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pmid:29720944

Identification of Biomarkers Associated With Pathological Stage and Prognosis of Clear Cell Renal Cell Carcinoma by Co-expression Network Analysis.

Liang Chen · Lushun Yuan · Kaiyu Qian · Guofeng Qian · Yuan Zhu · Chin-Lee Wu · Han C Dan · Yu Xiao · Xinghuan Wang

Frontiers in physiology · 2018 doi:10.3389/fphys.2018.00399

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title
Identification of Biomarkers Associated With Pathological Stage and Prognosis of Clear Cell Renal Cell Carcinoma by Co-expression Network Analysis.
doi
10.3389/fphys.2018.00399
pmid
29720944
authors array · 9 items
[0]
Liang Chen
[1]
Lushun Yuan
[2]
Kaiyu Qian
[3]
Guofeng Qian
[4]
Yuan Zhu
[5]
Chin-Lee Wu
[6]
Han C Dan
[7]
Yu Xiao
[8]
Xinghuan Wang
journal
Frontiers in physiology
year
2018
abstract
Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer whose prognostic is affected by the tumor progression associated with complex gene interactions. However, there is currently no available molecular markers associated with ccRCC progression and used or clinical application. In our study, microarray data of 101 ccRCC samples and 95 normal kidney samples were analyzed and 2,425 differentially expressed genes (DEGs) were screened. Weighted gene co-expression network analysis (WGCNA) was then conducted and 11 co-expressed gene modules were identified. Module preservation analysis revealed that two modules (red and black) were found to be most stable. In addition, Pearson's correlation analysis identified the module most relevant to pathological stage(patho-module) (r = 0.44, p = 3e-07). Functional enrichment analysis showed that biological processes of the patho-module focused on cell cycle and cell division related biological process and pathway. In addition, 29 network hub genes highly related to ccRCC progression were identified from the stage module. These 29 hub genes were subsequently validated using 2 other independent datasets including GSE53757 (n = 72) and TCGA (n = 530), and the results indicated that all hub genes were significantly positive correlated with the 4 stages of ccRCC progression. Kaplan-Meier survival curve showed that patients with higher expression of each hub gene had significantly lower overall survival rate and disease-free survival rate, indicating that all hub genes could act as prognosis and recurrence/progression biomarkers of ccRCC. In summary, we identified 29 molecular markers correlated with different pathological stages of ccRCC. They may have important clinical implications for improving risk stratification, therapeutic decision and prognosis prediction in ccRCC patients.
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The “WGCNA” (Langfelder and Horvath, 2008) package in R was applied to performed co-expression network using the expression values of 2,425 DEGs from 125 tumor samples and complete clinical data (GSE73731).
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sections[5] "Weighted gene co-expression network construction" paragraph 2
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The “WGCNA” (Langfelder and Horvath, 2008) package in R was applied to performed co-expression network using the expression values of 2,425 DEGs from 125 tumor samples and complete clinical data (GSE73731).
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The headline result was that WGCNA-based analysis identified 29 hub genes associated with pathological stages of ccRCC, which were validated in independent datasets and associated with prognosis: “29 genes with the high connectivity in patho-module were selected as hub genes” and “Patients with higher each hub gene showed significantly shorter overall survival rate and disease-free survival rate, indicating that all hub genes could act as prognosis and recurrence biomarkers of ccRCC”. The statistical route used differential-expression screening followed by weighted co-expression network analysis, module preservation, correlation with clinical traits, one-way ANOVA for stage/grade comparisons, and Kaplan–Meier survival analysis.
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sections[14] "Identification of hub genes" paragraph 2
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29 genes with the high connectivity in patho-module were selected as hub genes
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sections[16] "Survival analysis" paragraph 2
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Patients with higher each hub gene showed significantly shorter overall survival rate and disease-free survival rate, indicating that all hub genes could act as prognosis and recurrence biomarkers of ccRCC
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The headline result was that WGCNA-based analysis identified 29 hub genes associated with pathological stages of ccRCC, which were validated in independent datasets and associated with prognosis: “29 genes with the high connectivity in patho-module were selected as hub genes” and “Patients with higher each hub gene showed significantly shorter overall survival rate and disease-free survival rate, indicating that all hub genes could act as prognosis and recurrence biomarkers of ccRCC”. The statistical route used differential-expression screening followed by weighted co-expression network analysis, module preservation, correlation with clinical traits, one-way ANOVA for stage/grade comparisons, and Kaplan–Meier survival analysis.
