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pmid:30816522

Analysis of gene co‑expression network reveals prognostic significance of CNFN in patients with head and neck cancer.

Baoling Liu · Guanhong Huang · Hongming Zhu · Zhaoming Ma · Xiaokang Tian · Li Yin · Xingya Gao · Xia He

Oncology reports · 2019 doi:10.3892/or.2019.7019

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title
Analysis of gene co‑expression network reveals prognostic significance of CNFN in patients with head and neck cancer.
doi
10.3892/or.2019.7019
pmid
30816522
authors array · 8 items
[0]
Baoling Liu
[1]
Guanhong Huang
[2]
Hongming Zhu
[3]
Zhaoming Ma
[4]
Xiaokang Tian
[5]
Li Yin
[6]
Xingya Gao
[7]
Xia He
journal
Oncology reports
year
2019
abstract
In patients with head and neck cancer (HNC), lymph node (N) metastases are associated with cancer aggressiveness and poor prognosis. Identifying meaningful gene modules and representative biomarkers relevant to the N stage helps predict prognosis and reveal mechanisms underlying tumor progression. The present study used a step‑wise approach for weighted gene co‑expression network analysis (WGCNA). Dataset GSE65858 was subjected to WGCNA. RNA sequencing data of HNC downloaded from the Cancer Genome Atlas (TCGA) and dataset GSE39366 were utilized to validate the results. Following data preprocessing, 4,295 genes were screened, and blue and black modules associated with the N stage of HNC were identified. A total of 16 genes [keratinocyte differentiation associated protein, suprabasin, cornifelin (CNFN), small proline rich protein 1B, desmoglein 1 (DSG1), chromosome 10 open reading frame 99, keratin 16 pseudogene 3, gap junction protein β2, dermokine, LY6/PLAUR domain containing 3, transmembrane protein 79, phospholipase A2 group IVE, transglutaminase 5, potassium two pore domain channel subfamily K member 6, involucrin, kallikrein related peptidase 8] that had a negative association with the N‑stage in the blue module, and two genes (structural maintenance of chromosomes 4 and mutS homolog 6) that had a positive association in the black module, were identified to be candidate hub genes. Following further validation in TCGA and dataset GSE65858, it was identified that CNFN and DSG1 were associated with the clinical stage of HNC. Survival analysis of CNFN and DSG1 was subsequently performed. Patients with increased expression of CNFN displayed better survival probability in dataset GSE65858 and TCGA. Therefore, CNFN was selected as the hub gene for further verification in the Gene Expression Profiling Interactive Analysis database. Finally, functional enrichment and gene set enrichment analyses were performed using datasets GSE65858 and GSE39366. Three gene sets, namely 'P53 pathway', 'estrogen response early' and 'estrogen response late', were enriched in the two datasets. In conclusion, CNFN, identified via the WGCNA algorithm, may contribute to the prediction of lymph node metastases and prognosis, probably by regulating the pathways associated with P53, and the early and late estrogen response.
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To construct a scale-free gene co-expression network, the WGCNA package in R (14,22,23) was used.
