Literature DB >> 33851708

Identified a disintegrin and metalloproteinase with thrombospondin motifs 6 serve as a novel gastric cancer prognostic biomarker by bioinformatics analysis.

Ya-Zhen Zhu1, Yi Liu1, Xi-Wen Liao2, Shan-Shan Luo1.   

Abstract

OBJECTIVE: We aimed to explore the prognostic value of a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS) genes in gastric cancer (GC).
METHODS: The RNA-sequencing (RNA-seq) expression data for 351 GC patients and other relevant clinical data were acquired from The Cancer Genome Atlas (TCGA). Survival analysis and a genome-wide gene set enrichment analysis (GSEA) were performed to define the underlying molecular value of the ADAMTS genes in GC development. Besides, qRT-PCR and immunohistochemistry were all employed to validate the relationship between the expression of these genes and GC patient prognosis.
RESULTS: The Log rank test with both Cox regression and Kaplan-Meier survival analyses showed that ADAMTS6 expression profile correlated with the GC patients clinical outcome. Patients with a high expression of ADAMTS6 were associated with poor overall survival (OS). Comprehensive survival analysis of the ADAMTS genes suggests that ADAMTS6 might be an independent predictive factor for the OS in patients with GC. Besides, GSEA demonstrated that ADAMTS6 might be involved in multiple biological processes and pathways, such as the vascular endothelial growth factor A (VEGFA), kirsten rat sarcoma viral oncogene (KRAS), tumor protein P53, c-Jun N-terminal kinase (JNK), cadherin (CDH1) or tumor necrosis factor (TNF) pathways. It was also confirmed by immunohistochemistry and qRT-PCR that ADAMTS6 is highly expressed in GC, which may be related to the prognosis of GC patients.
CONCLUSION: In summary, our study demonstrated that ADAMTS6 gene could be used as a potential molecular marker for GC prognosis.
© 2021 The Author(s).

Entities:  

Keywords:  ADAMTS; gastric cancer; mRNA; prognosis

Mesh:

Substances:

Year:  2021        PMID: 33851708      PMCID: PMC8065180          DOI: 10.1042/BSR20204359

Source DB:  PubMed          Journal:  Biosci Rep        ISSN: 0144-8463            Impact factor:   3.840


Introduction

Gastric cancer (GC) is the fourth most common cancer and the second cause of mortality worldwide [1]. The median survival time of patients with GC recurrence and metastasis is less than 1 year [2,3]. According to the 2015 data at the National Cancer Center, there were ∼679000 new stomach cancer cases and 498000 deaths [4]. China accounts for 430000 new GC cases and 300000 deaths every year [5]. The GC development may be a process of long-term synergistic action of multiple factors. GC might be triggered by pathogenic infections, such as Helicobacter pylori (HP) [6-9] or gastric ulcers [10], chronic atrophic gastritis [11], carcinogens such as nitrite in food [12], smoking [13,14], and long-term drinking [15]. Despite the immense progress made in the clinical management of GC, the prognosis and survival rates of patients remain poor. Low early diagnosis rate and high local recurrence cases coupled with distant metastasis rates for advanced GC results in the poor GC prognosis in China. It is, therefore, vital to further interrogate the carcinogenesis and development of GC in order to develop new prognostic molecular markers and targeted therapy. A disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS) genes are zinc-dependent metalloproteases. Previous research has demonstrated a close association between ADAMTS and tumor invasion and metastasis [16]. ADAMTS participate in multiple biological pathways, including histomorphogenesis, pathophysiological reconstruction, inflammation, angiogenesis, or tumorigenesis [17,18]. However, the diagnostic, prognostic, or therapeutic value of the ADAMTS genes in the development of GC is yet to be defined. Here, using The Cancer Genome Atlas (TCGA) and the Kaplan–Meier plotter (KM plotter) tools, we explored the diagnostic and underlying prognostic value of the ADAMTS family of genes in stomach cancer.

Methods

Public database source

The RNA-sequencing (RNA-seq) expression data for 383 patients and relevant clinical data were acquired from the TCGA database (https://portal.gdc.cancer.gov/; accessed 15 May 2019). The mined data comprised 351 GC tumors and 32 normal gastric samples. The raw data were normalized via the DESeq (https://www.bioconductor.org/packages/release/bioc/html/DESeq.html) [19].

Bioinformatics analysis

We used GraphPad Prism 8 to draw the scatter diagram and receiver operating characteristic (ROC) curves of the expression distribution for both the GC and the normal samples. The unpaired t test was used to compare the differences shown in the scatter diagram and the area under ROC curve. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID, (https://david.ncifcrf.gov/home.jsp; accessed 1 December 2019; v.6.8), we then investigated the Gene Ontology (GO) enrichment of ADAMTS genes [20]. We used the gene multiple association network integration algorithm (Gene MANIA; http://www.genemania.org/; accessed 1 December 2019) to construct the gene–gene networks [21,22] and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING v.10.0; https://string-db.org/; accessed 1 December 2019) to define the protein–protein interaction (PPI) networks [23,24].

Comprehensive survival analysis of ADAMTS genes

Using the median values for the gene expression profile, GC patients were classified into two categories based on survival analysis. Both the ADAMTS expression and clinical data in GC tissues or adjacent tissues were analyzed using the log-rank test, while the clinical characteristics related to overall survival (OS) were selected and adjusted by the multivariate Cox regression survival analysis. Besides, the clinical pathological features were further analyzed in the subgroups. We then conducted full survival analysis using the prognosis-related ADAMTS genes, and assessed the survival ROC curves using the R package platform, as well as the total and subgroup survival analysis. Finally, the prognostic relationship between ADAMTS family of genes and GC was verified by the KM plotter database (www.kmplot.com).

Gene set enrichment analysis

We used gene set enrichment analysis (GSEA) (http://software.broadinstitute.org/gsea/index.jsp; accessed 15 December 2020) [25,26] to study the biological differences and pathways affected by the differential expression of the ADAMTS gene pool and their relationship with GC clinical outcome. The GSEA software applies the Molecular Signatures Database (MSigDB) c2 (c2.cp.kegg.v7.0.symbols.gmt) and c5 (c5.all.v7.0.symbols.gmt) [27] in gene set analysis. Then a probability value of <0.05 and a false discovery rate (FDR) < 0.25 for GSEA were considered statistically significant.

qRT-PCR

We collected 24 pairs of GC and adjacent normal tissue samples surgically from December 2020 to February 2021 at Guangxi Medical University Affiliated Tumor Hospital for RNA extraction. A cDNA reverse transcription kit (RR036A; TaKaRa, U.S.A.) and TB Green kit (RR820A; TaKaRa, U.S.A.) were used for reverse transcription into cDNA and qRT-PCR. Primers used were: ADAMTS6-F: 5′-CCTCCCAAGCGTGACTTTCT-3′; ADAMTS6-R: 5′-AGACACCAGAGCTCTCTACACACTT-3′. GAPDH-F: 5′-CAGGAGGCATTGCTGATGAT-3′, and GAPDH-R: 5′-GAAGGCTGGGGCTCATTT-3′.

