MOLECULAR MEDICINE REPORTS
Abstract. The present study aimed to classify gastric cancer
(GC) into subtypes and to screen the subtype‑specic genes,
their targeted microRNAs (miRNAs) and enriched pathways
to explore the putative mechanism of each GC subtypes.
The GSE13861 data set was downloaded from the Gene
Expression Omnibus and used to screen differential expres‑
sion genes (DEGs) in GC samples based on the detection of
imbalanced differential signal algorithm. The specic genes
in each subtype were identied with the cut‑off criterion of
U>0.04, pathway enrichment analysis was performed and the
subtype‑specific subpaths of miRNA‑target pathway were
determined. A total of 1,263 DEGs were identified in the
primary gastric adenocarcinoma (PGD) samples, which were
subsequently divided into four subtypes, according to the hier‑
archy cluster analysis. Identication of the subpaths of each
subtype indicated that the subpath related to subtype 1 was
miRNA (miR)‑202/calcium voltage‑gated channel subunit α1
(CACNA1E)/type II diabetes mellitus. The nuclear factor‑κB
signaling pathway was the most signicantly specic pathway
and subpath identified for subtype 2, which was regulated
by miR‑338‑targeted suppression of C‑C motif chemokine
ligand 21 (CCL21). For subtype 3, signicant related pathways
included ubiquitin‑mediated proteolysis and proteasome,
and the important subpath was miR‑146B/proteasome 26S
subunit, non‑ATPase 3 (PSMD3)/proteasome; focal adhesion
was the signicant pathway indicated for subtype 4, and the
subpaths were miR‑34A/vinculin (VCL)/focal adhesion and
mi R‑3 4C/VCL/focal adhesion. In addition, Helicobacter pylori
infection was higher in GC subtype 1 than in other subtypes.
Specic genes, such as CACNA 1E, CCL21, PSMD3 and VCL,
may be used as potential feature genes to identify different
subtypes of GC, and their associated subpaths may partially
explain the pathogenetic mechanism of each GC subtype.
Gastric cancer (GC) is a common malignant neoplasm that
is derived from gastric epithelial dysplasia and intestinal
metaplasia (1); GC is the third leading cause of malignant
neoplasm‑related mortalities worldwide, with ~989,600 new
cases and ~738,000 mortalities in 2008 (2). GC has high
heterogeneity with histopathologic and epidemiologic char‑
acteristics (3), and can be divided into several classications,
including proximal nondiffuse, diffuse and distal nondiffuse
GC (4). A previous study identied DNA content heterogeneity
in 12 (33%) patients with primary GC that were examined (5);
however, DNA content heterogeneity was independent of
histological heterogeneity. The incidence and mortality rates
of GC are declining worldwide, owing to the notable progress
made in diagnosis, prevention and treatment; however, as the
rate of relapse is high and we do not completely understand the
pathogenesis, additional long‑term studies are required if GC
is to be cured.
A number of previous studies have attempted to iden‑
tify new potential therapeutic targets of GC. For example,
the upregulated expression of the transcription factor
hepatocyte nuclear factor 4α by AMP‑activated protein
kinase signaling is a main event in GC development (6).
Vestigial‑like family member 4 (VGLL4) was reported
to be a promising therapeutic target for GC inhibition, as
VGLL4 competes with yes‑associated protein (YAP) for
binding with TEA domain transcription factor 1, and YAP
is involved in overgrowth and tumor formation of multiple
cancers (7). microRNA (miRNA) miR‑329 was also previ‑
ously revealed to reduce the expression of T‑lymphoma
invasion and metastasis‑inducing 1, and may be a potential
therapeutic target for suppression of GC cell invasion and
proliferation (8). In addition, miR‑7 expression was reported
to be signicantly reduced in highly metastatic GC cells,
and insulin‑like growth factor‑1 receptor (IGF1R) oncogene
overexpression, as a direct target of miR‑7, may attenuate
the function of miR‑7 in GC cells (9); thus, miR‑7/IGF1R
may be a therapeutic approach to inhibit GC metastasis.
