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pmid:32617019

Decreased PTGDS Expression Predicting Poor Survival of Endometrial Cancer by Integrating Weighted Gene Co-Expression Network Analysis and Immunohistochemical Validation.

Ruoyao Zou · Mingjun Zheng · Mingzi Tan · Haoya Xu · Nannan Luan · Liancheng Zhu

Cancer management and research · 2020 doi:10.2147/CMAR.S255753

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complete

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:32617019
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2026-11-01T15:41:05Z
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paper object · 10 fields
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title
Decreased PTGDS Expression Predicting Poor Survival of Endometrial Cancer by Integrating Weighted Gene Co-Expression Network Analysis and Immunohistochemical Validation.
doi
10.2147/CMAR.S255753
pmid
32617019
authors array · 6 items
[0]
Ruoyao Zou
[1]
Mingjun Zheng
[2]
Mingzi Tan
[3]
Haoya Xu
[4]
Nannan Luan
[5]
Liancheng Zhu
journal
Cancer management and research
year
2020
abstract
PURPOSE: To identify key pathogenic genes and reveal the potential molecular mechanisms of endometrial cancer (EC) using bioinformatics analysis and immunohistochemistry validation. MATERIALS AND METHODS: Through weighted gene co-expression network analysis (WGCNA), a co-expression network was constructed based on the top 25% variant genes in the GSE50830 dataset downloaded from gene expression omnibus (GEO). GO and KEGG pathway enrichment analyses were performed using the DAVID online tool. Candidate genes were selected using the cytoHubba plug-in of Cytoscape, mRNA expression levels and prognostic values in EC were analyzed by Oncomine, GEPIA, and Kaplan-Meier Plotter database to determine hub genes. One hub gene was validated by immunohistochemical (IHC) staining of 116 paraffin-embedded endometrial tissues and TCGA-UCEC cohort. Genes co-expressed with this hub gene were identified by LinkedOmics. Finally, its correlation with immune infiltration was evaluated by TIMER. RESULTS: Three co-expression modules and five candidate genes in each module were obtained by WGCNA; four hub genes were identified (LGR5, SST, ZNF558, and PTGDS). The mRNA levels of LGR5 and SST were significantly upregulated in EC, whereas those of ZNF558 and PTGDS were significantly downregulated; the expression of all four genes was associated with EC prognosis. Further validation demonstrated that PTGDS was significantly downregulated in the EC group compared with the atypical hyperplasia and normal endometrial groups, and its low expression was an independent risk factor for worse prognosis of EC. Biological function analysis indicated that PTGDS might be involved in the adaptive immune response, leukocyte migration, as well as in the regulation of cell adhesion molecules and chemokine signaling. Additionally, PTGDS expression was positively correlated with immune infiltration status of B cells, CD4+ T cells and macrophages. CONCLUSION: LGR5, SST, ZNF558, and PTGDS may participate in the development, progression, and prognosis of EC, in which PTGDS may be a novel biomarker and therapeutic target for EC.
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The Kaplan–Meier method was used for survival analysis and the Log rank test was used to test for significance.
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The Kaplan–Meier method was used for survival analysis and the Log rank test was used to test for significance.
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Screening Hub Genes in Pivotal Modules
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Screening of Hub Genes by cytoHubba (Degrees Ranking)
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PTGDS Expression in 116 Cases of Endometrial Tissues
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Expression of PTGDS in Endometrial Tissue in the Different Groups
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PTGDS Expression in 116 Cases of Endometrial Tissues
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Relationship Between PTGDS Expression and Clinical Pathological Parameters in 87 Cases of Malignant Endometrial Cancer
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Relationship Between PTGDS Expression and Clinical Pathological Parameters of EC