Immunohistochemistry

We collected 18 pairs of GC tissues and paired paracancerous tissue samples in the Guangxi Medical University Affiliated Tumor Hospital from January 2018 to December 2018. The experiments were approved by the Ethical Committee of the Guangxi Medical University Affiliated Tumor Hospital and written informed consent was signed by each participant. The sections were placed in an oven at 65°C for 2 h, dewaxed with xylene, hydrated with a graded series of ethanol, and repaired with antigen repair buffer using the EDTA method. Endogenous antigens were blocked with 3% hydrogen peroxide. The sections were incubated with Rabbit Anti-ADAMTS6 antibody (bs-8009R) overnight at 4°C. The sections were heated for 30 min to bring them to room temperature and incubated with secondary antibody for 20 min. Staining was visualized using DAB color developing solution. The sections were stained with Hematoxylin, differentiated in 1% hydrochloric acid in alcohol, and dehydrated. Thereafter, the sections were dried naturally in a fume hood, transparentized with xylene, and sealed with neutral resin. The negative control contained PBS instead of primary antibody, and a proven positive section served as the positive control. The staining index was scored according to staining intensity (0, no staining; 1, weak, light yellow; 2, moderate, yellow brown; 3, strong, brown) and the proportion of positive cells (0, 0%; 1, <10%; 2, <50%; 3, <75%; 4, >76%). An ‘immunoreactive score’ was determined to be the product of the intensity and percentage of positive cells, which ranged from 0 to 12. Cases with scores of 0–7 were defined as the negative group and those with scores of 8–12 were the positive group [28].

Statistical analysis

We employed the Benjamini–Hochberg program to adjust FDRs in GSEA for multiple tests [29-31]. The statistical analysis was conducted via SPSS version 23.0 (IBM Corporation, Armonk, NY, U.S.A.). A probability value of P<0.05 was considered statistically significant.

Results

In determining the potential diagnostic value of ADAMTS family in distinguishing between GC tumor tissue and adjacent normal tissue, the results of unpaired t test showed that ADAMTS1 and ADAMTS8 mRNA expression was down-regulated in GC tumor tissues as compared with the adjacent normal tissues (P<0.05). On the other side, ADAMTS2, ADAMTS6, and ADAMTS7 genes mRNA expression were up-regulated in GC tissues (P<0.05), as shown in Figure 1. The ROC curves for the GC and adjacent normal samples showed that the tumor tissues could be effectively identified based on the risk score (Figure 2).
Figure 1

The scatter plot of ADAMTS mRNA expression in GC and adjacent tissues based on TCGA database

****P<0.0001.

Figure 2

The ROC curve of ADAMTS mRNA expression in GC and adjacent tissues based on TCGA database

The scatter plot of ADAMTS mRNA expression in GC and adjacent tissues based on TCGA database

****P<0.0001. The GO analysis indicated that the ADAMTS genes were linked with extracellular matrix/structure organization, extracellular matrix disassembly, cell adhesion molecule binding, or collagen catabolic/metabolic processes, as shown in Figure 3. Our interaction network analysis showed that ADAMTS genes and other related genes formed an intricate network together. The gene–gene interaction network showed that the ADAMTS genes are strongly co-expressed (Figure 4A), while the PPI network analysis showed that the ADAMTS proteins directly interact with each other (Figure 4B). Furthermore, there was co-expression among the ADAMTS1–8 genes in the GC tumor tissues (Figure 5), demonstrating that the genes are positively correlated with GC development (Pearson correlation coefficient range: 0.11–0.69; P<0.05). Besides, our analysis of the ADAMTS genes expression in different tumor stages and histologic grades showed that the ADAMTS6 expression was significantly different in G1/G2 and G3 tumor tissues (Figure 6A). However, there was no significant difference in the expression of ADAMTS genes in stages I/II and III/IV tumor samples (Figure 6B). Together, these data show that ADAMTS6 overexpression is associated with G3 tumor tissues samples in GC patients, P=0.01 (Figure 6A).
Figure 3

GO enrichment analysis of ADAMTS genes

Figure 4

Gene and protein interaction networks of ADAMTS genes

(A) Gene multiple association network integration algorithm. (B) PPI networks.

Figure 5

Co-expression matrix of ADAMTS genes in GC tumor tissues, and demonstrated that ADAMTS1–8 were positively correlated and co-expressed with each other in GC tumor tissues

Figure 6

Gene expression distribution of ADAMTS genes in different GC histologic grades and tumor stages

(A) Gene expression distribution of ADAMTS genes in different GC histologic grades. (B) Gene expression distribution of ADAMTS genes in different GC tumor stages. *P<0.05.

Gene and protein interaction networks of ADAMTS genes

(A) Gene multiple association network integration algorithm. (B) PPI networks.

Gene expression distribution of ADAMTS genes in different GC histologic grades and tumor stages

(A) Gene expression distribution of ADAMTS genes in different GC histologic grades. (B) Gene expression distribution of ADAMTS genes in different GC tumor stages. *P<0.05.

ADAMTS1–8 genes and OS in GC patients

The clinical characteristics of the 351 GC patients is shown in Table 1. The results from the univariate analysis found that the high ADAMTS1 and ADAMTS6 mRNA expression were associated with poor survival rates of GC patients (hazard ratio (HR) = 1.52, 95% confidence interval (CI) = 1.09–2.12, P=0.013 and HR = 1.80, 95% CI = 1.29–2.51, P=0.001, respectively) as shown in Table 2. Unlike the other ADAMTS genes, the survival analysis indicated that the up-regulation of ADAMTS1, ADAMTS3, or ADAMTS6 increased risk of death in GC patients (P<0.05) (Figure 7A–H). Clinical parameters such as age, TNM stage, cancer status, primary therapy outcome, residual tumor, and therapy were remarkably correlated with OS in GC. What needs to be adjusted in multivariate Cox regression and indicated that ADAMTS6 overexpression was remarkably raised death rate in GC (adjusted HR = 1.89, 95% CI = 1.19–3.01, adjusted P=0.007, Table 2, Figure 7F) and accelerated a worse OS (high ADAMTS6 vs low ADAMTS6; 21 vs 56 months, Table 2, Figures 7F and 8A). There was, however, no correlation between other ADAMTS genes and the GC OS. In addition, as shown in Table 3, subgroup analysis indicated that overexpression of the ADAMTS6 genes increased the death rate in GC patients who are ≥60 years old; female patients; patients with gastric antrum cancer; intestinal type adenocarcinoma; G2, G3 histologic grade; microsatellite instability-altitude (MSI-H), MMS; lymph node metastasis; T3, T4 stage; tumor free status; pathologic stage III; R0 resection; those untreated with radiation therapy or targeted therapy, without distant metastasis and HP infection. Time-dependent ROC analysis proved that ADAMTS6 expression profile could reliably predict OS in GC patients. The area under the 1-, 2-, or 3-year curve (AUC) was 0.613, 0.607, or 0.759, respectively (Figure 8B). Besides, we demonstrate that ADAMTS6, together with the OS-related clinical characteristics give superior performance in the prediction OS in GC patients (Figure 9A–E and Table 4).
Table 1

Correlation between OS and clinicopathologic features of GC patients in TCGA cohort