Furthermore, several signaling pathways have been revealed
to be associated with GC. For example, the inactivation
Identifying heterogeneous subtypes of gastric cancer and
subtype‑specic subpaths of microRNA‑target pathways
YUANHANG LI1, WEIJUN BAI1 and XU ZHANG2
1Medical Department; 2Radiotherapy Department, Cancer Hospital of
China Medical University, Shenyang, Liaoning 110042, P.R. China
Received December 12, 2016; Accepted November 15, 2017
Correspondence to: Dr Yuanhang Li, Medical Department,
Cancer Hospital of China Medical University, 44 Xiaoheyan Road,
Dadong, Shenyang, Liaoning 110042, P.R. China
Key word s: gastric cancer, differentially expressed gene, specific
gene, microRNA, subpath
LI et al: IDENTIFY ING SUBTY PE‑SPECI FIC SUBPATHS OF GC
of transforming growth factor‑β and hedgehog signaling
pathways have been reported as useful therapeutic pathways
to prevent GC progression, by inhibiting the migration
and invasion of GC cells (10,11). However, these previous
reports did not identify the GC subtypes of the patients
in their study and, thus, the subtype‑specific subpaths
of miRNAs, their targeted genes and related pathways
The present study reanalyzed the data set GSE13861
that was published by Cho et al (12). That study generated
and analyzed microarray data from 65 patients with GC to
identify feature genes related to relapse and subsequently
predicted the relapse of patients who received gastrectomy.
Conversely, the present study aimed to screen specic genes
and to use those genes to divide the patients into different
subtypes; as well as to identify the subtype‑specic subpaths
of miRNA‑target pathway for comprehensive understanding
the mechanisms of GC through bioinformatical prediction
Materials and methods
Data access and data preprocessing. The microarray raw
data were downloaded from Gene Expression Omnibus
(https://www.ncbi.nlm.nih.gov/geo; accession number
GSE13861) database, which were based on the Illumina
HumanWG‑6 v3.0 Expression Beadchip platform. A total of 90
samples were obtained, comprising 65 samples from primary
gastric adenocarcinoma (PGD) tissues, 6 samples from
gastrointestinal stromal tumor (GIST) tissues and 19 samples
from normal gastric tissues. The probes were transformed to
corresponding gene symbols and merged according to the
application programing of Python. Mean expression values of
the same gene were obtained and all expression values were
revised using Z‑score (13).
Differentially expressed genes (DEGs) analysis. Owing to
high heterogeneity, the changes of expression in some impor‑
tant genes that may induce GC only occur in heterogeneous
populations. Thus, to capture those important genes within
a group, a new method, detection of imbalanced differential
signal (DIDS), was adopted to identify subgroup DEGs in
heterogeneous populations (14). Based on the DIDS algorithm,
the normal reference interval of each gene expression value
was stipulated between the maximum and minimum value,
and they were respectively calculated as the corresponding
mean values in the normal group ±1.96 x standard deviation.
Subsequently, random disturbance was conducted and multiple
testing adjustments were performed by Benjamini‑Hochberg
method, which revised the raw P‑value into the false discovery
rate (FDR) (15). FDR <0.01 was used as the cut‑off criterion
to lter DEGs.
