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pmid:32391054

Transcription Factor Profiling to Predict Recurrence-Free Survival in Breast Cancer: Development and Validation of a Nomogram to Optimize Clinical Management.

Hengyu Chen · Xianxiong Ma · Ming Yang · Mengyi Wang · Lei Li · Tao Huang

Frontiers in genetics · 2020 doi:10.3389/fgene.2020.00333

Paper

complete

Experiment

complete

Data

partial

Analysis

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Execution

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pmid:32391054
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paper object · 10 fields
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title
Transcription Factor Profiling to Predict Recurrence-Free Survival in Breast Cancer: Development and Validation of a Nomogram to Optimize Clinical Management.
doi
10.3389/fgene.2020.00333
pmid
32391054
authors array · 6 items
[0]
Hengyu Chen
[1]
Xianxiong Ma
[2]
Ming Yang
[3]
Mengyi Wang
[4]
Lei Li
[5]
Tao Huang
journal
Frontiers in genetics
year
2020
abstract
Breast cancer (BC) is the most frequently diagnosed cancer and the leading cause of cancer-related death in young women. Several prognostic and predictive transcription factor (TF) markers have been reported for BC; however, they are inconsistent due to small datasets, the heterogeneity of BC, and variation in data pre-processing approaches. This study aimed to identify an effective predictive TF signature for the prognosis of patients with BC. We analyzed the TF data of 868 patients with BC in The Cancer Genome Atlas (TCGA) database to investigate TF biomarkers relevant to recurrence-free survival (RFS). These patients were separated into training and internal validation datasets, with GSE2034 and GSE42568 used as external validation sets. A nine-TF signature was identified as crucially related to the RFS of patients with BC by univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and multivariate Cox proportional hazard analysis in the training dataset. Kaplan-Meier analysis revealed that the nine-TF signature could significantly distinguish high- and low-risk patients in both the internal validation dataset and the two external validation sets. Receiver operating characteristic (ROC) analysis further verified that the nine-TF signature showed a good performance for predicting the RFS of patients with BC. In addition, we developed a nomogram based on risk score and lymph node status, with C-index, ROC, and calibration plot analysis, suggesting that it displays good performance and clinical value. In summary, we used integrated bioinformatics approaches to identify an effective predictive nine-TF signature which may be a potential biomarker for BC prognosis.
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Breast cancer relapse free survival
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[0]
T4
[1]
33 (3.8)
[2]
23 (3.8)
[3]
10 (3.8)
[4]
[5]
[12] array · 6 items
[0]
TX
[1]
2 (0.23)
[2]
1 (0.16)
[3]
1 (0.38)
[4]
[5]
[13] array · 6 items
[0]
Lymph node status
[1]
[2]
[3]
[4]
[5]
[14] array · 6 items
[0]
N0
[1]
398 (45.9)
[2]
274 (45.1)
[3]
124 (47.7)
[4]
[5]
[15] array · 6 items
[0]
N1
[1]
318 (36.6)
[2]
225 (37.0)
[3]
93 (35.8)
[4]
[5]
[16] array · 6 items
[0]
N2
[1]
100 (11.5)
[2]
70 (11.5)
[3]
30 (11.5)
[4]
[5]
[17] array · 6 items
[0]
N3
[1]
40 (4.6)
[2]
31 (5.1)
[3]
9 (3.5)
[4]
[5]
[18] array · 6 items
[0]
NX
[1]
12 (1.38)
[2]
8 (1.32)
[3]
4 (1.54)
[4]
[5]
[19] array · 6 items
[0]
Metastasis
[1]
[2]
[3]
[4]
[5]
[20] array · 6 items
[0]
M0
[1]
753 (86.75)
[2]
522 (85.86)
[3]
231 (88.85)
[4]
[5]
[21] array · 6 items
[0]
M1
[1]
16 (1.84)
[2]
14 (2.3)
[3]
2 (0.77)
[4]
[5]
[22] array · 6 items
[0]
MX
[1]
92 (10.6)
[2]
66 (10.86)
[3]
26 (10)
[4]
[5]
[23] array · 6 items
[0]
TNM stage
[1]
[2]
[3]
[4]
[5]
[24] array · 6 items
[0]
Stage I
[1]
157 (18.1)
[2]
102 (16.8)
[3]
55 (21.2)
[4]
[5]