VariablesEvents/totalMST (months)HR (95% CI)P-value
Age (years)144/348290.022
  <6036/108601
  ≥60108/240261.55 (1.06–2.27)
  Missing3
Gender144/351290.178
  Male100/226291
  Female44/125350.78 (0.55–1.12)
  Missing0
Anatomic neoplasm138/337290.919
  Gastroesophageal junction36/84261
  Fundus gastric body50/123280.92 (0.60–1.41)
  Gastric antrum52/130350.94 (0.61–1.43)
  Missing14
HP infection66/161430.304
  Positive6/18581
  Negative60/143431.55 (0.67–3.61)
  Missing190
Histologic type144/350290.057
  Intestinal64/160381
  Diffuse type24/61601.00 (0.63–1.60)
  Signet ring type8/11132.52 (1.20–5.25)
  Other types48/118261.29 (0.88–1.88)
  Missing1
Histologic grade140/342290.169
  G12/9NA1
  G248/127431.67 (0.41–6.86)
  G390/206262.22 (0.55–9.01)
  Missing9
MSS1144/350290.225
  MSI-H99/240281
  MSI-L22/51291.26 (0.79–2.01)
  MMS23/59350.76 (0.48–1.19)
  Missing1
Pathological M138/336290.012
  M113/23121
  M0125/313350.49 (0.28–0.86)
  Missing15
Pathological N139/341290.004
  N028/103601
  N+111/238251.83 (1.21–2.76)
  Missing10
Pathological T140/347310.009
  T1/T228/91701
  T3/T4112/256261.73 (1.14–2.63)
  Missing4
TNM stage136/33829<0.001
  Stage I11/47731
  Stage II34/109561.61 (0.81–3.18)
  Stage III69/147262.44 (1.29–4.61)
  Stage IV22/35163.79 (1.84–7.82)
  Missing13
Cancer status121/32437<0.001
  Tumor free35/206NA1
  With tumor86/118175.53 (3.70–8.26)
  Missing27
Primary therapy outcome114/30338<0.001
  CR55/209731
  PR4/5174.23 (1.52–11.78)
  SD7/25311.89 (0.86–4.18)
  PD48/64134.33 (2.91–6.45)
 Missing47
Radiation therapy135/328310.001
  Yes19/62NA1
  No116/266262.32 (1.42–3.80)
  Missing23
Residual tumor121/31637<0.001
  R19/14131
  R212/1491.88 (0.95–3.73)
  R0100/288477.19 (3.88–13.34)
  Missing35
Targeted therapy134/326310.022
  Yes56/151431
  No78/175261.49 (1.06–2.10)
  Missing25

Abbreviations: CR, complete response; G1, highly differentiated; G2, moderately differentiated; G3, poorly differentiated; MSI-L, microsatellite instability-altitude; MST, median survival time; PD, progressive disease; PR, partial response; R0, no residual tumor; R1, microscopic residual tumor; R2, macroscopic residual tumor; SD, stable disease.

Table 2

Prognostic values of ADAMTS genes expression in GC OS of TCGA cohort

Gene expressionEvents/total (n=351)MST (months)Crude HR (95% CI)Crude P-valueAdjusted HR (95% CI)Adjusted P-value1
ADAMTS1
 Low61/1765611
 High83/175261.52 (1.09–2.12)0.0131.39 (0.91–2.11)0.125
ADAMTS2
 Low64/1764711
 High80/175261.38 (0.99–1.92)0.0551.30 (0.85–1.98)0.226
ADAMTS3
 Low63/1763711
 High81/175271.25 (0.90–1.73)0.191.36 (0.88–2.09)0.168
ADAMTS4
 Low63/1764311
 High81/175281.21 (0.87–1.68)0.2661.34 (0.87–2.07)0.189
ADAMTS5
 Low70/1762711
 High74/175351.09 (0.79–1.52)0.5990.84 (0.54–1.31)0.441
ADAMTS6
 Low59/1765611
 High85/175211.80 (1.29–2.51)0.0011.89 (1.19–3.01)0.007
ADAMTS7
 Low64/1764311
 High80/175261.33 (0.95–1.85)0.0951.41 (0.91–2.18)0.120
ADAMTS8
 Low67/1763711
 High77/175281.15 (0.83–1.60)0.4011.52 (0.97–2.39)0.067

Abbreviation: MST, median survival time.

Adjusted for age, TNM stage, cancer status, primary therapy outcome, residual tumor, targeted molecular therapy, and radiation therapy.

Figure 7

Kaplan–Meier survival curves for ADAMTS genes in GC of TCGA cohort

OS stratified by ADAMTS1 (A), ADAMTS2 (B), ADAMTS3 (C), ADAMTS4 (D), ADAMTS5 (E), ADAMTS6 (F), ADAMTS7 (G), ADAMTS8 (H).

Figure 8

Prognostic value evaluation of ADAMTS6 in patients with GC

(A) From top to bottom: are the expression values of ADAMTS6, patients’ survival status distribution, and the expression heat map of ADAMTS6 in the low- and high-expression groups. (B) ROC curve for predicting OS in GC patients by the ADAMTS6.

Table 3

Stratified analysis of ADAMTS6 gene expression in clinicopathologic features of GC cases

VariablesCasesHR (95% CI)Log-rank P-value
Age (years)348
 <601081.01 (0.46–2.21)0.102
 ≥602401.94 (1.25–3.01)0.048
Gender351
 Male2261.66 (1.05–2.62)0.021
 Female1252.26 (1.14–4.50)0.293
Anatomic neoplasm
Gastroesophageal337
 Junction840.79 (0.29–2.15)0.501
 Fundus gastric body1230.70 (0.39–1.24)0.001
 Gastric antrum1301.97 (1.01–3.85)0.001
HP infection161
 Positive183.61 (0.22–59.81)0.180
 Negative1431.20 (0.65–2.23)0.005
Histologic type350
 Intestinal1602.53 (1.43–4.46)P<0.001
 Diffuse type610.67 (0.28–1.59)0.158
 Signet ring type110.64 (0.02–26.22)0.312
 Other types1181.96 (1.01–3.82)0.005
Histologic grade342
 G19NANA
 G21271.15 (0.59–2.26)0.006
 G32062.38 (1.49–3.81)P<0.001
MSS1350
 MSI-H2401.67 (1.08–2.59)P<0.001
 MSI-L512.15 (0.81–5.73)0.099
 MMS592.62 (0.88–7.81)0.005
Pathological M336
 M1231.19 (0.07–19.35)0.870
 M03131.63 (1.12–2.38)P<0.001
Pathological N139/341
 N01030.90 (0.35–2.30)0.005
 N+111/2382.20 (1.41–3.42)P<0.001
Pathological T347
 T1/T2910.60 (0.24–1.51)0.118
 T3/T42562.24 (1.44–3.50)P<0.001
Pathologic stage136/338
 Stage I471.60 (0.48–5.29)0.402
 Stage II1091.55 (0.79–3.07)0.478
 Stage III1472.00 (1.16–3.44)0.039
 Stage IV353.11 (0.70–13.86)0.132
Cancer status121/324
 Tumor free2062.26 (1.07–4.80)P<0.001
 With tumor1181.71 (1.06–2.76)0.108
Radiation therapy135/328
 Yes622.26 (0.80–6.39)0.287
 No2661.75 (1.78–2.59)P<0.001
Residual tumor121/316
 R114NANA
 R2140.78 (0.02–41.15)0.531
 R02881.71 (1.13–2.58)0.090
Targeted therapy134/326
 Yes1511.36 (0.77–2.39)0.388
 No1751.92 (1.18–3.12)P<0.001

Abbreviations: CR, complete response; G1, highly differentiated; G2, moderately differentiated; G3, poorly differentiated; MSI-L, microsatellite instability-low; MST, median survival time; M, metastasis; N, node; NA, not applicable; PD, progressive disease; PR, partial response; R0, no residual tumor; R1, microscopic residual tumor; R2, macroscopic residual tumor; SD, stable disease; T, tumor.

