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pmid:32293338

INHBA is a prognostic predictor for patients with colon adenocarcinoma.

Xueying Li · Weiming Yu · Chao Liang · Yuan Xu · Miaozun Zhang · Xiaoyun Ding · Xianlei Cai

BMC cancer · 2020 doi:10.1186/s12885-020-06743-2

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:32293338
kind
science-record
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sha256:88bfb619b3bb0e361f13d0e3628a99d90b2f08a82779962e8218be9ca18158c1
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2026-11-01T15:40:59Z
updated_at
2026-11-01T22:01:30Z
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paper object · 10 fields
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title
INHBA is a prognostic predictor for patients with colon adenocarcinoma.
doi
10.1186/s12885-020-06743-2
pmid
32293338
authors array · 7 items
[0]
Xueying Li
[1]
Weiming Yu
[2]
Chao Liang
[3]
Yuan Xu
[4]
Miaozun Zhang
[5]
Xiaoyun Ding
[6]
Xianlei Cai
journal
BMC cancer
year
2020
abstract
BACKGROUND: Colon adenocarcinoma (COAD) is one of the most lethal cancers. It is particularly important to accurately predict prognosis and to provide individualized treatment. Several lines of evidence suggest that genetic factors and clinicopathological characteristics are related to cancer onset and progression. The aim of this study was to identify potential prognostic genes and to develop a nomogram to predict survival and recurrence of COAD. METHODS: To identify potential prognostic genes in COAD, microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained from GEO2R. Venn diagram was drawn to select those genes that were overexpressed in all datasets, and survival analyses were performed to determine the prognostic values of the selected genes. New nomograms were developed based on the genes that were significantly associated with prognosis. Clinicopathological data were obtained from The Cancer Genome Atlas (TCGA). Finally, the new nomograms were compared head-to-head comparison with the TNM nomogram. RESULTS: From GSE21510, GSE110223, GSE113513 and GSE110224, a total of 834, 218, 236 and 613 overexpressed DEGs were screened out, respectively. The Venn diagram revealed that 12 genes appeared in all four profiles. After survival analyses, only INHBA expression was associated with both overall survival (OS) and disease-free survival (DFS). Multivariate analyses revealed that age, pathological N and pathological M were significant independent risk factors for OS. Age, pathological N, pathological M and INHBA were significant independent risk factors for DFS. Two prediction models predicted the probability of 3-year survival and 5-year survival for OS and DFS, respectively. The concordance indexes were 0.785 for 3-year overall survival, 0.759 for 5-year overall survival, 0.789 for 3-year disease-free survival and 0.757 for 5-year disease-free survival. The head-to-head comparison according to time-dependent ROC curves indicated that the new models had higher predictive accuracy. Decision curve analyses (DCA) indicated that the clinical value of the new models were higher than TNM models for predicting disease-free survival. CONCLUSION: The combination of INHBA expression with a clinical nomogram improves prognostic power in colon adenocarcinoma, especially for predicting recurrence.
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We used the Cox proportional hazard regression model to estimate hazard ratio (and its 95% confidence interval (CI)) for each potential risk factor.
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sections[25] "notes" paragraph 1
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The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Expression data from 13 patients with colorectal cancer
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Homo sapiens
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Expression data from 17 patients with colorectal cancer
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15207
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Homo sapiens
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Gene expression profile of human colorectal carcinoma
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The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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[53]
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[54]
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taxon
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title
Clinical Significance of Osteoprotegerin Expression in Human Colorectal Cancer
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Clinicopathological characteristics of the development set
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Baseline patient characteristics in TCGA
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caption
Clinicopathological characteristics of the development set