[25] array · 6 items
[0]
Stage II
[1]
514 (59.2)
[2]
361 (59.4)
[3]
153 (58.8)
[4]
[5]
[26] array · 6 items
[0]
Stage III
[1]
167 (19.2)
[2]
122 (20.1)
[3]
45 (17.3)
[4]
[5]
[27] array · 6 items
[0]
Stage IV
[1]
14 (1.61)
[2]
13 (2.14)
[3]
1 (0.38)
[4]
[5]
[28] array · 6 items
[0]
Indeterminate
[1]
16 (1.8)
[2]
10 (1.6)
[3]
6 (2.3)
[4]
[5]
[29] array · 6 items
[0]
Site
[1]
[2]
[3]
[4]
[5]
[30] array · 6 items
[0]
Left
[1]
462 (53.2)
[2]
321 (52.8)
[3]
141 (54.2)
[4]
[5]
[31] array · 6 items
[0]
Right
[1]
406 (46.8)
[2]
287 (47.2)
[3]
119 (45.8)
[4]
[5]
[32] array · 6 items
[0]
Estrogen receptor
[1]
[2]
[3]
[4]
[5]
[33] array · 6 items
[0]
Positive
[1]
600 (69.12)
[2]
432 (71.1)
[3]
168 (64.6)
[4]
209 (73.1)
[5]
67 (64.4)
[34] array · 6 items
[0]
Negative
[1]
223 (25.69)
[2]
147 (24.2)
[3]
76 (29.2)
[4]
77 (26.9)
[5]
34 (32.7)
[35] array · 6 items
[0]
Indeterminate
[1]
45 (5.2)
[2]
29 (4.8)
[3]
16 (6.2)
[4]
[5]
3 (2.9)
[36] array · 6 items
[0]
Progesterone receptor
[1]
[2]
[3]
[4]
[5]
[37] array · 6 items
[0]
Positive
[1]
515 (59.3)
[2]
369 (60.7)
[3]
146 (56.2)
[4]
[5]
[38] array · 6 items
[0]
Negative
[1]
305 (35.1)
[2]
207 (34.0)
[3]
98 (37.7)
[4]
[5]
[39] array · 6 items
[0]
Indeterminate
[1]
48 (5.5)
[2]
32 (5.3)
[3]
16 (6.2)
[4]
[5]
[40] array · 6 items
[0]
Her2 receptor
[1]
[2]
[3]
[4]
[5]
[41] array · 6 items
[0]
Positive
[1]
139 (16.0)
[2]
106 (17.4)
[3]
33 (12.7)
[4]
[5]
[42] array · 6 items
[0]
Negative
[1]
440 (50.7)
[2]
304 (50.0)
[3]
136 (52.3)
[4]
[5]
[43] array · 6 items
[0]
Indeterminate
[1]
289 (33.3)
[2]
198 (32.6)
[3]
91 (35)
[4]
[5]
[44] array · 6 items
[0]
Margin status
[1]
[2]
[3]
[4]
[5]
[45] array · 6 items
[0]
Positive
[1]
47 (5.41)
[2]
33 (5.43)
[3]
14 (5.38)
[4]
[5]
[46] array · 6 items
[0]
Negative
[1]
732 (84.33)
[2]
513 (84.38)
[3]
219 (84.23)
[4]
[5]
[47] array · 6 items
[0]
Indeterminate
[1]
89 (10.3)
[2]
62 (10.2)
[3]
27 (10.4)
[4]
[5]
[48] array · 6 items
[0]
Race
[1]
[2]
[3]
[4]
[5]
[49] array · 6 items
[0]
American Indian or Alaska Native
[1]
1 (0.12)
[2]
1 (0.16)
[3]
[4]
[5]
[50] array · 6 items
[0]
Asian
[1]
46 (5.3)
[2]
32 (5.26)
[3]
14 (5.38)
[4]
[5]
[51] array · 6 items
[0]
Black or African American
[1]
155 (17.86)
[2]
115 (18.91)
[3]
40 (15.38)
[4]
[5]
[52] array · 6 items
[0]
White
[1]
594 (68.43)
[2]
408 (67.11)
[3]
186 (71.54)
[4]
[5]
[53] array · 6 items
[0]
Not available
[1]
72 (8.29)
[2]
52 (8.55)
[3]
20 (7.69)
[4]
[5]
locator
section[8]/table-wrap[1]#T1
[4] object · 16 fields
id
asset:table:pmid:32391054:2
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:43bcd514e568cfc2cbceb1a89d6bb2a2bd49cd2b1922b76b175647923d750f43
bytes
1894
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[13]/table-wrap[1]#T2
version
null
note
Univariate Cox regression analysis and multivariate Cox regression analysis outcomes based on methylation risk score and other clinical factors.
structure object · 5 fields
pmcid
PMC7193038
section_index
13
section_title
Nomogram Development and Assessment
row_count
13
column_count
9
table object · 8 fields
label
TABLE 2
caption
Univariate Cox regression analysis and multivariate Cox regression analysis outcomes based on methylation risk score and other clinical factors.
rows array · 13 items
[0] array · 3 items
[0]
[1]
Univariate Cox regression analysis
[2]
Multivariate Cox regression analysis
[1] array · 9 items
[0]
Characteristics
[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]
2.738045421
[2]
2.048628528
[3]
3.659469066
[4]
1.01E-11
[5]
2.510661624
[6]
1.788829617
[7]
3.523768687
[8]
1.02E-07
[3] array · 9 items
[0]
Estrogen receptor status
[1]
0.692324993
[2]
0.342239438
[3]
1.400522099
[4]
0.306355543
[5]
0.768993041
[6]
0.260500851
[7]
2.270051305
[8]
0.634356191
[4] array · 9 items
[0]
Progesterone receptor status
[1]
0.592458976
[2]
0.30230991
[3]
1.161085454
[4]
0.127286775
[5]
0.981504947
[6]
0.33480194
[7]
2.877378671
[8]
0.972862011
[5] array · 9 items
[0]