Figure 9

Joint effects analysis of OS stratified by ADAMTS6 and GC clinical parameters

Joint effects analysis stratified by ADAMTS6 and following clinical parameters: histologic grade (A), radiation therapy (B), radical resection (C), targeted molecular therapy (D), stage (E).

Table 4

Joint effects survival analysis of clinical factors and the ADAMTS6 expression with OS

GroupADAMTS6VariablesEvents/totalMST (months)Crude HR (95% CI)Crude P-valueAdjusted HR (95% CI)Adjusted P-value1
Histologic grade
ALow expressionG1 + G227/745611
BLow expressionG3 + G432/98471.005 (0.602–1.680)0.9840.734 (0.394–1.371)0.332
CHigh expressionG1 + G223/6231I.226 (0.701–2.142)0.4751.171 (0.600–2.286)0.644
DHigh expressionG3 + G458/108182.045 (1.291–3.237)0.0021.815 (1.035–3.184)0.038
Radiation therapy
aLow expressionYes6/314711
bLow expressionNo47/131583.104 (1.321–7.29400.0093.265 (1.276–8.353)0.014
cHigh expressionYes13/31202.821 (1.071–7.432)0.0362.644 (0.954–7.324)0.062
dHigh expressionNo69/135315.665 (2.434–13.188)<0.0015.917 (2.349–14.900)<0.001
Radical resection
ILow expressionR044/1557011
IILow expressionR1+R2 5/983.046 (1.204–7.707)0.0193.308 (0.931–11.751)0.064
IVHigh expressionR056/133271.829 (1.230–2.720)0.0032.055 (1.349–3.129)0.001
IIIHigh expressionR1+R216/19125.009 (2.807–8.937)<0.0013.485 (1.719–7.067)0.001
Targeted molecular therapy
iLow expressionYes22/747011
iiLow expressionNo31/89471.400 (0.810–2.420)0.2280.928 (0.478–1.801)0.825
iiiHigh expressionYes34/77291.829 (1.068–3.133)0.0281.626 (0.891–2.966)0.113
ivHigh expressionNo47/86183.002 (1.802–5.001)<0.0012.030 (1.087–3.793)0.026
Stage
ELow expressionI + II24/895611
FLow expressionIII+IV34/84471.398 (0.829–2.358)0.2091.839 (0.975–3.469)0.06
GHigh expressionI + II21/67601.214 (0.675–2.181)0.5171.579 (0.794–3.142)0.193
HHigh expressionIII+IV57/98172.916 (1.807–4.704)<0.0013.897 (2.117–7.176)<0.001

Abbreviation: MST, median survival time.

Adjusted for histologic grade, radiation therapy, radical resection, targeted molecular therapy, and stage.

Kaplan–Meier survival curves for ADAMTS genes in GC of TCGA cohort

OS stratified by ADAMTS1 (A), ADAMTS2 (B), ADAMTS3 (C), ADAMTS4 (D), ADAMTS5 (E), ADAMTS6 (F), ADAMTS7 (G), ADAMTS8 (H).

Prognostic value evaluation of ADAMTS6 in patients with GC

(A) From top to bottom: are the expression values of ADAMTS6, patients’ survival status distribution, and the expression heat map of ADAMTS6 in the low- and high-expression groups. (B) ROC curve for predicting OS in GC patients by the ADAMTS6.

Joint effects analysis of OS stratified by ADAMTS6 and GC clinical parameters

Joint effects analysis stratified by ADAMTS6 and following clinical parameters: histologic grade (A), radiation therapy (B), radical resection (C), targeted molecular therapy (D), stage (E). Abbreviations: CR, complete response; G1, highly differentiated; G2, moderately differentiated; G3, poorly differentiated; MSI-L, microsatellite instability-altitude; MST, median survival time; PD, progressive disease; PR, partial response; R0, no residual tumor; R1, microscopic residual tumor; R2, macroscopic residual tumor; SD, stable disease. Abbreviation: MST, median survival time. Adjusted for age, TNM stage, cancer status, primary therapy outcome, residual tumor, targeted molecular therapy, and radiation therapy. Abbreviations: CR, complete response; G1, highly differentiated; G2, moderately differentiated; G3, poorly differentiated; MSI-L, microsatellite instability-low; MST, median survival time; M, metastasis; N, node; NA, not applicable; PD, progressive disease; PR, partial response; R0, no residual tumor; R1, microscopic residual tumor; R2, macroscopic residual tumor; SD, stable disease; T, tumor. Abbreviation: MST, median survival time. Adjusted for histologic grade, radiation therapy, radical resection, targeted molecular therapy, and stage.

K–M plotter survival analysis

The expression profile for the ADAMTS genes in the K–M plotter database demonstrated that patients with high expression had poor clinical outcomes (Figure 10 and Table 5). The data from the Lauren classification of the ADAMTS mRNA expression of the GC patients showed that the patients with high expression of diffuse ADAMTS5 (Affymetrix ID: 235368_at) gene, intestinal ADAMTS1 (Affymetrix ID: 222486_s_at), ADAMTS2 (Affymetrix ID: 226311_at), ADAMTS3 (Affymetrix ID: 214913_at), ADAMTS4 (Affymetrix ID: 214913_at), ADAMTS6 (Affymetrix ID: 237411_at) or ADAMTS7 (Affymetrix ID: 228911_at) had shorter survival time compared with those with low expression (Figures 11 and 12 and Table 6). Tables 7-10 show the stratified results of the ADAMTS expression in TNM stage, tumor differentiation degree, different treatment strategies, and HER2 status of the GC cases. The high expression of ADAMTS1 in tumor stage IV (adjusted P=0.036, adjusted HR = 1.52, 95% CI = 1.02−2.27), ADAMTS2 in stage III (adjusted P=0.02, adjusted HR = 1.56, 95% CI = 1.07−2.28), ADAMTS5 in stages III and IV (adjusted P=0.002, adjusted HR = 1.81, 95% CI = 1.24−2.64 and adjusted P=0.04, adjusted HR = 1.51, 95% CI = 1.02−2.25), ADAMTS7 in stages III and IV (adjusted P=0.0026, adjusted HR = 1.77, 95% CI = 1.21−2.57 and adjusted P=0.0047, adjusted HR = 1.76, 95% CI = 1.18−2.63) increased the mortality rate, while a high expression of ADAMTS2 in tumor stage I (adjusted P=0.03, adjusted HR = 0.26, 95% CI = 0.07−0.96) increased the survival rate in GC patients. Similarly, the high expression in G2 degree of tumor differentiation of ADAMTS7 (adjusted P=0.0074, adjusted HR = 2.39, 95% CI = 1.24−4.59) increased the death rate in GC patients. In addition, the mortality rate in patients with high expression of ADAMTS genes after surgery was higher than those with low expression. The HER2 state subgroup analysis showed that the high expression of ADAMTS family of genes increased mortality in patients with HER2 positive and HER2 negative status, but not in ADAMTS5, ADAMTS7 HER2 positive patients.
Figure 10

Survival analysis of ADAMTS mRNA expression in GC based on K–M plotter database

Survival analysis of ADAMTS1 (A), ADAMTS2 (B), ADAMTS3 (C), ADAMTS4 (D), ADAMTS5 (E), ADAMTS6 (F), ADAMTS7 (G), ADAMTS8 (H).

