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pmid:28808310

Genome-scale analysis identifies NEK2, DLGAP5 and ECT2 as promising diagnostic and prognostic biomarkers in human lung cancer.

Yuan-Xiang Shi · Ji-Ye Yin · Yao Shen · Wei Zhang · Hong-Hao Zhou · Zhao-Qian Liu

Scientific reports · 2017 doi:10.1038/s41598-017-08615-5

Paper

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Experiment

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Data

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title
Genome-scale analysis identifies NEK2, DLGAP5 and ECT2 as promising diagnostic and prognostic biomarkers in human lung cancer.
doi
10.1038/s41598-017-08615-5
pmid
28808310
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Yuan-Xiang Shi
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Ji-Ye Yin
[2]
Yao Shen
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Wei Zhang
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Hong-Hao Zhou
[5]
Zhao-Qian Liu
journal
Scientific reports
year
2017
abstract
This study aims to identify promising biomarkers for the early detection of lung cancer and evaluate the prognosis of lung cancer patients. Genome-wide mRNA expression data obtained from the Gene Expression Omnibus (GSE19188, GSE18842 and GSE40791), including 231 primary tumor samples and 210 normal samples, were used to discover differentially expressed genes (DEGs). NEK2, DLGAP5 and ECT2 were found to be highly expressed in tumor samples. These results were experimentally confirmed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The elevated expression of the three candidate genes was also validated using the Cancer Genome Atlas (TCGA) datasets, which consist of 349 tumor and 58 normal tissues. Furthermore, we performed receiver operating characteristics (ROC) analysis to assess the diagnostic value of these lung cancer biomarkers, and the results suggested that NEK2, DLGAP5 and ECT2 expression levels could robustly distinguish lung cancer patients from normal subjects. Finally, Kaplan-Meier analysis revealed that elevated NEK2, DLGAP5 and ECT2 expression was negatively correlated with both overall survival (OS) and relapse-free survival (RFS). Taken together, these findings indicate that these three genes might be used as promising biomarkers for the early detection of lung cancer, as well as predicting the prognosis of lung cancer patients.
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Survival analysis was carried out according to Kaplan–Meier analysis and the Log-rank test.
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GSM1001671
[162]
GSM1001672
[163]
GSM1001673
[164]
GSM1001674
[165]
GSM1001675
[166]
GSM1001676
[167]
GSM1001677
[168]
GSM1001678
[169]
GSM1001679
[170]
GSM1001680
[171]
GSM1001681
[172]
GSM1001682
[173]
GSM1001683
[174]
GSM1001684
[175]
GSM1001685
[176]
GSM1001686
[177]
GSM1001687
[178]
GSM1001688
[179]
GSM1001689
[180]
GSM1001690
[181]
GSM1001691
[182]
GSM1001692
[183]
GSM1001693
[184]
GSM1001694
[185]
GSM1001695
[186]
GSM1001696
[187]
GSM1001697
[188]
GSM1001698
[189]
GSM1001699
[190]
GSM1001700
[191]
GSM1001701
[192]
GSM1001702
[193]
GSM1001703
sample_count
194
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Usp44 binds centrin to regulate centrosome positioning and suppress tumorigenesis
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[4] object · 16 fields
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asset:table:pmid:28808310:1
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jats-table
media_type
application/vnd.jats.table+json
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null
sha256
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bytes
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reader_status
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version
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note
Clinicopathological characteristics of patients for clinical validation cohorts.
structure object · 5 fields
pmcid
PMC5556079
section_index
4
section_title
Independent validation
row_count
20
column_count
2
table object · 8 fields
label
Table 1
caption
Clinicopathological characteristics of patients for clinical validation cohorts.
