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pmid:33520450

Gene signatures with predictive and prognostic survival values in human osteosarcoma.

Zhongpeng Qiu · Xinhui Du · Kai Chen · Yi Dai · Sibo Wang · Jun Xiao · Gang Li

PeerJ · 2021 doi:10.7717/peerj.10633

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

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:33520450
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science-record
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sha256:ce003d7c840e219f7c409fff818b4dd62c4a592acf5536a3e93277a0a20cfbf9
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2026-11-01T15:41:23Z
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2026-11-01T21:24:35Z
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title
Gene signatures with predictive and prognostic survival values in human osteosarcoma.
doi
10.7717/peerj.10633
pmid
33520450
authors array · 7 items
[0]
Zhongpeng Qiu
[1]
Xinhui Du
[2]
Kai Chen
[3]
Yi Dai
[4]
Sibo Wang
[5]
Jun Xiao
[6]
Gang Li
journal
PeerJ
year
2021
abstract
Osteosarcoma is a common malignancy seen mainly in children and adolescents. The disease is characterized by poor overall prognosis and lower survival due to a lack of predictive markers. Many gene signatures with diagnostic, prognostic, and predictive values were evaluated to achieve better clinical outcomes. Two public data series, GSE21257 and UCSC Xena, were used to identify the minimum number of robust genes needed for a predictive signature to guide prognosis of patients with osteosarcoma. The lasso regression algorithm was used to analyze sequencing data from TCGA-TARGET, and methods such as Cox regression analysis, risk factor scoring, receiving operating curve, KMplot prognosis analysis, and nomogram were used to characterize the prognostic predictive power of the identified genes. Their utility was assessed using the GEO osteosarcoma dataset. Finally, the functional enrichment analysis of the identified genes was performed. A total of twenty-gene signatures were found to have a good prognostic value for predicting patient survival. Gene ontology analysis showed that the key genes related to osteosarcoma were categorized as peptide-antigen binding, clathrin-coated endocytic vesicle membrane, peptide binding, and MHC class II protein complex. The osteosarcoma related genes in these modules were significantly enriched in the processes of antigen processing and presentation, phagocytosis, cell adhesion molecules, Staphylococcus aureus infection. Twenty gene signatures were identified related to osteosarcoma, which would be helpful for predicting prognosis of patients with OS. Further, these signatures can be used to determine the subtypes of osteosarcoma.
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The method chosen by lasso Cox was 10-fold cross-validation.
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sections[4] "Construction of gene signatures" paragraph 2
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The method chosen by lasso Cox was 10-fold cross-validation.
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The following information was supplied regarding data availability: The raw measurements are available in the Supplemental Files.
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sections[25] "Data Availability Statement" paragraphs 1-3
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The following information was supplied regarding data availability: The raw measurements are available in the Supplemental Files.
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The claim, method and evidence span published with the source article.
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the published estimate and its evidence
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title
Genome-wide gene expression profiling on pre-chemotherapy biopsies of osteosarcoma patients who developed metastases within 5yrs (n=34) and patients who did not develop metastases within 5yrs (n=19)
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Potential biomarkers for osteosarcoma.
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Introduction
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Potential biomarkers for osteosarcoma.