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[0] array · 4 items
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[1]
Subgroup
[2]
OS set (n = 420)No of patient (%)
[3]
DFS set (n = 388)No of patient (%)
[1] array · 4 items
[0]
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[1]
<65
[2]
165 (39.3)
[3]
156 (40.2)
[2] array · 3 items
[0]
65–74
[1]
127 (30.2)
[2]
118 (30.4)
[3] array · 3 items
[0]
≥75
[1]
128 (30.5)
[2]
114 (29.4)
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[1]
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[2]
226 (53.8)
[3]
212 (54.6)
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[0]
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[1]
194 (46.2)
[2]
176 (45.4)
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[0]
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[1]
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[2]
11 (2.6)
[3]
11 (2.8)
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[1]
74 (17.6)
[2]
72 (18.6)
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[0]
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[1]
286 (68.1)
[2]
268 (69.1)
[9] array · 3 items
[0]
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[1]
49 (11.7)
[2]
37 (9.5)
[10] array · 4 items
[0]
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[1]
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[2]
249 (59.3)
[3]
239 (61.60)
[11] array · 3 items
[0]
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[1]
99 (23.6)
[2]
91 (23.5)
[12] array · 3 items
[0]
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[1]
72 (17.1)
[2]
58 (14.9)
[13] array · 3 items
[0]
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[1]
0 (0)
[2]
0 (0)
[14] array · 4 items
[0]
pM
[1]
M0
[2]
364 (86.7)
[3]
347 (89.4)
[15] array · 3 items
[0]
M1
[1]
56 (13.3)
[2]
41 (10.6)
[16] array · 4 items
[0]
INHBA
[1]
Low
[2]
255 (60.7)
[3]
239 (61.6)
[17] array · 3 items
[0]
High
[1]
165 (39.3)
[2]
149 (38.4)
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section[12]/table-wrap[1]#Tab1
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Risk factors for overall survival and disease free survival according to Cox proportional hazards regression model
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Risk factors for overall survival and disease-free survival
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caption
Risk factors for overall survival and disease free survival according to Cox proportional hazards regression model
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[2]
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[3]
DFS set(n = 388)
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[0]
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[3]
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[4]
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[5]
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[10]
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[11]
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[2]
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[3]
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[6]
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1
[12]
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[1]
0.99
[2]
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0.960
[4]
1.29
[5]
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[6]
0.393
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0.47
[8]
0.27–0.81
[9]
0.007
[10]
0.50
[11]
0.28–0.88
[12]
0.016
[5] array · 13 items
[0]
≥75
[1]
1.86
[2]
1.13–3.09
[3]
0.016
[4]
2.28
[5]
1.36–3.83
[6]
0.002
[7]
0.90
[8]
0.60–1.57
[9]
0.897
[10]
1.23
[11]
0.75–2.02
[12]
0.421
[6] array · 14 items
[0]
Gender
[1]
Male
[2]
1
[3]
[4]
[5]
[6]
[7]
[8]
1
[9]
[10]
[11]
[12]
[13]
[7] array · 13 items
[0]
Female
[1]
1.18
[2]
0.55–1.31
[3]
0.466
[4]
[5]
[6]
[7]
0.75
[8]
0.49–1.16
[9]
0.199
[10]
[11]
[12]
[8] array · 14 items
[0]
pT
[1]
T1
[2]
1
[3]
[4]
[5]
1
[6]
[7]
[8]
1
[9]
[10]
[11]
1
[12]
[13]
[9] array · 13 items
[0]
T2
[1]
1.62
[2]
0.06–5.96
[3]
0.676
[4]
0.46
[5]
0.05–4.48
[6]
0.504
[7]
1.11
[8]
0.13–9.22
[9]
0.927
[10]
1.12
[11]
0.13–9.50
[12]
0.917
[10] array · 13 items
[0]
T3
[1]
2.28
[2]
0.32–16.49
[3]
0.414
[4]
1.16
[5]
0.16–8.61
[6]
0.886
[7]
2.79
[8]
0.39–20.16
[9]
0.309
[10]
1.95
[11]
0.26–14.50
[12]
0.514
[11] array · 13 items
[0]
T4
[1]
8.10
[2]
1.08–60.79
[3]
0.042
[4]
3.25
[5]
0.42–25.42
[6]
0.262
[7]
7.68
[8]
1.01–58.58
[9]
0.049
[10]
4.77
[11]
0.60–37.82
[12]
0.139
[12] array · 14 items
[0]
pN
[1]
N0
[2]
1
[3]
[4]
[5]
1
[6]
[7]
[8]
1
[9]
[10]
[11]
1
[12]
[13]
[13] array · 13 items
[0]
N1
[1]
1.72
[2]
0.98–2.99
[3]
0.056
[4]
1.25
[5]
0.68–2.29
[6]
0.467
[7]
1.41
[8]
0.83–2.40
[9]
0.206
[10]
0.78
[11]
0.44–1.40
[12]
0.406
[14] array · 13 items
[0]
N2
[1]
4.38
[2]
2.65–7.21
[3]
0.000
[4]
2.46
[5]
1.36–4.46
[6]
0.003
[7]
4.22
[8]
2.55–6.96
[9]
0.000
