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pmid:31354287

Identification of candidate biomarkers correlated with the diagnosis and prognosis of cervical cancer via integrated bioinformatics analysis.

Fangfang Dai · Gantao Chen · Yanqing Wang · Li Zhang · Youmei Long · Mengqin Yuan · Dongyong Yang · Shiyi Liu · Yanxiang Cheng · Liping Zhang

OncoTargets and therapy · 2019 doi:10.2147/OTT.S199615

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Experiment

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pmid:31354287
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title
Identification of candidate biomarkers correlated with the diagnosis and prognosis of cervical cancer via integrated bioinformatics analysis.
doi
10.2147/OTT.S199615
pmid
31354287
authors array · 10 items
[0]
Fangfang Dai
[1]
Gantao Chen
[2]
Yanqing Wang
[3]
Li Zhang
[4]
Youmei Long
[5]
Mengqin Yuan
[6]
Dongyong Yang
[7]
Shiyi Liu
[8]
Yanxiang Cheng
[9]
Liping Zhang
journal
OncoTargets and therapy
year
2019
abstract
Background: Cervical carcinoma is one of the most common malignant gynecological tumors and is associated with high rates of morbidity and mortality. Early diagnosis and early treatment can reduce the mortality rate of cervical cancer. However, there is still no specific biomarkers for the diagnosis and detection of cervical cancer prognosis. Therefore, it is greatly urgent in searching biomarkers correlated with the diagnosis and prognosis of cervical cancer. Results: The mRNA and microRNA expression profile datasets (GSE7803, GSE9750, GSE63514, and GSE30656) were downloaded from the Gene Expression Omnibus database (GEO). The three microarray datasets were integrated to one via integrated bioinformatics. Differentially expressed genes (DEGs) and microRNAs (DEMs) were obtained by R software. The protein-protein interaction (PPI) networks of the DEGs were performed from the STRING database and further visualized by Cytoscape software. A total of 83 DEGs and 14 DEMs were screened from the microarray expression profile datasets. The miRNAs validated to be associated with cervical cancer were obtained using HMDD online website and the target genes of DEMs were identified using the miRWalk2.0 online database. ESR1, PPP1R3C, NSG1, and TMPRSS11D were the gene targets of hsa-miR-21; the targets of hsa-miR-16 were GYS2, ENDOU, and KLF4. These targets were all downregulated in cervical cancer. Finally, we verified the expression of those targets in cervical tissues from TCGA and GTEx databases and analyzed their relationship with survival of cervical cancer patients. In the end, the expression of key genes in cervical cancer tissues was verified via experiment method, we found KLF4 and ESR1 were downregulated in tumor tissues. Conclusion: This study indicates that KLF4 and ESR1 are downregulated by the upregulated miR21 and miRNA16 in cervical cancer, respectively, using bioinformatics analysis, and the lower expression of KLF4 and ESR1 is closely related to the poor prognosis. They might be of clinical significance for the diagnosis and prognosis of cervical cancer, and provide effective targets for the treatment of cervical cancer.
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GraphPad Prism5 was used for statistical analysis, independent samples t-test was used to analyze the correlation between KLF4, ESR1 expression, and tissue parameters.
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AIM2
[3]
TOP2A
[4]
DTL
[5]
RFC4
[6]
APOBEC3B
[3] array · 7 items
[0]
Downregulated (71)
[1]
LOR
[2]
SULT2B1
[3]
GLTP
[4]
CYP3A5
[5]
FGFBP1
[6]
EMP1
[4] array · 7 items
[0]
[1]
S100A9
[2]
ZNF750
[3]
SERPINB1
[4]
SERPINB4
[5]
KLF4
[6]
SPRR2C
[5] array · 7 items
[0]
[1]
TGM3
[2]
ID4
[3]
SOSTDC1
[4]
KRT6B
[5]
SERPINB2
[6]
CDA
[6] array · 7 items
[0]
[1]
ZBED2
[2]
CFD
[3]
HSPB8
[4]
PRSS3
[5]
NSG1
[6]
TGM1
[7] array · 7 items
[0]
[1]
PPL
[2]
IL18
[3]
CRABP2
[4]
ALOX12B
[5]
ACPP
[6]
CXCL14
[8] array · 7 items
[0]
[1]
PI3
[2]
HOPX
[3]
GYS2
[4]
S100A12
[5]
IL1R2
[6]
AKR1B10
[9] array · 7 items
[0]
[1]
GREB1
[2]
CRYAB
[3]
KLK12
[4]
KLK11
[5]
GPX3
[6]
TMPRSS11D
[10] array · 7 items
[0]
[1]
KLK13
[2]
CWH43
[3]
SPRR1B
[4]
IVL
[5]
SPRR2B
[6]
ESR1
[11] array · 7 items
[0]
[1]
EDN3
[2]
SLURP1
[3]
HPGD
[4]
SPRR1A
[5]
TMPRSS11E
[6]
CLIC3
[12] array · 7 items
[0]
[1]
BBOX1
[2]
ENDOU
[3]
CLCA4
[4]
ALOX12
[5]
PPP1R3C
[6]
UPK1A
[13] array · 7 items