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Relationship Between PTGDS Expression and Clinical Pathological Parameters in 87 Cases of Malignant Endometrial Cancer
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Kaplan-Meier Survival Analysis of Clinicopathological Features and PTGDS Expression in 87 Cases of Malignant Endometrial Cancer
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Relationship Between PTGDS Expression and Prognosis in Patients with EC
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Kaplan-Meier Survival Analysis of Clinicopathological Features and PTGDS Expression in 87 Cases of Malignant Endometrial Cancer
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[3] array · 5 items
[0]
≥60
[1]
32 (36.8)
[2]
90.2 ± 7.1
[3]
76.3–104.1
[4]
[4] array · 5 items
[0]
FIGO stage
[1]
[2]
[3]
[4]
[5] array · 5 items
[0]
I–II
[1]
38 (43.7)
[2]
115.0 ± 4.4
[3]
106.4–123.7
[4]
13.234 (0.00027*)
[6] array · 5 items
[0]
III–IV
[1]
49 (56.3)
[2]
79.7 ± 4.5
[3]
70.9–88.6
[4]
[7] array · 5 items
[0]
Differentiation
[1]
[2]
[3]
[4]
[8] array · 5 items
[0]
Poor
[1]
37 (74.0)
[2]
82.9 ± 6.2
[3]
70.8–95.0
[4]
8.462 (0.0036*)
[9] array · 5 items
[0]
Well-moderate
[1]
50 (26.0)
[2]
108.2 ± 4.6
[3]
99.2–117.2
[4]
[10] array · 5 items
[0]
Myometrial invasion
[1]
[2]
[3]
[4]
[11] array · 5 items
[0]
<1/2
[1]
42 (48.3)
[2]
103.7 ± 4.4
[3]
95.1–112.3
[4]
8.701 (0.0032*)
[12] array · 5 items
[0]
≥1/2
[1]
45 (51.7)
[2]
87.4 ± 5.7
[3]
76.2–98.5
[4]
[13] array · 5 items
[0]
LN metastasis
[1]
[2]
[3]
[4]
[14] array · 5 items
[0]
No
[1]
49 (56.3)
[2]
110.0 ± 4.6
[3]
101.1–119.0
[4]
11.769 (6E-04*)
[15] array · 5 items
[0]
Yes
[1]
38 (43.7)
[2]
78.1 ± 5.0
[3]
68.3–88.0
[4]
[16] array · 5 items
[0]
Pathological subtype
[1]
[2]
[3]
[4]
[17] array · 5 items
[0]
non-endometrioid
[1]
17 (19.5)
[2]
88.6 ± 9.6
[3]
69.8–107.4
[4]
1.485 (0.223)
[18] array · 5 items
[0]
endometrioid
[1]
70 (80.5)
[2]
98.6 ± 4.1
[3]
90.6–106.6
[4]
[19] array · 5 items
[0]
PTGDS expression
[1]
[2]
[3]
[4]
[20] array · 5 items
[0]
Low
[1]
54 (62.1)
[2]
83.8 ± 4.6
[3]
74.6–92.9
[4]
9.515 (0.002*)
[21] array · 5 items
[0]
High
[1]
33 (37.9)
[2]
114.5 ± 4.5
[3]
105.6–123.4
[4]
locator
section[23]/table-wrap[1]#T0004
gaps array · 2 items
[0]
asset:data:geo:GSE50830 not downloaded
[1]
asset:data:geo:GSE50830 licence not read
analysis object · 4 fields
state
partial
methods array · 5 items
[0] object · 10 fields
id
method-1
family
survival_analysis
specific
kaplan_meier
label
Survival analysis
dsl_operations array · 1 item
[0]
survival_km
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.7
band
medium
quote
Kaplan-Meier
[1] object · 10 fields
id
method-2
family
survival_analysis
specific
cox_proportional_hazards
label
Survival analysis
dsl_operations array · 1 item
[0]
cox_ph
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
band
high
quote
Cox regression
[2] object · 10 fields
id
method-3
family
survival_analysis
specific
log_rank_test
label
Survival analysis
dsl_operations array · 1 item
[0]
logrank_test
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
band
high
quote
Log rank
[3] object · 10 fields
id
method-4
family
network_analysis
specific
pathway_enrichment
label
Network / pathway analysis
dsl_operations array · 1 item
[0]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.7
band
medium
quote
pathway enrichment
[4] object · 10 fields
id
method-5
family
null
specific
null
label
Kaplan–Meier method
dsl_operations array · 0 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 0 items
evidence object · 3 fields
score
1
band
verbatim
quote
The Kaplan–Meier method was used for survival analysis and the Log rank test was used to test for significance.
targets array · 0 items
gaps array · 1 item
[0]
method.reader_named_unmapped
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 1 item
[0]
survival_km
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 1 item
[0]
GSE50830
resolved_accessions array · 1 item
[0]
GSE50830
statements array · 1 item
[0]
"The GSE50830 gene expression profile dataset provided by Dezso et al (2014) in the GEO database (https://www.ncbi.nlm.nih.gov/geo) was retrieved and analyzed." "The gene expression data containing 168 EC samples was obtained based on the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array; Affymetrix, Santa Clara, CA, USA)."
