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pmid:32566639

Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis.

Yutao Wang · Jiaxing Lin · Kexin Yan · Jianfeng Wang

International journal of genomics · 2020 doi:10.1155/2020/1097602

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pmid:32566639
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2026-11-01T15:41:03Z
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title
Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis.
doi
10.1155/2020/1097602
pmid
32566639
authors array · 4 items
[0]
Yutao Wang
[1]
Jiaxing Lin
[2]
Kexin Yan
[3]
Jianfeng Wang
journal
International journal of genomics
year
2020
abstract
AIM: In this paper, we aimed to develop and validate a risk prediction method using independent prognosis genes selected robustly in prostate cancer. METHOD: We considered 723 samples obtained from TCGA (the Cancer Genome Atlas), GSE46602, and GSE21032. Prostate cancer prognosis-related genes with P < 0.05 were selected using Univariable Cox regression analysis. We then built the lowest AIC (Akaike information criterion score) optimal gene model using the "Rbsurv" package in TCGA train set. The coefficients were obtained by Multivariable Cox regression analysis. We named the new prognosis method CMU5. The CMU5 risk score was verified in TCGA test set, GSE46602, and GSE21032. RESULTS: FAM72D, ARHGAP33, TACR2, PLEK2, and FA2H were identified as independent prognosis factors in prostate cancer patients. We built the computing model as follows: CMU5 risk score = 1.158∗FAM72D + 1.737∗ARHGAP33 - 0.737∗TACR2 - 0.651∗PLEK2 - 0.793∗FA2H. The AUC of DFS was 0.809 in the train set (274 samples), 0.710 in the test set (273 samples), and 0.768 in the complete set (547 samples). The benign prediction capacity of CMU5 was verified by GSE46602 (36 samples; AUC = 0.6039) and GSE21032 GPL5188 (140 samples; AUC = 0.7083). Using the cut-off point of 2.056, a significant difference was shown between high- and low-risk groups. CONCLUSION: A prognosis-related risk score formula named CMU5 was built and verified, providing reliable prediction of prostate cancer outcome. This signature might provide a basis for individualized treatment of prostate cancer.
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The data used to support the findings of this study are available from the corresponding author upon request.
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[742]
GSM528049
sample_count
743
group_sizes
null
group_sizes_basis
null
platform
10264;8227;5188;4091
taxon
Homo sapiens
title
Integrative genomic profiling of human prostate cancer
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[2] object · 15 fields
id
data:geo:GSE46602
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=GSE46602
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE46602
version
null
note
The data used to support the findings of this study are available from the corresponding author upon request.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The data used to support the findings of this study are available from the corresponding author upon request.
access_statement_kind
data_availability_statement
sample_accessions array · 50 items
[0]
GSM1133136
[1]
GSM1133137
[2]
GSM1133138
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GSM1133139
[4]
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[5]
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[6]
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[7]
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[10]
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[22]
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[24]
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[25]
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[26]
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[27]
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[28]
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[29]
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[30]
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[31]
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[32]
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[34]
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[35]
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[44]
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[45]
GSM1133181
[46]
GSM1133182
[47]
GSM1133183
[48]
GSM1133184
[49]
GSM1133185
sample_count
50
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Expression data from prostate cancer and benign prostate glands
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 4 items
[0]
asset:data:geo:GSE21032 not downloaded
[1]
asset:data:geo:GSE21032 licence not read
[2]
asset:data:geo:GSE46602 not downloaded
[3]
asset:data:geo:GSE46602 licence not read
analysis object · 4 fields
state
partial
methods array · 2 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
0.7
band
medium
quote
Cox regression
[1] object · 10 fields
id
method-2
family
null
specific
null
label
Robust likelihood-based survival analysis using the Rbsurv package, with Akaike information criterion (AIC) model selection
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
reordered
quote
“The Rbsurv package in R was used to conduct robust likelihood-based survival analysis among survival-associated genes with the parameter as follows: iteration times = 100 and max concern genes = 20 [21, 22].”