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Candidate biomarker
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[4]
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[5]
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[6]
Reference
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[2]
C7orf24
[3]
↑
[4]
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[5]
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[6]
Uejima et al. (2011)
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[1]
miRNA signatures
[2]
−
[3]
Tissue; cell lines
[4]
qRT-PCR
[5]
Gougelet et al. (2011)
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[0]
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[1]
Multigene classifier
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−
[3]
Tissue
[4]
cDNA microarray; qRT-PCR
[5]
Man et al. (2005)
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[1]
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−
[3]
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[4]
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[5]
Mintz et al. (2005)
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miRNA signatures
[2]
−
[3]
Tissue
[4]
miRNA microarray; qRT-PCR; immunohistochemistry (IHC)
[5]
Jones et al. (2012)
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miR-21
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↑
[3]
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[4]
qRT-PCR
[5]
Yuan et al. (2012)
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Xiong et al. (2009)
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qRT-PCR
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[6]
Li et al. (2010)
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↑
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[4]
2D-DIGE; MALDI-TOF MS; WB; ELISA
[5]
Jin et al. (2007)
[16] array · 6 items
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[2]
↑(poor prognosis)
[3]
Tissue
[4]
2D-DIGE; LC-ESI-MS/MS; WB
[5]
Kikuta et al. (2010)
[17] array · 6 items
[0]
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[2]
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[4]
SELDI-TOF MS; WB
[5]
Li et al. (2011)
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Twenty genes significantly related to the overall survival in the training set underlying the LASSO model.
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Construction of genes classifier for OS
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Table 2
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Twenty genes significantly related to the overall survival in the training set underlying the LASSO model.
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Ensemble ID
[1]
Gene
[2]
Chromosome location
[3]
Coefficient of lasso model
[4]
HR
[5]
p value
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NUBP1
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Univariate and multivariate Cox regression analyses genes signiture for patients with osteosarcoma in study cohort.
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Validation of twenty-gene signature for survival prediction in the validation set
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Univariate and multivariate Cox regression analyses genes signiture for patients with osteosarcoma in study cohort.
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Relationship between risk score of the 20-marker-based prognostic classifier with os and clinical characteristics in the training set.
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Gene co-expression network analysis
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Relationship between risk score of the 20-marker-based prognostic classifier with os and clinical characteristics in the training set.
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[0]
Characters
[1]
Level
[2]
Low risk
[3]
High risk
[4]
P value
[1] array · 5 items
[0]
n
[1]
[2]
42
[3]
42
[4]
[2] array · 5 items
[0]
Gender
[1]
Female (%)
[2]
16 ( 38.1)
[3]
21 (50.0)
[4]
0.379
[3] array · 5 items
[0]
[1]
Male (%)
[2]
26 ( 61.9)
[3]
21 (50.0)
[4]
[4] array · 5 items
[0]
Age
[1]
<18 (%)
[2]
32 ( 76.2)
[3]
34 (81.0)
[4]
0.791
[5] array · 5 items
[0]
[1]
≥18 (%)
[2]
10 ( 23.8)
[3]
8 (19.0)
[4]
[6] array · 5 items
[0]
Overall survival
[1]
Alived (%)
[2]
42 (100.0)
[3]
13 (31.0)
[4]
<0.001
[7] array · 5 items
[0]
[1]
Dead (%)
[2]
0 ( 0.0)
[3]
29 (69.0)
[4]
[8] array · 5 items
[0]
Recurrence
[1]
No (%)
[2]
32 ( 76.2)
[3]
13 (31.0)
[4]
<0.001
[9] array · 5 items
[0]
[1]
Yes (%)
[2]
10 ( 23.8)
[3]
29 (69.0)
[4]
[10] array · 5 items
[0]
Metastasis
[1]
No (%)
[2]
37 ( 88.1)
[3]
26 (61.9)
[4]
0.011
[11] array · 5 items
[0]
[1]
Yes (%)
[2]
5 ( 11.9)
[3]
16 (38.1)
[4]
[12] array · 5 items
[0]
Primary tumor site
[1]
Arm/Hand OR Pelvis (%)
[2]
3 ( 7.1)
[3]
5 (11.9)
[4]
0.713
[13] array · 5 items
[0]
[1]
Leg/Foot (%)
[2]
39 ( 92.9)
[3]
37 (88.1)
[4]
[14] array · 5 items
[0]
Specific tumor site
[1]
Femur (%)
[2]
13 ( 31.0)
[3]
25 (59.5)
[4]
0.015
[15] array · 5 items
[0]
[1]
Tibia or others (%)
[2]
29 ( 69.0)
[3]
17 (40.5)
[4]
locator
section[12]/table-wrap[1]#table-4
gaps array · 2 items
[0]
asset:data:geo:GSE21257 not downloaded
[1]
asset:data:geo:GSE21257 licence not read
analysis object · 4 fields
state
complete
methods array · 5 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
1
band
verbatim
quote
The method chosen by lasso Cox was 10-fold cross-validation.
