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pmid:32530136

DNA methylation profiling to predict recurrence risk in stage Ι lung adenocarcinoma: Development and validation of a nomogram to clinical management.

Xianxiong Ma · Jiancheng Cheng · Peng Zhao · Lei Li · Kaixiong Tao · Hengyu Chen

Journal of cellular and molecular medicine · 2020 doi:10.1111/jcmm.15393

Paper

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Experiment

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Data

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Analysis

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Execution

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pmid:32530136
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title
DNA methylation profiling to predict recurrence risk in stage Ι lung adenocarcinoma: Development and validation of a nomogram to clinical management.
doi
10.1111/jcmm.15393
pmid
32530136
authors array · 6 items
[0]
Xianxiong Ma
[1]
Jiancheng Cheng
[2]
Peng Zhao
[3]
Lei Li
[4]
Kaixiong Tao
[5]
Hengyu Chen
journal
Journal of cellular and molecular medicine
year
2020
abstract
Increasing evidence suggested DNA methylation may serve as potential prognostic biomarkers; however, few related DNA methylation signatures have been established for prediction of lung cancer prognosis. We aimed at developing DNA methylation signature to improve prognosis prediction of stage I lung adenocarcinoma (LUAD). A total of 268 stage I LUAD patients from the Cancer Genome Atlas (TCGA) database were included. These patients were separated into training and internal validation datasets. GSE39279 was used as an external validation set. A 13-DNA methylation signature was identified to be crucially relevant to the relapse-free survival (RFS) of patients with stage I LUAD by the univariate Cox proportional hazard analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox proportional hazard analysis in the training dataset. The Kaplan-Meier analysis indicated that the 13-DNA methylation signature could significantly distinguish the high- and low-risk patients in entire TCGA dataset, internal validation and external validation datasets. The receiver operating characteristic (ROC) analysis further verified that the 13-DNA methylation signature had a better value to predict the RFS of stage I LUAD patients in internal validation, external validation and entire TCGA datasets. In addition, a nomogram combining methylomic risk scores with other clinicopathological factors was performed and the result suggested the good predictive value of the nomogram. In conclusion, we successfully built a DNA methylation-associated nomogram, enabling prediction of the RFS of patients with stage I LUAD.
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Then, LASSO Cox regression model was acted on these 530 DNA methylation sites and 25 methylation sites were identified as the candidate prognostic indicators for predicting RFS of stage I LUAD patients (Figure 2A,B).
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sections[9] "Identification of 13 methylation site signature" paragraph 2
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Then, LASSO Cox regression model was acted on these 530 DNA methylation sites and 25 methylation sites were identified as the candidate prognostic indicators for predicting RFS of stage I LUAD patients (Figure 2A,B).
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The data that support the findings of this study are available in the GEO database and TCGA data.
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[0]
Yes
[1]
173 (64.6)
[2]
122 (64.9)
[3]
51 (63.75)
[4]
99 (83.9)
[10] array · 5 items
[0]
No
[1]
90 (33.6)
[2]
63 (33.5)
[3]
27 (33.75)
[4]
16 (13.6)
[11] array · 5 items
[0]
Not available
[1]
5 (1.8)
[2]
3 (1.6)
[3]
2 (2.5)
[4]
3 (2.5)
[12] array · 1 item
[0]
Stage
[13] array · 5 items
[0]
Stage I
[1]
5 (1.87)
[2]
4 (2.13)
[3]
1 (1.25)
[4]
10 (8.5)
[14] array · 5 items
[0]
Stage IA
[1]
133 (49.63)
[2]
86 (45.74)
[3]
47 (58.75)
[4]
99 (83.9)
[15] array · 5 items
[0]
Stage IB
[1]
130 (48.51)
[2]
98 (52.13)
[3]
32 (40)
[4]
9 (7.6)
[16] array · 1 item
[0]
Tumour
[17] array · 5 items
[0]
T1
[1]
136 (50.75)
[2]