[10]
2.40
[11]
1.36–4.24
[12]
0.002
[15] array · 14 items
[0]
pM
[1]
M0
[2]
1
[3]
[4]
[5]
1
[6]
[7]
[8]
1
[9]
[10]
[11]
1
[12]
[13]
[16] array · 13 items
[0]
M1
[1]
4.08
[2]
2.55–6.53
[3]
0.000
[4]
2.25
[5]
1.29–3.91
[6]
0.004
[7]
4.69
[8]
2.85–7.70
[9]
0.000
[10]
3.66
[11]
2.06–6.53
[12]
0.000
[17] array · 14 items
[0]
INHBA
[1]
Low
[2]
1
[3]
[4]
[5]
1
[6]
[7]
[8]
1
[9]
[10]
[11]
1
[12]
[13]
[18] array · 13 items
[0]
High
[1]
1.65
[2]
1.07–2.55
[3]
0.024
[4]
1.44
[5]
0.93–2.25
[6]
0.100
[7]
1.94
[8]
1.27–2.97
[9]
0.002
[10]
1.71
[11]
1.10–2.65
[12]
0.017
locator
section[13]/table-wrap[1]#Tab2
gaps array · 8 items
[0]
asset:data:geo:GSE110223 not downloaded
[1]
asset:data:geo:GSE110223 licence not read
[2]
asset:data:geo:GSE110224 not downloaded
[3]
asset:data:geo:GSE110224 licence not read
[4]
asset:data:geo:GSE113513 not downloaded
[5]
asset:data:geo:GSE113513 licence not read
[6]
asset:data:geo:GSE21510 not downloaded
[7]
asset:data:geo:GSE21510 licence not read
analysis object · 4 fields
state
partial
methods array · 3 items
[0] object · 10 fields
id
method-1
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 regression
[1] object · 10 fields
id
method-2
family
model_calibration
specific
decision_curve
label
Model calibration / clinical utility
dsl_operations array · 2 items
[0]
calibration_bins
[1]
net_benefit
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
predictions_and_outcomes
evidence object · 3 fields
score
0.8
band
high
quote
decision curve analysis
[2] object · 10 fields
id
method-3
family
null
specific
null
label
Cox proportional hazard regression model
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
We used the Cox proportional hazard regression model to estimate hazard ratio (and its 95% confidence interval (CI)) for each potential risk factor.
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]
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.
data object · 6 fields
tier
R1
reachable
true
accessions array · 4 items
[0]
GSE21510
[1]
GSE110223
[2]
GSE113513
[3]
GSE110224
resolved_accessions array · 4 items
[0]
GSE21510
[1]
GSE110223
[2]
GSE113513
[3]
GSE110224
statements array · 1 item
[0]
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
basis
The paper names GSE21510, GSE110223, GSE113513, GSE110224 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]
cox_ph
assets array · 4 items
[0]
source:pmid:32293338
[1]
data:geo:GSE110223
[2]
asset:table:pmid:32293338:1
[3]
asset:table:pmid:32293338:2
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:GSE110223
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
2026-10-31T18:19:29Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
3
usage object · 3 fields
prompt_tokens
20368
completion_tokens
2780
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:32293338
type
paper
label
pmid:32293338
[1] object · 3 fields
id
source:pmid:32293338
type
manuscript
label
PMID 32293338 full text
[2] object · 3 fields
id
data:geo:GSE110223
type
dataset
label
GEO GSE110223
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:32293338
type
paper
label
pmid:32293338
[1] object · 3 fields
id
source:pmid:32293338
type
manuscript
label
PMID 32293338 full text
[2] object · 3 fields
id
data:geo:GSE110223
type
dataset
label
GEO GSE110223
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-32293338
type
extraction
used array · 1 item
[0]
source:pmid:32293338
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE110223
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE110223
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:32293338
parent_hash
sha256:3368ff1ad05e7036e258c945e574265bc8838dbc17adb5b9d08dea3b24c4d7f5
fork_of
null
gaps array · 13 items
[0]
asset:data:geo:GSE110223 licence not read
[1]
asset:data:geo:GSE110223 not downloaded
[2]
asset:data:geo:GSE110224 licence not read
[3]
asset:data:geo:GSE110224 not downloaded
[4]
asset:data:geo:GSE113513 licence not read
[5]
asset:data:geo:GSE113513 not downloaded
[6]
asset:data:geo:GSE21510 licence not read
[7]
asset:data:geo:GSE21510 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]
identify potential prognostic genes in COAD, microarray datasets were downloaded from the Gene Expre
order_basis
paper_text_order:536
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
survival_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 1 item
[0]
cox_ph
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
Cox regression