Hierarchical clustering. Cluster and TreeView are programs
that offer computational and graphical analyses of the results
from DNA microarray data (16). In the present study, hier‑
archical clustering analysis was performed among the 90
PGD samples, and the processing of expression prole data,
including filtering the data and data normalization, were
conducted by Cluster software (17‑19). Based on the clusters
of genes similarly expressed, the results of hierarchical clus‑
tering were used to identify the different GC subtypes and
were displayed as a heatmap (Version 1.2.0; ht tp://www.bioc
Identication of specic genes in each subtype. Following
identication of the subtypes of GC that were based on hier‑
archical clustering analysis, the specic gene expressions in
each subtype was examined. First, the mean expression values
of genes were distributed in each subtype. Second, to estimate
whether an identied DEG was a specic gene for a certain
subtype, the following formulas were used:
For each gene, score represented the deviation from normal
range, and score >0 indicated that the DEG was upregulated
in the PGD samples, and score <0 indicated that the DEG was
downregulated in the PGD samples. The U distribution of
genes related to GC is provided in Fig. 1. Specic genes were
identied from the DEGs with the cut‑off criterion of U >0.04,
otherwise the DEG was considered as common gene. For
example, one gene was indicated as ‘g’ and the mean expres‑
sion value of this gene in GC subtypes was indicated as ‘X1’,
‘X2’… ‘Xi’ and ‘Xm’. ‘Max’ represented the maximum mean
expression values in those GC subtypes, whereas ‘min’ repre‑
sented the minimum mean expression values among those
GC subtypes. ‘Xi’ represented the mean expression values of
one gene in subtype i, and it was evaluated if this gene was
specific to subtype i with the aforementioned formulas. If
Xi>max‑γ x U, the gene was specic to subtype i. Where γ
is the threshold value, and γ=1/m, in which m represents the
number of GC subtypes.
Pathway enrichment analysis. The Molecular Signatures
Database (MSigDB; http://software.broadinstitute
.org/gsea/msigdb/index.jsp) is a collection of annotated gene
sets used to perform gene set enrichment analysis (20). A total
of 186 Kyoto Encyclopedia of Genes and Genomes (KEGG)
pathways and their related gene sets data from MSigDB were
downloaded. By combing the pathway data, specic genes
were identified in PGD samples, and pathway enrichment
analysis was performed on specific genes of each subtype
using Fisher's exact test. Significant pathway terms were
selected with the threshold of P<0.05.
Identication of subtype‑specic subpaths of miRNA‑target
pa thw ay. Signicant drugs to diseases were predicted using
causal inference as previously described (21); this method
was used to construct CauseNet for the identification of
subtype‑specic subpath of miRNA‑target pathways. A layered
network from miRNAs to specic pathways is presented in
Fig. 2. Relationships between miRNAs, their targets genes,
specic genes, target‑related pathways and specic KEGG
pathways were calculated. If a miRNA regulated several
specic genes that were enriched in several signicant KEGG
pathways, those subpaths of miRNA‑target pathway may be
important subpaths for explaining the development of different
subtypes of GC.
MOLECULAR MEDICINE REPORTS 3
To identify those important subpaths, the following
algorithms were used:
Where weight1 is the weight of miRNA of each subpath,
P|G*| is the whole number of specic genes and P|G| is the
number of specic genes regulated by the miRNA. Weight2
represents the weight of a target gene, in which P|G*| is the
total KEGG pathways number in which all targets participated
and P|G| is the KEGG pathways number that was this target
participated. Weight3 is the weight of a pathway, in which
P|G*| is total gene number enriched in this pathway and P|G|
represents the number of specic genes. In addition, the scores
of all the subpaths in each subtype were repeatedly calculated
following the course of 1,000 times random disturbance,
and the subpath with the max score in a certain subtype was
chosen as the specic subpath of this subtype with the cut‑off
criteria of P<0.05. Furthermore, subpath analysis among the
specic genes was conducted to identify the subtype‑specic
regulation relationship of miRNA‑target pathway.
Helicobacter pylori in fection rate in each GC subt ype. H. pylori
infection is a known risk factor for GC progression (22);
however, whether H. pylori infection is a subtype‑specific
pathway for our predicted GC subtype is unknown. Thus, a
series of bioinformatics methods and clinic information of GC
samples with H. pylori infection were combined to calculate the
H. pylori rate in each of the predicted GC subtypes. The iden‑
tied specic genes in each subtype were used as characters to
build a neural network (NN) model using the neuralnet package
in R (Version 1.5.0; https://cran.r‑project.org/web/pack‑
ages/NeuralNetTools/index.html). The input layer was 24
neurons (also designated 24 gene feature) and the output layer
was 1 neuron, which was used to decide which subtype a
certain neuron belonged. The hidden layer was set as two layers
that included eight and ve neurons, respectively. Sigmoid
neural activation function was adopted for feed‑forward
neural network and backward propagation was used for weight
optimization. The maximum number of iterations to conver‑
gence to its stationary distribution. was 1,000. In addition,
logistic regression (LR) model was performed to compare
with NN model. Through building a NN model and training
the NN with analysis data, the prediction for the four GC
subtypes may be achieved. Following forecast classication of
independent test data in The Cancer Genome Atlas (TCGA;
https://cancergenome.nih.gov/), four testing‑set subtypes
were obtained. Subsequently, 100 GC samples (including 46
H. pylori infection samples and 54 without H. pylori infec‑
tion samples) were downloaded from the PMID:24816253
data set (23). According to the clinical information regarding
H. pylori infection rate in TCGA and the distribution of
H. pylori infection samples in the four subtypes, the H. pylori
infection rate in each subtype was calculated.