[0]
[1]
RHCG
[2]
CRCT1
[3]
SPINK5
[4]
KRT4
[5]
SPRR3
[6]
DSG1
[14] array · 7 items
[0]
[1]
KRT13
[2]
KRT1
[3]
MAL
[4]
CRISP3
[5]
CRNN
[6]
locator
section[12]/table-wrap[1]#T0003
[7] object · 16 fields
id
asset:table:pmid:31354287:3
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table
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jats-table
media_type
application/vnd.jats.table+json
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null
sha256
sha256:79c25e5009dcd269266bec2e42af2e8d76eac14f16aeacf3409cdd3f701df9d0
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467
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open
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verified
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read_table
reader_status
available
identifier
section[12]/table-wrap[2]#T0004
version
null
note
Screening DEMs in cervical cancer
structure object · 5 fields
pmcid
PMC6581759
section_index
12
section_title
Identification of DEGs and DEMs in cervical cancer
row_count
6
column_count
4
table object · 8 fields
label
Table 3
caption
Screening DEMs in cervical cancer
rows array · 6 items
[0] array · 3 items
[0]
DEMs
[1]
miRNA names
[2]
[1] array · 4 items
[0]
Upregulated (3)
[1]
hsa-miR-21
[2]
hsa-miR-106b
[3]
hsa-miR-16
[2] array · 4 items
[0]
Downregulated (11)
[1]
hsa-miR-572
[2]
hsa-miR-149
[3]
hsa-miR-494
[3] array · 4 items
[0]
[1]
hsa-miR-630
[2]
hsa-miR-638
[3]
hsa-miR-193b
[4] array · 4 items
[0]
[1]
hsa-miR-575
[2]
hsa-miR-99a
[3]
hsa-miR-205
[5] array · 4 items
[0]
[1]
hsa-miR-370
[2]
hsa-miR-203
[3]
locator
section[12]/table-wrap[2]#T0004
[8] object · 16 fields
id
asset:table:pmid:31354287:4
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table
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jats-table
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application/vnd.jats.table+json
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null
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sha256:8a59377669ff8732213741362ab231700a62f5e26e61cb3f90d8a2cdb0a75272
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966
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open
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verified
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read_table
reader_status
available
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section[13]/table-wrap[1]#T0005
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null
note
miRNAs which have been reported and validated in cervical cancer
structure object · 5 fields
pmcid
PMC6581759
section_index
13
section_title
Validated miRNAs in cervical cancer
row_count
8
column_count
7
table object · 8 fields
label
Table 4
caption
miRNAs which have been reported and validated in cervical cancer
rows array · 8 items
[0] array · 1 item
[0]
MiRNAs which have been reported and validated in cervical cancer
[1] array · 7 items
[0]
hsa-let-7a
[1]
hsa-miR-107
[2]
hsa-miR-10b
[3]
hsa-miR-124
[4]
hsa-miR-125b
[5]
hsa-miR-130a
[6]
hsa-miR-138
[2] array · 7 items
[0]
hsa-miR-143
[1]
hsa-miR-145
[2]
hsa-miR-146a
[3]
hsa-miR-146b
[4]
hsa-miR-150
[5]
hsa-miR-152
[6]
hsa-miR-155
[3] array · 7 items
[0]
hsa-miR-15b
[1]
hsa-miR-16
[2]
hsa-miR-195
[3]
hsa-miR-19a
[4]
hsa-miR-200a
[5]
hsa-miR-200b
[6]
hsa-miR-204
[4] array · 7 items
[0]
hsa-miR-205
[1]
hsa-miR-21
[2]
hsa-miR-210
[3]
hsa-miR-214
[4]
hsa-miR-215
[5]
hsa-miR-218
[6]
hsa-miR-218
[5] array · 7 items
[0]
hsa-miR-218–2
[1]
hsa-miR-27b
[2]
hsa-miR-29a
[3]
hsa-miR-29c
[4]
hsa-miR-31
[5]
hsa-miR-320
[6]
hsa-miR-328
[6] array · 7 items
[0]
hsa-miR-34a
[1]
hsa-miR-373
[2]
hsa-miR-376c
[3]
hsa-miR-425
[4]
hsa-miR-429
[5]
hsa-miR-483
[6]
hsa-miR-634
[7] array · 7 items
[0]
hsa-miR-644a
[1]
hsa-miR-744
[2]
hsa-miR-9
[3]
hsa-miR-93
[4]
hsa-miR-98
[5]
[6]
locator
section[13]/table-wrap[1]#T0005
[9] object · 16 fields
id
asset:table:pmid:31354287:5
role
table
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jats-table
media_type
application/vnd.jats.table+json
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null
sha256
sha256:be608d71a5bb4b5c347dc0c138e7414732c527678e42b2de98a73bcebf4f1108
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921
access
open
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verified
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read_table
reader_status
available
