circulating tumor cells (CTCs), and circulating
tumor DNA (ctDNA). Due to the extreme rarity and unreliable
detection, CTCs and ctDNA fail to serve as diagnostic bio-
markers and are commonly used for monitoring disease
progression.12–17
EVs, however, are highly stable and are
released by almost all cells, offering potential insight into the
heterogeneous biological changes associated with the tumor
microenvironment such that they are an attractive source of
diagnostic biomarkers for human malignancies.18,19
Numerous studies have underscored the potential clinical
significance of the high concentrations of long noncoding
RNAs (LRs) within circulating EVs, and several EV-derived LR
candidates have been established as potential diagnostic
*Authors share co-first authorship; §
Authors share co-senior authorship.
Abbreviations used in this paper: AGC, advanced-stage gastric cancer;
AUC, area under the curve; CAG, chronic atrophic gastritis; CI, confidence
interval; CRC, colorectal cancer; CTC, circulating tumor cells; ctDNA,
circulating tumor DNA; EGC, early-stage gastric cancer; EV, extracellular
vesicle; GC, gastric cancer; GEJ, gastroesophageal junction; HCC, he-
patocellular carcinoma; HD, healthy donor; IM, intestinal metaplasia; LR,
long noncoding RNA; PDAC, pancreatic ductal adenocarcinoma; qPCR,
quantitative reverse-transcription polymerase chain reaction.
Most current article
© 2023 by the AGA Institute.
0016-5085/$36.00
https://doi.org/10.1053/j.gastro.2023.02.044
Gastroenterology 2023;165:402–413
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2. biomarkers.20–22
The LR profiles of EVs are distinct from
those of tissues and circulating cells, providing a distinct
range of biological insights.23
Prior studies have suggested
the functionally oncogenic role of lncRNA GClnc1, which is
capable of promoting tumor progression.24,25
Because LR
expression is generally stable in tissues, blood, stool, and
other bodily fluids, they have emerged as promising candi-
dates for developing liquid biopsy biomarkers in human
cancers.26,27
However, although LR have been previously
explored in the diagnosis of GC, to the best of our knowledge,
no systematic studies have yet focused on identifying EV-
derived biomarkers for the detection of EGC in patients.28–33
Given the challenges for the detection of EGC, we used a
3-phase approach to perform a genome-wide analysis to
comprehensively identify a blood-based EV-derived LR signa-
ture for the detection of EGC in patients After a systematic
biomarker discovery, we ultimately affirmed the utility of EV-
derived lncRNA GClnc1 as a GC-specific biomarker, offering
particular benefit to individuals with EGC and GC whose test
results were negative for traditional gastrointestinal biomarkers
with multicentric validation, and finally established a novel
liquid biopsy signature for the detection of EGC in patients.
Methods
Study Design and Patients
The present study used a 3-phase design. In the discovery
phase, transcriptomic profiling of 10 paired American Joint
Committee on Cancer stage I tumor samples and corresponding
normal mucosae samples was systematically analyzed for the
identification of a clinically translatable LR that detects EGC. The
criteria of specimen selection during this phase were confined to
intestinal type of adenocarcinoma located mainly in the antrum/
body of the stomach to controlling for confounding factors. In the
validation phase, the criteria of specimen selection were extended
to all subtypes of GC to provide general validation. Plasma sam-
ples from multiple clinical cohorts were used to validate the
diagnostic performance of biomarkers selected in discovery phase.
Quantitative reverse-transcription polymerase chain reaction
(qPCR) was used to evaluate the expression of LR candidates in
plasma samples from EGC and controls. In the supplemental
phase, serial plasma samples were used to assess the stability of
this blood-based LR signature in clinical settings. In addition,
plasma samples from GC patients preoperatively and post-
operatively and from patients with other gastrointestinal tumors
were used to identify the GC-specific role of this biomarker.
During the discovery phase, tissue samples from 10 patients
with EGC and plasma samples from 184 participants (92 pa-
tients with EGC and 92 healthy donors [HDs]) were collected
from the Air Force 986th
Hospital (Xi’an, China) between 2019
and 2020. During the validation phase, a training cohort
including 242 patients with GC and 312 controls from the Air
Force 986th
Hospital (Xi’an, China) from 2018–2021, and 2
external validation cohorts (Xi’an cohort: 224 patients with GC
and 205 controls from Xijing Hospital from 2019–2022; Beijing
cohort: 261 patients with GC and 243 controls from the Chinese
People’s Liberation Army General Hospital from 2019–2022)
were enrolled. During the supplemental phase, samples from
69 patients with GC, 133 with hepatocellular carcinoma (HCC),
126 with pancreatic ductal adenocarcinoma (PDAC), and 142
with colorectal cancers (CRC) were collected from Xijing Hos-
pital (Xi’an, China) from 2012–2022. The overall workflow of
this study is summarized in Figure 1. Enrolled patient charac-
teristics are compiled in Table 1 and Supplementary Table 1.
The study was conducted in accordance with Declaration of
Helsinki. Diagnostic accuracy (Standards for Reporting of Diag-
nostic Accuracy Studies, STARD) guidelines were used to
conduct this study, which was performed in accordance with the
International Ethical Guidelines for Biomedical Research
Involving Human Subjects (Council for International Organiza-
tions of Medical Sciences, CIOMS) and the Reporting Recom-
mendations for Tumor Marker Prognostic Studies guidelines. All
patients provided informed written consent. This study was
approved by the Institutional Ethics Committees and Review
Board of the Air Force 986th Hospital, Xijing Hospital, and the
Chinese People’s Liberation Army General Hospital, respectively.
Plasma Sample Collection
Blood samples from each participant were collected in the
morning (between 7 and 12 AM) of the day before surgery with
ethylene diamine tetraacetic acid-coated tubes, and centrifuged
for plasma stratification within 2 hours of collection at room
temperature (3000g, 30 minutes). Each plasma sample was
labeled with unique number and stored at -80
C.
Isolation and Characterization of EVs
For each participant, 1 mL of plasma samples was used to
isolate EVs with an exoRNeasy Serum/Plasma Kit (Qiagen)
WHAT YOU NEED TO KNOW
BACKGROUND AND CONTEXT
Diagnosing patients with early-stage gastric cancer (EGC)
is challenging due to the lack of screening strategies,
hence, there is a clear unmet clinical need to develop
biomarkers for the early detection of this malignancy.
NEW FINDINGS
Through genome-wide transcriptomic profiling, we
identified and developed a blood-based extracellular
vesicle (EV)–derived lncRNA signature that offers value
as a robust, stable biomarker with a high level of
accuracy when used to detect EGC and distinguish
EGC from precancerous lesions in multicentric validation
analyses, and has the potential for use a noninvasive
assay for population screening.
LIMITATIONS
Although our cohorts included multicentric patient
populations, the sample sizes were modest; therefore,
future prospective studies with larger patient
populations will be needed.
CLINICAL RESEARCH RELEVANCE
Our EV-derived lncRNA signature has the potential to
transform clinical practice by allowing noninvasive and
timely detection of EGC in patients.
BASIC RESEARCH RELEVANCE
This EV-derived lncRNA may shed new light on tumor
cells regulating the tumor environment.
August 2023 Early Detection of Gastric Cancer 403
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3. according to the manufacturer’s instructions as follows: (1) the
centrifuged plasma was passed through a 0.20-mm membrane
filter to exclude particles 0.2 mm; (2) the samples were mixed
with binding buffer of equal volume, loaded onto the exoEasy
spin column, and spun for 1 minute at 500g; (3) 10 mL XWP was
added and spun 5 minutes at 5000g to wash the column and
remove residual buffer; (4) the harvested EVs were eluted with
400 mL of XE elution buffer; (5) the eluate volume was reduced to
50 mL and the buffer was exchanged with phosphate buffer sa-
line; and (6) the samples were ultrafiltered with Amicon Ultra-0.5
Centrifugal Filter 10 kDa (Merck Millipore, Germany). For EV
characterization, see the Supplementary Methods.
Quantitative Reverse-Transcription Polymerase
Chain Reaction
QIAzol (Qiagen, Germany) was used to harvest RNA from EVs
by lysing on the column, and total RNA was eluted and purified.
For each sample, 30 ng of RNA were prepared for generating
complementary DNA using an MMLV kit (Takara, Japan). All
qPCR analyses were performed as follows: 95
C for 5 minutes;
40 cycles of 95
C for 10 second, and 60
C for 30 seconds. The
primers of LR candidates are presented in Supplementary
Table 6, and were normalized using the 2-DDCt
method.