Table 5

Survival analysis of ADAMTS gene mRNA expression in GC cases in KM plotter database

Gene/Affymetrix IDLow/high expression casesHR (95% CI)P-value
ADAMTS1/222486_s_at317/3141.68 (1.35–2.09)2.9e-06
ADAMTS2/226311_at316/3151.49 (1.2–1.85)3e-04
ADAMTS3/214913_at448/4281.33 (1.12–1.58)0.00091
ADAMTS4/214913_at448/4281.33 (1.12–1.58)0.00091
ADAMTS5/235368_at317/3141.4 (1.12–1.74)0.0024
ADAMTS6/237411_at319/3121.7 (1.37–2.12)1.5e-06
ADAMTS7/228911_at344/2871.63 (1.32–2.03)7.2e-06
ADAMTS8/235649_at316/3151.72 (1.38–2.13)9.9e-07
Figure 11

Stratified analysis of ADAMTS1–4 gene mRNA in Lauren typing of GC cases in K–M plotter database

OS curves for (A) intestinal-type ADAMTS1, (B) diffuse-type ADAMTS1, (C) mixed-type ADAMTS1, (D) intestinal-type ADAMTS2, (E) diffuse-type ADAMTS2, (F) mixed-type ADAMTS2, (G) intestinal-type ADAMTS3, (H) diffuse-type ADAMTS3, (I) mixed-type ADAMTS3, (J) intestinal-type ADAMTS4, (K) diffuse-type ADAMTS4, (L) mixed-type ADAMTS4.

Figure 12

Stratified analysis of ADAMTS5-8 gene mRNA in Lauren typing of GC cases in K–M plotter database

OS curves for (A) intestinal-type ADAMTS5, (B) diffuse-type ADAMTS5, (C) mixed-type ADAMTS5, (D) intestinal-type ADAMTS6, (E) diffuse-type ADAMTS6, (F) mixed-type ADAMTS6, (G) intestinal-type ADAMTS7, (H) diffuse-type ADAMTS7, (I) mixed-type ADAMTS7, (J) intestinal-type ADAMTS8, (K) diffuse-type ADAMTS8, (L) mixed-type ADAMTS8.

Table 6

Stratified analysis of ADAMTS gene mRNA in Lauren typing in K–M plotter

GeneLauren typingLow/high expression casesHR (95% CI)P-value
ADAMTS1Intestinal136/1331.79 (1.23–2.59)0.0019
Diffuse120/1201.3 (0.92–1.83)0.13
Mixed14/151.96 (0.6–6.37)0.26
ADAMTS2Intestinal136/1331.55 (1.08–2.23)0.017
Diffuse120/1201.37 (0.97–1.94)0.069
Mixed14/150.9 (0.3–2.68)0.85
ADAMTS3Intestinal162/1581.55 (1.13–2.13)0.0063
Diffuse120/1211.35 (0.96–1.9)0.088
Mixed16/162.72 (0.92–8.06)0.061
ADAMTS4Intestinal162/1581.55 (1.13–2.13)0.0063
Diffuse120/1211.35 (0.96–1.9)0.088
Mixed16/162.72 (0.92–8.06)0.061
ADAMTS5Intestinal134/1351.23 (0.85–1.77)0.27
Diffuse120/1201.54 (1.09–2.18)0.013
Mixed14/150.9 (0.3–2.68)0.85
ADAMTS6Intestinal139/1301.81 (1.26–2.62)0.0013
Diffuse121/1191.3 (0.93–1.83)0.13
Mixed14/150.74 (0.25–2.24)0.6
ADAMTS7Intestinal147/1222.33 (1.61–3.38)4.3e-06
Diffuse133/1071.4 (0.99–1.96)0.054
Mixed16/131.54 (0.51–4.58)0.44
ADAMTS8Intestinal134/1351.42 (0.99–2.05)0.057
Diffuse121/1191.34 (0.95–1.89)0.093
Mixed14/152.21 (0.68–7.21)0.18

Survival analysis of ADAMTS mRNA expression in GC based on K–M plotter database

Survival analysis of ADAMTS1 (A), ADAMTS2 (B), ADAMTS3 (C), ADAMTS4 (D), ADAMTS5 (E), ADAMTS6 (F), ADAMTS7 (G), ADAMTS8 (H).

Stratified analysis of ADAMTS1–4 gene mRNA in Lauren typing of GC cases in K–M plotter database

OS curves for (A) intestinal-type ADAMTS1, (B) diffuse-type ADAMTS1, (C) mixed-type ADAMTS1, (D) intestinal-type ADAMTS2, (E) diffuse-type ADAMTS2, (F) mixed-type ADAMTS2, (G) intestinal-type ADAMTS3, (H) diffuse-type ADAMTS3, (I) mixed-type ADAMTS3, (J) intestinal-type ADAMTS4, (K) diffuse-type ADAMTS4, (L) mixed-type ADAMTS4.

Stratified analysis of ADAMTS5-8 gene mRNA in Lauren typing of GC cases in K–M plotter database

OS curves for (A) intestinal-type ADAMTS5, (B) diffuse-type ADAMTS5, (C) mixed-type ADAMTS5, (D) intestinal-type ADAMTS6, (E) diffuse-type ADAMTS6, (F) mixed-type ADAMTS6, (G) intestinal-type ADAMTS7, (H) diffuse-type ADAMTS7, (I) mixed-type ADAMTS7, (J) intestinal-type ADAMTS8, (K) diffuse-type ADAMTS8, (L) mixed-type ADAMTS8.

GSEA

The GSEA data indicated that, in the c2 category, the high expression of ADAMTS6 may participate in extracellular matrix organization, development of advanced GC, metastasis, ECM receptor interaction, vascular endothelial growth factor A (VEGFA), kirsten rat sarcoma viral oncogene (KRAS), c-Jun N-terminal kinase (JNK), and cadherin (CDH1) signaling pathways (Figure 13A–I). In the c5 category, down-regulation of the ADAMTS6 expression might be linked with DNA damage, cell cycle, apoptosis, glycolysis, fatty acid metabolism, mRNA catabolism, tumor protein p53, and tumor necrosis factor (TNF) signaling pathways (Figure 14A–I).
Figure 13

GSEA of C2 gene sets for high ADAMTS6 expression groups

(A) ‘Extracellular matrix organization’, (B) ‘Gastric cancer advanced vs early up’, (C) ‘ECM receptor interaction’, (D) ‘Tavazoie metastasis’, (E) ’Alonso metastasis EMT up’, (F) ‘KRAS targets up’ (G) ‘VEGFA targets 12HR’, (H) ‘JNK signaling up’ and (I) ‘CDH1 signaling pathway’ were enriched in the ADAMTS6 high-expression groups.