88 (46.81)
[3]
48 (60.00)
[4]
75 (63.6)
[18] array · 5 items
[0]
T2
[1]
132 (49.25)
[2]
100 (53.19)
[3]
32 (40.00)
[4]
43 (36.4)
[19] array · 1 item
[0]
Location in lung parenchyma
[20] array · 5 items
[0]
Central lung
[1]
27 (10.07)
[2]
15 (7.98)
[3]
12 (15)
[4]
[21] array · 5 items
[0]
Peripheral lung
[1]
50 (18.66)
[2]
34 (18.09)
[3]
16 (20)
[4]
[22] array · 5 items
[0]
Not available
[1]
191 (71.27)
[2]
139 (73.94)
[3]
52 (65)
[4]
[23] array · 1 item
[0]
History of neoadjuvant treatment
[24] array · 5 items
[0]
No
[1]
268 (100)
[2]
188 (100)
[3]
80 (100)
[4]
[25] array · 1 item
[0]
Anatomic neoplasm subdivision
[26] array · 5 items
[0]
L‐lower
[1]
39 (14.55)
[2]
29 (15.43)
[3]
10 (12.5)
[4]
[27] array · 5 items
[0]
L‐upper
[1]
64 (23.88)
[2]
40 (21.28)
[3]
24 (30)
[4]
[28] array · 5 items
[0]
R‐lower
[1]
39 (14.55)
[2]
24 (12.77)
[3]
15 (18.75)
[4]
[29] array · 5 items
[0]
R‐middle
[1]
8 (2.99)
[2]
4 (2.13)
[3]
4 (5)
[4]
[30] array · 5 items
[0]
R‐upper
[1]
113 (42.16)
[2]
89 (47.34)
[3]
24 (30)
[4]
[31] array · 5 items
[0]
Not available
[1]
5 (1.87)
[2]
2 (1.06)
[3]
3 (3.75)
[4]
[32] array · 1 item
[0]
Residual_tumour
[33] array · 5 items
[0]
R0
[1]
172 (64.18)
[2]
118 (62.77)
[3]
54 (67.5)
[4]
[34] array · 5 items
[0]
R1
[1]
1 (0.37)
[2]
1 (0.53)
[3]
[4]
[35] array · 5 items
[0]
RX
[1]
95 (35.44)
[2]
69 (36.71)
[3]
26 (32.5)
[4]
[36] array · 1 item
[0]
Race
[37] array · 5 items
[0]
Asian
[1]
4 (1.49)
[2]
3 (1.6)
[3]
1 (1.25)
[4]
[38] array · 5 items
[0]
Black or African‐American
[1]
30 (11.19)
[2]
19 (10.11)
[3]
11 (13.75)
[4]
[39] array · 5 items
[0]
White
[1]
215 (80.22)
[2]
150 (79.79)
[3]
65 (81.25)
[4]
[40] array · 5 items
[0]
Not available
[1]
19 (7.09)
[2]
16 (8.51)
[3]
3 (3.75)
[4]
locator
section[8]/table-wrap[1]#jcmm15393-tbl-0001
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asset:table:pmid:32530136:2
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jats-table
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application/vnd.jats.table+json
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null
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sha256:192eccedb29ab182f401b8b450a76f3b29de9eab50b586a6fc4f874b158b4331
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open
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verified
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read_table
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available
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section[13]/table-wrap[1]#jcmm15393-tbl-0002
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null
note
Univariate Cox regression analysis and multivariate Cox regression analysis outcome based on methylation risk score and other clinical factors
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pmcid
PMC7339160
section_index
13
section_title
Nomogram development and assessment
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17
column_count
9
table object · 8 fields
label
TABLE 2
caption
Univariate Cox regression analysis and multivariate Cox regression analysis outcome based on methylation risk score and other clinical factors
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[0] array · 2 items
[0]
Univariate Cox analysis
[1]
Multivariate Cox analysis
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[0]
ID
[1]
HR
[2]
HR.95L
[3]
HR.95H
[4]
P value
[5]
HR
[6]
HR.95L
[7]
HR.95H
[8]
P value
[2] array · 9 items
[0]
Score
[1]
2.718281827
[2]
2.257482625
[3]
3.27313974
[4]
4.99E−26
[5]
2.519216037
[6]
2.070251915
[7]
3.065544535
[8]
2.80E−20
[3] array · 9 items
[0]
Sex
[1]
1.190788066
[2]
0.785542651
[3]
1.805091317
[4]
0.410678663
[5]
1.234569867
[6]
0.783432222
[7]
1.94549409
[8]
0.363812717
[4] array · 9 items
[0]
T
[1]
1.414801719
[2]
0.924381171
[3]
2.165409646
[4]
0.110071377
[5]
[6]
[7]
[8]
[5] array · 9 items
[0]
N
[1]
3.71E−08
[2]
0
[3]
Inf
[4]
0.993929691
[5]
[6]
[7]
[8]
[6] array · 9 items
[0]
M
[1]
0.978813086
[2]
0.627736375
[3]
1.526237917
[4]
0.924724548
[5]
[6]
[7]
[8]
[7] array · 9 items
[0]
Cancer status
[1]
0.495199898
[2]
0.374942683
[3]
0.654027803
[4]
7.36E−07
[5]
0.61831797
[6]
0.472305909
[7]
0.809469254
[8]
0.000468889
[8] array · 9 items
[0]
Age
[1]
1.007645831
[2]
0.994324503