Serum Biomarker Analyses
Levels of the traditional gastrointestinal biomarkers CEA,
CA72-4, and CA19-9 were analyzed using Elecsys-
electrochemical Immune Assays (Roche, Switzerland). The
cutoff values of each biomarker were 5 ng/mL, 5.3 U/mL, and
27 U/mL, respectively.
Statistical Analysis
SPSS 18.0 (IBM) and GraphPad Prism 8.0 (GraphPad) were
used to analyze data, which are reported as means (standard
deviation). R software (Version 4.2.2) with “rms” package was
used to draw calibration curves. Transcriptomic profiling raw
read counts were converted to TPM values to scale all com-
parable variates and normalized across all samples. Variates
with frequencies of 25% (ie, expressed in 25% of the entire
samples) were omitted, and remaining markers were used for
subsequent statistical analyses. The Mann-Whitney U test was
used to assess differential expression of LRs in the tumor and
normal mucosae samples. LRs with false discovery rate 0.05
and fold change 2.0 were retained and intersected with dif-
ferential RNA-sequencing profiles based on Genotype-Tissue
Expression data sets. Diagnostic efficiency was assessed using
receiver operating characteristic (ROC) curves, areas under
Discovery phase
(Tissue)
Expression analysis of 5 LR candidates in six public datasets
(GSE26595, GSE33743, GSE23739, GSE70880, GSE28700, GSE93147)
Identification of 5 top LR candidates up-regulated in EGC
(GClnc1, CDC6, GCMA, RMRP, MT1JP)
A genome-wide tissue-based transcriptomic profiling
AJCC stage I tumor samples vs matched normal mucosae samples (n=10 pairs)
Identification of 3 LR candidates relevant to EGC
(GClnc1, GCMA, MT1JP)
1 LR candidate biomarker detectable in circulating EVs
(GClnc1)
Validation phase
(Blood)
Supplemental phase
(Blood)
Training cohort from Xi’an (n=554)
GC (EGC and AGC) vs controls (precancerous lesion and HD)
External validation cohorts
from Xi’an (n=429) and Beijing (n=504)
GC (EGC and AGC) vs controls (precancerous lesion and HD)
Validation of LR biomarker in subgroups of GC patients
EGC vs AGC, Diffuse vs Intestinal, GEJ vs non-GEJ
Validation of LR biomarker in pre post operative serum specimens
Validation of LR biomarker in different gastrointestinal cancers
GC vs HCC vs PDAC vs CRC
Stability validation of LR biomarker in circulating EVs in clinical use
Figure 1. Flow diagram corresponding to patient inclusion in this study. Overall, these analyses included a discovery phase, a
validation phase, and a supplemental phase. Circulating EVs were isolated from patient plasma samples, with qPCR being
used to detect lncRNA GClnc1 within these EV samples. AJCC, American Joint Committee on Cancer.
404 Guo et al Gastroenterology Vol. 165, Iss. 2
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4. Table 1.Clinicopathologic Data of Patients in 3-Step Phases
Characteristics
Discovery phase
Validation phase
Supplemental phase
Training cohort Xi’an cohort Beijing cohort
GC Controls GC Controls GC Controls GC Controls GC
Total 92 92 242 312 224 205 261 243 69
Gender
Male (%) 65 (70.7) 56 (60.9) 165 (68.2) 204 (65.4) 144 (64.3) 126 (61.5) 188 (72.0) 150 (61.7) 42 (60.9)
Female (%) 27 (29.3) 36 (39.1) 77 (31.8) 108 (34.6) 80 (35.7) 79 (38.5) 73 (28.0) 93 (38.3) 27 (39.1)
Age
Median (range) 52 (34–78) 56 (41–79) 56 (31–84) 51 (29–79) 59 (27–80) 49 (32–84) 61 (30–86) 43 (26–83) 54 (37–79)
Tumor location
GEJ (%) 25 (27.2) – 48 (19.8) – 50 (22.3) – 60 (23.0) – 12 (17.4)
Non-GEJ (%) 67 (72.8) – 194 (80.2) – 174 (77.7) – 201 (77.0) – 57 (82.6)
Differentiation
High (%) 14 (15.2) – 46 (19.0) – 50 (22.3) – 66 (25.3) – 8 (11.6)
Middle (%) 29 (31.5) – 67 (27.7) – 44 (19.6) – 38 (14.6) – 19 (27.5)
Low/middle-low (%) 49 (53.3) – 129 (53.3) – 130 (58.1) – 157 (60.1) – 42 (60.9)
Lauren’s type
Intestinal (%) 71 (77.2) – 188 (77.7) – 172 (76.8) – 192 (73.6) – 51 (73.9)
Diffuse (%) 16 (17.4) – 36 (14.9) – 40 (17.9) – 40 (15.3) – 13 (18.8)
Mixed (%) 5 (5.4) – 18 (7.4) – 12 (5.3) – 29 (11.1) – 5 (7.3)
Controls
CAG – – – 67 (21.5) – 54 (26.3) – 37 (15.2) –
IM – – – 86 (27.6) – 43 (21.0) – 64 (26.3) –
HD – 92 (100.0) – 159 (50.9) – 108 (52.7) – 142 (58.4) –
Tumor stage
T1 (%) 9 (9.8) – 29 (11.9) – 26 (11.6) – 30 (11.5) – 4 (5.8)
T2 (%) 21 (22.8) – 36 (14.9) – 33 (14.7) – 52 (19.9) – 14 (20.3)
T3 (%) 37 (40.2) – 105 (43.4) – 96 (42.9) – 126 (48.3) – 30 (43.5)
T4 (%) 25 (27.2) – 72 (29.8) – 69 (30.8) – 53 (20.3) – 21 (30.4)
Lymph node stage –
N0 (%) 15 (16.3) – 21 (8.7) – 24 (10.7) – 18 (6.9) – 9 (13.0)
N1 (%) 27 (29.4) – 59 (24.4) – 56 (25.0) – 68 (26.1) – 17 (24.6)
N2 (%) 35 (38.0) – 121 (50.0) – 106 (47.3) – 136 (52.1) – 29 (42.0)
N3 (%) 15 (15.3) – 51 (21.1) – 38 (17.0) – 39 (14.9) 14 (20.4)
Clinical stage
EGC (stage I/II, %) 44 (47.8) – 112 (46.3) – 93 (41.5) – 108 (41.4) – 26 (37.7)
AGC (stage III/IV, %) 48 (52.2) – 130 (53.7) – 131 (58.5) – 153 (58.6) – 43 (62.3)
Controls included CAG, IM, and HD.
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5. curve (AUC), sensitivity, specificity, false-negative rate
(1-sensitivity), and false-positive rate (1-specificity). The Youden
index (Youden index ¼ specificity þ sensitivity 1) was used to
determine the cutoff value in the training cohort. Pearson corre-
lation analyses were used to examine relationships between vari-
ables. Differences between groups were compared using Student t
tests, whereas clinical variables were compared using Pearson c2
tests. A 2-tailed P 0.05 was the significance threshold.
Results
Genome-Wide Transcriptomic Profiling Identifies
a 5-LR Tissue-Based Signature for the Detection
of EGC in Patients
The first goal of this study was to identify a systematic and
comprehensive LR signature for the detection of EGC. To this
end, we conducted a genome-wide transcriptomic profiling
using tissue samples from patients with American Joint
Committee on Cancer stage I GC (n ¼ 10 pairs;, tumor and
matched normal mucosae samples; Supplementary Table 1).
Each sample was reliably detected with nearly 14,000 an-
notated genes, including messenger RNAs, LRs, and pseudo-
genes. Numbers of detected RNA species did not significantly
differ between tumor and normal mucosae samples
(Figure 2A). A 3-dimensional data scatterplot indicated that the
transcriptomic profiles of tumor samples generally differed
from those of normal mucosae samples (Figure 2B). In total,
362 LRs were identified that were differentially expressed in
tumor samples compared with normal mucosae samples using
Mann-Whitney U test (false discovery rate 0.05; fold change
2). Unsupervised hierarchical clustering revealed a clear
separation of tumor and normal mucosae samples (Figure 2C).
