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pmid:34603375

Identification of Transcription Factor-Related Gene Signature and Risk Score Model for Colon Adenocarcinoma.

Jianwei Lin · Zichao Cao · Dingye Yu · Wei Cai

Frontiers in genetics · 2021 doi:10.3389/fgene.2021.709133

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Data

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title
Identification of Transcription Factor-Related Gene Signature and Risk Score Model for Colon Adenocarcinoma.
doi
10.3389/fgene.2021.709133
pmid
34603375
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[0]
Jianwei Lin
[1]
Zichao Cao
[2]
Dingye Yu
[3]
Wei Cai
journal
Frontiers in genetics
year
2021
abstract
The prognosis of colon adenocarcinoma (COAD) remains poor. However, the specific and sensitive biomarkers for diagnosis and prognosis of COAD are absent. Transcription factors (TFs) are involved in many biological processes in cells. As the molecule of the signal pathway of the terminal effectors, TFs play important roles in tumorigenesis and development. A growing body of research suggests that aberrant TFs contribute to the development of COAD, as well as to its clinicopathological features and prognosis. In consequence, a few studies have investigated the relationship between the TF-related risk model and the prognosis of COAD. Therefore, in this article, we hope to develop a prognostic risk model based on TFs to predict the prognosis of patients with COAD. The mRNA transcription data and corresponding clinical data were downloaded from TCGA and GEO. Then, 141 differentially expressed genes, validated by the GEPIA2 database, were identified by differential expression analysis between normal and tumor samples. Univariate, multivariate and Lasso Cox regression analysis were performed to identify seven prognostic genes (E2F3, ETS2, HLF, HSF4, KLF4, MEIS2, and TCF7L1). The Kaplan-Meier curve and the receiver operating characteristic curve (ROC, 1-year AUC: 0.723, 3-year AUC: 0.775, 5-year AUC: 0.786) showed that our model could be used to predict the prognosis of patients with COAD. Multivariate Cox analysis also reported that the risk model is an independent prognostic factor of COAD. The external cohort (GSE17536 and GSE39582) was used to validate our risk model, which indicated that our risk model may be a reliable predictive model for COAD patients. Finally, based on the model and the clinicopathological factors, we constructed a nomogram with a C-index of 0.802. In conclusion, we emphasize the clinical significance of TFs in COAD and construct a prognostic model of TFs, which could provide a novel and reliable model for the prognosis of COAD.
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Differentially expressed genes were identified by the linear models for microarray data (limma) R package based on TCGA–COAD data.
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Differentially expressed genes were identified by the linear models for microarray data (limma) R package based on TCGA–COAD data.
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The mRNA expression data (Workflow Type: HT Seq-FPKM) and clinical information pertaining to survival time for COAD patients downloaded from TCGA website (https://portal.gdc.cancer.gov/repository) were used as the training set.
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Metastasis Gene Expression Profile Predicts Recurrence and Death in Colon Cancer Patients (Moffitt Samples)
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[436]
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[439]
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[440]
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[443]
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[446]
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[447]
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[448]
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[450]
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[451]
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[452]
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[453]
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[456]
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[457]
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[460]
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[462]
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[463]
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[464]
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[470]
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[472]
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[474]
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[501]
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[536]
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sample_count
585
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Gene expression Classification of Colon Cancer defines six molecular subtypes with distinct clinical, molecular and survival characteristics [Expression]
resolution_checked_at
2026-11-01T15:39:54Z
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identifier_resolution_only_no_download_no_licence_read
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asset:table:pmid:34603375:1
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null
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The clinical information of colon adenocarcinoma (COAD) patients from The Cancer Genome Atlas (TCGA).
structure object · 5 fields
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PMC8485095
section_index
12
section_title
Overall Design of the Study
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17
column_count
3
table object · 8 fields
label
TABLE 1
caption
The clinical information of colon adenocarcinoma (COAD) patients from The Cancer Genome Atlas (TCGA).
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[0] array · 2 items
[0]
[1]
Patient n = 384
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[0]
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[1]
≤65
[2]
159
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[1]
225
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[2]
180
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[0]
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[1]
204
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[2]
216
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157
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[0]
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[1]
11
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[1]
T1–2
[2]
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306
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285
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54
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[1]
39
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N
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[2]
230
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[0]
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[1]
154
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section[12]/table-wrap[1]#T1
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The results of multivariate Cox analysis in COAD.
structure object · 5 fields
pmcid
PMC8485095
section_index
14
section_title
Construction of Risk Score Model by Univariate, Multivariate, and Lasso Cox Regression Analysis
row_count
8
column_count
6
table object · 8 fields
label
TABLE 2
caption
The results of multivariate Cox analysis in COAD.
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[0]
ID
[1]
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[2]
HR
[3]
HR.95L
[4]
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[5]
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[1] array · 6 items
[0]
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[1]
0.735185
[2]
2.085867
[3]
1.1849
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3.671907
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0.744583
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0.341165
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0.608,272
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0.91836
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2.505179
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0.006262
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0.551359
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1.735609
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0.954879
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3.154683
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0.070526
locator
section[14]/table-wrap[1]#T2
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[0]
asset:data:geo:GSE17536 not downloaded
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asset:data:geo:GSE17536 licence not read
[2]
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[3]
asset:data:geo:GSE39582 licence not read
analysis object · 4 fields
state
partial
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[0] object · 10 fields
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method-1
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differential_expression
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Differential expression analysis
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[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
parameters object · 0 fields
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[0]
count_matrix_with_condition
evidence object · 3 fields
score
1