Figure 14

GSEA of C5 gene sets for low ADAMTS6 expression groups

The GO terms (A) ‘Intrinsic apoptotic signaling pathway’, (B) ‘Regulation of apoptotic signaling pathway’, (C) ‘Intrinsic apoptotic signaling pathway by P53 class mediator’, (D) ‘G1 DNA damage checkpoint’, (E) ‘Glycolysis process’, (F) ‘Fatty acid metabolic process’, (G) ‘Regulation of mRNA catabolic process’, (H) ‘Signal transduction by P53 class mediator’ and (I) ‘Tumor necrosis factor-mediated signaling pathways’ were enriched in the ADAMTS6 low-expression groups.

GSEA of C2 gene sets for high ADAMTS6 expression groups

(A) ‘Extracellular matrix organization’, (B) ‘Gastric cancer advanced vs early up’, (C) ‘ECM receptor interaction’, (D) ‘Tavazoie metastasis’, (E) ’Alonso metastasis EMT up’, (F) ‘KRAS targets up’ (G) ‘VEGFA targets 12HR’, (H) ‘JNK signaling up’ and (I) ‘CDH1 signaling pathway’ were enriched in the ADAMTS6 high-expression groups.

GSEA of C5 gene sets for low ADAMTS6 expression groups

The GO terms (A) ‘Intrinsic apoptotic signaling pathway’, (B) ‘Regulation of apoptotic signaling pathway’, (C) ‘Intrinsic apoptotic signaling pathway by P53 class mediator’, (D) ‘G1 DNA damage checkpoint’, (E) ‘Glycolysis process’, (F) ‘Fatty acid metabolic process’, (G) ‘Regulation of mRNA catabolic process’, (H) ‘Signal transduction by P53 class mediator’ and (I) ‘Tumor necrosis factor-mediated signaling pathways’ were enriched in the ADAMTS6 low-expression groups.

qRT-PCR and immunohistochemistry

Although the application of bioinformatics has given us directions, we still needed to combine sample verification to provide guidance for clinical research. We found that ADAMTS6 is mainly expressed in the cytoplasm of GC tissues. The positive expression of ADAMTS6 was observed in 10 (55.56%) and 4 (22.22%) cases of GC and adjacent normal tissues, respectively (Table 11, P<0.05), which proved that ADAMTS6 was up-regulated in GC compared with adjacent tissues (Figure 15A,B), consistent with TCGA findings (Figure 1, P<0.05).
Table 11

ADAMTS6 expression in GC and paracarcioma tissues

ADAMTS6 expressionP-value
Positive, n (%)Negative, n (%)
GC tissue (n=18)10 (55.56)8 (44.44)0.04
Paracarcioma tissues (n=18)4 (22.22)14 (77.78)
Figure 15

ADAMTS6 expression in GC patients

(A) Representative images of immunostaining for ADAMTS6 in GC and adjacent normal tissues. (B) ADAMTS6 levels were detected in 24 pairs of GC tissues by qRT-PCR, revealing higher ADAMTS6 expression in GC tissues relative to paracancerous tissues. *P<0.05.

ADAMTS6 expression in GC patients

(A) Representative images of immunostaining for ADAMTS6 in GC and adjacent normal tissues. (B) ADAMTS6 levels were detected in 24 pairs of GC tissues by qRT-PCR, revealing higher ADAMTS6 expression in GC tissues relative to paracancerous tissues. *P<0.05.

Discussions

Invasive growth and distant metastasis are the two key features that characterize malignancy, which are also the primary reason for high mortality. Rapid proliferation and metastasis of tumors is facilitated by emergence of new blood vessels in the stroma [32]. Thus, inhibiting angiogenesis may be an effective strategy to inhibit cancer growth. The most significant feature of the ADAMTS family of enzymes are their diversity in thrombospondin type 1 (TSP1) motifs at the C-terminus. The TSP1 motifs are highly conserved and are an endogenous inhibitor of angiogenesis. It inhibits endothelial cell proliferation, induce endothelial cell apoptosis and anti-angiogenesis through its interaction with CD36 receptor [33]. In our study, GO enrichment analysis revealed that ADAMTS gene family is related to extracellular matrix/structure organization, extracellular matrix disassembly, and binding of cell adhesion molecules. Cell adhesion molecules mediate the interaction between cells or between the cells and matrix [34]. These adhesion molecules are synthesized and secreted by a variety of cells, and participate in the occurrence and metastasis of tumors [35]. In preventing tumor metastasis, extracellular matrix organization plays an important regulatory roles and its dissolution can promote tumor growth and metastasis. Previous data have suggested that the ADAMTS family of genes plays a vital role in the degradation of extracellular matrix [36]. Thus, the ADAMTS genes might be key anti-invasive molecules. The co-expression of the ADAMTS1–8 genes shows positive correlation with GC tumors. Nevertheless, data on the molecular expression and the role of ADAMTS set of genes in GC are still scant. Several studies have revealed that ADAMTS1 is down-regulated in a variety of tumors [37-39], as well as in GC tumor tissue [40]. In our study, there was low expression of ADAMTS1 mRNA in GC tissues compared with those in adjacent tissues, P<0.001. Besides, the survival analysis also demonstrated that up-regulation of the ADAMTS1 is associated with poor survival time in GC patients (P=0.013), but not in multifactor analysis (P>0.05). Whereas the ADAMTS1 gene expression level correlates with OS in stomach cancer patients, it is not an independent prognostic biomarker for GC, thus more validation studies are required. Compared with the normal tissues, ADAMTS2 expression in GC tumor cells and fibroblasts was significantly increased, and the up-regulation of the ADAMTS2 was associated with poor prognosis of GC patients [41]. Our data demonstrated that the expression of ADAMTS2 increases in GC, P<0.001, but the high expression did not affect mortality in GC (P>0.05). It has also been observed that the increased expression of ADAMTS3 gene takes a major part in the development of myocardial infarction, osteoarthritis, and breast cancer [42]. Our study found the expression level of ADAMTS3 gene is not associated with the GC OS. Whereas ADAMTS4 expression was significantly up-regulated in invasive breast cancer tissues [43] and human glioma [44], our study showed that the expression of ADAMTS4 gene does not affect the OS of GC patients. Similarly, unlike previous studies [45-48], our findings shows that the expression profile of ADAMTS5 gene does not correlate with survival of GC patients. In addition, ADAMTS7 is involved in the migration and proliferation of smooth muscle and the development of atherosclerosis and restenosis [49]. Our current findings indicates the expression level of the ADAMTS7 gene is not related to OS in GC patients. Previous studies have shown that the ADAMTS8 expression is down-regulated in breast cancer, brain cancer, and non-small cell lung cancer [50-52]. Whereas our data showed down-regulation of the ADAMTS8 mRNA expression in GC, P<0.001, the low expression was not correlated with the risk of GC-specific mortality (P>0.05). The up-regulation of ADAMTS6 is a molecular marker for poor prognosis in esophageal squamous cell carcinoma [53]. Besides, some researches have demonstrated that ADAMTS6 is dysregulated in breast cancer [54], prolactin tumors [55], and colorectal cancer [56]. In this study, we revealed that ADAMTS6 mRNA is overexpressed in GC tissues, P<0.001. ADAMTS6 expression is largely correlated with tumor stage, targeted molecular therapy, radical resection, radiation therapy, and histological grade of the GC. It was confirmed by immunohistochemistry and qRT-PCR that ADAMTS6 is up-regulated in GC, consistent with TCGA findings, which may be related to the prognosis of GC patients. Stratified analysis of the clinic pathological parameters such as age, gender, and TNM stage also showed that patients with the high expression of ADAMTS6 have reduced survival than those with low expression. Similarly, the multivariate survival and stratified analysis showed that the ADAMTS6 mRNA was up-regulated in GC patients and led to poor survival time. In addition, the ADAMTS6 gene was shown to promote the occurrence and development of stomach cancer. Our study revealed that ADAMTS6 is a tumor promoter, whose overexpression mediates occurrence, proliferation, invasion, or metastasis of stomach tumor, thus leading to a high mortality. Therefore, the ADAMTS6 gene may be a potential therapeutic target for GC. The GSEA indicated that ADAMTS6 enriched cancer-related pathways, such as apoptosis, VEGF, KRAS, P53, JNK, CDH1, or TNF pathways, which may affect GC prognosis. Apoptosis plays a significant role in maintaining the stability of the internal environment. The balance between cell proliferation and apoptosis is pivotal to the stability of human internal environment, otherwise, any perturbation of this state might lead to tumorigenesis [57]. VEGF mediates angiogenesis, which has been considered as the strongest cytokine promoting tumor angiogenesis [58]. It has been shown that inhibition of the VEGF signaling pathway inhibits neovascularization, thus blocking the occurrence and metastasis of tumors [59]. KRAS is the most common mutation type in the RAS family of genes that affects the development of tumors [60,61]. Once KRAS mutates, it will lose the activity of GTP hydrolase, and thus continue to activate, promoting the uncontrolled cell proliferation and carcinogenesis. Besides, mutations in the p53 gene renders it ineffective in regulating cell growth, apoptosis, DNA repair and so on, causing cell transformation and cancer [62,63]. Deveci et al. demonstrated that the P53 gene is associated with the occurrence and development of GC [64]. JNK signaling pathway plays a significant part in regulation of cell cycle, reproduction, apoptosis, and cell stress. Moreover, Yan et al., emphasized that inhibition of JNK signaling pathway may lead to apoptosis and metastasis of GC [65,66]. CDH1 gene mutation is a marker for poor GC prognosis [67-70]. TNF is involved in the inflammation and cellular immune response as well as tumor regulatory mechanisms [71-73]. The findings infer that ADAMTS6 may be involved in cancer-related pathways, including VEGF, KRAS, P53, JNK, CDH1, or TNF pathways, which play a crucial role in GC prognosis. Our study has several limitations. Our study is dependent on data from a public database, thus the ADAMTS and survival analysis findings require further validation. Therefore, further experiment verification of the expression and function of ADAMTS is very necessary to improve the credibility of our current study. Besides, the underlying molecular mechanisms by which ADAMTS6 affects the occurrence and prognosis of GC needs further interrogation. In addition, because the information on TCGA database is incomplete, we were unable to clearly evaluate the relationship between the mRNA expression of the ADAMTS family of genes and protein expression. Subsequent studies need to reveal the biological mechanism of ADAMTS6 in the development and metastasis of GC from various aspects. Despite these limitations, the present study is the first to investigate the correlation between ADAMTS mRNA expression and survive time in patients with stomach cancer. Log-rank test with Cox regression survival analysis and K–M survival analysis method were performed and found that ADAMTS6 expression level was largely correlated to GC patient clinical prognosis outcome. Thus, the prognostic relationship between ADAMTS family and GC was verified in the KM plotter database. Finally, GSEA found the differences in biological processes and related tumor pathways. Once these consequences are confirmed, we anticipate that ADAMTS6-targeted therapy drugs will be used in GC patients.