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[4]
TCGA HNSC
[5]
GSE65858
[6]
TCGA HNSC
[3] array · 7 items
[0]
[1]
[2]
[3]
[4]
[5]
[6]
[4] array · 13 items
[0]
Gene
[1]
Correlation
[2]
P-value
[3]
Correlation
[4]
P-value
[5]
Correlation
[6]
P-value
[7]
Correlation
[8]
P-value
[9]
F
[10]
P-value
[11]
F
[12]
P-value
[5] array · 13 items
[0]
C10orf99
[1]
−0.205
[2]
6.80×10−4
[3]
−0.218
[4]
9.08×10−6
[5]
−0.175
[6]
3.94×10−3
[7]
−0.113
[8]
1.91×10−2
[9]
2.799
[10]
4.05×10−2
[11]
2.119
[12]
9.71×10−2
[6] array · 13 items
[0]
CNFN
[1]
−0.144
[2]
1.77×10−2
[3]
−0.148
[4]
2.79×10−3
[5]
−0.144
[6]
1.82×10−2
[7]
−0.128
[8]
7.61×10−3
[9]
2.672
[10]
4.79×10−2
[11]
3.642
[12]
1.29×10−2
[7] array · 13 items
[0]
DMKN
[1]
−0.208
[2]
5.94×10−4
[3]
−0.089
[4]
7.20×10−2
[5]
−0.132
[6]
2.98×10−2
[7]
−0.019
[8]
6.87×10−1
[9]
1.724
[10]
1.63×10−1
[11]
0.298
[12]
8.27×10−1
[8] array · 13 items
[0]
DSG1
[1]
−0.287
[2]
1.56×10−6
[3]
−0.172
[4]
5.03×10−4
[5]
−0.279
[6]
3.22×10−6
[7]
−0.125
[8]
9.19×10−3
[9]
8.185
[10]
3.13×10−5
[11]
2.982
[12]
3.11×10−2
[9] array · 13 items
[0]
GJB2
[1]
−0.19
[2]
1.72×10−3
[3]
−0.166
[4]
7.85×10−4
[5]
−0.153
[6]
1.18×10−2
[7]
−0.09
[8]
6.18×10−2
[9]
2.242
[10]
8.38×10−2
[11]
1.322
[12]
2.67×10−1
[10] array · 13 items
[0]
IVL
[1]
−0.222
[2]
2.31×10−4
[3]
−0.23
[4]
2.87×10−6
[5]
−0.134
[6]
2.78×10−2
[7]
−0.116
[8]
1.58×10−2
[9]
1.695
[10]
1.68×10−1
[11]
3.098
[12]
2.67×10−2
[11] array · 13 items
[0]
KCNK6
[1]
−0.187
[2]
2.07×10−3
[3]
−0.12
[4]
1.56×10−2
[5]
−0.111
[6]
6.84×10−2
[7]
−0.037
[8]
4.49×10−1
[9]
1.829
[10]
1.42×10−1
[11]
0.423
[12]
7.37×10−1
[12] array · 13 items
[0]
KLK8
[1]
−0.224
[2]
2.13×10−4
[3]
−0.141
[4]
4.51×10−3
[5]
−0.166
[6]
6.33×10−3
[7]
−0.084
[8]
8.21×10−2
[9]
2.782
[10]
4.14×10−2
[11]
1.165
[12]
3.23×10−1
[13] array · 13 items
[0]
KRT16P3
[1]
−0.215
[2]
3.72×10−4
[3]
−0.074
[4]
1.36×10−1
[5]
−0.177
[6]
3.46×10−3
[7]
−0.019
[8]
6.87×10−1
[9]
3.073
[10]
2.83×10−2
[11]
0.19
[12]
9.03×10−1
[14] array · 13 items
[0]
KRTDAP
[1]
−0.183
[2]
2.49×10−3
[3]
−0.129
[4]
9.30×10−3
[5]
−0.175
[6]
3.90×10−3
[7]
−0.052
[8]
2.82×10−1
[9]
2.9
[10]
3.55×10−2
[11]
0.417
[12]
7.41×10−1
[15] array · 13 items
[0]
LYPD3
[1]
−0.177
[2]
3.56×10−3
[3]
−0.176
[4]
3.73×10−4
[5]
−0.127
[6]
3.75×10−2
[7]
−0.121
[8]
1.15×10−2
[9]
1.929
[10]
1.25×10−1
[11]
2.563
[12]
5.43×10−2
[16] array · 13 items
[0]
MSH6
[1]
0.2
[2]
9.52×10−4
[3]
0.13
[4]
8.90×10−3
[5]
0.121
[6]
4.69×10−2
[7]
0.022
[8]
6.44×10−1
[9]
2.432
[10]
6.55×10−2
[11]
0.422
[12]
7.37×10−1
[17] array · 13 items
[0]
PLA2G4E
[1]
−0.209
[2]
5.39×10−4
[3]
−0.219
[4]
8.40×10−6
[5]
−0.095
[6]
1.19×10−1
[7]
−0.124
[8]
1.01×10−2
[9]
1.125
[10]
3.40×10−1
[11]
2.767
[12]
4.15×10−2
[18] array · 13 items
[0]
SBSN
[1]
−0.191
[2]
1.62×10−3
[3]
−0.125
[4]
1.15×10−2
[5]
−0.167
[6]
6.01×10−3
[7]
−0.084
[8]
8.05×10−2
[9]
2.66
[10]
4.86×10−2
[11]
1.746
[12]
1.57×10−1
[19] array · 13 items
[0]
SMC4
[1]
0.193
[2]
1.42×10−3
[3]
0.043
[4]
3.89×10−1
[5]
0.101
[6]
9.73×10−2
[7]
0
[8]
9.99×10−1
[9]
1.073
[10]
3.61×10−1
[11]
0.528
[12]
6.63×10−1
[20] array · 13 items
[0]
SPRR1B
[1]
−0.176
[2]
3.72×10−3
[3]
−0.185
[4]
1.80×10−4
[5]
−0.146
[6]
1.60×10−2
[7]
−0.115
[8]
1.71×10−2
[9]
2.02
[10]
1.11×10−1
[11]
3.132
[12]
2.55×10−2
[21] array · 13 items
[0]
TGM5
[1]
−0.22
[2]
2.76×10−4
[3]
−0.178
[4]
3.07×10−4
[5]
−0.165
[6]
6.48×10−3
[7]
−0.086
[8]
7.53×10−2
[9]
2.727
[10]
4.46×10−2
[11]
2.314
[12]
7.54×10−2
[22] array · 13 items
[0]
TMEM79
[1]
−0.186
[2]
2.19×10−3
[3]
−0.128
[4]
9.84×10−3
[5]
−0.139
[6]
2.20×10−2
[7]
−0.095
[8]
4.79×10−2
[9]
2.128
[10]
9.70×10−2
[11]
1.57
[12]
1.96×10−1
locator
section[15]/table-wrap[1]#tI-or-41-04-2168
gaps array · 4 items
[0]
asset:data:geo:GSE39366 not downloaded
[1]
asset:data:geo:GSE39366 licence not read
[2]
asset:data:geo:GSE65858 not downloaded
[3]
asset:data:geo:GSE65858 licence not read
analysis object · 4 fields
state
partial