[3]
1.021145631
[4]
0.261973795
[5]
[6]
[7]
[8]
[9] array · 9 items
[0]
Anatomic neoplasm subdivision
[1]
1.1414812
[2]
1.026606451
[3]
1.269210151
[4]
0.01447795
[5]
1.102553661
[6]
0.984284201
[7]
1.235034122
[8]
0.09172864
[10] array · 9 items
[0]
Ethnicity
[1]
0.607929916
[2]
0.367987477
[3]
1.004324347
[4]
0.052002005
[5]
0.535327155
[6]
0.325704964
[7]
0.879861206
[8]
0.013708055
[11] array · 9 items
[0]
Location in lung parenchyma
[1]
0.800719177
[2]
0.548018068
[3]
1.169945367
[4]
0.250676677
[5]
[6]
[7]
[8]
[12] array · 9 items
[0]
The number of pack‐years smoked
[1]
1.007926688
[2]
1.001037526
[3]
1.014863261
[4]
0.024051348
[5]
1.001385061
[6]
0.993810733
[7]
1.009017117
[8]
0.720871674
[13] array · 9 items
[0]
Other family disease history
[1]
0.645154686
[2]
0.34305619
[3]
1.213283952
[4]
0.173823123
[5]
0.802567865
[6]
0.412774092
[7]
1.560454469
[8]
0.516783907
[14] array · 9 items
[0]
Race
[1]
1.204479117
[2]
0.885896961
[3]
1.637628311
[4]
0.235231162
[5]
[6]
[7]
[8]
[15] array · 9 items
[0]
Residual tumour
[1]
0.96206214
[2]
0.616497209
[3]
1.501326443
[4]
0.864745533
[5]
[6]
[7]
[8]
[16] array · 9 items
[0]
Tobacco smoking history
[1]
1.086624927
[2]
0.88332982
[3]
1.336707655
[4]
0.431810059
[5]
[6]
[7]
[8]
locator
section[13]/table-wrap[1]#jcmm15393-tbl-0002
gaps array · 2 items
[0]
asset:data:geo:GSE39279 not downloaded
[1]
asset:data:geo:GSE39279 licence not read
analysis object · 4 fields
state
complete
methods array · 3 items
[0] object · 10 fields
id
method-1
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survival_analysis
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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
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time_to_event_with_censoring
evidence object · 3 fields
score
1
band
verbatim
quote
Then, LASSO Cox regression model was acted on these 530 DNA methylation sites and 25 methylation sites were identified as the candidate prognostic indicators for predicting RFS of stage I LUAD patients (Figure 2A,B).
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survival_analysis
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Survival analysis
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survival_km
missing_operations array · 0 items
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data_shapes array · 1 item
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time_to_event_with_censoring
evidence object · 3 fields
score
0.7
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medium
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Kaplan-Meier
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id
method-3
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diagnostic_evaluation
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roc
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Diagnostic test evaluation
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confusion_counts
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roc_curve
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sensitivity_specificity
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solve_confusion_matrix
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classification_metrics
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published_precision_envelope
missing_operations array · 0 items
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reference_standard_and_test
evidence object · 3 fields
score
0.8
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high
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ROC analysis
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capability object · 3 fields
state
complete
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capability_id
capability:computational_analysis
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meta_analysis
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machine_learning
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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
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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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tier
R1