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section[22]/table-wrap[1]#ST0001
version
null
note
The log FC and P.Value of top 10 up and down-DEGs
structure object · 5 fields
pmcid
PMC6581759
section_index
22
section_title
Supplementary materials
row_count
13
column_count
4
table object · 8 fields
label
Table S1
caption
The log FC and P.Value of top 10 up and down-DEGs
rows array · 13 items
[0] array · 4 items
[0]
Genes
[1]
Official full name
[2]
Log FC
[3]
P-value
[1] array · 1 item
[0]
Upregulated
[2] array · 4 items
[0]
CDKN2A
[1]
Cyclin dependent kinase inhibitor 2A
[2]
1.975412633
[3]
5.31E-39
[3] array · 4 items
[0]
SPP1
[1]
Secreted phosphoprotein 1
[2]
1.354063506
[3]
1.82E-13
[4] array · 4 items
[0]
SYCP2
[1]
Synaptonemal complex protein 2
[2]
1.265154917
[3]
4.27E-16
[5] array · 4 items
[0]
MMP12
[1]
Matrix metallopeptidase 12
[2]
1.248880396
[3]
1.85E-18
[6] array · 4 items
[0]
CXCL8
[1]
C-X-C motif chemokine ligand 8
[2]
1.181026013
[3]
5.60E-16
[7] array · 1 item
[0]
Downregulated
[8] array · 4 items
[0]
KRT13
[1]
Keratin 13
[2]
−2.146391633
[3]
9.14E-15
[9] array · 4 items
[0]
KRT1
[1]
Keratin 1
[2]
−2.310120444
[3]
1.14E-23
[10] array · 4 items
[0]
MAL
[1]
Mal, T-cell differentiation protein
[2]
−2.677048339
[3]
4.26E-34
[11] array · 4 items
[0]
CRISP3
[1]
Cysteine rich secretory protein 3
[2]
−2.771740965
[3]
1.23E-38
[12] array · 4 items
[0]
CRNN
[1]
Cornulin
[2]
−2.987899149
[3]
1.23E-42
locator
section[22]/table-wrap[1]#ST0001
gaps array · 8 items
[0]
asset:data:geo:GSE30656 not downloaded
[1]
asset:data:geo:GSE30656 licence not read
[2]
asset:data:geo:GSE63514 not downloaded
[3]
asset:data:geo:GSE63514 licence not read
[4]
asset:data:geo:GSE7803 not downloaded
[5]
asset:data:geo:GSE7803 licence not read
[6]
asset:data:geo:GSE9750 not downloaded
[7]
asset:data:geo:GSE9750 licence not read
analysis object · 4 fields
state
partial
methods array · 2 items
[0] object · 10 fields
id
method-1
family
network_analysis
specific
ppi_network
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Network / pathway analysis
dsl_operations array · 3 items
[0]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
missing_operations array · 0 items
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data_shapes array · 1 item
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edge_list
evidence object · 3 fields
score
0.7
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medium
quote
protein-protein interaction
[1] object · 10 fields
id
method-2
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null
specific
null
label
Independent samples t-test
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evidence object · 3 fields
score
1
band
verbatim
quote
GraphPad Prism5 was used for statistical analysis, independent samples t-test was used to analyze the correlation between KLF4, ESR1 expression, and tissue parameters.
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gaps array · 1 item
[0]
method.reader_named_unmapped
capability object · 3 fields
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complete
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[0] object · 6 fields
capability_id
capability:computational_analysis
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Computational analysis platform
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[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
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statistical_regression
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pca
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molecular_docking
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network_analysis
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differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
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readiness object · 8 fields
state
complete
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R1
blocked_on
nothing:replicable_now
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method object · 5 fields
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R1
expressible
true
required_operations array · 3 items
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build_graph
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centrality_scores
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pathway_enrich
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: ppi_network.