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GSM1931047
[52]
GSM1931048
[53]
GSM1931049
[54]
GSM1931050
[55]
GSM1931051
[56]
GSM1931052
[57]
GSM1931053
[58]
GSM1931054
[59]
GSM1931055
[60]
GSM1931056
[61]
GSM1931057
[62]
GSM1931058
[63]
GSM1931059
[64]
GSM1931060
[65]
GSM1931061
[66]
GSM1931062
[67]
GSM1931063
[68]
GSM1931064
[69]
GSM1931065
[70]
GSM1931066
[71]
GSM1931067
[72]
GSM1931068
[73]
GSM1931069
[74]
GSM1931070
[75]
GSM1931071
[76]
GSM1931072
[77]
GSM1931073
[78]
GSM1931074
[79]
GSM1931075
[80]
GSM1931076
[81]
GSM1931077
[82]
GSM1931078
[83]
GSM1931079
[84]
GSM1931080
[85]
GSM1931081
[86]
GSM1931082
[87]
GSM1931083
[88]
GSM1931084
[89]
GSM1931085
[90]
GSM1931086
[91]
GSM1931087
[92]
GSM1931088
[93]
GSM1931089
[94]
GSM1931090
[95]
GSM1931091
[96]
GSM1931092
[97]
GSM1931093
[98]
GSM1931094
[99]
GSM1931095
[100]
GSM1931096
[101]
GSM1931097
[102]
GSM1931098
[103]
GSM1931099
[104]
GSM1931100
[105]
GSM1931101
[106]
GSM1931102
[107]
GSM1931103
[108]
GSM1931104
[109]
GSM1931105
[110]
GSM1931106
[111]
GSM1931107
[112]
GSM1931108
[113]
GSM1931109
[114]
GSM1931110
[115]
GSM1931111
[116]
GSM1931112
[117]
GSM1931113
[118]
GSM1931114
[119]
GSM1931115
[120]
GSM1931116
[121]
GSM1931117
[122]
GSM1931118
[123]
GSM1931119
[124]
GSM1931120
[125]
GSM1931121
[126]
GSM1931122
[127]
GSM1931123
[128]
GSM1931124
[129]
GSM1931125
[130]
GSM1931126
[131]
GSM1931127
[132]
GSM1931128
[133]
GSM1931129
[134]
GSM1931130
[135]
GSM1931131
[136]
GSM1931132
[137]
GSM1931133
[138]
GSM1931134
[139]
GSM1931135
[140]
GSM1931136
[141]
GSM1931137
[142]
GSM1931138
[143]
GSM1931139
[144]
GSM1931140
[145]
GSM1931141
[146]
GSM1931142
[147]
GSM1931143
[148]
GSM1931144
[149]
GSM1931145
[150]
GSM1931146
[151]
GSM1931147
[152]
GSM1931148
[153]
GSM1931149
[154]
GSM1931150
[155]
GSM1931151
[156]
GSM1931152
[157]
GSM1931153
[158]
GSM1931154
[159]
GSM1931155
[160]
GSM1931156
[161]
GSM1931157
[162]
GSM1931158
[163]
GSM1931159
[164]
GSM1931160
[165]
GSM1931161
[166]
GSM1931162
[167]
GSM1931163
[168]
GSM1931164
[169]
GSM1931165
[170]
GSM1931166
[171]
GSM1931167
[172]
GSM1931168
[173]
GSM1931169
[174]
GSM1931170
[175]
GSM1931171
[176]
GSM1931172
[177]
GSM1931173
[178]
GSM1931174
[179]
GSM1931175
[180]
GSM1931176
[181]
GSM1931177
[182]
GSM1931178
[183]
GSM1931179
[184]
GSM1931180
[185]
GSM1931181
[186]
GSM1931182
[187]
GSM1931183
[188]
GSM1931184
[189]
GSM1931185
[190]
GSM1931186
[191]
GSM1931187
[192]
GSM1931188
[193]
GSM1931189
[194]
GSM1931190
[195]
GSM1931191
[196]
GSM1931192
[197]
GSM1931193
[198]
GSM1931194
[199]
GSM1931195
[200]
GSM1931196
[201]
GSM1931197
[202]
GSM1931198
[203]
GSM1931199
[204]
GSM1931200
[205]
GSM1931201
[206]
GSM1931202
[207]
GSM1931203
[208]
GSM1931204
[209]
GSM1931205
[210]
GSM1931206
[211]
GSM1931207
[212]
GSM1931208
[213]
GSM1931209
[214]
GSM1931210
[215]
GSM1931211
[216]
GSM1931212
[217]
GSM1931213
[218]
GSM1931214
[219]
GSM1931215
[220]
GSM1931216
[221]
GSM1931217
[222]
GSM1931218