Conclusions

Our research reveals that the up-regulation of ADAMTS6 is significantly associated with poor prognosis, and might be used as an independent predictive factor for GC. The potential mechanism of ADAMTS6 in GC prognosis was involved in cancer-related biologic processes and pathways, including apoptosis, VEGF, KRAS, P53, JNK, CDH1, or TNF pathways. Nevertheless, the potential mechanism of ADAMTS6 still need further verification and investigation.
Table 7

Stratified analysis of ADAMTS gene mRNA in stage in K–M plotter database

GeneStageLow/high expression casesHR (95% CI)P-value
ADAMTS1I31/311.95 (0.6–6.4)0.26
II69/661.57 (0.83–2.97)0.16
III98/991.11 (0.77–1.62)0.57
IV70/701.52 (1.02–2.27)0.036
ADAMTS2I31/310.26 (0.07–0.96)0.03
II68/670.62 (0.33–1.19)0.15
III98/991.56 (1.07–2.28)0.02
IV70/701.29 (0.87–1.91)0.2
ADAMTS3I35/321.18 (0.43–3.23)0.75
II70/701.52 (0.83–2.76)0.17
III152/1531.13 (0.85–1.5)0.4
IV74/741.46 (1–2.15)0.051
ADAMTS4I35/321.18 (0.43–3.23)0.75
II70/701.52 (0.83–2.76)0.17
III152/1531.13 (0.85–1.5)0.4
IV74/741.46 (1–2.15)0.051
ADAMTS5I31/310.48 (0.16–1.49)0.19
II68/671.66 (0.86–3.18)0.13
III98/991.81 (1.24–2.64)0.002
IV71/691.51 (1.02–2.25)0.04
ADAMTS6I32/300.41 (0.12–1.33)0.12
II68/671.4 (0.74–2.63)0.29
III98/991.36 (0.93–1.98)0.11
IV70/701.31 (0.88–1.95)0.18
ADAMTS7I34/282.56 (0.77–8.56)0.11
II82/531.35 (0.71–2.57)0.35
III110/871.77 (1.21–2.57)0.0026
IV74/661.76 (1.18–2.63)0.0047
ADAMTS8I31/311.87 (0.56–6.23)0.3
II68/671.52 (0.8–2.88)0.19
III98/991.2 (0.82–1.74)0.34
IV70/701.4 (0.94–2.09)0.096
Table 8

Stratified analysis of ADAMTS gene mRNA in degree of tumor differentiation in K–M plotter

GeneDifferentiationLow/high expression casesHR (95% CI)P-value
ADAMTS1G12/31142066032.99 (0–Inf)0.41
G234/330.89 (0.46–1.7)0.72
G360/610.87 (0.53–1.4)0.55
ADAMTS2G12/31142066039.57 (0–Inf)0.41
G234/331.31 (0.69–2.52)0.41
G360/611.34 (0.82–2.18)0.24
ADAMTS3G116/161.52 (0.64–3.61)0.34
G234/331.35 (0.71–2.59)0.36
G382/831.23 (0.82–1.83)0.32
ADAMTS4G116/161.52 (0.64–3.61)0.34
G234/331.35 (0.71–2.59)0.36
G382/831.23 (0.82–1.83)0.32
ADAMTS5G12/31142066032.99 (0–Inf)0.41
G234/330.96 (0.5–1.84)0.91
G360/611.16 (0.72–1.88)0.54
ADAMTS6G12/31142066039.57 (0–Inf)0.41
G236/311.49 (0.78–2.84)0.23
G362/591.07 (0.66–1.73)0.78
ADAMTS7G12/31142066042.6 (0–Inf)0.41
G240/272.39 (1.24–4.59)0.0074
G360/610.77 (0.48–1.26)0.3
ADAMTS8G12/31142066039.57 (0–Inf)0.41
G234/331.43 (0.75–2.74)0.28
G360/611.1 (0.68–1.79)0.69
Table 9

