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pmid:31803620

Identification of Subtype-Specific Three-Gene Signature for Prognostic Prediction in Diffuse Type Gastric Cancer.

Bowen Bao · Chunlei Zheng · Bowen Yang · Yue Jin · Kezuo Hou · Zhi Li · Xueying Zheng · Shitong Yu · Xiaojie Zhang · Yibo Fan · Xiujuan Qu · Yunpeng Liu · Xiaofang Che

Frontiers in oncology · 2019 doi:10.3389/fonc.2019.01243

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title
Identification of Subtype-Specific Three-Gene Signature for Prognostic Prediction in Diffuse Type Gastric Cancer.
doi
10.3389/fonc.2019.01243
pmid
31803620
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[0]
Bowen Bao
[1]
Chunlei Zheng
[2]
Bowen Yang
[3]
Yue Jin
[4]
Kezuo Hou
[5]
Zhi Li
[6]
Xueying Zheng
[7]
Shitong Yu
[8]
Xiaojie Zhang
[9]
Yibo Fan
[10]
Xiujuan Qu
[11]
Yunpeng Liu
[12]
Xiaofang Che
journal
Frontiers in oncology
year
2019
abstract
Gastric cancer (GC), with high heterogeneity, can be mainly classified into intestinal type and diffuse type according to the Lauren classification system. Although a number of differences were reported between these two types, no study on the Lauren subtype-specific multi-gene signature for evaluation of GC prognosis has been conducted, and the molecular mechanism underlying its poor prognosis has still remained elusive. Therefore, this study aimed to explore subtype-specific multi-gene signature for prognostic prediction in different subtypes of Lauren classification. With combination of the least absolute shrinkage and selection operator (LASSO) algorithm and the Akaike information criterion (AIC), the 3-gene subtype-specific prognostic signature was successfully established in diffuse type GC using GSE62254 dataset. Following the calculation of risk score (RS) based on 3-gene signature, the nomogram models were established to predict 1-, 3-, and 5-year overall survival in diffuse type GC. Moreover, the prognostic predictive nomogram model of diffuse type GC was also proved to be effective for validation of GSE1549 dataset and by a Gene Expression Omnibus (GEO)-based meta-analysis. In the analysis of the correlation between RS and clinical-pathological characteristics, RS and two genes of the 3-gene signature (EMCN and COL4A5) were found to be positively correlated with peritoneal metastasis. Furthermore, EMCN and COL4A5, rather than CCL11, were proved to be able to enhance the adhesion ability of MKN45 and NUGC4 cells to peritoneal mesothelial cell line HMR-SV5. Eventually, it was proved that COL4A5 promoted peritoneal metastasis by activating Wnt signaling pathway, whereas the upregulation of integrin family genes mediated by FAK-AKT/ERK/STAT3 signaling pathway activation is involved in peritoneal metastasis promotion function of EMCN. Taken together, our study identified the subtype-specific 3-gene signature in diffuse type GC, which could effectively predict the patients' OS and might explain the molecular mechanisms in presence of its poor prognosis.
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With combination of the least absolute shrinkage and selection operator (LASSO) algorithm and the Akaike information criterion (AIC), the 3-gene subtype-specific prognostic signature was successfully established in diffuse type GC using GSE62254 dataset.
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With combination of the least absolute shrinkage and selection operator (LASSO) algorithm and the Akaike information criterion (AIC), the 3-gene subtype-specific prognostic signature was successfully established in diffuse type GC using GSE62254 dataset.
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Publicly available datasets were analyzed in this study. This data can be found here: GSE62254, GSE15459, GSE57303.
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[213]
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[215]
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[217]
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[219]
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[220]
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[221]
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[222]
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[223]
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[224]
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[225]
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title
Molecular analysis of gastric cancer identifies discrete subtypes associated with distinct clinical characteristics and survival outcomes: the ACRG (Asian Cancer Research Group) study [gastric tumors]
resolution_checked_at
2026-11-01T15:39:55Z
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The univariate and multivariate Cox regression analysis between 10 robust markers and OS in diffuse type GC.
structure object · 5 fields
pmcid
PMC6869510
section_index
14
section_title
Identification of Subtype-Specific Multi-Gene Signatures in Diffuse and Intestinal Type GC
row_count
12
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9
table object · 8 fields
label
Table 1
caption
The univariate and multivariate Cox regression analysis between 10 robust markers and OS in diffuse type GC.