[223]
GSM1931219
[224]
GSM1931220
[225]
GSM1931221
[226]
GSM1931222
[227]
GSM1931223
[228]
GSM1931224
[229]
GSM1931225
[230]
GSM1931226
[231]
GSM1931227
[232]
GSM1931228
[233]
GSM1931229
[234]
GSM1931230
[235]
GSM1931231
[236]
GSM1931232
[237]
GSM1931233
[238]
GSM1931234
[239]
GSM1931235
[240]
GSM1931236
[241]
GSM1931237
[242]
GSM1931238
[243]
GSM1931239
[244]
GSM1931240
[245]
GSM1931241
[246]
GSM1931242
[247]
GSM1931243
[248]
GSM1931244
[249]
GSM1931245
[250]
GSM1931246
[251]
GSM1931247
[252]
GSM1931248
[253]
GSM1931249
[254]
GSM1931250
[255]
GSM1931251
[256]
GSM1931252
[257]
GSM1931253
[258]
GSM1931254
[259]
GSM1931255
[260]
GSM1931256
[261]
GSM1931257
[262]
GSM1931258
[263]
GSM1931259
[264]
GSM1931260
sample_count
265
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Clear cell renal cell carcinoma (ccRCC) samples
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[5] object · 16 fields
id
asset:table:pmid:29720944:1
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:b578cb8b750596fa55f588d8bc9241e2009052b22738d419b650aba64d4290b5
bytes
2015
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[14]/table-wrap[1]#T1
version
null
note
Hub genes in the modules related with pathological stages.
structure object · 5 fields
pmcid
PMC5915556
section_index
14
section_title
Identification of hub genes
row_count
31
column_count
7
table object · 8 fields
label
Table 1
caption
Hub genes in the modules related with pathological stages.
rows array · 31 items
[0] array · 5 items
[0]
Gene
[1]
Entrez ID
[2]
Probe ID
[3]
Co-expression analysis
[4]
DEG analysis
[1] array · 7 items
[0]
[1]
[2]
[3]
cor.geneModuleMembership
[4]
cor.gene TraitSignificance
[5]
Fold change
[6]
FDR
[2] array · 7 items
[0]
ANLN
[1]
54443
[2]
222608_s_at
[3]
0.918
[4]
0.345
[5]
4.33
[6]
0
[3] array · 7 items
[0]
ASPM
[1]
259266
[2]
219918_s_at
[3]
0.870
[4]
0.383
[5]
4.95
[6]
0
[4] array · 7 items
[0]
ATAD2
[1]
29028
[2]
222740_at
[3]
0.813
[4]
0.356
[5]
2.57
[6]
0
[5] array · 7 items
[0]
BUB1B
[1]
701
[2]
203755_at
[3]
0.916
[4]
0.414
[5]
2.61
[6]
0
[6] array · 7 items
[0]
CCNA2
[1]
890
[2]
213226_at
[3]
0.872
[4]
0.311
[5]
2.57
[6]
0
[7] array · 7 items
[0]
CCNB1
[1]
891
[2]
214710_s_at
[3]
0.887
[4]
0.415
[5]
2.20
[6]
0
[8] array · 7 items
[0]
CDK1
[1]
983
[2]
203213_at
[3]
0.850
[4]
0.221
[5]
2.28
[6]
0
[9] array · 7 items
[0]
CDKN3
[1]
1033
[2]
1555758_a_at
[3]
0.813
[4]
0.266
[5]
2.36
[6]
0
[10] array · 7 items
[0]
CENPF
[1]
1063
[2]
207828_s_at
[3]
0.861
[4]
0.332
[5]
2.49
[6]
0
[11] array · 7 items
[0]
CENPK
[1]
64105
[2]
222848_at
[3]
0.840
[4]
0.322
[5]
4.19
[6]
0
[12] array · 7 items
[0]
CENPW
[1]
387103
[2]
226936_at
[3]
0.805
[4]
0.373
[5]
2.01
[6]
0
[13] array · 7 items
[0]
CEP55
[1]
55165
[2]
218542_at
[3]
0.935
[4]
0.384
[5]
2.88
[6]
0
[14] array · 7 items
[0]
DLGAP5
[1]
9787
[2]
203764_at
[3]
0.840
[4]
0.298
[5]
2.63
[6]
0
[15] array · 7 items
[0]
FOXM1
[1]
2305
[2]
202580_x_at