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
analysis revealed that differentially expressed LRs were
enriched for some pathways involved in cancer, such as gastric
cancer pathways, mammalian target of rapamycin (mTOR)
signaling pathway, and P53 signaling (Figure 2D). The top 5 up-
regulated LR candidates are presented in Figure 2E.
To enhance the overall specificity of the discovered bio-
markers, our 5 top LR candidates were further overlapped
with 6 public profiling datasets (GSE26595, GSE33743,
GSE23739, GSE70880, GSE28700, and GSE93147), and sub-
sequently identified 3 LRs (GClnc1, GCMA, and MT1JP) that
represented up-regulated markers in patients with EGC
(Supplementary Figure 1). Finally, a multivariate logistic
regression model established the association of the 3-LR
panel in patients with EGC, which showed a diagnostic AUC
value of 0.9257 (95% confidence interval [CI], 0.9011–
0.9502; P 0.0001), with a sensitivity of 86.31% and spec-
ificity of 83.52% for the detection of EGC in patients
(Figure 2F). These results suggested that this 3-LR panel has
potential as a biomarker for the detection of EGC.
Establishment and Validation of Diagnostic
Performance of an EV-Derived LR Panel in
Independent Cohorts of Patients With EGC
Because the primary goal of this study was to develop a
liquid biopsy–based assay for the detection of EGC, the
validation of diagnostic performance of this 3-LR panel in
actual clinical settings was warranted. To this end, we first
investigated whether the tissue-based markers identified
during the discovery phase could be detected in cell-free
plasma. Disappointingly, the 3 LR candidates in GC plasma
samples did not significantly differ from HD ones, and
showed as low levels as the HDs (Supplementary Figure 2A).
Because several studies have attempted to investigate the
diagnostic value of LRs in circulating EVs,26,31,34
we further
focused on whether the 3 LR candidates could be detected
in circulating EVs.
Circulating EVs were collected from patient plasma sam-
ples and analyzed via transmission electron microscope,
revealing the expected rounded, cup-shaped, double
membrane–enclosed characteristics consistent with EVs
(Supplementary Figure 3A). A nanoparticle tracking analysis
revealed these EVs to primarily be 50–200 nm in diameter
(Supplementary Figure 3B), and Western immunoblotting
confirmed that they were positive for the EV biomarkers CD9,
CD63, and TSG101, whereas these markers were absent on
circulating mononuclear cells (Supplementary Figure 3C).
Conversely, the cell-enriched biomarker calnexin was absent
in EVs and present in mononuclear cells, thus affirming the
highly purified nature of isolated EVs. After quality control of
EVs, the 3 LR candidates were assessed using qPCR with
isolated circulating EVs. Only 1 of the tissue-based markers
(GClnc1) can be detected in circulating EVs from GC samples
compared with HD (Supplementary Figure 2B). Furthermore,
circulating free lncRNA GClnc1 was primarily encapsulated
within EVs given that levels of this lncRNA were significantly
lower when analyzing EV-depleted plasma (P 0.0001;
Supplementary Figure 2C). Additionally, the EV-derived
lncRNA GClnc1 was significantly correlated with total circu-
lating free levels of lncRNA GClnc1 (r ¼ 0.7246; P 0.0001;
Supplementary Figure 2D). These results strongly highlighted
that this tissue-based marker lncRNA GClnc1 can be detected
in plasma samples with EV-encapsulated forms.
Next, to establish the diagnostic potential of EV-derived
lncRNA GClnc1 for EGC, its performance in plasma samples
obtained from 1 independent clinical cohort was examined
using qPCR assays. Using receiver operating characteristic
analysis, this biomarker was trained in a cohort of patients
(112 with EGC and 159 HDs). Significantly increased levels
of EV-derived lncRNA GClnc1 and the 3 tested gastrointes-
tinal tumor-related biomarkers (CEA, CA72-4, and CA19-9)
were all evident in samples from patients with EGC as
compared with HD (P 0.05; Figure 3A–3D). However, EV-
derived lncRNA GClnc1 demonstrated an excellent diag-
nostic performance in patients with EGC with an AUC value
of 0.9369 (95% CI, 0.9073–0.9664; P 0.0001), and a
corresponding sensitivity of 87.42% and specificity of
84.82%, which was significantly higher than those of CEA
(AUC, 0.6831; sensitivity, 83.65%; specificity, 49.11%),
CA72-4 (AUC, 0.6204; sensitivity, 74.84%; specificity,
50.00%), and CA19-9 (AUC, 0.6066; sensitivity, 59.12%;
specificity, 58.04%; Figure 3E–3H and Supplementary
Table 2). The optimal cutoff value of EV-derived lncRNA
GClnc1 was determined when the Youden Index (Youden
Index ¼ specificity þ sensitivity - 1) was the highest. Thus,
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6. -
4
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Tumor
Normal
A B
C D
E F
Tumor Normal Mucosae
3 2 1 0 1 2 3
Log (TPM+1)
Low High
Probeset-ID Tumor Normal fold change Gene Symbol P-value Type
TC0200008378.hg.1 8.01 2.34 3.42 GClnc1 0.0000 lncRNA
TC0200008386.hg.1 8.23 2.62 3.14 CDC6 0.0001 lncRNA
TC0200008485.hg.1 7.86 3.02 2.60 GCMA 0.0002 lncRNA
TC0200009349.hg.1 7.13 2.93 2.43 RMRP 0.0008 lncRNA
TC0200009918.hg.1 6.72 3.04 2.21 MT1JP 0.0015 lncRNA
40
50
60
70
80
Figure 2. Genome-wide tissue-based transcriptomic profiling results. (A) Distribution of number of detected genes per sample among
tumor and NMS. (B) Three-dimensional scatter plot generated from t-SNE analysis for the differential LR profiles of GC tumor from those
of NMS. (C) Heatmap of unsupervised hierarchical clustering of the LRs differentially expressed between tumor and NMS. Each column
represents an individual sample, and each row represents an LR. The scale represents the expression beta values. (D) KEGG pathway
enrichment analysis for differentially expressed LRs. (E) The top 5 up-regulated LRs in tumor compared with NMS. (F) ROC curve
analysis with the selected 5 candidate LRs for discriminating stage I GC tumors. KEGG, Kyoto Encyclopedia of Genes and Genomes;
NMS, normal mucosae specimen; ROC, receiver operating characteristics; t-SNE, t-distributed stochastic neighbor embedding.
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7. the optimal cutoff value was 4.400 with the highest Youden
Index of 0.7224. The distribution of positive/negative re-
sults is listed in Supplementary Table 3. The calibration
curves showed satisfactory consistency of Hosmer-
Lemeshow goodness-of-fit tests (Supplementary Figure 4).
In view of the encouraging results of EV-derived lncRNA
GClnc1 for the detection of EGC in the training cohort, the
robustness and accuracy of this biomarker were further
assessed in 2 external independent validation cohorts from
Xi’an (93 with EGC and 108 HDs) and Beijing (108 with EGC
and 142 HDs). These validation efforts confirmed the earlier
findings and yielded an AUC value of 0.8839 (95% CI, 0.8336–
0.9342; P 0.0001) with a sensitivity of 89.81% and a
specificity of 78.49% in the Xi’an cohort, and an AUC value of
0.9018 (95% CI, 0.8597–0.9439; P 0.0001) with a sensi-
tivity of 89.44% and a specificity of 80.56% in the Beijing
cohort (Figure 4A and 4E and Supplementary Figure 5A and
5E). The 3 traditional gastrointestinal biomarkers still
revealed low AUC values, sensitivity, and specificity in both
external validation cohorts (Figure 4A and Supplementary
Figure 5B–5D). The calibration curves remained stable for
the 2 external validation cohorts (Supplementary Figure 6A
and 6E), and the distribution of positive/negative results are
listed in Supplementary Table 3. Together, the genome-wide
transcriptomic profiling approach was indeed robust because
it identified the biomarker GClnc1 that were successfully
trained and validated in plasma samples from independent
cohorts of patients with EGC, hence highlighting their trans-
lational potential in the clinic for the detection of this malig-
nancy in early stages.