[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
KM
[2] object · 10 fields
id
method-3
family
correlation_analysis
specific
pearson
label
Correlation analysis
dsl_operations array · 1 item
[0]
pearson_r
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
paired_numeric_columns
evidence object · 3 fields
score
0.8
band
high
quote
Pearson correlation
[3] object · 10 fields
id
method-4
family
clustering
specific
hierarchical
label
Clustering
dsl_operations array · 1 item
[0]
hierarchical_cluster
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
feature_matrix
evidence object · 3 fields
score
0.8
band
high
quote
dendrogram
[4] object · 10 fields
id
method-5
family
network_analysis
specific
pathway_enrichment
label
Network / pathway analysis
dsl_operations array · 1 item
[0]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.8
band
high
quote
pathway enrichment
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]
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 · 1 item
[0]
GSE21257
resolved_accessions array · 1 item
[0]
GSE21257
statements array · 1 item
[0]
The following information was supplied regarding data availability: The raw measurements are available in the Supplemental Files.
basis
The paper names GSE21257 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 · 6 items
[0]
source:pmid:33520450
[1]
data:geo:GSE21257
[2]
asset:table:pmid:33520450:1
[3]
asset:table:pmid:33520450:2
[4]
asset:table:pmid:33520450:3
[5]
asset:table:pmid:33520450:4
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:GSE21257
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
2026-10-31T18:26:30Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
3
usage object · 3 fields
prompt_tokens
34395
completion_tokens
1213
cached_tokens
792
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:33520450
type
paper
label
pmid:33520450
[1] object · 3 fields
id
source:pmid:33520450
type
manuscript
label
PMID 33520450 full text
[2] object · 3 fields
id
data:geo:GSE21257
type
dataset
label
GEO GSE21257
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:33520450
type
paper
label
pmid:33520450
[1] object · 3 fields
id
source:pmid:33520450
type
manuscript
label
PMID 33520450 full text
[2] object · 3 fields
id
data:geo:GSE21257
type
dataset
label
GEO GSE21257
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-33520450
type
extraction
used array · 1 item
[0]
source:pmid:33520450
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE21257
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE21257
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:33520450
parent_hash
sha256:d99a70e7c5de1ee95967f3257e7606e13dfb3ce4433ea9f7826d630429bd59b1
fork_of
null
gaps array · 6 items
[0]
asset:data:geo:GSE21257 licence not read
[1]
asset:data:geo:GSE21257 not downloaded
[2]
execution.not_run
[3]
review.human_critique
[4]
review.independent_reader
[5]
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]
cox_ph
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
The method chosen by lasso Cox was 10-fold cross-validation.