[3]
0.832
[4]
0.378
[5]
2.03
[6]
0
[16] array · 7 items
[0]
KIF11
[1]
3832
[2]
204444_at
[3]
0.855
[4]
0.395
[5]
2.61
[6]
0
[17] array · 7 items
[0]
KIF14
[1]
9928
[2]
236641_at
[3]
0.930
[4]
0.333
[5]
2.11
[6]
0
[18] array · 7 items
[0]
KIF20A
[1]
10112
[2]
218755_at
[3]
0.847
[4]
0.368
[5]
3.08
[6]
0
[19] array · 7 items
[0]
KIF4A
[1]
24137
[2]
218355_at
[3]
0.883
[4]
0.338
[5]
2.42
[6]
0
[20] array · 7 items
[0]
MELK
[1]
9833
[2]
204825_at
[3]
0.850
[4]
0.323
[5]
2.23
[6]
0
[21] array · 7 items
[0]
NCAPG
[1]
54892
[2]
218662_s_at
[3]
0.823
[4]
0.317
[5]
2.12
[6]
0
[22] array · 7 items
[0]
NEK2
[1]
4751
[2]
204641_at
[3]
0.857
[4]
0.345
[5]
2.41
[6]
0
[23] array · 7 items
[0]
NUF2
[1]
83540
[2]
223381_at
[3]
0.891
[4]
0.416
[5]
2.30
[6]
0
[24] array · 7 items
[0]
PRC1
[1]
9055
[2]
218009_s_at
[3]
0.909
[4]
0.366
[5]
4.01
[6]
0
[25] array · 7 items
[0]
PTTG1
[1]
9232
[2]
203554_x_at
[3]
0.829
[4]
0.285
[5]
2.76
[6]
0
[26] array · 7 items
[0]
RRM2
[1]
6241
[2]
201890_at
[3]
0.888
[4]
0.370
[5]
5.29
[6]
0
[27] array · 7 items
[0]
TOP2A
[1]
7153
[2]
201291_s_at
[3]
0.917
[4]
0.392
[5]
5.59
[6]
0
[28] array · 7 items
[0]
TPX2
[1]
22974
[2]
210052_s_at
[3]
0.833
[4]
0.360
[5]
2.91
[6]
0
[29] array · 7 items
[0]
TTK
[1]
7272
[2]
204822_at
[3]
0.846
[4]
0.443
[5]
2.55
[6]
0
[30] array · 7 items
[0]
UHRF1
[1]
29128
[2]
225655_at
[3]
0.867
[4]
0.331
[5]
3.65
[6]
0
locator
section[14]/table-wrap[1]#T1
gaps array · 8 items
[0]
asset:data:geo:GSE36895 not downloaded
[1]
asset:data:geo:GSE36895 licence not read
[2]
asset:data:geo:GSE40355 not downloaded
[3]
asset:data:geo:GSE40355 licence not read
[4]
asset:data:geo:GSE53757 not downloaded
[5]
asset:data:geo:GSE53757 licence not read
[6]
asset:data:geo:GSE73731 not downloaded
[7]
asset:data:geo:GSE73731 licence not read
analysis object · 4 fields
state
partial
methods array · 3 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
correlation_analysis
specific
pearson
label
Correlation analysis
dsl_operations array · 1 item
[0]
pearson_r
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
paired_numeric_columns
evidence object · 3 fields
score
0.7
band
medium
quote
Pearson's correlation
[2] object · 10 fields
id
method-3
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
The “WGCNA” (Langfelder and Horvath, 2008) package in R was applied to performed co-expression network using the expression values of 2,425 DEGs from 125 tumor samples and complete clinical data (GSE73731).
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 · 4 items
[0]
GSE53757
[1]
GSE36895
[2]
GSE73731
[3]
GSE40355
resolved_accessions array · 4 items
[0]
GSE53757
[1]
GSE36895
[2]
GSE73731
[3]
GSE40355
statements array · 1 item
[0]
Datasets GSE53757 Von Roemeling et al., 2014 and GSE36895 (Peña-Llopis et al., 2012) performed on the same platform Affymetrix Human Genome U133 Plus 2.0 Array (HG U133 Plus 2.0) were combined and analyzed to screen DEGs.