EV-Derived lncRNA GClnc1 Reliably
Distinguishes EGC From Precancerous Lesions
To our knowledge, 1 important feature of an effective
biomarker is to distinguish early-stage cancer from pre-
cancerous lesions. Therefore, we wondered whether EV-
derived lncRNA GClnc1 could distinguish EGC from gastric
precancerous lesions (chronic atrophic gastritis [CAG] and
intestinal metaplasia [IM]). It was exciting to observe that, in
the Xi’an validation cohort, this biomarker retained an
excellent diagnostic performance with AUC values of 0.8523
(95% CI, 0.7921–0.9126; sensitivity, 85.19%; specificity,
73.12%) and 0.8401 (95% CI, 0.7718–0.9084; sensitivity,
85.71%; specificity, 74.19%) when differentiating EGC from
CAG and IM (Figure 4B and 4C). In line with these results, in
the Beijing validation cohort, this biomarker revealed better
diagnostic performance with AUC values of 0.8702 (95% CI,
0.8131–0.9274; sensitivity, 86.49%; specificity, 79.63%) and
0.8510 (95% CI, 0.7907–0.9113; sensitivity, 84.38%; speci-
ficity, 80.56%), when differentiating EGC from CAG and IM,
and when compared with the 3 traditional gastrointestinal
biomarkers (Figure 4F and 4G). In comparison, the traditional
gastrointestinal biomarkers CEA, CA72-4, and CA19-9 all
failed to distinguish EGC from precancerous lesions with
satisfactory sensitivity and specificity in both external vali-
dation cohorts (Figure 4B and 4C and 4E and 4F). The cali-
bration curves retained satisfactory consistency for the 2
external validation cohorts (Supplementary Figure 6B–6D and
6F–6H). Taken together, the evaluation of diagnostic perfor-
mance of EV-derived lncRNA GClnc1 in distinguishing EGC
from precancerous lesions was indeed crucial because this
A B C D
E F G H
Figure 3. Analysis of the expression and diagnostic utility of EV-derived lncRNA GClnc1 and traditional gastrointestinal bio-
markers in EGC in training cohort. (A–D) The expression of EV-derived lncRNA GClnc1 (A), CEA (B), CA72-4 (C), and CA19-9
(D) were analyzed in EGC (n ¼ 112), CAG (n ¼ 67), IM (n ¼ 86), HD-
(n ¼ 93), and HDþ
(n ¼ 66) patients. (E–H) ROC curves
corresponding to EV-derived lncRNA GClnc1, CEA, CA72-4, and CA19-9 when used to differentiate between EGC and HD (E),
CAG (F), IM (G), and controls (H). Controls include CAG, IM, and HD patients. Data are means (SD). HD-
, healthy donor with
negative Helicobacter pylori infection; HDþ
, healthy donor with positive H pylori infection; NS, not significant.
408 Guo et al Gastroenterology Vol. 165, Iss. 2
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8. biomarker may represent the evident molecular marker
changes from benign diseases to malignant, hence, providing a
blood-based biomarker for the detection of this malignancy
once the benign disease turns to malignancy.
EV-Derived lncRNA GClnc1 Identifies Early-
Stage (Stage I/II) and Advanced Stage (Stage
III/IV) GC in Patients
An important approach for improving prognosis in pa-
tients with GC is to attain an earlier diagnosis. Hence,
whether EV-derived lncRNA GClnc1 performed better in
subgroups of GC based on tumor stage is crucial. Because
we have identified the diagnostic value in EGC, we further
wondered whether this biomarker can be applied to
advanced-stage GC (AGC; stage III/IV). In the Xi’an valida-
tion cohort, we evaluated the distribution of EV-derived
lncRNA GClnc1 in EGC vs AGC relative to controls. It was
reassuring to observe that this biomarker successfully
identified patients with both EGC and AGC (Supplementary
Figure 7A–7D). Furthermore, this biomarker revealed
excellent diagnostic performance for the detection of AGC
with an AUC value of 0.8910 (95% CI, 0.8500–0.9320;
sensitivity, 85.78%; specificity, 83.21%), which was supe-
rior to that of the 3 other tested gastrointestinal tumor-
related biomarkers (CEA: AUC, 0.8060; sensitivity,
77.29%; specificity, 76.34%; CA72-4: AUC, 0.7012; sensi-
tivity, 82.61%; specificity, 59.54%; and CA19-9: AUC,
0.6926; sensitivity, 71.98%; specificity, 56.49%; Figure 5A–
5D and Supplementary Table 4). In line with these results, in
the Beijing validation cohort, this biomarker retained its
excellent diagnostic performance for the detection of AGC
with an AUC of 0.8978 (95% CI, 0.8642–0.9314; sensitivity,
90.53%; specificity, 76.47%), and was better than CEA
(AUC, 0.6616; sensitivity, 76.54%; specificity, 48.37%),
CA72-4 (AUC, 0.6642; sensitivity, 79.01%; specificity,
51.63%), and CA19-9 (AUC, 0.5823; sensitivity, 65.43%;
specificity, 48.37%; Figure 5E–5H and Supplementary
Figure 7E–7H). The calibration curves remained stable for
the 2 external validation cohorts (Supplementary Figure 8).
Because specimens selected in the discovery phase were
mainly of the intestinal type and located in the antrum/body
of the stomach, we further investigated the performance of
GClnc1 in subgroups of GC based on Lauren’s type and tu-
mor location in the validation phase. In the Xi’an validation
cohort, GClnc1 showed no significant differences among
patients with intestinal and disuse type, and those with
gastroesophageal junction (GEJ) and non-GEJ tumors (P ¼
0.903 and P ¼ 0.633, respectively; Supplementary Figure 9A
and 9B). In addition, this biomarker revealed excellent
diagnostic performance for the detection of EGC and AGC in
the subgroups of patients with diffuse type (EGC: AUC,
0.8821; sensitivity, 84.80%; specificity, 82.61%; AGC: AUC,
0.9242; sensitivity, 93.63%; specificity, 86.21%) or GEJ tu-
mors (EGC: AUC, 0.8904; sensitivity, 84.80%; specificity,
85.00%; AGC: AUC, 0.9102; sensitivity, 86.27%; specificity,
80.00%; Supplementary Figure 9C–9F). In line with these
results, in the Beijing validation cohort, GClnc1 retained
high diagnostic performance in the subgroups of patients
with diffuse type (EGC: AUC, 0.8786; sensitivity, 88.48%;
specificity, 76.92%; AGC: AUC, 0.8980; sensitivity, 86.83%;
specificity, 79.07%) or GEJ tumors (EGC: AUC, 0.9114;
A B C D
E F G H
Figure 4. Examination of the diagnostic utility of EV-derived lncRNA GClnc1 and traditional gastrointestinal biomarkers in EGC
in the 2 external validation cohorts. (A–D) ROC curves corresponding to EV-derived lncRNA GClnc1, CEA, CA72-4, and CA19-
9 when used to differentiate between EGC and HD (A), CAG (B), IM (C), and controls (D) in the Xi’an validation cohort. (E–H)
ROC curves corresponding to EV-derived lncRNA GClnc1, CEA, CA72-4, and CA19-9 when used to differentiate between
EGC and HD (E), CAG (F), IM (G), and controls (H) in the Beijing validation cohort. Controls include CAG, IM, and HD patients.
August 2023 Early Detection of Gastric Cancer 409
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9. sensitivity, 91.77%; specificity, 84.00%; AGC: AUC, 0.8825;
sensitivity, 86.83%; specificity, 77.14%; Supplementary
Figure 9G–9L). The calibration curves remained stable for
the 2 external validation cohorts (Supplementary
Figure 10). Collectively, these results suggest that EV-
derived lncRNA GClnc1 is robust in detection of EGC in
patients as readily as AGC, independent of Lauren’s type or
tumor location, highlighting the potential clinical trans-
formation because early detection is surely crucial to
improve prognosis in patients with GC.
EV-Derived lncRNA GClnc1 Effectively Identifies
GC Patients With Negative Traditional
Gastrointestinal Tumor-Related Biomarkers
It is reported that 20%–40% of patients with GC have
negative traditional gastrointestinal biomarkers including
CEA, CA72-4, and CA19-9 due to Lewis a-
b-
genotype,35
hence
identifying these subgroups of GC patients is warranted. With
respect to patients with GC with negative gastrointestinal
biomarkers in the Xi’an validation cohort, EV-derived lncRNA
GClnc1 revealed high diagnostic efficiency when discrimi-
nating between these tumors and both precancerous lesions
and HD samples (Supplementary Figure 11A–11D and
Supplementary Table 5). In line with these results, in the
Beijing validation cohort, this biomarker retained its excel-
lent diagnostic performance in identifying patients with
negative gastrointestinal biomarkers (Supplementary
Figure 11E–11H and Supplementary Table 5). Furthermore,
the calibration curves still remained stable for the 2 external
validation cohorts (Supplementary Figure 12). Together,
these results shed new light that EV-derived lncRNA GClnc1 is
robust in identifying subgroups of patients with GC who have
negative gastrointestinal biomarkers.