methods array · 2 items
[0] object · 10 fields
id
method-1
family
group_comparison
specific
anova
label
Group comparison
dsl_operations array · 4 items
[0]
group_summary
[1]
welch_t
[2]
bootstrap_mean_difference
[3]
group_comparison_anova
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
two_or_more_groups_continuous
evidence object · 3 fields
score
0.8
band
high
quote
ANOVA
[1] object · 10 fields
id
method-2
family
null
specific
null
label
Weighted gene co-expression network analysis (WGCNA)
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
To construct a scale-free gene co-expression network, the WGCNA package in R (14,22,23) was used.
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 · 4 items
[0]
group_summary
[1]
welch_t
[2]
bootstrap_mean_difference
[3]
group_comparison_anova
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: anova.
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 · 2 items
[0]
GSE65858
[1]
GSE39366
resolved_accessions array · 2 items
[0]
GSE65858
[1]
GSE39366
statements array · 1 item
[0]
All data generated or analyzed during this study are included in this published article.
basis
The paper names GSE65858, GSE39366 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 · 4 items
[0]
group_summary
[1]
welch_t
[2]
bootstrap_mean_difference
[3]
group_comparison_anova
assets array · 3 items
[0]
source:pmid:30816522
[1]
data:geo:GSE39366
[2]
asset:table:pmid:30816522:1
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:GSE39366
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
2026-10-31T18:14:07Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
3
usage object · 3 fields
prompt_tokens
34194
completion_tokens
1840
cached_tokens
11656
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:30816522
type
paper
label
pmid:30816522
[1] object · 3 fields
id
source:pmid:30816522
type
manuscript
label
PMID 30816522 full text
[2] object · 3 fields
id
data:geo:GSE39366
type
dataset
label
GEO GSE39366
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:30816522
type
paper
label
pmid:30816522
[1] object · 3 fields
id
source:pmid:30816522
type
manuscript
label
PMID 30816522 full text
[2] object · 3 fields
id
data:geo:GSE39366
type
dataset
label
GEO GSE39366
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-30816522
type
extraction
used array · 1 item
[0]
source:pmid:30816522
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE39366
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE39366
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:30816522
parent_hash
sha256:9c80374c4bbfc34113917fa62140c8d984e9a762bccf468352ef31efe51b1b1c
fork_of
null
gaps array · 9 items
[0]
asset:data:geo:GSE39366 licence not read
[1]
asset:data:geo:GSE39366 not downloaded
[2]
asset:data:geo:GSE65858 licence not read
[3]
asset:data:geo:GSE65858 not downloaded
[4]
execution.not_run
[5]
method.reader_named_unmapped
[6]
review.human_critique
[7]
review.independent_reader
[8]
review.machine_critique
assays array · 2 items
[0] object · 11 fields
role
data_generation
definition_id
rna_seq
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.93
evidence array · 1 item
[0]
A). Dataset GSE65858 was subjected to WGCNA. RNA sequencing data of HNC downloaded from the Cancer Genom
order_basis
paper_text_order:515
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
group_comparison
definition_version
f2132e96ae6c7a4e
required_operations array · 4 items
[0]
group_summary
[1]
welch_t
[2]
bootstrap_mean_difference
[3]
group_comparison_anova
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
ANOVA