basis
The paper names GSE53757, GSE36895, GSE73731, GSE40355 in Gene Expression Omnibus (GEO) database 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 · 3 items
[0]
source:pmid:29720944
[1]
data:geo:GSE36895
[2]
asset:table:pmid:29720944:1
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:GSE36895
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
2026-10-31T18:34:21Z
independence
same_provider
evidence_check object · 2 fields
method
verbatim
availability
null
calls
8
usage object · 3 fields
prompt_tokens
74471
completion_tokens
5306
cached_tokens
10784
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:29720944
type
paper
label
pmid:29720944
[1] object · 3 fields
id
source:pmid:29720944
type
manuscript
label
PMID 29720944 full text
[2] object · 3 fields
id
data:geo:GSE36895
type
dataset
label
GEO GSE36895
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:29720944
type
paper
label
pmid:29720944
[1] object · 3 fields
id
source:pmid:29720944
type
manuscript
label
PMID 29720944 full text
[2] object · 3 fields
id
data:geo:GSE36895
type
dataset
label
GEO GSE36895
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-29720944
type
extraction
used array · 1 item
[0]
source:pmid:29720944
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE36895
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE36895
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:29720944
parent_hash
sha256:eb0623a8e834333f5834fbafa47ab7d2c251a7eaf418da7514e48dae1e3c3d04
fork_of
null
gaps array · 13 items
[0]
asset:data:geo:GSE36895 licence not read
[1]
asset:data:geo:GSE36895 not downloaded
[2]
asset:data:geo:GSE40355 licence not read
[3]
asset:data:geo:GSE40355 not downloaded
[4]
asset:data:geo:GSE53757 licence not read
[5]
asset:data:geo:GSE53757 not downloaded
[6]
asset:data:geo:GSE73731 licence not read
[7]
asset:data:geo:GSE73731 not downloaded
[8]
execution.not_run
[9]
method.reader_named_unmapped
[10]
review.human_critique
[11]
review.independent_reader
[12]
review.machine_critique
assays array · 2 items
[0] object · 11 fields
role
data_generation
definition_id
microarray
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]
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Complete file
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However, there is currently no available molecular markers associated with ccRCC progression and used or clinical application. In our study, microarray data of 101 ccRCC samples and 95 normal kidney samples were analyzed and 2,425 differentially expressed genes (DEGs) were screened. Weighted gene co-expression network analysis (WGCNA) was then conducted and 11 co-expressed gene modules were identified. Module preservation analysis revealed that two modules (red and black) were found to be most stable. In addition, Pearson's correlation analysis identified the module most relevant to pathological stage(patho-module) (r = 0.44, p = 3e-07). Functional enrichment analysis showed that biological processes of the patho-module focused on cell cycle and cell division related biological process and pathway. In addition, 29 network hub genes highly related to ccRCC progression were identified from the stage module. These 29 hub genes were subsequently validated using 2 other independent datasets including GSE53757 (n = 72) and TCGA (n = 530), and the results indicated that all hub genes were significantly positive correlated with the 4 stages of ccRCC progression. Kaplan-Meier survival curve showed that patients with higher expression of each hub gene had significantly lower overall survival rate and disease-free survival rate, indicating that all hub genes could act as prognosis and recurrence/progression biomarkers of ccRCC. In summary, we identified 29 molecular markers correlated with different pathological stages of ccRCC. They may have important clinical implications for improving risk stratification, therapeutic decision and prognosis prediction in ccRCC patients.", "claims": [{"id": "claim-method", "statement": "The “WGCNA” (Langfelder and Horvath, 2008) package in R was applied to performed co-expression network using the expression values of 2,425 DEGs from 125 tumor samples and complete clinical data (GSE73731).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29720944", "locator": "sections[5] \"Weighted gene co-expression network construction\" paragraph 2", "quote": "The “WGCNA” (Langfelder and Horvath, 2008) package in R was applied to performed co-expression network using the expression values of 2,425 DEGs from 125 tumor samples and complete clinical data (GSE73731)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Datasets GSE53757 Von Roemeling et al., 2014 and GSE36895 (Peña-Llopis et al., 2012) performed on the same platform Affymetrix Human Genome U133 Plus 2.0 Array (HG U133 Plus 2.0) were combined and analyzed to screen DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29720944", "locator": "sections[3] \"Data collection\" paragraph 2", "quote": "Datasets GSE53757 Von Roemeling et al., 2014 and GSE36895 (Peña-Llopis et al., 2012) performed on the same platform Affymetrix Human Genome U133 Plus 2.0 Array (HG U133 Plus 2.0) were combined and analyzed to screen DEGs."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The headline result was that WGCNA-based analysis identified 29 hub genes associated with pathological stages of ccRCC, which were validated in independent datasets and associated with prognosis: “29 genes with the high connectivity in patho-module were selected as hub genes” and “Patients with higher each hub gene showed significantly shorter overall survival rate and disease-free survival rate, indicating that all hub genes could act as prognosis and recurrence biomarkers of ccRCC”. 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The statistical route used differential-expression screening followed by weighted co-expression network analysis, module preservation, correlation with clinical traits, one-way ANOVA for stage/grade comparisons, and Kaplan–Meier survival analysis.", "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:29720944"], "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. 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