EV-Derived lncRNA GClnc1 Provides Stable
Application in Clinical Settings
Given the evidence supporting the diagnostic perfor-
mance of EV-derived lncRNA GClnc1 as a biomarker suited
to the detection of EGC, we next sought to further probe its
feasibility for use in a clinical setting. These experiments
were designed to address 3 specific concerns: (1) whether
EV-derived lncRNA GClnc1 levels decrease after surgical
removal of GC as the expression of circulating biomarkers is
that tumor cells constantly shed cellular cargo into the
systemic circulation; (2) whether EV-derived lncRNA
GClnc1 remains sufficiently stable for routine clinical
testing; and (3) whether EV-derived lncRNA GClnc1 is a
specific biomarker for GC. To address these concerns, paired
plasma samples of presurgery and postsurgery (n ¼ 69),
serial plasma samples (n ¼ 69), and plasma samples of
gastrointestinal cancers other than GC (HCC ¼ 133, PDAC ¼
126, and CRC ¼ 142) were collected.
First, relative to preoperative samples, postoperative
plasma EV-derived lncRNA GClnc1 were significantly
decreased in expression (P 0.0001; Supplementary
Figure 13A). Second, with respect to stability, EV-derived
lncRNA GClnc1 were approximately unchanged by sample
treatment with RNase (P ¼ 0.2968; Supplementary
Figure 13B), prolonged room temperature storage (P ¼
0.3231; Supplementary Figure 13C), or repeated freeze/thaw
A B C D
E F G H
Figure 5. Examination of the diagnostic utility of EV-derived lncRNA GClnc1 and traditional gastrointestinal biomarkers in AGC
in the 2 external validation cohorts. (A–D) ROC curves corresponding to EV-derived lncRNA GClnc1, CEA, CA72-4, and CA19-
9 when used to differentiate between AGC and HD (A), CAG (B), IM (C), and controls (D) in the Xi’an validation cohort. (E–H)
ROC curves corresponding to EV-derived lncRNA GClnc1, CEA, CA72-4, and CA19-9 when used to differentiate between
AGC and HD (E), CAG (F), IM (G), and controls (H) in the Beijing validation cohort. Controls include CAG, IM, and HD patients.
410 Guo et al Gastroenterology Vol. 165, Iss. 2
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10. cycling (P ¼ 0.4028; Supplementary Figure 13D). These
findings reveal the robust clinical applicability of efforts to
detect EV-derived lncRNA GClnc1 when diagnosing GC.
Last, the levels of EV-derived lncRNA GClnc1 were
measured in other gastrointestinal tumors including HCC,
PDAC, and CRC. Relative to GC samples, HCC, PDAC, and CRC
showed significantly lower levels of EV-derived lncRNA
GClnc1 (Supplementary Figure 13E). However, no obvious
changes were detected among HCC, PDAC, and CRC samples
and when compared with HDs (Supplementary Figure 13E).
Together, these results reaffirmed the potential diagnostic
utility of EV-derived lncRNA GClnc1 as a tool for the
detection of GC, particularly in patients with EGC, and pre-
liminarily confirmed the GC-specific diagnostic role of this
biomarker.
Discussion
In contrast to the decreasing incidence of GC, the mor-
tality of this malignancy has been steadily increasing. What
is worse, patients with GC are often diagnosed at advanced
stage, which may lead to missing chances of radical removal
of tumor, thus highlighting the significance for early detec-
tion of GC to improve patients’ survival outcomes. Although
endoscopy is currently the gold standard for the diagnosis
of GC, it has several major disadvantages, such as high
uncomfortableness and costs, and potential risk of compli-
cations. In view of these limitations, identifying novel
diagnostic approaches that facilitate early detection of GC is
urgently warranted. This study is a significant step toward
this direction, where we systematically and comprehen-
sively developed an EV-derived LR biomarker that is highly
robust in the early detection of GC in patients. In addition,
this biomarker could reliably and effectively distinguish GC
from precancerous lesions and those with negative tradi-
tional gastrointestinal-related biomarkers, suggesting the
warning role of molecular marker changes during precan-
cerous lesions turning into malignancy. Furthermore, evi-
dence that the levels of EV-derived lncRNA GCln1 were
significantly reduced after surgery and showed low levels in
other gastrointestinal tumors highlighted that EV-derived
lncRNA GClnc1 was indeed secreted by GC tumors.
Currently, several attempts of noninvasive tests for the
detection of GC have been investigated. Saliva-based tests
have been reported to show promising diagnostic perfor-
mance because several noncoding RNA can be detected
either in saliva or saliva-derived EVs. However, due to the
unstable quality control of saliva and the disturbance of oral
micro-organism, the efficacy of these tests is limited. Several
other blood-based tests have also been used for detecting
GC, yet no serum biomarkers have been successfully
translated into clinical practice. Liquid biopsy–based bio-
markers including ctDNA, CTCs, and EVs have been
demonstrated as tools for patient detection and treatment
monitoring given that they can be routinely monitored in a
largely noninvasive manner.36–39
However, both CTCs and
ctDNA are extremely rare, fragile, and heterogeneous, thus
restricting their practical value. In addition, the membrane
affinity method has already been widely applied for EV
isolation with commercial kits, suggesting the wide maturity
and generalizability of this method.40–42
Thus, EV is a po-
tential ideal target for liquid biopsy efforts aimed at early
detection of tumor.43–45
This study is a significant step in
this direction, where we developed a blood-based EV-
derived LR signature that can robustly identify patients with
EGC. Furthermore, this blood-based assay revealed suffi-
cient stability to meet clinical needs. Accordingly, these
findings highlight that this novel biomarker has the poten-
tial to complement current clinical practice and serve as a
noninvasive assay for detecting EGC.
Several studies have demonstrated the oncogenic role of
EV-derived lncRNA GClnc1 in different cancers. The high
expression of this LR was significantly correlated with poor
progression-free and overall survival in bladder cancer, and
promoted proliferation and metastasis via activation of
oncogenic protein MYC.46
In vitro and in vivo studies indi-
cated that lncRNA GClnc1 contributed tumorigenesis in os-
teosarcoma through ubiquitination degradation of p53
signaling.47
In addition, studies have proved the epigenetic
regulating role of lncRNA GClnc1 in regulating the tran-
scription of various target genes, hence, this LR is mecha-
nistically, functionally, and clinically oncogenic in GC.24,48
Specifically, GClnc1 acted as a scaffold to recruit WDR5
(histone methyltransferase) and KAT2A (histone acetyl-
transferase), thus modifying the transcription of targeted
genes and consequently altering GC cell biology.49
However,
the potential role of lncRNA GCln1 in circulating EVs has
been poorly understood. This study, to our knowledge, is the
first one to evaluate the diagnostic role of EV-derived
lncRNA GClnc1 for EGC with multicentric validation based
on genome-wide profiling. This study is a significant step,
where we further elucidated the additional biological role of
lncRNA GClnc1 in circulating EVs. Hence, the excellent
diagnostic performance of this biomarker was based on the
important oncogenic role in tumorigenesis of GC.
This study has several major strengths: (1) the use of
this noninvasive biomarker offers patients with GC the op-
portunity to undergo curative treatment while their disease
is still eligible for surgical resection, thereby improving their
potential survival outcomes; (2) this biomarker offers
certain advantages over other gastrointestinal biomarkers
currently used to detect GC, allowing for the detection of
EGC even in patients negative for traditional gastrointestinal
biomarkers while accurately differentiating between GC and
precancerous lesions; and (3) this study enrolled multiple
cohorts from the North and West of China to systematically
confirm the diagnostic value of EV-derived lncRNA GClnc1.
As such, EV-derived lncRNA GClnc1 may hold value as a
blood-based GC biomarker for patient early detection in the
clinic.