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 · 2 items
[0]
GSE46602
[1]
GSE21032
resolved_accessions array · 2 items
[0]
GSE46602
[1]
GSE21032
statements array · 1 item
[0]
The data used to support the findings of this study are available from the corresponding author upon request.
basis
The paper names GSE46602, GSE21032 in GEO database 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 · 2 items
[0]
source:pmid:32566639
[1]
data:geo:GSE21032
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:GSE21032
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
2026-10-31T18:20:43Z
independence
single_reader
evidence_check object · 2 fields
method
reordered
availability
verbatim
calls
3
usage object · 3 fields
prompt_tokens
19331
completion_tokens
2026
cached_tokens
776
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
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[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:32566639
type
paper
label
pmid:32566639
[1] object · 3 fields
id
source:pmid:32566639
type
manuscript
label
PMID 32566639 full text
[2] object · 3 fields
id
data:geo:GSE21032
type
dataset
label
GEO GSE21032
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:32566639
type
paper
label
pmid:32566639
[1] object · 3 fields
id
source:pmid:32566639
type
manuscript
label
PMID 32566639 full text
[2] object · 3 fields
id
data:geo:GSE21032
type
dataset
label
GEO GSE21032
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-32566639
type
extraction
used array · 1 item
[0]
source:pmid:32566639
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE21032
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE21032
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:32566639
parent_hash
sha256:e9c121ecf735ec9279fa063fa3a217418f3b8e53b0a368bad1cd869df248dfd5
fork_of
null
gaps array · 9 items
[0]
asset:data:geo:GSE21032 licence not read
[1]
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[2]
asset:data:geo:GSE46602 licence not read
[3]
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[4]
execution.not_run
[5]
method.reader_named_unmapped
[6]
review.human_critique
[7]
review.independent_reader
[8]
review.machine_critique
assays array · 2 items
[0] object · 11 fields
role
data_generation
definition_id
clinical_trial
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]
considered DFS (disease-free survival) data. Patients were randomly assigned to a train set (n = 274) containing 22 nontu
order_basis
paper_text_order:2025
id
assay:1
[1] object · 11 fields
role
analysis
definition_id
survival_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 1 item
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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:32566639", "kind": "science-record", "revision": 6, "content_hash": "sha256:095ef0a41a1287b61d6a963d036edf7b5194e410e7bab43a04752858bec2dfee", "created_at": "2026-11-01T15:41:03Z", "updated_at": "2026-11-01T21:23:22Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis.", "doi": "10.1155/2020/1097602", "pmid": "32566639", "authors": ["Yutao Wang", "Jiaxing Lin", "Kexin Yan", "Jianfeng Wang"], "journal": "International journal of genomics", "year": 2020, "abstract": "AIM: In this paper, we aimed to develop and validate a risk prediction method using independent prognosis genes selected robustly in prostate cancer.\nMETHOD: We considered 723 samples obtained from TCGA (the Cancer Genome Atlas), GSE46602, and GSE21032. Prostate cancer prognosis-related genes with P < 0.05 were selected using Univariable Cox regression analysis. We then built the lowest AIC (Akaike information criterion score) optimal gene model using the \"Rbsurv\" package in TCGA train set. The coefficients were obtained by Multivariable Cox regression analysis. We named the new prognosis method CMU5. The CMU5 risk score was verified in TCGA test set, GSE46602, and GSE21032.\nRESULTS: FAM72D, ARHGAP33, TACR2, PLEK2, and FA2H were identified as independent prognosis factors in prostate cancer patients. We built the computing model as follows: CMU5 risk score = 1.158∗FAM72D + 1.737∗ARHGAP33 - 0.737∗TACR2 - 0.651∗PLEK2 - 0.793∗FA2H. The AUC of DFS was 0.809 in the train set (274 samples), 0.710 in the test set (273 samples), and 0.768 in the complete set (547 samples). The benign prediction capacity of CMU5 was verified by GSE46602 (36 samples; AUC = 0.6039) and GSE21032 GPL5188 (140 samples; AUC = 0.7083). Using the cut-off point of 2.056, a significant difference was shown between high- and low-risk groups.\nCONCLUSION: A prognosis-related risk score formula named CMU5 was built and verified, providing reliable prediction of prostate cancer outcome. This signature might provide a basis for individualized treatment of prostate cancer.", "claims": [{"id": "claim-data-access", "statement": "The data used to support the findings of this study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32566639", "locator": "sections[18] \"Data Availability\" paragraph 2", "quote": "The data used to support the findings of this study are available from the corresponding author upon 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:32566639"], "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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