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:33520450", "kind": "science-record", "revision": 6, "content_hash": "sha256:ce003d7c840e219f7c409fff818b4dd62c4a592acf5536a3e93277a0a20cfbf9", "created_at": "2026-11-01T15:41:23Z", "updated_at": "2026-11-01T21:24:35Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Gene signatures with predictive and prognostic survival values in human osteosarcoma.", "doi": "10.7717/peerj.10633", "pmid": "33520450", "authors": ["Zhongpeng Qiu", "Xinhui Du", "Kai Chen", "Yi Dai", "Sibo Wang", "Jun Xiao", "Gang Li"], "journal": "PeerJ", "year": 2021, "abstract": "Osteosarcoma is a common malignancy seen mainly in children and adolescents. The disease is characterized by poor overall prognosis and lower survival due to a lack of predictive markers. Many gene signatures with diagnostic, prognostic, and predictive values were evaluated to achieve better clinical outcomes. Two public data series, GSE21257 and UCSC Xena, were used to identify the minimum number of robust genes needed for a predictive signature to guide prognosis of patients with osteosarcoma. The lasso regression algorithm was used to analyze sequencing data from TCGA-TARGET, and methods such as Cox regression analysis, risk factor scoring, receiving operating curve, KMplot prognosis analysis, and nomogram were used to characterize the prognostic predictive power of the identified genes. Their utility was assessed using the GEO osteosarcoma dataset. Finally, the functional enrichment analysis of the identified genes was performed. A total of twenty-gene signatures were found to have a good prognostic value for predicting patient survival. Gene ontology analysis showed that the key genes related to osteosarcoma were categorized as peptide-antigen binding, clathrin-coated endocytic vesicle membrane, peptide binding, and MHC class II protein complex. The osteosarcoma related genes in these modules were significantly enriched in the processes of antigen processing and presentation, phagocytosis, cell adhesion molecules, Staphylococcus aureus infection. Twenty gene signatures were identified related to osteosarcoma, which would be helpful for predicting prognosis of patients with OS. Further, these signatures can be used to determine the subtypes of osteosarcoma.", "claims": [{"id": "claim-method", "statement": "The method chosen by lasso Cox was 10-fold cross-validation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33520450", "locator": "sections[4] \"Construction of gene signatures\" paragraph 2", "quote": "The method chosen by lasso Cox was 10-fold cross-validation."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:\nThe raw measurements are available in the Supplemental Files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33520450", "locator": "sections[25] \"Data Availability Statement\" paragraphs 1-3", "quote": "The following information was supplied regarding data availability:\nThe raw measurements are available in the Supplemental Files."}], "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:33520450"], "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:GSE21257"], "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:GSE21257"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33520450", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.10633", "sha256": "sha256:a7f9c82dd111434ae194b0d618531e61af6581a2a78c37f071d6998b8e52f3e6", "bytes": 42680, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7717/peerj.10633", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7812922", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE21257", "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=GSE21257", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE21257", "version": null, "note": "The following information was supplied regarding data availability:\nThe raw measurements are available in the Supplemental Files.