This study is subject to several limitations. First, although
EV-derived lncRNA GClnc1 offers great potential as a nonin-
vasive biomarker, to offer maximal clinical value before the
disease can be detected via endoscopic examination, this
study only incorporated retrospective validation cohorts,
highlighting the need for further prospective validation in
large asymptomatic patient populations in longitudinal ana-
lyses. Second, the sample size of specimens used for
August 2023 Early Detection of Gastric Cancer 411
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11. transcriptomic profiling is relatively small. A larger sample
size may result in different LR selection and allow analysis of
association of lncRNA GClnc1 with clinical characteristics of
the patients. Third, although lncRNA GClnc1 has been re-
ported to serve as an oncogenic mediator in the progression
of GC, no functional studies have firmly established its
oncogenic role in circulating EVs. Accordingly, further cell-
and animal-based studies of the mechanistic importance of
this EV cargo are warranted. Last, we have only compared the
diagnostic performance of EV-derived lncRNA GClnc1 and 3
gastrointestinal tumor-related biomarkers while several
other biomarkers including pepsinogens or PG1/PG2 ratio
have been ignored. Owing to the retrospective nature of the
present analyses, these factors were not available, high-
lighting a potential source of bias that should be taken into
consideration when interpreting these results.
In summary, a series of systematic approaches were
herein used to identify and develop a novel blood-based EV-
derived biomarker for the detection of EGC in patients. This
biomarker was successfully validated in 3 independent co-
horts and revealed robust diagnostic performance in dis-
tinguishing EGC from precancerous lesions and nondisease
controls. We envisage that EV-derived lncRNA GClnc1 may
aid in transforming the screening of patients with GC and
subsequently reduce the mortality rates through further
validation in prospective, larger studies.
Supplementary Material
Note: To access the supplementary material accompanying
this article, visit the online version of Gastroenterology at
www.gastrojournal.org, and at https://doi.org/10.1053/
j.gastro.2023.02.044.
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Received October 8, 2022. Accepted February 20, 2023.
Correspondence
Address correspondence to: Gang Ji, MD, Department of Digestive Surgery,
Xijing Hospital, Fourth Military Medical University, 169 Changle Road, Xi’an,
China. e-mail: jigang@fmmu.edu.cn; or Xiaohui Lv, MD, Department of
Gynecology and Obstetrics, Xijing Hospital, Fourth Military Medical University,
169 Changle Road, Xi’an, China. e-mail: lvxiaohuiss@126.com; or Bo Wei, MD,
Department of General Surgery, Chinese People’s Liberation Army General
Hospital, 28 Fuxing Road, Beijing, China. e-mail: weibo@vip163.com.
CRediT Authorship Contributions
Xin Guo, MD (Conceptualization: Lead; Project administration: Lead; Writing –
original draft: Lead).
Yunhua Peng, MD (Data curation: Equal; Project administration: Equal;
Software: Equal).
Qiying Song, MD (Resources: Equal; Software: Equal).
Jiangpeng Wei, MD (Investigation: Lead; Methodology: Lead).
Xinxin Wang, MD (Resources: Lead; Supervision: Lead).
Yi Ru, MD (Software: Equal; Validation: Equal).
Shenhui Xu, MD (Visualization: Lead).
Xin Cheng, MD (Visualization: Equal).
Xiaohua Li, MD (Data curation: Lead; Formal analysis: Lead).
Di Wu, MD (Resources: Equal).
Lubin Chen, MD (Supervision: Equal).
Bo Wei, MD (Resources: Equal; Supervision: Lead).
Xiaohui Lv, MD (Supervision: Equal; Validation: Equal; Visualization: Equal;
Writing – original draft: Equal). Gang Ji, MD (Supervision: Lead).
Conflicts of interest
The authors disclose no conflicts.
Funding
This study was funded by the National Natural Science Foundation of China
grant/award no. 81903073 (to Xin Guo), no. 82002740 (to Xiaohui Lv), and no.
82073192 (to Bo Wei), the Natural Science Foundation of Shaanxi Province
grant/award no. 2023-YBSF-481, Shaanxi Provincial Youth Science and
Technology Nova Cultivation Project no. 2021KJXX-25 (to Xin Guo), and the
National Basic Research Program of China no. 2019YFB1311505 (to Bo Wei).
Data Availability
Data are available on reasonable request to the corresponding author.
August 2023 Early Detection of Gastric Cancer 413
GI
CANCER

13. Supplementary Methods
EV Characterization
After isolation, the size distributions and morphologic
characteristics of these EVs were examined via transmission
electron microscopy (Thermo Scientific) and nanoparticle
tracking analysis (NanoSight NS300). Western immunoblotting
was additionally used to confirm the presence of EV surface
markers by using radio immunoprecipitation assay buffer to
lyse these samples, followed by their separation via sodium
dodecyl sulfate-polyacrylamide gel electrophoresis, transfer
onto polyvinylidene fluoride membranes, and probing
overnight with anti-CD9 (1:2000, CST), anti-CD63 (1:1000,
CST), anti-TSG101 (1:2000, CST), and anti-calnexin (1:1000,
CST) at 4
C.
Supplementary Figure 1. Expression of the 5 top LR candidates in public profiling datasets. The expression levels of the 5 LR
candidates (GClnc1, CDC6, GCMA, RMRP, and MT1JP) were assessed in 6 public GEO datasets including (A) GSE26595, (B)
GSE33743, (C) GSE23749, (D) GSE70880, (E) GSE28700, and (F) GSE93147. GEO, Gene Expression Omnibus. *P 0.05. **P
0.01. ***P 0.001. ****P 0.0001.
413.e1 Guo et al Gastroenterology Vol. 165, Iss. 2

14. Supplementary Figure 2. Expression of LR candidates in cell-free plasma samples and circulating EVs. The expression of 3
LR candidates in (A) cell-free plasma samples and (B) circulating EVs from patients with GC (n ¼ 92) and controls (n ¼ 92) in
discovery phase. (C) GClnc1 levels were assessed in both EVs and EV-depleted plasma isolated from patients with GC. (D)
Analysis of the correlation between plasma EV-derived lncRNA GClnc1 expression and total circulating lncRNA GClnc1
expression in patients with GC. Controls, healthy donors.
Supplementary Figure 3. EV isolation and characterization. (A) Electron microscopy revealed that isolated EVs exhibited the
expected characteristic vesicular characteristics. Scale bar: 100 nm. (B) A NanoSight particle-tracking analysis was used to
analyze EV size distributions, revealing them to range from 50–200 nm in size. (C) The EV marker proteins TSG101, CD63, and
CD9 were detected using Western immunoblotting with the negative control proteins calnexin. PBMC, peripheral blood
mononuclear cell.
August 2023 Early Detection of Gastric Cancer 413.e2

15. Supplementary Figure 4. Calibration curves of EV-derived lncRNA GClnc1 in the training cohort. (A–D) Calibration curves
corresponding to GClnc1 when used to differentiate between EGC and HD (A), CAG (B), IM (C), and controls (D) in the training
cohort. Controls include CAG, IM, and HD patients.
413.e3 Guo et al Gastroenterology Vol. 165, Iss. 2

16. Supplementary Figure 5. Analysis of the expression of EV-derived lncRNA GClnc1 and traditional gastrointestinal biomarkers
in EGC and controls in the 2 external validation cohorts. (A–D) The expression of GClnc1 (A), CEA (B), CA72-4 (C), and CA19-9
(D) were analyzed in EGC (n ¼ 93), CAG (n ¼ 54), IM (n ¼ 43), HD-
(n ¼ 68), and HDþ
(n ¼ 40) patients in the Xi’an validation
cohort. (E–H) The expression of GClnc1 (E), CEA (F), CA72-4 (G), and CA19-9 (H) were analyzed in EGC (n ¼ 108), CAG (n ¼
37), IM (n ¼ 64), HD-
(n ¼ 95), and HDþ
(n ¼ 47) patients in the Beijing validation cohort.
Supplementary Figure 6. Calibration curves of EV-derived lncRNA GClnc1 for patients with EGC in the 2 external validation
cohorts. (A–D) Calibration curves corresponding to GClnc1 when used to differentiate between EGC and HD (A), CAG (B), IM
(C), and controls (D) in the Xi’an validation cohort. (E–H) Calibration curves corresponding to GClnc1 when used to differentiate
between EGC and HD (E), CAG (F), IM (G), and controls (H) in the Beijing validation cohort. Controls include CAG, IM, and HD
patients.