DEG screening and hierarchical clustering. Based on the
aforementioned criteria, a total of 1,263 DEGs that were related
to GC were identied, including 392 downregulated genes
and 871 upregulated genes in the PGD samples. Additionally,
hierarchy cluster analysis indicated that the 1,263 DEGs could
be used to divide the 65 PGD samples into four subtypes
with correlated expression proles. The four subtypes of GC
were: i) Subtype 1 in blue with 11 samples; ii) subtype 2 in
red with 29 samples; iii) subtype 3 in pink with 13 samples;
and iv) subtype 4 in purple with 12 samples. Although three
of the normal samples were wrongly identied as subtype 1,
the other PGD, GIST and normal samples were placed among
different clusters and were classied correctly. In addition,
the results indicated that there was no heterogeneity of gene
expression within subtypes, but there was high heterogeneity
between different subtypes (Fig. 3).
Identication of specic genes in each subtype. According to
the formulas described in the Methods section, specic genes
of the four subtypes and common genes were identied. A
total of 33 specic genes were identied in subtype 1, 318 in
subtype 2, 161 in subtype 3 and 157 in subtype 4. In addition, a
total of 631 common genes were detected, which were signi‑
cantly different between the GC group and normal group, but
exhibited no notable difference within the four subtypes.
KEGG pa thway enrichment analysis. To explore the signicant
differences among the four GC subtypes at the molecular
Figure 1. U distribution of gastric cancer‑related genes. The horizontal axis
represents the gastric cancer related genes, and the vertical axis shows the U
value of the corresponding gene. Thu blue curve is the U distribution of all
Figure 2. The network model for identifying the subtype‑specic subpath of
miRNA‑target pathway in each subtype.
LI et al: IDENTIFY ING SUBTY PE‑SPECI FIC SUBPATHS OF GC
level, four subtype‑specic pathway analyses were conducted.
Five specific pathways, such as renin‑angiotensin system
and H. pylori infection, were associated with GC subtype 1
(Tab le I ); two specic pathways were identied in subtype 2,
including nuclear factor (NF)‑κB signaling pathway and tight
junction. The specic genes related to GC subtype 3 were
enriched in six specic pathways that were mainly associated
with metabolic process, such as fatty acid metabolism, protea‑
some and ubiquitin‑mediated proteolysis; the data indicated
that carbohydrate metabolism may serve an important role in
the progression of GC subtype 3. The specic genes of GC
subtype 4 were enriched in 14 specic pathways, including
phosphoinositide 3 kinase/Akt signaling pathway, focal adhe‑
sion, vascular smooth muscle contraction and cardiac muscle
Identication of subtype‑specic subpath of miRNA‑target
pathway. According to the aforementioned Methods and
criteria, specic subpaths of each subtype were identied.