basis
The paper names GSE50830 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 1 item
[0]
survival_km
assets array · 6 items
[0]
source:pmid:32617019
[1]
data:geo:GSE50830
[2]
asset:table:pmid:32617019:1
[3]
asset:table:pmid:32617019:2
[4]
asset:table:pmid:32617019:3
[5]
asset:table:pmid:32617019:4
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE50830
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-10-31T18:20:56Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
null
calls
3
usage object · 3 fields
prompt_tokens
41718
completion_tokens
2020
cached_tokens
784
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
gaps array · 3 items
[0]
review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
mode
null
runs array · 0 items
gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
[0] object · 3 fields
id
pmid:32617019
type
paper
label
pmid:32617019
[1] object · 3 fields
id
source:pmid:32617019
type
manuscript
label
PMID 32617019 full text
[2] object · 3 fields
id
data:geo:GSE50830
type
dataset
label
GEO GSE50830
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
[0] object · 3 fields
id
pmid:32617019
type
paper
label
pmid:32617019
[1] object · 3 fields
id
source:pmid:32617019
type
manuscript
label
PMID 32617019 full text
[2] object · 3 fields
id
data:geo:GSE50830
type
dataset
label
GEO GSE50830
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-32617019
type
extraction
used array · 1 item
[0]
source:pmid:32617019
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE50830
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE50830
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
label
Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
type
software
label
MiniMax literature reader
source_run_id
reading-sci-2026-11-01
dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
snapshot_id
ba34b49490d9bfaf
lineage object · 4 fields
parent_id
pmid:32617019
parent_hash
sha256:84f4b37f24540e6fd5c90352155ca676846c95ec5f846fd94fa08dbbc20e6df1
fork_of
null
gaps array · 7 items
[0]
asset:data:geo:GSE50830 licence not read
[1]
asset:data:geo:GSE50830 not downloaded
[2]
execution.not_run
[3]
method.reader_named_unmapped
[4]
review.human_critique
[5]
review.independent_reader
[6]
review.machine_critique
assays array · 2 items
[0] object · 11 fields
role
data_generation
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.99
evidence array · 4 items
[0]
hted Gene Co-Expression Network Analysis and Immunohistochemical Validation.
[1]
ancer (EC) using bioinformatics analysis and immunohistochemistry validation. MATERIALS AND METHODS: Through w
[2]
ine hub genes. One hub gene was validated by immunohistochemical (IHC) staining of 116 paraffin-embedded endo
[3]
b gene was validated by immunohistochemical (IHC) staining of 116 paraffin-embedded endometri
order_basis
paper_text_order:93
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
survival_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 1 item
[0]
survival_km
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
Kaplan-Meier
order_basis
consumes_earlier_output
id
assay:2
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32617019", "kind": "science-record", "revision": 7, "content_hash": "sha256:87bca8d85c815687e17a0407b4313aae6cc832cbb556e201e292526aa6a6f2d1", "created_at": "2026-11-01T15:41:05Z", "updated_at": "2026-11-01T21:23:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Decreased PTGDS Expression Predicting Poor Survival of Endometrial Cancer by Integrating Weighted Gene Co-Expression Network Analysis and Immunohistochemical Validation.", "doi": "10.2147/CMAR.S255753", "pmid": "32617019", "authors": ["Ruoyao Zou", "Mingjun Zheng", "Mingzi Tan", "Haoya Xu", "Nannan Luan", "Liancheng Zhu"], "journal": "Cancer management and research", "year": 2020, "abstract": "PURPOSE: To identify key pathogenic genes and reveal the potential molecular mechanisms of endometrial cancer (EC) using bioinformatics analysis and immunohistochemistry validation.\nMATERIALS AND METHODS: Through weighted gene co-expression network analysis (WGCNA), a co-expression network was constructed based on the top 25% variant genes in the GSE50830 dataset downloaded from gene expression omnibus (GEO). GO and KEGG pathway enrichment analyses were performed using the DAVID online tool. Candidate genes were selected using the cytoHubba plug-in of Cytoscape, mRNA expression levels and prognostic values in EC were analyzed by Oncomine, GEPIA, and Kaplan-Meier Plotter database to determine hub genes. One hub gene was validated by immunohistochemical (IHC) staining of 116 paraffin-embedded endometrial tissues and TCGA-UCEC cohort. Genes co-expressed with this hub gene were identified by LinkedOmics. Finally, its correlation with immune infiltration was evaluated by TIMER.