rows array · 20 items
[0] array · 2 items
[0]
Clinical and pathological variables
[1]
Clinical validation cohorts (N = 56)
[1] array · 1 item
[0]
Age (years)
[2] array · 2 items
[0]
<60
[1]
29
[3] array · 2 items
[0]
≥60
[1]
27
[4] array · 1 item
[0]
Gender
[5] array · 2 items
[0]
Male
[1]
54
[6] array · 2 items
[0]
Female
[1]
2
[7] array · 1 item
[0]
Smoking status
[8] array · 2 items
[0]
Smoker
[1]
48
[9] array · 2 items
[0]
Non-smoker
[1]
8
[10] array · 1 item
[0]
Clinical stage
[11] array · 2 items
[0]
I-II
[1]
28
[12] array · 2 items
[0]
III-IV
[1]
28
[13] array · 1 item
[0]
Differentiation
[14] array · 2 items
[0]
Well
[1]
8
[15] array · 2 items
[0]
Moderate
[1]
27
[16] array · 2 items
[0]
Poor
[1]
21
[17] array · 1 item
[0]
Lymph node metastasis
[18] array · 2 items
[0]
Yes
[1]
21
[19] array · 2 items
[0]
No
[1]
35
locator
section[4]/table-wrap[1]#Tab1
[5] object · 16 fields
id
asset:table:pmid:28808310:2
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jats-table
media_type
application/vnd.jats.table+json
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note
Correlation between NEK2/ DLGAP5/ECT2 expression and clinical characteristics in 349 lung cancer patients.
structure object · 5 fields
pmcid
PMC5556079
section_index
5
section_title
Correlation between the three biomarkers and clinicopathologic variables
row_count
21
column_count
11
table object · 8 fields
label
Table 2
caption
Correlation between NEK2/ DLGAP5/ECT2 expression and clinical characteristics in 349 lung cancer patients.
rows array · 21 items
[0] array · 5 items
[0]
Characteristic
[1]
n = 349
[2]
NEK2 expression levels
[3]
DLGAP5 expression levels
[4]
ECT2 expression levels
[1] array · 9 items
[0]
low (n = 174)
[1]
high (n = 175)
[2]
P
[3]
low (n = 174)
[4]
high (n = 175)
[5]
P
[6]
low (n = 174)
[7]
high (n = 175)
[8]
P
[2] array · 1 item
[0]
Age (years)
[3] array · 11 items
[0]
<65
[1]
159
[2]
69
[3]
90
[4]
0.027
[5]
69
[6]
90
[7]
0.027
[8]
74
[9]
85
[10]
0.257
[4] array · 11 items
[0]
≥65
[1]
190
[2]
105
[3]
85
[4]
[5]
105
[6]
85
[7]
[8]
100
[9]
90
[10]
[5] array · 1 item
[0]
Gender
[6] array · 11 items
[0]
Female
[1]
190
[2]
113
[3]
77
[4]
<0.001
[5]
114
[6]
76
[7]
<0.001
[8]
109
[9]
81
[10]
0.002
[7] array · 11 items
[0]
Male
[1]
159
[2]
61
[3]
98
[4]
[5]
60
[6]
99
[7]
[8]
65
[9]
94
[10]
[8] array · 1 item
[0]
Clinical stage
[9] array · 11 items
[0]
I & II
[1]
281
[2]
148
[3]
133
[4]
0.033
[5]
149
[6]
132
[7]
0.016
[8]
144
[9]
137
[10]
0.292
[10] array · 11 items
[0]
III & IV
[1]
68
[2]
26
[3]
42
[4]
[5]
25
[6]
43
[7]
[8]
30
[9]
38
[10]
[11] array · 1 item
[0]
New tumor event
[12] array · 11 items
[0]
YES
[1]
99
[2]
40
[3]
59
[4]
0.26
[5]
39
[6]
60
[7]
0.014
[8]
40
[9]
59
[10]
0.026
[13] array · 11 items
[0]
NO
[1]
250
[2]
134
[3]
116
[4]
[5]
135
[6]
115
[7]
[8]
134
[9]
116
[10]
[14] array · 1 item
[0]
Pathologic T stage
[15] array · 11 items
[0]
T1
[1]
127
[2]
82
[3]
45
[4]
<0.001
[5]
79
[6]
48
[7]
0.001
[8]
79
[9]
48
[10]
0.002
[16] array · 11 items
[0]
T2
[1]
180
[2]
76
[3]
104
[4]
[5]
74
[6]
106
[7]
[8]
77
[9]
103
[10]
[17] array · 11 items
[0]
T3 + T4
[1]
40
[2]
16
[3]
24
[4]
[5]
21
[6]
21
[7]
[8]
18
[9]
24
[10]
[18] array · 1 item
[0]
Therapy outcome
[19] array · 11 items
[0]
CR + PR
[1]
145
[2]
81
[3]
64
[4]
0.004
[5]
81
[6]
64
[7]
0.01
[8]
82
[9]
63
[10]
0.012
[20] array · 11 items
[0]
SD + PD
[1]
86
[2]
31
[3]
55
[4]
[5]
33
[6]
53
[7]
[8]
34
[9]
52
[10]
locator
section[5]/table-wrap[1]#Tab2
[6] object · 16 fields
id
asset:table:pmid:28808310:3
role
table
format
jats-table
media_type
application/vnd.jats.table+json
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null
sha256
sha256:d80f4b16c9fb5b851707ba94be8c53b1a5541ce725fbf062f1b2e6227b11130c
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read_table
reader_status
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identifier
section[6]/table-wrap[1]#Tab3
version
null
note
ROC curve analyses using NEK2/DLGAP5/ECT2 for distinguishing patients with lung cancer from normal control subjects.