Four or ve specic subpaths were identied for each subtype
(Table II). In subtype 1, ARF GTPase‑activating protein
GIT1 was indicated to be regulated by miR‑199B, miR‑122A
and miR‑199A through the H. pylori infection pathway, and
calcium voltage‑gated channel subunit α1 E (CACNA1E) was
indicated as regulated by miR‑202 through the type II diabetes
mellitus pathway. For subtype 2, protein inhibitor of acti‑
vated STAT 4 may be regulated by miR‑198, and C‑C motif
chemokine ligand 21 (CCL21) may be regulated by miR‑338
and miR‑370 by participating in NF‑κB signaling pathway; in
addition, miR‑508 may regulate VAMP‑associated protein A
through tight junction pathway. In GC subtype 3, miR‑146B
and miR‑146A were indicated to regulate proteasome 26S
subunit, non‑ATPase 3 (PSMD3) through the proteasome
pathway. Five important subpaths of subtype 3 were identi‑
fied, including miR‑429 and miR‑205 regulation of LDL
receptor‑related 1 through the Salmonella infection pathway,
and miR‑34A, miR‑34C and miR‑449 regulation of vinculin
(VCL) through the focal adhesion pathway.
H. pylori infection rate in each GC subtype. H. pylori infection
rate in each GC subtype was analyzed as aforementioned. The
NN model was a more accurate method to distinguish the four
GC subtypes compared with the LR model (Fig. 4A and B,
respectively); the NN model was therefore used to predict
the GC subtypes for all samples (Table III), and all the GC
samples were divided into the four testing‑set. Subsequently,
the four testing‑set was used to predict the subtype of the
100 GC samples in the PMID:24816253 data set. Notably, the
H. pylori infection rate in subtype 1 was higher than in other
subtypes (Table IV), indicating that there was an increased
susceptibility to H. pylori infection in subtype 1 compared
with other subtypes. This outcome was consistent with the
aforementioned analysis, which indicated that H. pylori
infection may be a specic pathway for GC subtype 1.
In the present study, a total of 1,263 DEGs in the 65 PGD
samples were identified, which allowed the samples to be
divided into four subtypes based on hierarchy cluster analysis. In
addition, a total of 33 specic genes were screened in subtype 1,
318 in subtype 2, 161 in subtype 3 and 157 in subtype 4.
The subpaths miR‑202/CACNA1E/type II diabetes mellitus,
mi R‑338/CCL21/NF‑κB signaling, miR‑146B/PSMD3/protea‑
some, miR‑34A/VCL/focal adhesion and miR‑34C/VCL/focal
adhesion were identied more than once and therefore may be
important specic subpaths of the four GC subtypes, respectively.
That H. pylori infection may serve a role in the progres‑
sion of GC is widely accepted (24). Notably, results from
the present study demonstrated that several specic genes of
subtype 1 were signicantly enriched in H. pylori infection
pathway and that the H. pylori infection rate in GC subtype 1
was higher than in other subtypes. Therefore, the present study
hypothesized that H. pylori infection was a specic pathway
for GC subtype 1.
CACNA1E encodes a Cav2. 3 R‑type voltage‑activated Ca2+
channel that is involved in gene expression regulation, cell
differentiation and cell death (25). In addition, CAC NA1E has
Figure 3. Hierarchical cluster map of DEGs. The horizontal axis indicates the
different sample types by color: Gastric cancer subtype 1, blue; subtype 2,
red; subtype 3, pink; subtype 4, pur ple; normal, light green; gastrointestinal
stromal tumors, yellow. The right vertical axis shows clusters of DEGs. Red
represents higher expression values and green represents lower expression
values. DEGs, differentially expressed genes.
MOLECULAR MEDICINE REPORTS 5
been reported to be upregulated in air pollution‑associated lung
cancer (26), and the abnormal expression of CACNA1E may be
used to predict the occurrence of cancers (27). Results from the
present study revealed that CACNA 1E may be a specic gene
of GC subtype 1, and miR‑202/CACNA1E/type II diabetes
mellitus was predicted to be an important subpath of subtype 1.
In addition, the downregulated expression of miR‑202 may
suppress GC cell proliferation (28). Furthermore, CACNA1E
expression may increase the risk of the type 2 diabetes, and
there is close correlation between the metabolic syndrome and
the development of gastric adenocarcinoma (29,30). Therefore,
it was inferred that CACNA1E, as a target of miR‑202, may be
related to GC subtype 1 by participating in the type II diabetes
mellitus related metabolic pathway.