\nRESULTS: Three co-expression modules and five candidate genes in each module were obtained by WGCNA; four hub genes were identified (LGR5, SST, ZNF558, and PTGDS). The mRNA levels of LGR5 and SST were significantly upregulated in EC, whereas those of ZNF558 and PTGDS were significantly downregulated; the expression of all four genes was associated with EC prognosis. Further validation demonstrated that PTGDS was significantly downregulated in the EC group compared with the atypical hyperplasia and normal endometrial groups, and its low expression was an independent risk factor for worse prognosis of EC. Biological function analysis indicated that PTGDS might be involved in the adaptive immune response, leukocyte migration, as well as in the regulation of cell adhesion molecules and chemokine signaling. Additionally, PTGDS expression was positively correlated with immune infiltration status of B cells, CD4+ T cells and macrophages.\nCONCLUSION: LGR5, SST, ZNF558, and PTGDS may participate in the development, progression, and prognosis of EC, in which PTGDS may be a novel biomarker and therapeutic target for EC.", "claims": [{"id": "claim-method", "statement": "The Kaplan–Meier method was used for survival analysis and the Log rank test was used to test for significance.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32617019", "locator": "sections[12] \"Statistical Analysis\" paragraph 2", "quote": "The Kaplan–Meier method was used for survival analysis and the Log rank test was used to test for significance."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:32617019"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE50830"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE50830"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32617019", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/CMAR.S255753", "sha256": "sha256:e16749e877140c0dd6883a4f615deddb69b1d3af8d1cf4fada006f9d4491c9c7", "bytes": 55225, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/CMAR.S255753", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7326400", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE50830", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50830", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE50830", "version": null, "note": "\"The GSE50830 gene expression profile dataset provided by Dezso et al (2014) in the GEO database (https://www.ncbi.nlm.nih.gov/geo) was retrieved and analyzed.\" \"The gene expression data containing 168 EC samples was obtained based on the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array; Affymetrix, Santa Clara, CA, USA).\"", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "\"The GSE50830 gene expression profile dataset provided by Dezso et al (2014) in the GEO database (https://www.ncbi.nlm.nih.gov/geo) was retrieved and analyzed.\" \"The gene expression data containing 168 EC samples was obtained based on the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array; Affymetrix, Santa Clara, CA, USA).\"", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM1230533", "GSM1230534", "GSM1230535", "GSM1230536", "GSM1230537", "GSM1230538", "GSM1230539", "GSM1230540", "GSM1230541", "GSM1230542", "GSM1230543", "GSM1230544", "GSM1230545", "GSM1230546", "GSM1230547", "GSM1230548", "GSM1230549", "GSM1230550", "GSM1230551", "GSM1230552", "GSM1230553", "GSM1230554", "GSM1230555", "GSM1230556", "GSM1230557", "GSM1230558", "GSM1230559", "GSM1230560", "GSM1230561", "GSM1230562", "GSM1230563", "GSM1230564", "GSM1230565", "GSM1230566", "GSM1230567", "GSM1230568", "GSM1230569", "GSM1230570", "GSM1230571", "GSM1230572", "GSM1230573", "GSM1230574", "GSM1230575", "GSM1230576", "GSM1230577", "GSM1230578", "GSM1230579", "GSM1230580", "GSM1230581", "GSM1230582", "GSM1230583", "GSM1230584", "GSM1230585", "GSM1230586", "GSM1230587", "GSM1230588", "GSM1230589", "GSM1230590", "GSM1230591", "GSM1230592", "GSM1230593", "GSM1230594", "GSM1230595", "GSM1230596", "GSM1230597", "GSM1230598", "GSM1230599", "GSM1230600", "GSM1230601", "GSM1230602", "GSM1230603", "GSM1230604", "GSM1230605", "GSM1230606", "GSM1230607", "GSM1230608", "GSM1230609", "GSM1230610", "GSM1230611", "GSM1230612", "GSM1230613", "GSM1230614", "GSM1230615", "GSM1230616", "GSM1230617", "GSM1230618", "GSM1230619", "GSM1230620", "GSM1230621", "GSM1230622", "GSM1230623", "GSM1230624", "GSM1230625", "GSM1230626", "GSM1230627", "GSM1230628", "GSM1230629", "GSM1230630", "GSM1230631", "GSM1230632", "GSM1230633", "GSM1230634", "GSM1230635", "GSM1230636", "GSM1230637", "GSM1230638", "GSM1230639", "GSM1230640", "GSM1230641", "GSM1230642", "GSM1230643", "GSM1230644", "GSM1230645", "GSM1230646", "GSM1230647", "GSM1230648", "GSM1230649", "GSM1230650", "GSM1230651", "GSM1230652", "GSM1230653", "GSM1230654", "GSM1230655", "GSM1230656", "GSM1230657", "GSM1230658", "GSM1230659", "GSM1230660", "GSM1230661", "GSM1230662", "GSM1230663", "GSM1230664", "GSM1230665", "GSM1230666", "GSM1230667", "GSM1230668", "GSM1230669", "GSM1230670", "GSM1230671", "GSM1230672", "GSM1230673", "GSM1230674", "GSM1230675", "GSM1230676", "GSM1230677", "GSM1230678", "GSM1230679", "GSM1230680", "GSM1230681", "GSM1230682", "GSM1230683", "GSM1230684", "GSM1230685", "GSM1230686", "GSM1230687", "GSM1230688", "GSM1230689", "GSM1230690", "GSM1230691", "GSM1230692", "GSM1230693", 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