August 2023 Early Detection of Gastric Cancer 413.e4

17. Supplementary Figure 7. Analysis of the expression of EV-derived lncRNA GClnc1 and traditional gastrointestinal biomarkers
in AGC and controls in the 2 external validation cohorts. (A–D) The expression of GClnc1 (A), CEA (B), CA72-4 (C), and CA19-9
(D) were analyzed in EGC (n ¼ 93), AGC (n ¼ 131), CAG (n ¼ 54), IM (n ¼ 43), and HD (n ¼ 108) patients in the Xi’an validation
cohort. (E–H) The expression of GClnc1 (E), CEA (F), CA72-4 (G), and CA19-9 (H) were analyzed in EGC (n ¼ 108), AGC (n ¼
153), CAG (n ¼ 37), IM (n ¼ 64), and HD (n ¼ 142) patients in the Beijing validation cohort.
Supplementary Figure 8. Calibration curves of EV-derived lncRNA GClnc1 for patients with AGC in the 2 external validation
cohorts. (A–D) Calibration curves corresponding to GClnc1 when used to differentiate between AGC and HD (A), CAG (B), IM
(C), and controls (D) in the Xi’an validation cohort. (E–H) Calibration curves corresponding to GClnc1 when used to differentiate
between AGC and HD (E), CAG (F), IM (G), and controls (H) in the Beijing validation cohort. Controls include CAG, IM, and HD
patients.
413.e5 Guo et al Gastroenterology Vol. 165, Iss. 2

18. Supplementary Figure 9. Analysis of the expression and diagnostic utility of EV-derived lncRNA GClnc1 in subgroups of
patients with GC in the 2 external validation cohorts. (A and B) The expression of GClnc1 were analyzed in subgroups of GC
according to Lauren type (A) and tumor location (B) in the Xi’an validation cohort. (C and D) ROC curves corresponding to
GClnc1 when used to differentiate between diffuse type EGC and controls (C), and diffuse type AGC and controls (D) in the
Xi’an validation cohort. (E and F) ROC curves corresponding to GClnc1 when used to differentiate between GEJ EGC and
controls (E), and GEJ AGC and controls (F) in the Xi’an validation cohort. (G and H) The expression of GClnc1 was analyzed in
subgroups of GC according to Lauren type (G) and tumor location (H) in the Beijing validation cohort. (I and J) ROC curves
corresponding to GClnc1 when used to differentiate between diffuse type EGC and controls (I), and diffuse type AGC and
controls (J) in the Beijing validation cohort. (K and L) ROC curves corresponding to when used to differentiate between GEJ
EGC and controls (K), and GEJ AGC and controls (L) in the Beijing validation cohort. Controls include CAG, IM, and HD
patients. Data are means (SD). Diffuse type indicated as diffuse type and mixed type. SD, standard deviation.
August 2023 Early Detection of Gastric Cancer 413.e6

19. Supplementary Figure 10. Calibration curves of EV-derived lncRNA GClnc1 for subgroup patients with GC in the 2 external
validation cohorts. (A and B) Calibration curves corresponding to GClnc1 between diffuse type EGC and controls (A), and
diffuse type AGC and controls (B) in the Xi’an validation cohort. (C and D) Calibration curves corresponding to GClnc1 when
used to differentiate between GEJ EGC and controls (C), and GEJ AGC and controls (D) in the Xi’an validation cohort. (E and F)
Calibration curves corresponding to GClnc1 when used to differentiate between diffuse type EGC and controls (E), and diffuse
type AGC and controls (F) in the Beijing validation cohort. (G and H) Calibration curves corresponding to when used to
differentiate between GEJ EGC and controls (G), and GEJ AGC and controls (H) in the Beijing validation cohort. Controls
include CAG, IM, and HD patients. Diffuse type indicated as diffuse type and mixed type.
Supplementary Figure 11. Examination of the diagnostic utility of EV-derived lncRNA GClnc1 in patients with GC with
negative gastrointestinal biomarkers in the 2 external validation cohorts. (A–D) ROC curves corresponding to GClnc1 when
used to differentiate between GC (negative) and HD (A), CAG (B), IM (C), and controls (D) in the Xi’an validation cohort. (E–H)
ROC curves corresponding to GClnc1 when used to differentiate between GC (negative) and HD (E), CAG (F), IM (G), and
controls (H) in the Beijing validation cohort. Controls include CAG, IM, and HD patients. GC (negative), GC with negative
gastrointestinal biomarkers.
413.e7 Guo et al Gastroenterology Vol. 165, Iss. 2

20. Supplementary Figure 12. Calibration curves of EV-derived lncRNA GClnc1 for patients with GC with negative gastroin-
testinal biomarkers in the 2 external validation cohorts. (A–D) Calibration curves corresponding to GClnc1 when used to
differentiate between GC with negative biomarkers and HD (A), CAG (B), IM (C), and controls (D) in the Xi’an validation cohort.
(E–H) Calibration curves corresponding to GClnc1 when used to differentiate between GC with negative biomarkers and HD
(E), CAG (F), IM (G), and controls (H) in the Beijing validation cohort. Controls include CAG, IM, and HD patients.
Supplementary Figure 13. The utility of EV-derived lncRNA GClnc1 when used as a serum biomarker for the detection of GC
in a clinical setting in the supplemental phase. (A) Changes in GClnc1 levels in patients with GC were compared prior to
surgery and 5 days postsurgery (n ¼ 69). (B–D) The stability of GClnc1 encapsulated in EVs was examined in patients with GC
(n ¼ 50) when EVs were (B) treated with or without RNase A (5 mg/mL) for 30 min, (C) incubated at room temperature for an
extended period of time, or (D) repeatedly frozen and thawed. (E) Levels of GClnc1 were measured in 4 gastrointestinal cancers
including GC, HCC, PDAC, and CRC and HD controls. Data are means (SD).
August 2023 Early Detection of Gastric Cancer 413.e8

21. Supplementary Table 1.Clinicopathologic Characteristics of Patients for Genome-Wide Transcriptomic Profiling
Patient ID Age Gender Tumor location Pathologic status
Pathologic
differentiation Lauren type TNM stage
Y3036757 56 Male Antrum Adenocarcinoma Middle Intestinal T1N0M0
Y2927251 64 Male Antrum Adenocarcinoma Middle-low Intestinal T1N0M0
Y3316069 65 Female Body Adenocarcinoma Low Intestinal T1N0M0
Y3285038 68 Male Body Adenocarcinoma Low Intestinal T1N0M0
Y3262582 55 Female Antrum Adenocarcinoma Low Intestinal T1N0M0
G167198 59 Female Antrum Adenocarcinoma Middle-low Intestinal T1N0M0
Y2892543 61 Male Body Adenocarcinoma Middle-low Intestinal T1N0M0
K0302909 62 Male Body Adenocarcinoma Low Intestinal T1N0M0
Y2698894 62 Female Antrum Adenocarcinoma Middle-low Intestinal T1N0M0
G102930 64 Female Antrum Adenocarcinoma Middle-low Intestinal T1N0M0
ID, identification; TNM, tumor node metastasis.