For GC subtype 2, the results indicated that
mi R‑338‑CCL21‑NF‑κB signaling was one of the important
subpaths. C CL 21 encodes a C‑C chemokine that is mainly
presented in lymphoid tissue and serves an important role
in dendritic cell recruitment and lymphoid neogenesis (31).
In addition, NF‑κB signaling is a major link between cancer
and inflammation, which is triggered by proinf lammatory
cytokines such as CCL21 (32,33); several previous studies
have indicated that the activation of NF‑κB signaling is
Table I. Subtype‑specic pathways related to gastric cancer and common pathways of all subtypes.
Subtype KEGG pathway Counta Allb P‑valuec
Subtype 1 Renin‑angiotensin system 3 17 0.007398
Folate biosynthesis 2 10 0.014313
Type II diabetes mellitus 1 9 0.01947
Hedgehog signaling pathway 2 13 0.024601
Helicobacter pylori infection 1 8 0.03013
Subtype 2 NF‑κB signaling pathway 6 9 0.01016
Tight junction 4 5 0.015905
Subtype 3 Fatty acid metabolism 2 3 0.044476
Ribosome biogenesis in eukaryotes 4 7 0.006553
Proteasome 5 10 0.004685
Nucleotide excision repair 3 7 0.048337
Cell cycle 4 11 0.040908
Ubiquitin mediated proteolysis 6 11 0.001051
Subtype 4 PI3K/Akt signaling pathway 9 22 0.000675
Vascular smooth muscle contraction 5 10 0.004185
Alzheimer's disease 4 7 0.00597
Focal adhesion 5 11 0.006911
Cardiac muscle contraction 3 5 0.015628
Pertussis 3 5 0.015628
Hypertrophic cardiomyopathy 4 10 0.026485
Dilated cardiomyopathy 4 10 0.026485
Long‑term depression 3 6 0.028424
Porphyrin and chlorophyll metabolism 2 3 0.042385
Salmonella infection 2 3 0.042385
Glioma 2 3 0.042385
Dopaminergic synapse 3 7 0.045265
Melanoma 3 7 0.045265
aNumber of specic genes enriched in the corresponding pathways. bTotal number of differentially expressed genes. cSignicance level
determined by Fisher's exact test. KEGG, Kyoto Encyclopedia of Genes and Genomes; PI3K, phosphoinositide 3 kinase.
Figure 4. Results of test training set. (A) The predicted results of the GC
subtypes by using t he NN model. (B) The predicted re sults of the GC subtypes
by using LR model. The x axis represents real category labels, with the values
of the four GC subtypes determined as 0, 0.33, 0.66 and 1, respectively. The
y axis represents predicted category labels. GC, gastric cancer; NN, neural
network; LR, logistic regression.
LI et al: IDENTIFY ING SUBTY PE‑SPECI FIC SUBPATHS OF GC
related to GC oncogenesis (34‑36). In addition, miR‑338 was
highly associated with GC through the inhibition the GC cell
proliferation (37), which is similar with the present data. These
results suggested that miR‑338 may promote apoptosis of GC
subtype 2 cells by activating the NF‑κB signaling pathway
through targeting CCL 21.
Pathway enrichment ana lysis of the speci c gene s in subtype
3 demonstrated that most of the identied pathways were related
to carbohydrate metabolism, such as fatty acid metabolism,
ribosome biogenesis, ubiquitin‑mediated proteolysis and prot ea‑
some. Proteasome is protein complex which degrades unneeded
or damaged proteins by proteolysis and mediates protein folding.
In addition, PSMD3 was identied as a proteasome‑pathway
related gene that may be regulated by miR‑146A. Previous
studies reported that PSMD3 was highly related to the progres‑
sion of breast cancer and lung cancer (38,39). In addition, it
has been indicated that miR‑146A serves a key function in GC
development by suppressing proliferation of GC cells (40,41).
Therefore, the present study hypothesized that miR‑146A may
be related to GC subtype 3 by targeting PSMD3.