structure object · 5 fields
pmcid
PMC5556079
section_index
6
section_title
Diagnostic value of NEK2, DLGAP5 and ECT2 in lung cancer
row_count
13
column_count
7
table object · 8 fields
label
Table 3
caption
ROC curve analyses using NEK2/DLGAP5/ECT2 for distinguishing patients with lung cancer from normal control subjects.
rows array · 13 items
[0] array · 7 items
[0]
[1]
Lung cancer datasets
[2]
AUC value
[3]
P value
[4]
95% CI
[5]
Cut off value
[6]
Specificity, sensitivity
[1] array · 7 items
[0]
NEK2
[1]
GSE19188
[2]
0.9272
[3]
<0.0001
[4]
0.8826–0.9719
[5]
−0.718
[6]
0.890, 0.923
[2] array · 6 items
[0]
GSE18842
[1]
1
[2]
<0.0001
[3]
1.0000–1.0000
[4]
4.384
[5]
1.000, 1.000
[3] array · 6 items
[0]
GSE40791
[1]
0.9665
[2]
<0.0001
[3]
0.9415–0.9915
[4]
3.991
[5]
0.926, 0.910
[4] array · 6 items
[0]
TCGA
[1]
0.9733
[2]
<0.0001
[3]
0.9647–0.9883
[4]
−0.871
[5]
0.868, 0.983
[5] array · 7 items
[0]
DLGAP5
[1]
GSE19188
[2]
0.9158
[3]
<0.0001
[4]
0.8703–0.9614
[5]
−0.401
[6]
0.846, 0.892
[6] array · 6 items
[0]
GSE18842
[1]
1
[2]
<0.0001
[3]
1.0000–1.0000
[4]
4.505
[5]
1.000, 1.000
[7] array · 6 items
[0]
GSE40791
[1]
0.9572
[2]
<0.0001
[3]
0.9297–0.9847
[4]
4.043
[5]
0.883, 0.940
[8] array · 6 items
[0]
TCGA
[1]
0.973
[2]
<0.0001
[3]
0.9578–0.9881
[4]
−1.338
[5]
0.905, 0.983
[9] array · 7 items
[0]
ECT2
[1]
GSE19188
[2]
0.9013
[3]
<0.0001
[4]
0.8495–0.9530
[5]
−0.008
[6]
0.802, 0.969
[10] array · 6 items
[0]
GSE18842
[1]
0.9986
[2]
<0.0001
[3]
0.9950–1.002
[4]
6.439
[5]
0.978, 1.000
[11] array · 6 items
[0]
GSE40791
[1]
0.9728
[2]
<0.0001
[3]
0.9488–0.9967
[4]
8.008
[5]
0.926, 0.950
[12] array · 6 items
[0]
TCGA
[1]
0.9601
[2]
<0.0001
[3]
0.9421–0.9782
[4]
−0.628
[5]
0.888, 0.948
locator
section[6]/table-wrap[1]#Tab3
[7] object · 16 fields
id
asset:table:pmid:28808310:4
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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:b0b7b4260797b916add6a6cee7c9a3579b7903ccfe682f9c55cd2c7fec461e84
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read_table
reader_status
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identifier
section[7]/table-wrap[1]#Tab4
version
null
note
Univariate and multivariate Cox regression analyses for overall survival and recurrence-free survival.
structure object · 5 fields
pmcid
PMC5556079
section_index
7
section_title
Prognostic value of NEK2, DLGAP5 and ECT2 in lung cancer
row_count
26
column_count
5
table object · 8 fields
label
Table 4
caption
Univariate and multivariate Cox regression analyses for overall survival and recurrence-free survival.