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 · 4 items
[0]
GSE7803
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GSE9750
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GSE63514
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GSE30656
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[0]
GSE7803
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GSE9750
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GSE63514
[3]
GSE30656
statements array · 1 item
[0]
The platform for GSE7803 is GPL96, [HG-U133A] Affymetrix Human Genome U133A Array, which contains 10 normal cervical samples and 21 cervical cancer specimens;
basis
The paper names GSE7803, GSE9750, GSE63514, GSE30656 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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build_graph
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centrality_scores
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pathway_enrich
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source:pmid:31354287
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data:geo:GSE30656
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asset:table:pmid:31354287:1
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asset:table:pmid:31354287:3
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asset:table:pmid:31354287:4
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asset:table:pmid:31354287:5
steps array · 1 item
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run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
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capabilities array · 0 items
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[0]
data:geo:GSE30656
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review object · 5 fields
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partial
machine_critique object · 5 fields
kind
extraction_review
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model
MiniMax-M3.1-Flash
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minimax
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minimax
read_at
2026-10-31T18:15:44Z
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single_reader
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verbatim
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null
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3
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30771
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2693
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784
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extraction
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complete
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minimax
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review.human_critique
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absent
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not_run
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execution.not_run
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pmid:31354287
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paper
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pmid:31354287
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source:pmid:31354287
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manuscript
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PMID 31354287 full text
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data:geo:GSE30656
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GEO GSE30656
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pmid:31354287
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PMID 31354287 full text
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GEO GSE30656
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agent:corpus-assay-engine
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agent:minimax-reader
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[0]
data:geo:GSE30656
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agent:corpus-assay-engine
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agent:minimax-reader
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MiniMax literature reader
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343fb30756ba6ec3
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parent_id
pmid:31354287
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asset:data:geo:GSE30656 licence not read
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asset:data:geo:GSE63514 licence not read
[3]
asset:data:geo:GSE63514 not downloaded
[4]
asset:data:geo:GSE7803 licence not read
[5]
asset:data:geo:GSE7803 not downloaded
[6]
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[7]
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execution.not_run
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method.reader_named_unmapped
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partial
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[0]
Expression Omnibus database (GEO). The three microarray datasets were integrated to one via integrat
[1]
f 83 DEGs and 14 DEMs were screened from the microarray expression profile datasets. The miRNAs vali
[2]
after three cervical cancer gene expression microarray datasets were integrated, (B) GSE30656 data.
[3]
after three cervical cancer gene expression microarray datasets were integrated; (B) GSE30656 data.
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supported
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protein-protein interaction
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31354287", "kind": "science-record", "revision": 8, "content_hash": "sha256:f6519fa8f070fc0834baec39292942e6f56c6bdebf9d31c318425f2696c86248", "created_at": "2026-11-01T15:40:48Z", "updated_at": "2026-11-01T22:00:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of candidate biomarkers correlated with the diagnosis and prognosis of cervical cancer via integrated bioinformatics analysis.", "doi": "10.2147/OTT.S199615", "pmid": "31354287", "authors": ["Fangfang Dai", "Gantao Chen", "Yanqing Wang", "Li Zhang", "Youmei Long", "Mengqin Yuan", "Dongyong Yang", "Shiyi Liu", "Yanxiang Cheng", "Liping Zhang"], "journal": "OncoTargets and therapy", "year": 2019, "abstract": "Background: Cervical carcinoma is one of the most common malignant gynecological tumors and is associated with high rates of morbidity and mortality. Early diagnosis and early treatment can reduce the mortality rate of cervical cancer. However, there is still no specific biomarkers for the diagnosis and detection of cervical cancer prognosis. Therefore, it is greatly urgent in searching biomarkers correlated with the diagnosis and prognosis of cervical cancer. Results: The mRNA and microRNA expression profile datasets (GSE7803, GSE9750, GSE63514, and GSE30656) were downloaded from the Gene Expression Omnibus database (GEO). The three microarray datasets were integrated to one via integrated bioinformatics. Differentially expressed genes (DEGs) and microRNAs (DEMs) were obtained by R software. The protein-protein interaction (PPI) networks of the DEGs were performed from the STRING database and further visualized by Cytoscape software. A total of 83 DEGs and 14 DEMs were screened from the microarray expression profile datasets. The miRNAs validated to be associated with cervical cancer were obtained using HMDD online website and the target genes of DEMs were identified using the miRWalk2.0 online database. ESR1, PPP1R3C, NSG1, and TMPRSS11D were the gene targets of hsa-miR-21; the targets of hsa-miR-16 were GYS2, ENDOU, and KLF4. These targets were all downregulated in cervical cancer. Finally, we verified the expression of those targets in cervical tissues from TCGA and GTEx databases and analyzed their relationship with survival of cervical cancer patients. In the end, the expression of key genes in cervical cancer tissues was verified via experiment method, we found KLF4 and ESR1 were downregulated in tumor tissues. Conclusion: This study indicates that KLF4 and ESR1 are downregulated by the upregulated miR21 and miRNA16 in cervical cancer, respectively, using bioinformatics analysis, and the lower expression of KLF4 and ESR1 is closely related to the poor prognosis. 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