rows array · 12 items
[0] array · 3 items
[0]
[1]
Variate COX
[2]
Multivariate COX
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[1]
Coef
[2]
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P
[4]
[5]
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[6]
HR
[7]
P
[8]
[2] array · 9 items
[0]
CCL11
[1]
−0.1378
[2]
0.8712
[3]
0.0403
[4]
*
[5]
−0.2733
[6]
0.7609
[7]
0.0015
[8]
**
[3] array · 9 items
[0]
RORA
[1]
0.5163
[2]
1.6759
[3]
0.0004
[4]
***
[5]
[6]
[7]
[8]
[4] array · 9 items
[0]
COL4A5
[1]
0.3200
[2]
1.3771
[3]
0.0002
[4]
***
[5]
0.1769
[6]
1.1936
[7]
0.0445
[8]
*
[5] array · 9 items
[0]
A2M
[1]
0.5455
[2]
1.7255
[3]
0.0009
[4]
***
[5]
[6]
[7]
[8]
[6] array · 9 items
[0]
TENT5C
[1]
−0.2647
[2]
0.7675
[3]
0.0232
[4]
*
[5]
[6]
[7]
[8]
[7] array · 9 items
[0]
TLR8
[1]
−0.2285
[2]
0.7957
[3]
0.0186
[4]
*
[5]
[6]
[7]
[8]
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NR4A3
[1]
0.3354
[2]
1.3985
[3]
0.0014
[4]
**
[5]
[6]
[7]
[8]
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[0]
TUSC3
[1]
0.3335
[2]
1.3959
[3]
0.0004
[4]
***
[5]
[6]
[7]
[8]
[10] array · 9 items
[0]
ACKR4
[1]
−0.1860
[2]
0.8303
[3]
0.0138
[4]
*
[5]
[6]
[7]
[8]
[11] array · 9 items
[0]
EMCN
[1]
0.5177
[2]
1.6781
[3]
0.0002
[4]
***
[5]
0.4744
[6]
1.6071
[7]
0.0063
[8]
**
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section[14]/table-wrap[1]#T1
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The univariate and multivariate Cox regression analysis between RS and other clinical characteristics and OS in diffuse type GC.
structure object · 5 fields
pmcid
PMC6869510
section_index
15
section_title
Establishment and Evaluation of the Prognostic Predictive Nomogram Model in Diffuse- and Intestinal Type GC
row_count
8
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9
table object · 8 fields
label
Table 2
caption
The univariate and multivariate Cox regression analysis between RS and other clinical characteristics and OS in diffuse type GC.
rows array · 8 items
[0] array · 3 items
[0]
[1]
Variate COX
[2]
Multivariate COX
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[0]
[1]
Coef
[2]
HR
[3]
P
[4]
[5]
Coef
[6]
HR
[7]
P
[8]
[2] array · 9 items
[0]
Sex
[1]
−0.2759
[2]
0.7589
[3]
0.2389
[4]
[5]
[6]
[7]
[8]
[3] array · 9 items
[0]
Age
[1]
0.0037
[2]
1.0037
[3]
0.7016
[4]
[5]
[6]
[7]
[8]
[4] array · 9 items
[0]
T
[1]
0.5508
[2]
1.7347
[3]
0.0016
[4]
**
[5]
0.0372
[6]
1.0379
[7]
0.8305
[8]
[5] array · 9 items
[0]
N
[1]
0.9641
[2]
2.6224
[3]
<0.001
[4]
***
[5]
0.7609
[6]
2.1402
[7]
<0.001
[8]
***
[6] array · 9 items
[0]
M
[1]
1.3385
[2]
3.8133
[3]
<0.001
[4]
***
[5]
0.7513
[6]
2.1198
[7]
0.0094
[8]
**
[7] array · 9 items
[0]
RS
[1]
1.6409
[2]
5.1598
[3]
<0.001
[4]
***
[5]
1.3075
[6]
3.6969
[7]
<0.001
[8]
***
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section[15]/table-wrap[1]#T2
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The correlation between RS and clinical pathological parameters in diffuse type GC.
structure object · 5 fields
pmcid
PMC6869510
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17
section_title
Analysis of Correlation Between 3-Gene Signature and Clinical-Pathological Parameters in Diffuse Type GC
row_count
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table object · 8 fields
label
Table 3
caption
The correlation between RS and clinical pathological parameters in diffuse type GC.