413.e9 Guo et al Gastroenterology Vol. 165, Iss. 2

22. Supplementary Table 2.Results of ROC Curves of EV-Derived lncRNA GClnc1 and Traditional Gastrointestinal Biomarkers for
Detecting EGC
Variables AUC 95% CI Sensitivity (%) Specificity (%)
Training cohort
EGC vs HD
GClnc1 0.9369 0.9073–0.9664 87.42 84.82
CEA 0.6831 0.6153–0.7508 83.65 49.11
CA72-4 0.6204 0.5480–0.6928 74.84 50.00
CA19-9 0.6066 0.5382–0.6750 59.12 58.04
EGC vs CAG
GClnc1 0.9272 0.8916–0.9629 91.04 80.36
CEA 0.6051 0.5224–0.6879 79.10 49.11
CA72-4 0.5665 0.4828–0.6502 64.18 50.00
CA19-9 0.5628 0.4763–0.6494 56.72 51.79
EGC vs IM
GClnc1 0.9101 0.8714–0.9489 93.02 74.11
CEA 0.6673 0.5911–0.7435 58.14 71.43
CA72-4 0.6485 0.5721–0.7249 73.26 56.25
CA19-9 0.6712 0.5966–0.7458 69.77 59.82
EGC vs controls
GClnc1 0.9274 0.8969–0.9579 90.71 80.36
CEA 0.6620 0.5981–0.7258 81.73 49.11
CA72-4 0.6166 0.5490–0.6841 66.67 56.25
CA19-9 0.6150 0.5536–0.6749 64.10 53.57
Xi’an cohort
EGC vs HD
GClnc1 0.8839 0.8336–0.9342 89.81 78.49
CEA 0.5632 0.4795–0.6469 86.11 40.86
CA72-4 0.6120 0.5291–0.6949 71.30 53.76
CA19-9 0.5739 0.4946–0.6532 57.41 54.84
EGC vs CAG
GClnc1 0.8523 0.7921–0.9126 85.19 73.12
CEA 0.5893 0.4972–0.6813 78.57 44.09
CA72-4 0.5973 0.5047–0.6898 76.79 47.31
CA19-9 0.5689 0.4733–0.6645 64.29 50.54
EGC vs IM
GClnc1 0.8401 0.7718–0.9084 85.71 74.19
CEA 0.5748 0.4726–0.6770 62.79 49.46
CA72-4 0.6930 0.6019–0.7842 65.12 63.44
CA19-9 0.6178 0.5222–0.7133 72.09 50.54
EGC vs controls
GClnc1 0.8665 0.8160–0.9170 88.24 74.19
CEA 0.5726 0.4970–0.6483 71.50 46.24
CA72-4 0.6248 0.5507–0.6989 71.50 53.76
CA19-9 0.5817 0.5111–0.6523 63.77 50.54
Beijing cohort
EGC vs HD
GClnc1 0.9018 0.8597–0.9439 89.44 80.56
CEA 0.7703 0.7103–0.8303 83.80 56.48
CA72-4 0.6330 0.5611–0.7050 78.87 50.00
CA19-9 0.5397 0.4659–0.6135 56.34 50.93
EGC vs CAG
GClnc1 0.8702 0.8131–0.9274 86.49 79.63
CEA 0.7212 0.6392–0.8033 75.68 59.26
CA72-4 0.7321 0.6457–0.8185 81.08 56.48
CA19-9 0.6453 0.5440–0.7465 72.97 58.33
EGC vs IM
GClnc1 0.8510 0.7907–0.9113 84.38 80.56
CEA 0.7572 0.6862–0.8281 82.81 58.33
CA72-4 0.6162 0.5320–0.7005 75.00 49.07
CA19-9 0.5466 0.4588–0.6344 67.19 45.37
August 2023 Early Detection of Gastric Cancer 413.e10

23. Supplementary Table 2.Continued
Variables AUC 95% CI Sensitivity (%) Specificity (%)
EGC vs controls
GClnc1 0.8836 0.8397–0.9276 86.83 80.56
CEA 0.7594 0.7006–0.8181 79.01 59.26
CA72-4 0.6437 0.5771–0.7102 79.42 49.07
CA19-9 0.5576 0.4899–0.6253 65.43 45.37
AUC, area under curve; CI, confidence interval; controls, CAG þ IM þ HD.
Supplementary Table 3.Distribution of Positive/Negative
Results of EV-Derived lncRNA
GClnc1 in Training and 2 External
Validation Cohorts
Levels of EV-derived
lncRNA GClnc1
Negative
(4.400, %)
Positive
(4.400, %)
Training cohort
EGC 15 (6.2) 97 (40.1)
AGC 6 (2.5) 124 (51.2)
Controls
(CAG þ IM þ HD)
283 (90.7) 29 (9.3)
Xi’an cohort
EGC 13 (5.8) 80 (35.7)
AGC 10 (4.5) 121 (54.0)
Controls
(CAG þ IM þ HD)
176 (87.5) 28 (12.5)
Beijing cohort
EGC 16 (6.1) 92 (35.2)
AGC 13 (5.0) 140 (53.7)
Controls
(CAG þ IM þ HD)
219 (90.1) 24 (9.9)
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24. Supplementary Table 4.Results of ROC Curves of EV-Derived lncRNA GClnc1 and Traditional Gastrointestinal Biomarkers for
Detecting AGC
Variables AUC 95% CI Sensitivity (%) Specificity (%)
Xi’an cohort
AGC vs HD
GClnc1 0.9018 0.8603–0.9433 89.81 83.21
CEA 0.8077 0.7493–0.8660 83.33 75.57
CA72-4 0.6962 0.6275–0.7650 71.30 65.65
CA19-9 0.6841 0.6176–0.7505 70.37 56.49
AGC vs CAG
GClnc1 0.8899 0.8434–0.9365 87.04 79.39
CEA 0.8131 0.7529–0.8733 82.14 74.81
CA72-4 0.6720 0.5932–0.7509 78.57 59.54
CA19-9 0.6825 0.6017–0.7634 73.21 50.38
AGC vs IM
GClnc1 0.8645 0.8069–0.9221 85.71 79.39
CEA 0.7927 0.7239–0.8614 81.40 71.76
CA72-4 0.7515 0.6769–0.8260 74.42 66.41
CA19-9 0.7273 0.6494–0.8053 79.07 56.49
AGC vs controls
GClnc1 0.8910 0.8500–0.9320 85.78 83.21
CEA 0.8060 0.7510–0.8610 77.29 76.34
CA72-4 0.7012 0.6387–0.7637 82.61 59.54
CA19-9 0.6926 0.6343–0.7509 71.98 56.49
Beijing cohort
AGC vs HD
GClnc1 0.9139 0.8817–0.9461 90.85 80.39
CEA 0.6926 0.6328–0.7524 70.42 58.82
CA72-4 0.6542 0.5918–0.7166 78.87 51.63
CA19-9 0.5783 0.5133–0.6434 56.34 54.90
AGC vs CAG
GClnc1 0.8924 0.8463–0.9386 86.49 80.39
CEA 0.5819 0.4955–0.6682 75.68 42.48
CA72-4 0.7506 0.6718–0.8294 64.86 73.20
CA19-9 0.6658 0.5719–0.7597 67.57 65.36
AGC vs IM
GClnc1 0.8651 0.8161–0.9141 87.50 73.86
CEA 0.6356 0.5603–0.7108 75.00 48.05
CA72-4 0.6365 0.5601–0.7130 71.88 52.29
CA19-9 0.5717 0.4911–0.6524 67.19 48.37
AGC vs controls
GClnc1 0.8978 0.8642–0.9314 90.53 76.47
CEA 0.6616 0.6041–0.7190 76.54 48.37
CA72-4 0.6642 0.6073–0.7212 79.01 51.63
CA19-9 0.5823 0.5231–0.6415 65.43 48.37
August 2023 Early Detection of Gastric Cancer 413.e12

25. Supplementary Table 5.Results of ROC Curves of EV-Derived lncRNA GClnc1 for Detecting GC With Negative
Gastrointestinal Biomarkers
Variables AUC 95% CI Sensitivity (%) Specificity (%)
Xi’an cohort
GC (negative) vs HD GClnc1 0.9397 0.9106–0.9689 90.22 87.40
GC (negative) vs CAG GClnc1 0.9248 0.8910–0.9586 91.77 81.10
GC (negative) vs IM GClnc1 0.9228 0.8819–0.9565 88.60 83.46
GC (negative) vs controls GClnc1 0.9397 0.9106–0.9689 90.22 87.40
Beijing cohort
GC (negative) vs HD GClnc1 0.9234 0.8983–0.9485 90.46 80.86
GC (negative) vs CAG GClnc1 0.9017 0.8695–0.9338 82.91 80.86
GC (negative) vs IM GClnc1 0.8911 0.8576–0.9245 82.90 80.86
GC (negative) vs controls GClnc1 0.9107 0.8845–0.9368 86.97 80.86
GC (negative): gastric cancer with negative gastrointestinal biomarkers including CEA, CA72-4, and CA19-9.
Supplementary Table 6.Primers of LR Candidates for qPCR
LR Forward (5’-3’) Reverse (5’-3’)
GClnc1 TGGGGTAACTTAGCAGTTTCAAT GGCAAGCAGTAATCTTACATGCAC
CDC6 CTCTGAAATGAACACTACCCAC CCATCAGCCTTCGGACA
GCMA TTCCAAAGTGTGTGCTCAGAGG TCGTTAGGAAGCATTCAGACCG
RMRP ACTCCAAAGTCCGCCAAGA TGC GTAACTAGAGGGAGCTGAC
MT1JP CTCCTGCAAGAAGAGCT TGCAGCAAATGGCTCAGTA
413.e13 Guo et al Gastroenterology Vol. 165, Iss. 2