Her2 receptor status
[1]
1.257944488
[2]
0.823912985
[3]
1.9206207
[4]
0.287844565
[5]
1.373910032
[6]
0.865382257
[7]
2.181265863
[8]
0.178008111
[6] array · 9 items
[0]
Tumor
[1]
1.754711187
[2]
0.617255141
[3]
4.988231197
[4]
0.291488237
[5]
1.101502581
[6]
0.3254944
[7]
3.727584669
[8]
0.87648392
[7] array · 9 items
[0]
Lymph node status
[1]
2.17751336
[2]
1.49200771
[3]
3.177975825
[4]
5.48E-05
[5]
1.651534043
[6]
1.069601814
[7]
2.550074858
[8]
0.023602411
[8] array · 9 items
[0]
Metastasis status
[1]
2.520159824
[2]
0.343448624
[3]
18.49244717
[4]
0.363359385
[5]
1.858617241
[6]
0.226521566
[7]
15.25001838
[8]
0.563807742
[9] array · 9 items
[0]
Margin status
[1]
0.7974526
[2]
0.396241415
[3]
1.604907073
[4]
0.525907749
[5]
0.634209273
[6]
0.307262431
[7]
1.309048428
[8]
0.218093787
[10] array · 9 items
[0]
Ethnicity
[1]
0.634959689
[2]
0.086536008
[3]
4.659029397
[4]
0.655117076
[5]
1.014927373
[6]
0.132510023
[7]
7.773582306
[8]
0.988619118
[11] array · 9 items
[0]
Age
[1]
0.97574987
[2]
0.948136617
[3]
1.004167323
[4]
0.093730595
[5]
0.979959839
[6]
0.95094581
[7]
1.009859108
[8]
0.186780098
[12] array · 9 items
[0]
Anatomic neoplasm subdivision
[1]
1.039911088
[2]
0.843603069
[3]
1.281900351
[4]
0.713889548
[5]
0.97189382
[6]
0.782429155
[7]
1.207237219
[8]
0.796654191
locator
section[13]/table-wrap[1]#T2
gaps array · 4 items
[0]
asset:data:geo:GSE2034 not downloaded
[1]
asset:data:geo:GSE2034 licence not read
[2]
asset:data:geo:GSE42568 not downloaded
[3]
asset:data:geo:GSE42568 licence not read
analysis object · 4 fields
state
complete
methods array · 3 items
[0] object · 10 fields
id
method-1
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
1
band
reordered
quote
“Kaplan–Meier (KM) and log-rank methods were used to compare the survival rates of the groups using the R “survival” package (Moreno-Betancur et al., 2017).”
[1] object · 10 fields
id
method-2
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
[2] object · 10 fields
id
method-3
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
0.7
band
medium
quote
Cox regression
targets array · 0 items
gaps array · 0 items
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]
logrank_test
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: log_rank_test.
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]
GSE2034
[1]
GSE42568
resolved_accessions array · 2 items
[0]
GSE2034
[1]
GSE42568
statements array · 1 item
[0]
All data generated or analyzed during the present study are included in this published article or are available from the corresponding author on reasonable request.
basis
The paper names GSE2034, GSE42568 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 · 1 item
[0]
logrank_test
assets array · 4 items
[0]
source:pmid:32391054
[1]
data:geo:GSE2034
[2]
asset:table:pmid:32391054:1
[3]
asset:table:pmid:32391054:2
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:GSE2034
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
2026-10-31T18:19:48Z
independence
single_reader
evidence_check object · 2 fields
method
reordered
availability
verbatim
calls
3
usage object · 3 fields
prompt_tokens
22670
completion_tokens
1815
cached_tokens
784
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:32391054
type
paper
label
pmid:32391054
[1] object · 3 fields
id
source:pmid:32391054
type
manuscript
label
PMID 32391054 full text
[2] object · 3 fields
id
data:geo:GSE2034
type
dataset
label
GEO GSE2034
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:32391054
type
paper
label
pmid:32391054
[1] object · 3 fields
id
source:pmid:32391054
type
manuscript
label
PMID 32391054 full text
[2] object · 3 fields
id
data:geo:GSE2034
type
dataset
label
GEO GSE2034
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-32391054
type
extraction
used array · 1 item
[0]
source:pmid:32391054
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE2034
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE2034