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[0]
[1]
[2]
Low-RS
[3]
High-RS
[4]
P
[5]
[1] array · 6 items
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<60
[2]
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28
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0.1868
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37
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27
[3]
31
[4]
0.6517
[5]
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[0]
[1]
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37
[3]
34
[4]
[5]
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T
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2
[2]
35
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27
[4]
0.2944
[5]
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[0]
[1]
3
[2]
24
[3]
33
[4]
[5]
[7] array · 6 items
[0]
[1]
4
[2]
5
[3]
5
[4]
[5]
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[0]
N
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0
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6
[3]
2
[4]
0.0265
[5]
*
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[1]
1
[2]
32
[3]
21
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[1]
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[2]
16
[3]
20
[4]
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[11] array · 6 items
[0]
[1]
3
[2]
10
[3]
22
[4]
[5]
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M
[1]
0
[2]
59
[3]
52
[4]
0.0813
[5]
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[1]
1
[2]
5
[3]
13
[4]
[5]
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[0]
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[1]
No
[2]
56
[3]
30
[4]
<0.001
[5]
***
[15] array · 6 items
[0]
[1]
Yes
[2]
8
[3]
35
[4]
[5]
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[0]
Ascites
[1]
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[2]
58
[3]
33
[4]
<0.001
[5]
***
[17] array · 6 items
[0]
[1]
Yes
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6
[3]
32
[4]
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[2]
61
[3]
57
[4]
0.2171
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[1]
YES
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3
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8
[4]
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[2]
64
[3]
63
[4]
0.4829
[5]
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[1]
Yes
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[3]
2
[4]
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[2]
63
[3]
61
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Yes
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1
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4
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[5]
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The correlation of EMCN, COL4A5, and CCL11 with the clinical pathological parameters related to metastasis in diffuse type GC.
structure object · 5 fields
pmcid
PMC6869510
section_index
17
section_title
Analysis of Correlation Between 3-Gene Signature and Clinical-Pathological Parameters in Diffuse Type GC
row_count
33
column_count
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table object · 8 fields
label
Table 4
caption
The correlation of EMCN, COL4A5, and CCL11 with the clinical pathological parameters related to metastasis in diffuse type GC.
rows array · 33 items
[0] array · 6 items
[0]
[1]
[2]
Low-EMCN
[3]
High-EMCN
[4]
P
[5]
[1] array · 6 items
[0]
Peritoneal seeding
[1]
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[2]
54
[3]
32
[4]
0.0001
[5]
***
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[0]
[1]
Yes
[2]
10
[3]
33
[4]
0.0001
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[0]
Ascites
[1]
No
[2]
54
[3]
37
[4]
0.0013
[5]
**
[4] array · 6 items
[0]
[1]
Yes
[2]
10
[3]
28
[4]
0.0013
[5]
[5] array · 6 items
[0]
Liver
[1]
No
[2]
57
[3]
61
[4]
0.5109
[5]
[6] array · 6 items
[0]
[1]
Yes
[2]
7
[3]
4
[4]
0.5109
[5]
[7] array · 6 items
[0]
Distant lymph node
[1]
No
[2]
62
[3]
65
[4]
0.4692
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[0]
[1]
Yes
[2]
2
[3]
0
[4]
0.4692
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[0]
Bone
[1]
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[2]
61
[3]
63
[4]
0.9859
[5]
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[0]
[1]
Yes
[2]
3
[3]
2
[4]
0.9859
[5]
[11] array · 6 items
[0]
[1]
[2]
Low-COL4A5
[3]
High-COL4A5
[4]
P
[5]
[12] array · 6 items
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Peritoneal seeding
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51
[3]
35
[4]
0.0034
[5]
**
[13] array · 6 items
[0]
[1]
Yes
[2]
13
[3]
30
[4]
0.0034
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[14] array · 6 items
[0]
Ascites
[1]
No
[2]
55
[3]
36
[4]
0.0003
[5]
***
[15] array · 6 items
[0]
[1]
Yes
[2]
9
[3]
29
[4]
0.0003
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[16] array · 6 items