rows array · 26 items
[0] array · 3 items
[0]
Variable
[1]
OS
[2]
RFS
[1] array · 4 items
[0]
HR (95% CI)
[1]
P-value
[2]
HR (95% CI)
[3]
P-value
[2] array · 1 item
[0]
Univariate analysis
[3] array · 5 items
[0]
Age (<65 vs. ≥65)
[1]
1.262(0.826–1.929)
[2]
0.281
[3]
1.415(0.943–2.123)
[4]
0.094
[4] array · 5 items
[0]
Gender (Female vs. Male)
[1]
1.172(0.774–1.775)
[2]
0.454
[3]
0.992(0.666–1.477)
[4]
0.968
[5] array · 5 items
[0]
Clinical stage (I–II vs. III–IV)
[1]
2.439(1.579–3.768)
[2]
<0.001
[3]
1.453(0.911–2.318)
[4]
0.117
[6] array · 5 items
[0]
Smoking history (yes vs. no)
[1]
0.815(0.516–1.286)
[2]
0.378
[3]
0.847(0.551–1.301)
[4]
0.448
[7] array · 5 items
[0]
NEK2 (low vs. high)
[1]
1.749(1.140–2.681)
[2]
0.010
[3]
1.738(1.161–2.601)
[4]
0.007
[8] array · 5 items
[0]
DLGAP5 (low vs. high)
[1]
1.986(1.285–3.070)
[2]
0.002
[3]
1.798(1.201–2.692)
[4]
0.004
[9] array · 5 items
[0]
ECT2 (low vs. high)
[1]
1.779(1.162–2.722)
[2]
0.008
[3]
1.752(1.172–2.619)
[4]
0.006
[10] array · 1 item
[0]
Multivariate analysis
[11] array · 5 items
[0]
Age (<65 vs. ≥65)
[1]
1.719(1.050–2.815)
[2]
0.031
[3]
1.919(1.210–3.043)
[4]
0.006
[12] array · 5 items
[0]
Gender (Female vs. Male)
[1]
1.218(0.780–1.903)
[2]
0.386
[3]
0.991(0.650–1.513)
[4]
0.968
[13] array · 5 items
[0]
Clinical stage (I–II vs. III–IV)
[1]
2.372(1.484–3.790)
[2]
<0.001
[3]
1.400(0.845–2.318)
[4]
0.191
[14] array · 5 items
[0]
Smoking history (yes vs. no)
[1]
0.826(0.495–1.378)
[2]
0.464
[3]
0.779(0.489–1.241)
[4]
0.294
[15] array · 5 items
[0]
NEK2 (low vs. high)
[1]
1.718(1.073–2.751)
[2]
0.024
[3]
1.771(1.142–2.745)
[4]
0.011
[16] array · 5 items
[0]
Age (<65 vs. ≥65)
[1]
1.880(1.131–3.123)
[2]
0.015
[3]
1.978(1.240–3.153)
[4]
0.004
[17] array · 5 items
[0]
Gender (Female vs. Male)
[1]
1.155(0.736–1.811)
[2]
0.531
[3]
0.935(0.610–1.432)
[4]
0.756
[18] array · 5 items
[0]
Clinical stage (I–II vs. III–IV)
[1]
2.388(1.493–3.821)
[2]
<0.001
[3]
1.400(0.846–2.319)
[4]
0.191
[19] array · 5 items
[0]
Smoking history (yes vs. no)
[1]
0.826(0.492–1.386)
[2]
0.469
[3]
0.769(0.482–1.226)
[4]
0.270
[20] array · 5 items
[0]
DLGAP5 (low vs. high)
[1]
2.101(1.294–3.412)
[2]
0.003
[3]
1.895(1.218–2.949)
[4]
0.005
[21] array · 5 items
[0]
Age (<65 vs. ≥65)
[1]
1.586(0.971–2.590)
[2]
0.066
[3]
1.742(1.101–2756)
[4]
0.018
[22] array · 5 items
[0]
Gender (Female vs. Male)
[1]
1.242(0.793–1.945)
[2]
0.344
[3]
1.005(0.659–1.533)
[4]
0.982
[23] array · 5 items
[0]
Clinical stage (I–II vs. III–IV)
[1]
2.501(1.565–3.996)
[2]
<0.001
[3]
1.465(0.886–2.421)
[4]
0.137
[24] array · 5 items
[0]
Smoking history (yes vs. no)
[1]
0.820(0.491–1.372)
[2]
0.450
[3]
0.770(0483–1.227)
[4]
0.272
[25] array · 5 items
[0]
ECT2 (low vs. high)
[1]
1.805(1.142–2.853)
[2]
0.012
[3]
1.699(1.105–2.612)
[4]
0.016
locator
section[7]/table-wrap[1]#Tab4
gaps array · 6 items
[0]
asset:data:geo:GSE18842 not downloaded
[1]
asset:data:geo:GSE18842 licence not read
[2]
asset:data:geo:GSE19188 not downloaded
[3]
asset:data:geo:GSE19188 licence not read
[4]
asset:data:geo:GSE40791 not downloaded
[5]
asset:data:geo:GSE40791 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 proportional hazards
[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
diagnostic_evaluation
specific
roc
label
Diagnostic test evaluation
dsl_operations array · 6 items