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:30816522", "kind": "science-record", "revision": 7, "content_hash": "sha256:fcbe68026cb07402a1c66c82909ead5f99d72714b97e0ec294b09e79072bbd81", "created_at": "2026-11-01T15:40:42Z", "updated_at": "2026-11-01T22:00:24Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analysis of gene co‑expression network reveals prognostic significance of CNFN in patients with head and neck cancer.", "doi": "10.3892/or.2019.7019", "pmid": "30816522", "authors": ["Baoling Liu", "Guanhong Huang", "Hongming Zhu", "Zhaoming Ma", "Xiaokang Tian", "Li Yin", "Xingya Gao", "Xia He"], "journal": "Oncology reports", "year": 2019, "abstract": "In patients with head and neck cancer (HNC), lymph node (N) metastases are associated with cancer aggressiveness and poor prognosis. Identifying meaningful gene modules and representative biomarkers relevant to the N stage helps predict prognosis and reveal mechanisms underlying tumor progression. The present study used a step‑wise approach for weighted gene co‑expression network analysis (WGCNA). Dataset GSE65858 was subjected to WGCNA. RNA sequencing data of HNC downloaded from the Cancer Genome Atlas (TCGA) and dataset GSE39366 were utilized to validate the results. Following data preprocessing, 4,295 genes were screened, and blue and black modules associated with the N stage of HNC were identified. A total of 16 genes [keratinocyte differentiation associated protein, suprabasin, cornifelin (CNFN), small proline rich protein 1B, desmoglein 1 (DSG1), chromosome 10 open reading frame 99, keratin 16 pseudogene 3, gap junction protein β2, dermokine, LY6/PLAUR domain containing 3, transmembrane protein 79, phospholipase A2 group IVE, transglutaminase 5, potassium two pore domain channel subfamily K member 6, involucrin, kallikrein related peptidase 8] that had a negative association with the N‑stage in the blue module, and two genes (structural maintenance of chromosomes 4 and mutS homolog 6) that had a positive association in the black module, were identified to be candidate hub genes. Following further validation in TCGA and dataset GSE65858, it was identified that CNFN and DSG1 were associated with the clinical stage of HNC. Survival analysis of CNFN and DSG1 was subsequently performed. Patients with increased expression of CNFN displayed better survival probability in dataset GSE65858 and TCGA. Therefore, CNFN was selected as the hub gene for further verification in the Gene Expression Profiling Interactive Analysis database. Finally, functional enrichment and gene set enrichment analyses were performed using datasets GSE65858 and GSE39366. Three gene sets, namely 'P53 pathway', 'estrogen response early' and 'estrogen response late', were enriched in the two datasets. In conclusion, CNFN, identified via the WGCNA algorithm, may contribute to the prediction of lymph node metastases and prognosis, probably by regulating the pathways associated with P53, and the early and late estrogen response.", "claims": [{"id": "claim-method", "statement": "To construct a scale-free gene co-expression network, the WGCNA package in R (14,22,23) was used.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30816522", "locator": "sections[7] \"Weighted gene co-expression network construction\" paragraph 2", "quote": "To construct a scale-free gene co-expression network, the WGCNA package in R (14,22,23) was used."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during this study are included in this published article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30816522", "locator": "sections[21] \"Availability of data and materials\" paragraph 2", "quote": "All data generated or analyzed during this study are included in this published article."}], "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:30816522"], "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:GSE39366"], "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:GSE39366"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:30816522", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3892/or.2019.7019", "sha256": 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