[0]
Liver
[1]
No
[2]
52
[3]
54
[4]
0.9673
[5]
[17] array · 6 items
[0]
[1]
Yes
[2]
12
[3]
11
[4]
0.9673
[5]
[18] array · 6 items
[0]
Distant lymph node
[1]
No
[2]
61
[3]
57
[4]
0.2171
[5]
[19] array · 6 items
[0]
[1]
Yes
[2]
3
[3]
8
[4]
0.2171
[5]
[20] array · 6 items
[0]
Bone
[1]
No
[2]
63
[3]
61
[4]
0.3710
[5]
[21] array · 6 items
[0]
[1]
Yes
[2]
1
[3]
4
[4]
0.3710
[5]
[22] array · 6 items
[0]
[1]
[2]
Low-CCL11
[3]
High-CCL11
[4]
P
[5]
[23] array · 6 items
[0]
Peritoneal seeding
[1]
No
[2]
40
[3]
46
[4]
0.4183
[5]
[24] array · 6 items
[0]
[1]
Yes
[2]
24
[3]
19
[4]
[5]
[25] array · 6 items
[0]
Ascites
[1]
No
[2]
44
[3]
47
[4]
0.8026
[5]
[26] array · 6 items
[0]
[1]
Yes
[2]
20
[3]
18
[4]
[5]
[27] array · 6 items
[0]
Liver
[1]
No
[2]
55
[3]
63
[4]
0.0551
[5]
[28] array · 6 items
[0]
[1]
Yes
[2]
9
[3]
2
[4]
[5]
[29] array · 6 items
[0]
Distant lymph node
[1]
No
[2]
62
[3]
65
[4]
0.4692
[5]
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[0]
[1]
Yes
[2]
2
[3]
0
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[5]
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[0]
Bone
[1]
No
[2]
61
[3]
63
[4]
0.9859
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[1]
Yes
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3
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2
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locator
section[17]/table-wrap[2]#T4
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asset:data:geo:GSE15459 not downloaded
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With combination of the least absolute shrinkage and selection operator (LASSO) algorithm and the Akaike information criterion (AIC), the 3-gene subtype-specific prognostic signature was successfully established in diffuse type GC using GSE62254 dataset.
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Publicly available datasets were analyzed in this study. This data can be found here: GSE62254, GSE15459, GSE57303.
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With combination of the least absolute shrinkage and selection operator (LASSO) algorithm and the Akaike information criterion (AIC), the 3-gene subtype-specific prognostic signature was successfully established in diffuse type GC using GSE62254 dataset.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31803620", "kind": "science-record", "revision": 6, "content_hash": "sha256:ed43f4831546bf1bba2b269aba623edb09f57f3099895873426d2116d4c28865", "created_at": "2026-11-01T15:40:52Z", "updated_at": "2026-11-01T21:22:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Subtype-Specific Three-Gene Signature for Prognostic Prediction in Diffuse Type Gastric Cancer.", "doi": "10.3389/fonc.2019.01243", "pmid": "31803620", "authors": ["Bowen Bao", "Chunlei Zheng", "Bowen Yang", "Yue Jin", "Kezuo Hou", "Zhi Li", "Xueying Zheng", "Shitong Yu", "Xiaojie Zhang", "Yibo Fan", "Xiujuan Qu", "Yunpeng Liu", "Xiaofang Che"], "journal": "Frontiers in oncology", "year": 2019, "abstract": "Gastric cancer (GC), with high heterogeneity, can be mainly classified into intestinal type and diffuse type according to the Lauren classification system. Although a number of differences were reported between these two types, no study on the Lauren subtype-specific multi-gene signature for evaluation of GC prognosis has been conducted, and the molecular mechanism underlying its poor prognosis has still remained elusive. Therefore, this study aimed to explore subtype-specific multi-gene signature for prognostic prediction in different subtypes of Lauren classification. With combination of the least absolute shrinkage and selection operator (LASSO) algorithm and the Akaike information criterion (AIC), the 3-gene subtype-specific prognostic signature was successfully established in diffuse type GC using GSE62254 dataset. Following the calculation of risk score (RS) based on 3-gene signature, the nomogram models were established to predict 1-, 3-, and 5-year overall survival in diffuse type GC. Moreover, the prognostic predictive nomogram model of diffuse type GC was also proved to be effective for validation of GSE1549 dataset and by a Gene Expression Omnibus (GEO)-based meta-analysis. In the analysis of the correlation between RS and clinical-pathological characteristics, RS and two genes of the 3-gene signature (EMCN and COL4A5) were found to be positively correlated with peritoneal metastasis. Furthermore, EMCN and COL4A5, rather than CCL11, were proved to be able to enhance the adhesion ability of MKN45 and NUGC4 cells to peritoneal mesothelial cell line HMR-SV5. Eventually, it was proved that COL4A5 promoted peritoneal metastasis by activating Wnt signaling pathway, whereas the upregulation of integrin family genes mediated by FAK-AKT/ERK/STAT3 signaling pathway activation is involved in peritoneal metastasis promotion function of EMCN. 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