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:32293338", "kind": "science-record", "revision": 7, "content_hash": "sha256:88bfb619b3bb0e361f13d0e3628a99d90b2f08a82779962e8218be9ca18158c1", "created_at": "2026-11-01T15:40:59Z", "updated_at": "2026-11-01T22:01:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "INHBA is a prognostic predictor for patients with colon adenocarcinoma.", "doi": "10.1186/s12885-020-06743-2", "pmid": "32293338", "authors": ["Xueying Li", "Weiming Yu", "Chao Liang", "Yuan Xu", "Miaozun Zhang", "Xiaoyun Ding", "Xianlei Cai"], "journal": "BMC cancer", "year": 2020, "abstract": "BACKGROUND: Colon adenocarcinoma (COAD) is one of the most lethal cancers. It is particularly important to accurately predict prognosis and to provide individualized treatment. Several lines of evidence suggest that genetic factors and clinicopathological characteristics are related to cancer onset and progression. The aim of this study was to identify potential prognostic genes and to develop a nomogram to predict survival and recurrence of COAD.\nMETHODS: To identify potential prognostic genes in COAD, microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained from GEO2R. Venn diagram was drawn to select those genes that were overexpressed in all datasets, and survival analyses were performed to determine the prognostic values of the selected genes. New nomograms were developed based on the genes that were significantly associated with prognosis. Clinicopathological data were obtained from The Cancer Genome Atlas (TCGA). Finally, the new nomograms were compared head-to-head comparison with the TNM nomogram.\nRESULTS: From GSE21510, GSE110223, GSE113513 and GSE110224, a total of 834, 218, 236 and 613 overexpressed DEGs were screened out, respectively. The Venn diagram revealed that 12 genes appeared in all four profiles. After survival analyses, only INHBA expression was associated with both overall survival (OS) and disease-free survival (DFS). Multivariate analyses revealed that age, pathological N and pathological M were significant independent risk factors for OS. Age, pathological N, pathological M and INHBA were significant independent risk factors for DFS. Two prediction models predicted the probability of 3-year survival and 5-year survival for OS and DFS, respectively. The concordance indexes were 0.785 for 3-year overall survival, 0.759 for 5-year overall survival, 0.789 for 3-year disease-free survival and 0.757 for 5-year disease-free survival. The head-to-head comparison according to time-dependent ROC curves indicated that the new models had higher predictive accuracy. Decision curve analyses (DCA) indicated that the clinical value of the new models were higher than TNM models for predicting disease-free survival.\nCONCLUSION: The combination of INHBA expression with a clinical nomogram improves prognostic power in colon adenocarcinoma, especially for predicting recurrence.", "claims": [{"id": "claim-method", "statement": "We used the Cox proportional hazard regression model to estimate hazard ratio (and its 95% confidence interval (CI)) for each potential risk factor.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32293338", "locator": "sections[8] \"Statistical analysis\" paragraph 2", "quote": "We used the Cox proportional hazard regression model to estimate hazard ratio (and its 95% confidence interval (CI)) for each potential risk factor."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32293338", "locator": "sections[25] \"notes\" paragraph 1", "quote": "The datasets used and/or analyzed during the current study 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:32293338"], "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:GSE110223"], "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:GSE110223"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32293338", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-020-06743-2", "sha256": "sha256:ef9a04569b1e4b9b6c73cd52d655e114486a343a7a414ffe85747ac40ee1f2a9", "bytes": 25811, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12885-020-06743-2", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7161248", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE110223", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110223", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE110223", "version": null, "note": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2982906", "GSM2982907", "GSM2982908", "GSM2982909", "GSM2982910", "GSM2982911", "GSM2982912", "GSM2982913", "GSM2982914", "GSM2982915", "GSM2982916", "GSM2982917", "GSM2982918", "GSM2982919", "GSM2982920", "GSM2982921", "GSM2982922", "GSM2982923", "GSM2982924", "GSM2982925", "GSM2982926", "GSM2982927", "GSM2982928", "GSM2982929", "GSM2982930", "GSM2982931"], "sample_count": 26, "group_sizes": null, "group_sizes_basis": null, "platform": "96", "taxon": "Homo sapiens", "title": "Expression data from 13 patients with colorectal cancer", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE110224", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110224", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE110224", "version": null, "note": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2982932", "GSM2982933", "GSM2982934", "GSM2982935", "GSM2982936", "GSM2982937", "GSM2982938", "GSM2982939", "GSM2982940", "GSM2982941", 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