VCL encodes a cytoskeletal protein that contributes to the
function of cell‑cell and cell‑matrix junctions, and is predicted
to be associated with GC (42). This was consistent with the
present results, which demonstrated that VCL was a specic
gene for GC subtype 4. In addition, it has been reported that
VCL may be a potential biomarker in many cancers, including
GC, pancreatic cancer and colorectal cancer, as the downregu‑
lated expression of VCL may promote metastasis and tumor
progression (43‑45). In addition, the miR‑34 family/yin yang
1 axis was reported to serve a crucial role in gastric carcino‑
genesis (46). Therefore, miR‑34A and miR‑34C may depend
on VCL to inhibit the spreading of GC subtype 4 cells by
improving focal adhesion.
In summary, GC was divided into four subtypes
based on the identied 1,263 DEGs in the PGD samples.
Table II. Subtype‑specic subpaths of gastric cancer.
Subtype miRNA Pathway Targeta Score P‑value
Subtype 1 miR‑199B Helicobacter pylori infection GIT1 1.256062 0.0307
miR‑122A Helicobacter pylori infection GIT1 1.256062 0.0314
miR‑199A Helicobacter pylori infection GIT1 1.256062 0.0317
miR‑202 Type II diabetes mellitus CACNA1E 0.610109 0.0356
Subtype 2 miR‑198 NF‑κB signaling pathway PIAS4 1.156533 0.0181
miR‑338 NF‑κB signaling pathway CCL21 1.170037 0.0195
miR‑370 NF‑κB signaling pathway CCL21 1.16555 0.0211
miR‑508 Tight junction VA PA 1.857042 0.0372
Subtype 3 miR‑146B Proteasome PSMD3 1.187736 0.008
miR‑524 Nucleotide excision repair ERCC8 1.532384 0.009
miR‑146A Proteasome PSMD3 1.187736 0.011
miR‑193A Fatty acid metabolism ACACA 2.006123 0.049
Subtype 4 miR‑429 Salmonella infection LRP1 and CACNA1C 2.278013 0.022
miR‑34A Focal adhesion VCL 0.760521 0.029
miR‑205 Salmonella infection LRP1 1.085376 0.031
miR‑34C Focal adhesion VCL 0.760521 0.032
miR‑449 Focal adhesion VCL 0.760521 0.041
aSpecic genes in the corresponding subtype. ACACA, acetyl‑CoA carboxylase α; CACNA1, calcium voltage‑gated channel subunit α1; CCL21,
C‑C motif chemokine ligand 21; ERCC8, ERCC excision repair 8, CSA ubiquitin ligase complex subunit; GIT1, ARF GTPase‑activating
protein GIT1; LRP1, LDL receptor‑related 1; NF‑κB, nuclear factor‑κB; PIAS4, protein inhibitor of activated STAT 4; PSMD3, proteasome
26S subunit, non‑ATPase 3; V A PA , VAMP‑associated protein A; VCL, vinculin.
Table III. Predicting gastric cancer subtypes using the neural
Type Subtype 1 Subtype 2 Subtype 3 Subtype 4
Subtype 1 11 1 1 1
Subtype 2 0 25 0 2
Subtype 3 1 3 9 0
Subtype 4 1 0 0 13
Table IV. Helicobacter pylori infection rate of four gastric
Subtype Infection ratio n
Subtype 1 0.67 24
Subtype 2 0.34 29
Subtype 3 0.58 19
Subtype 4 0.32 28
MOLECULAR MEDICINE REPORTS 7
Additionally, specific genes such as CACNA1E, CCL21,
PSMD3 and VCL may be used as potential feature genes
to identify different types of GC. It was concluded that the
subtype‑specic subpaths such as miR‑202/CACNA1E/type
II diabetes mellitus, miR‑338/CCL21/NF‑κB signaling,
miR‑14 6B/PSM D3/proteasome and miR‑34A/VCL/foca l
adhesion and miR‑34C/VCL/focal adhesion may serve crucial
roles in the development of GC subtypes. Furthermore,
the present study speculated that H. pylori infection was a
specic pathway for GC subtype 1. However, further experi‑
mentation is required to conrm these predicted outcomes.
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