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required_operations array · 1 item
[0]
cox_ph
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: cox_proportional_hazards.
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.
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tier
R1
reachable
true
accessions array · 1 item
[0]
GSE39279
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[0]
GSE39279
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The data that support the findings of this study are available in the GEO database and TCGA data.
basis
The paper names GSE39279 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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cox_ph
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source:pmid:32530136
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run_analysis
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data:geo:GSE39279
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state
partial
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extraction_review
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MiniMax-M3.1-Flash
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minimax
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minimax
read_at
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verbatim
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minimax
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paper
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pmid:32530136
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source:pmid:32530136
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PMID 32530136 full text
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data:geo:GSE39279
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Then, LASSO Cox regression model was acted on these 530 DNA methylation sites and 25 methylation sites were identified as the candidate prognostic indicators for predicting RFS of stage I LUAD patients (Figure 2A,B).
order_basis
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32530136", "kind": "science-record", "revision": 7, "content_hash": "sha256:6e6768bb0337eb1e38bf53bd02c150356823543033b7ec273a38ee3ca36c133c", "created_at": "2026-11-01T15:41:03Z", "updated_at": "2026-11-01T22:01:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "DNA methylation profiling to predict recurrence risk in stage Ι lung adenocarcinoma: Development and validation of a nomogram to clinical management.", "doi": "10.1111/jcmm.15393", "pmid": "32530136", "authors": ["Xianxiong Ma", "Jiancheng Cheng", "Peng Zhao", "Lei Li", "Kaixiong Tao", "Hengyu Chen"], "journal": "Journal of cellular and molecular medicine", "year": 2020, "abstract": "Increasing evidence suggested DNA methylation may serve as potential prognostic biomarkers; however, few related DNA methylation signatures have been established for prediction of lung cancer prognosis. We aimed at developing DNA methylation signature to improve prognosis prediction of stage I lung adenocarcinoma (LUAD). A total of 268 stage I LUAD patients from the Cancer Genome Atlas (TCGA) database were included. These patients were separated into training and internal validation datasets. GSE39279 was used as an external validation set. A 13-DNA methylation signature was identified to be crucially relevant to the relapse-free survival (RFS) of patients with stage I LUAD by the univariate Cox proportional hazard analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox proportional hazard analysis in the training dataset. The Kaplan-Meier analysis indicated that the 13-DNA methylation signature could significantly distinguish the high- and low-risk patients in entire TCGA dataset, internal validation and external validation datasets. The receiver operating characteristic (ROC) analysis further verified that the 13-DNA methylation signature had a better value to predict the RFS of stage I LUAD patients in internal validation, external validation and entire TCGA datasets. In addition, a nomogram combining methylomic risk scores with other clinicopathological factors was performed and the result suggested the good predictive value of the nomogram. 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