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verbatim
quote
Differentially expressed genes were identified by the linear models for microarray data (limma) R package based on TCGA–COAD data.
[1] object · 10 fields
id
method-2
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survival_analysis
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kaplan_meier
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Survival analysis
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[0]
survival_km
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parameters object · 0 fields
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[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.7
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medium
quote
Kaplan-Meier
[2] object · 10 fields
id
method-3
family
survival_analysis
specific
cox_proportional_hazards
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Survival analysis
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[0]
cox_ph
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parameters object · 0 fields
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[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.7
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medium
quote
Cox regression
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gaps array · 1 item
[0]
method.reader_differs_from_extractor
capability object · 3 fields
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complete
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[0] object · 6 fields
capability_id
capability:computational_analysis
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[0]
meta_analysis
[1]
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[2]
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[3]
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[4]
pca
[5]
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[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
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readiness object · 8 fields
state
complete
tier
R1
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nothing:replicable_now
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method object · 5 fields
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normalise_counts
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differential_expression_fit
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fdr_bh
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basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom.
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
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[0]
GSE17536
[1]
GSE39582
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[0]
GSE17536
[1]
GSE39582
statements array · 1 item
[0]
The mRNA expression data (Workflow Type: HT Seq-FPKM) and clinical information pertaining to survival time for COAD patients downloaded from TCGA website (https://portal.gdc.cancer.gov/repository) were used as the training set.
basis
The paper names GSE17536, GSE39582 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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fdr_bh
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run_analysis
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[0]
data:geo:GSE17536
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partial
machine_critique object · 5 fields
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extraction_review
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MiniMax-M3.1-Flash
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minimax
provider_family
minimax
read_at
2026-10-31T19:51:12Z
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single_reader
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verbatim
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null
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3
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528
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extraction
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complete
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minimax
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review.human_critique
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execution.not_run
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pmid:34603375
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paper
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pmid:34603375
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source:pmid:34603375
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PMID 34603375 full text
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data:geo:GSE17536
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dataset
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GEO GSE17536
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classification
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pmid:34603375
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PMID 34603375 full text
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GEO GSE17536
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agent:corpus-assay-engine
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agent:minimax-reader
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del based on TFs to predict the prognosis of patients with COAD. The mRNA transcription data and corres
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el could be used to predict the prognosis of patients with COAD. Multivariate Cox analysis also reporte
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Differentially expressed genes were identified by the linear models for microarray data (limma) R package based on TCGA–COAD data.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34603375", "kind": "science-record", "revision": 9, "content_hash": "sha256:caf1d358d70dc00711f80d395675213b3595436052833f212227631d27b81b27", "created_at": "2026-11-01T15:41:55Z", "updated_at": "2026-11-01T22:47:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Transcription Factor-Related Gene Signature and Risk Score Model for Colon Adenocarcinoma.", "doi": "10.3389/fgene.2021.709133", "pmid": "34603375", "authors": ["Jianwei Lin", "Zichao Cao", "Dingye Yu", "Wei Cai"], "journal": "Frontiers in genetics", "year": 2021, "abstract": "The prognosis of colon adenocarcinoma (COAD) remains poor. However, the specific and sensitive biomarkers for diagnosis and prognosis of COAD are absent. Transcription factors (TFs) are involved in many biological processes in cells. As the molecule of the signal pathway of the terminal effectors, TFs play important roles in tumorigenesis and development. A growing body of research suggests that aberrant TFs contribute to the development of COAD, as well as to its clinicopathological features and prognosis. In consequence, a few studies have investigated the relationship between the TF-related risk model and the prognosis of COAD. Therefore, in this article, we hope to develop a prognostic risk model based on TFs to predict the prognosis of patients with COAD. The mRNA transcription data and corresponding clinical data were downloaded from TCGA and GEO. Then, 141 differentially expressed genes, validated by the GEPIA2 database, were identified by differential expression analysis between normal and tumor samples. Univariate, multivariate and Lasso Cox regression analysis were performed to identify seven prognostic genes (E2F3, ETS2, HLF, HSF4, KLF4, MEIS2, and TCF7L1). The Kaplan-Meier curve and the receiver operating characteristic curve (ROC, 1-year AUC: 0.723, 3-year AUC: 0.775, 5-year AUC: 0.786) showed that our model could be used to predict the prognosis of patients with COAD. Multivariate Cox analysis also reported that the risk model is an independent prognostic factor of COAD. The external cohort (GSE17536 and GSE39582) was used to validate our risk model, which indicated that our risk model may be a reliable predictive model for COAD patients. Finally, based on the model and the clinicopathological factors, we constructed a nomogram with a C-index of 0.802. In conclusion, we emphasize the clinical significance of TFs in COAD and construct a prognostic model of TFs, which could provide a novel and reliable model for the prognosis of COAD.", "claims": [{"id": "claim-method", "statement": "Differentially expressed genes were identified by the linear models for microarray data (limma) R package based on TCGA–COAD data.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34603375", "locator": "sections[4] \"Identification of Differentially Expressed Genes and Prognostic Genes\" paragraph 2", "quote": "Differentially expressed genes were identified by the linear models for microarray data (limma) R package based on TCGA–COAD data."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The mRNA expression data (Workflow Type: HT Seq-FPKM) and clinical information pertaining to survival time for COAD patients downloaded from TCGA website (https://portal.gdc.cancer.gov/repository) were used as the training set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34603375", "locator": "sections[3] \"Data Collection\" paragraph 2", "quote": "The mRNA expression data (Workflow Type: HT Seq-FPKM) and clinical information pertaining to survival time for COAD patients downloaded from TCGA website (https://portal.gdc.cancer.gov/repository) were used as the training set."}], "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:34603375"], "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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