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:32391054
parent_hash
sha256:a4884005fbfa049edc012a4cca06df70f1e38364cd6617b631f8834e7d16cdee
fork_of
null
gaps array · 8 items
[0]
asset:data:geo:GSE2034 licence not read
[1]
asset:data:geo:GSE2034 not downloaded
[2]
asset:data:geo:GSE42568 licence not read
[3]
asset:data:geo:GSE42568 not downloaded
[4]
execution.not_run
[5]
review.human_critique
[6]
review.independent_reader
[7]
review.machine_critique
assays array · 1 item
[0] object · 11 fields
role
analysis
definition_id
survival_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 1 item
[0]
logrank_test
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
“Kaplan–Meier (KM) and log-rank methods were used to compare the survival rates of the groups using the R “survival” package (Moreno-Betancur et al., 2017).”
order_basis
analysis_only
id
assay:1
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32391054", "kind": "science-record", "revision": 6, "content_hash": "sha256:a3c7077ae6b1be378e8a10573a63634dae3fa8c9ebad5caf4a3cba15e0cf34f4", "created_at": "2026-11-01T15:41:01Z", "updated_at": "2026-11-01T21:23:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Transcription Factor Profiling to Predict Recurrence-Free Survival in Breast Cancer: Development and Validation of a Nomogram to Optimize Clinical Management.", "doi": "10.3389/fgene.2020.00333", "pmid": "32391054", "authors": ["Hengyu Chen", "Xianxiong Ma", "Ming Yang", "Mengyi Wang", "Lei Li", "Tao Huang"], "journal": "Frontiers in genetics", "year": 2020, "abstract": "Breast cancer (BC) is the most frequently diagnosed cancer and the leading cause of cancer-related death in young women. Several prognostic and predictive transcription factor (TF) markers have been reported for BC; however, they are inconsistent due to small datasets, the heterogeneity of BC, and variation in data pre-processing approaches. This study aimed to identify an effective predictive TF signature for the prognosis of patients with BC. We analyzed the TF data of 868 patients with BC in The Cancer Genome Atlas (TCGA) database to investigate TF biomarkers relevant to recurrence-free survival (RFS). These patients were separated into training and internal validation datasets, with GSE2034 and GSE42568 used as external validation sets. A nine-TF signature was identified as crucially related to the RFS of patients with BC by univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and multivariate Cox proportional hazard analysis in the training dataset. Kaplan-Meier analysis revealed that the nine-TF signature could significantly distinguish high- and low-risk patients in both the internal validation dataset and the two external validation sets. Receiver operating characteristic (ROC) analysis further verified that the nine-TF signature showed a good performance for predicting the RFS of patients with BC. In addition, we developed a nomogram based on risk score and lymph node status, with C-index, ROC, and calibration plot analysis, suggesting that it displays good performance and clinical value. In summary, we used integrated bioinformatics approaches to identify an effective predictive nine-TF signature which may be a potential biomarker for BC prognosis.", "claims": [{"id": "claim-data-access", "statement": "All data generated or analyzed during the present study are included in this published article or are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32391054", "locator": "sections[15] \"Data Availability Statement\" paragraph 2", "quote": "All data generated or analyzed during the present study are included in this published article or are available from the corresponding author on reasonable request."}], "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:32391054"], "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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