[0]
confusion_counts
[1]
roc_curve
[2]
sensitivity_specificity
[3]
solve_confusion_matrix
[4]
classification_metrics
[5]
published_precision_envelope
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
reference_standard_and_test
evidence object · 3 fields
score
0.8
band
high
quote
ROC curve
[4] object · 10 fields
id
method-5
family
null
specific
null
label
Kaplan–Meier analysis
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
Survival analysis was carried out according to Kaplan–Meier analysis and the Log-rank test.
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
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Three lung cancer datasets (GSE19188, GSE18842, GSE40791) generated from the Affymetrix platform and corresponding clinical information of lung cancer patients were retrieved from the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo). GSE19188, including 91 tumors and 65 adjacent normal lung tissues, GSE18842, which includes 46 tumors and 45 controls, and GSE40791 containing 94 tumors and 100 non-tumor tissues.Validation datasets were acquired from the Cancer Genome Atlas (TCGA) data portal (http://tcga-data.nci. nih.gov).
basis
The paper names GSE19188, GSE18842, GSE40791 in Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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Genome-wide mRNA expression data obtained from the Gene Expression Omnibus (GSE19188, GSE18842 and GSE40791), including 231 primary tumor samples and 210 normal samples, were used to discover differentially expressed genes (DEGs). NEK2, DLGAP5 and ECT2 were found to be highly expressed in tumor samples. These results were experimentally confirmed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The elevated expression of the three candidate genes was also validated using the Cancer Genome Atlas (TCGA) datasets, which consist of 349 tumor and 58 normal tissues. Furthermore, we performed receiver operating characteristics (ROC) analysis to assess the diagnostic value of these lung cancer biomarkers, and the results suggested that NEK2, DLGAP5 and ECT2 expression levels could robustly distinguish lung cancer patients from normal subjects. 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subjects.", "rows": [["", "Lung cancer datasets", "AUC value", "P value", "95% CI", "Cut off value", "Specificity, sensitivity"], ["NEK2", "GSE19188", "0.9272", "<0.0001", "0.8826–0.9719", "−0.718", "0.890, 0.923"], ["GSE18842", "1", "<0.0001", "1.0000–1.0000", "4.384", "1.000, 1.000"], ["GSE40791", "0.9665", "<0.0001", "0.9415–0.9915", "3.991", "0.926, 0.910"], ["TCGA", "0.9733", "<0.0001", "0.9647–0.9883", "−0.871", "0.868, 0.983"], ["DLGAP5", "GSE19188", "0.9158", "<0.0001", "0.8703–0.9614", "−0.401", "0.846, 0.892"], ["GSE18842", "1", "<0.0001", "1.0000–1.0000", "4.505", "1.000, 1.000"], ["GSE40791", "0.9572", "<0.0001", "0.9297–0.9847", "4.043", "0.883, 0.940"], ["TCGA", "0.973", "<0.0001", "0.9578–0.9881", "−1.338", "0.905, 0.983"], ["ECT2", "GSE19188", "0.9013", "<0.0001", "0.8495–0.9530", "−0.008", "0.802, 0.969"], ["GSE18842", "0.9986", "<0.0001", "0.9950–1.002", "6.439", "0.978, 1.000"], ["GSE40791", "0.9728", "<0.0001", "0.9488–0.9967", "8.008", "0.926, 0.950"], ["TCGA", 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