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The following information was supplied regarding data availability:\nThe raw measurements are available in the Supplemental Files.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM530667", "GSM530899", "GSM531283", "GSM531284", "GSM531285", "GSM531286", "GSM531287", "GSM531288", "GSM531289", "GSM531290", "GSM531291", "GSM531292", "GSM531293", "GSM531294", "GSM531295", "GSM531296", "GSM531297", "GSM531298", "GSM531299", "GSM531300", "GSM531301", "GSM531302", "GSM531303", "GSM531304", "GSM531305", "GSM531306", "GSM531307", "GSM531308", "GSM531309", "GSM531310", "GSM531311", "GSM531312", "GSM531313", "GSM531314", "GSM531319", "GSM531320", "GSM531321", "GSM531322", "GSM531323", "GSM531324", "GSM531325", "GSM531326", "GSM531327", "GSM531328", "GSM531329", "GSM531330", "GSM531331", "GSM531332", "GSM531333", "GSM531334", "GSM531335", "GSM531351", "GSM531352"], "sample_count": 53, "group_sizes": null, "group_sizes_basis": null, "platform": "10295", "taxon": "Homo sapiens", "title": "Genome-wide gene expression profiling on pre-chemotherapy biopsies of osteosarcoma patients who developed metastases within 5yrs (n=34) and patients who did not develop metastases within 5yrs (n=19)", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "asset:table:pmid:33520450:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:c252dcb48d671318d615a0d81cfaa9a1f9115add640bb5a92ec4c052abc3061a", "bytes": 2251, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[1]/table-wrap[1]#table-1", "version": null, "note": "Potential biomarkers for osteosarcoma.", "structure": {"pmcid": "PMC7812922", "section_index": 1, "section_title": "Introduction", "row_count": 18, "column_count": 7}, "table": {"label": "Table 1", "caption": "Potential biomarkers for osteosarcoma.", "rows": [["Study approach", "Type of biomarker", "Candidate biomarker", "Regulation", "Sample", "Technique", "Reference"], ["Genomics", "Diagnostic", "C7orf24", "↑", "Tissue; cell lines", "RT-PCR; qRT-PCR; Western blot (WB); siRNA transfection", "Uejima et al. (2011)"], ["Diagnostic And Predictive", "miRNA signatures", "−", "Tissue; cell lines", "qRT-PCR", "Gougelet et al. (2011)"], ["Predictive", "Multigene classifier", "−", "Tissue", "cDNA microarray; qRT-PCR", "Man et al. (2005)"], ["Predictive", "Multigene classifier", "−", "Tissue", "cDNA microarray; RT-PC", "Mintz et al. (2005)"], ["Predictive And Prognosis", "miRNA signatures", "−", "Tissue", "miRNA microarray; qRT-PCR; immunohistochemistry (IHC)", "Jones et al. (2012)"], ["Predictive And Prognosis", "miR-21", "↑", "Serum", "qRT-PCR", "Yuan et al. (2012)"], ["Prognosis", "Tenascin-C", "↓", "Cell lines", "cDNA microarray; RT-PCR; WB; IHC", "Xiong et al. (2009)"], ["Prognostic", "miRNA-214", "↑", "Tissue", "qRT-PCR", "Wang et al. (2014)"], ["Proteomics", "Diagnostic", "Serum amyloid A (SAA)", "↑", "Plasma", "SELDI-TOF MS; WB", "Li et al. (2010)"], ["Diagnostic", "Ezrin (EZR); a crystallin β chain (CRYAB)", "↑", "Tissue", "2D-DIGE; LC-ESI-MS/MS; RT-PCR for mRNA; tissue microarray and IHC", "Folio et al. (2009)"], ["Diagnostic", "Cytochrome C1 (CYC-1)", "↑", "Serum; cell lines", "SELDI-TOF MS; Gene microarray (cell lines)", "Li et al. (2009)"], ["Diagnostic", "Zinc finger protein 133 (ZNF 133); tubulin-a1c (TUBA1C)", "↑", "Tissue", "2-DE; MALDI-TOF MS; WB; IHC", "Li et al. (2010)"], ["Diagnostic", "Protein NDRG 1", "↑", "Plasma membrane from cell line and tissue", "2-DE; LC-ESI-MS/MS; IHC; WB", "Hua et al. (2011)"], ["Diagnostic", "Gelsolin", "↓", "Serum", "2D-DIGE; MALDIHTOF; WB; ELISA", "Jin et al. (2012)"], ["Predictive And Prognostic", "SAA", "↑", "Serum", "2D-DIGE; MALDI-TOF MS; WB; ELISA", "Jin et al. (2007)"], ["Predictive", "Peroxiredoxin 2 (PRDX2)", "↑(poor prognosis)", "Tissue", "2D-DIGE; LC-ESI-MS/MS; WB", "Kikuta et al. (2010)"], ["Predictive", "SAA; transthyretin (TTR)", "↑(poor prognosis)", "Plasma", "SELDI-TOF MS; WB", "Li et al. (2011)"]], "row_count": 18, "column_count": 7, "numeric_cells": 30, "header_rows": 1, "locator": "section[1]/table-wrap[1]#table-1"}}, {"id": "asset:table:pmid:33520450:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:7012df299348deed48055a39b9e1e9f3a6015cfd10eaaf2c73dca19364b5dbc0", "bytes": 2380, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[9]/table-wrap[1]#table-2", "version": null, "note": "Twenty genes significantly related to the overall survival in the training set underlying the LASSO model.", "structure": {"pmcid": "PMC7812922", "section_index": 9, "section_title": "Construction of genes classifier for OS", "row_count": 21, "column_count": 6}, "table": {"label": "Table 2", "caption": "Twenty genes significantly related to the overall survival in the training set underlying the LASSO model.", "rows": [["Ensemble ID", "Gene", "Chromosome location", "Coefficient of lasso model", "HR", "p value"], ["ENSG00000103274.9", "NUBP1", "chr16:10743786-10769351:(+)", "−0.2216", "0.905 (0.86, 0.953)", "0.000"], ["ENSG00000163219.10", "ARHGAP25", "chr2:68679601-68826833:(+)", "−0.0503", "0.795 (0.685, 0.924)", "0.003"], ["ENSG00000077420.14", "APBB1IP", "chr10:26438203-26567803:(+)", "−0.0429", "0.924 (0.88, 0.97)", "0.001"], ["ENSG00000162517.11", "PEF1", "chr1:31629862-31644896:(-)", "−0.0230", "0.979 (0.966, 0.992)", "0.002"], ["ENSG00000102226.8", "USP11", "chrX:47232690-47248328:(+)", "−0.0123", "0.982 (0.97, 0.994)", "0.004"], ["ENSG00000179163.11", "FUCA1", "chr1:23845077-23868294:(-)", "−0.0104", "0.969 (0.949, 0.99)", "0.003"], ["ENSG00000189171.12", "S100A13", "chr1:153618787-153634092:(-)", "0.0019", "1.007 (1.004, 1.011)", "0.000"], ["ENSG00000132535.17", "DLG4", "chr17:7189890-7219702:(-)", "0.0088", "1.064 (1.021, 1.109)", "0.003"], ["ENSG00000158315.9", "RHBDL2", "chr1:38885807-38941799:(-)", "0.0144", 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["ENSG00000138028.13", "CGREF1", "chr2:27098889-27119115:(-)", "0.2105", "1.015 (1.009, 1.021)", "0.000"], ["ENSG00000241563.3", "CORT", "chr1:10449719-10451902:(+)", "0.2504", "1.026 (1.016, 1.036)", "0.000"]], "row_count": 21, "column_count": 6, "numeric_cells": 107, "header_rows": 1, "locator": "section[9]/table-wrap[1]#table-2"}}, {"id": "asset:table:pmid:33520450:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:4dfd6875d2db9cf42459ecc76dda04ac6bf5a71f82f366b1fb46bba0c1ba022d", "bytes": 831, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[1]#table-3", "version": null, "note": "Univariate and multivariate Cox regression analyses genes signiture for patients with osteosarcoma in study cohort.", "structure": {"pmcid": "PMC7812922", "section_index": 11, "section_title": "Validation of twenty-gene signature for survival prediction in the validation set", "row_count": 8, "column_count": 5}, "table": {"label": "Table 3", "caption": "Univariate and multivariate Cox regression analyses genes signiture for patients with osteosarcoma in study cohort.", "rows": [["", "Univariate", "Multivariate"], ["Characteristics", "HR", "p.value", "HR", "p.value"], ["Gender (Female vs Male)", "0.666 (0.317, 1.399)", "0.283", "", ""], ["Age (<18 vs ≥18)", "0.909 (0.345, 2.393)", "0.846", "", ""], ["Recurrence (No vs Yes)", "19.348 (4.589, 81.563)", "<0.001", "5.374 (1.183, 24.404)", "0.029"], ["Primary tumor site (Arm/Hand OR Pelvis vs Leg/Foot)", "0.487 (0.168, 1.413)", "0.185", "", ""], ["Specific tumor site (Femur vs Tibia or others)", "0.642 (0.309, 1.337)", "0.236", "", ""], ["riskscore", "13.915 (7.067, 27.398)", "<0.001", "9.869 (4.663, 20.887)", "<0.001"]], "row_count": 8, "column_count": 5, "numeric_cells": 17, "header_rows": 2, "locator": "section[11]/table-wrap[1]#table-3"}}, {"id": "asset:table:pmid:33520450:4", "role": 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