Stratified analysis of ADAMTS genes mRNA in treatment method in K–M plotter

GeneTreatment methodLow/high expression casesHR (95% CI)P-value
ADAMTS1Surgery189/1911.66 (1.24–2.22)0.00065
5-FU17/170.99 (0.4–2.47)0.98
ADAMTS2Surgery192/1881.32 (0.99–1.76)0.061
5-FU17/172.26 (0.89–5.76)0.08
ADAMTS3Surgery194/1861.54 (1.15–2.06)0.0032
5-FU77/760.88 (0.63–1.24)0.47
ADAMTS4Surgery194/1861.54 (1.15–2.06)0.0032
5-FU77/760.88 (0.63–1.24)0.47
ADAMTS5Surgery190/1901.28 (0.96–1.71)0.096
5-FU17/170.68 (0.27–1.71)0.42
ADAMTS6Surgery190/1901.75 (1.31–2.34)0.00014
5-FU17/172.04 (0.81–5.16)0.12
ADAMTS7Surgery209/1711.57 (1.17–2.09)0.0021
5-FU17/171 (0.4–2.47)1
ADAMTS8Surgery190/1901.48 (1.11–1.98)0.0075
5-FU17/172.25 (0.85–5.95)0.094
Table 10

Stratified analysis of ADAMTS gene mRNA in HER2 state in K–M plotter database

GeneHER2 stateLow/high expression casesHR (95% CI)P-value
ADAMTS1Negative214/2151.6 (1.23–2.1)0.00049
Positive101/1011.94 (1.32–2.84)0.00063
ADAMTS2Negative214/2151.46 (1.12–1.91)0.0049
Positive101/1011.59 (1.09–2.32)0.015
ADAMTS3Negative266/2661.49 (1.19–1.87)0.00053
Positive172/1721.26 (0.97–1.63)0.082
ADAMTS4Negative266/2661.49 (1.19–1.87)0.00053
Positive172/1721.26 (0.97–1.63)0.082
ADAMTS5Negative216/2131.44 (1.1–1.87)0.0077
Positive102/1001.37 (0.94–1.99)0.1
ADAMTS6Negative215/2141.75 (1.33–2.29)4.2e-05
Positive101/1011.59 (1.09–2.31)0.015
ADAMTS7Negative225/2041.85 (1.42–2.43)4.6e-06
Positive104/981.33 (0.92–1.93)0.13
ADAMTS8Negative214/2151.75 (1.34–2.29)3.7e-05
Positive101/1011.64 (1.12–2.38)0.0096
  71 in total

1.  ADAMTS9 is a cell-autonomously acting, anti-angiogenic metalloprotease expressed by microvascular endothelial cells.

Authors:  Bon-Hun Koo; David M Coe; Laura J Dixon; Robert P T Somerville; Courtney M Nelson; Lauren W Wang; Mary Elizabeth Young; Daniel J Lindner; Suneel S Apte
Journal:  Am J Pathol       Date:  2010-01-21       Impact factor: 4.307

2.  HLA class II alleles in Chinese patients with hepatocellular carcinoma.

Authors:  P T Donaldson; S Ho; R Williams; P J Johnson
Journal:  Liver       Date:  2001-04

Review 3.  Hereditary diffuse gastric cancer: translation of CDH1 germline mutations into clinical practice.

Authors:  Parry Guilford; Bostjan Humar; Vanessa Blair
Journal:  Gastric Cancer       Date:  2010-04-07       Impact factor: 7.370

Review 4.  Adamalysins in biology and disease.

Authors:  Harry van Goor; Wynand B W H Melenhorst; Anthony J Turner; Stephen T Holgate
Journal:  J Pathol       Date:  2009-11       Impact factor: 7.996

5.  ADAMTS-6 is a predictor of poor prognosis in patients with esophageal squamous cell carcinoma.

Authors:  Lan Liu; Zhaoting Yang; Weidong Ni; Yanhua Xuan
Journal:  Exp Mol Pathol       Date:  2018-02-21       Impact factor: 3.362

6.  Quantitative assessment of common genetic variations in HLA-DP with hepatitis B virus infection, clearance and hepatocellular carcinoma development.

Authors:  Lei Yu; Yi-ju Cheng; Ming-liang Cheng; Yu-mei Yao; Quan Zhang; Xue-ke Zhao; Hua-juan Liu; Ya-xin Hu; Mao Mu; Bi Wang; Guo-zhen Yang; Li-li Zhu; Shuai Zhang
Journal:  Sci Rep       Date:  2015-10-14       Impact factor: 4.379

7.  Long-term risk of gastrointestinal cancers in persons with gastric or duodenal ulcers.

Authors:  Kirstine K Søgaard; Dóra K Farkas; Lars Pedersen; Jennifer L Lund; Reimar W Thomsen; Henrik T Sørensen
Journal:  Cancer Med       Date:  2016-02-29       Impact factor: 4.452

8.  High delta-like ligand 4 expression correlates with a poor clinical outcome in gastric cancer.

Authors:  Youjin Kim; Sun-Ju Byeon; Joonyoung Hur; Kangkook Lee; Dongin Kim; Jin-Hyung Ahn; Sang Hoon Lee; Weon-Kyoo You; Seung Tae Kim; Se Hoon Park; Won Ki Kang; Kyoung-Mee Kim; Jeeyun Lee
Journal:  J Cancer       Date:  2019-06-02       Impact factor: 4.207

9.  Helicobacter pylori infection assessed by ELISA and by immunoblot and noncardia gastric cancer risk in a prospective study: the Eurgast-EPIC project.

Authors:  C A González; F Megraud; A Buissonniere; L Lujan Barroso; A Agudo; E J Duell; M C Boutron-Ruault; F Clavel-Chapelon; D Palli; V Krogh; A Mattiello; R Tumino; C Sacerdote; J R Quirós; E Sanchez-Cantalejo; C Navarro; A Barricarte; M Dorronsoro; K-T Khaw; N Wareham; N E Allen; K K Tsilidis; H Bas Bueno-de-Mesquita; S M Jeurnink; M E Numans; P H M Peeters; P Lagiou; E Valanou; A Trichopoulou; R Kaaks; A Lukanova-McGregor; M M Bergman; H Boeing; J Manjer; B Lindkvist; R Stenling; G Hallmans; L M Mortensen; K Overvad; A Olsen; A Tjonneland; K Bakken; V Dumeaux; E Lund; M Jenab; I Romieu; D Michaud; T Mouw; F Carneiro; C Fenge; E Riboli
Journal:  Ann Oncol       Date:  2011-09-14       Impact factor: 32.976

10.  Dysregulated expression of adamalysin-thrombospondin genes in human breast carcinoma.

Authors:  Sarah Porter; Stuart D Scott; Elaine M Sassoon; Mark R Williams; J Louise Jones; Anne C Girling; Richard Y Ball; Dylan R Edwards
Journal:  Clin Cancer Res       Date:  2004-04-01       Impact factor: 12.531

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