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pmid:32669958

The prognostic value and potential mechanism of Matrix Metalloproteinases among Prostate Cancer.

Xinyu Geng · Chunyang Chen · Yuhua Huang · Jianquan Hou

International journal of medical sciences · 2020 doi:10.7150/ijms.46780

Paper

complete

Experiment

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Data

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Analysis

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pmid:32669958
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title
The prognostic value and potential mechanism of Matrix Metalloproteinases among Prostate Cancer.
doi
10.7150/ijms.46780
pmid
32669958
authors array · 4 items
[0]
Xinyu Geng
[1]
Chunyang Chen
[2]
Yuhua Huang
[3]
Jianquan Hou
journal
International journal of medical sciences
year
2020
abstract
Background: Matrix Metalloproteinases (MMPs) play an indispensable role in the initial alteration and development of PCa. We tried to generate an MMP-related prognostic signature (MMPS) in prostate cancer (PCa). Methods: TCGA-PRAD, MSKCC/GSE21032, GSE116918, GSE70769 cohorts were enrolled to assess the prognostic value of MMPs. The least absolute shrinkage and selection operator (LASSO) Cox regression was employed to generate the MMPS signature. The log-rank test and Kaplan-Meier (K-M) survival curve were applied to show the difference RFS, The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) was plotted to predict the accuracy of signature. CIBERSORT was conducted to analyze the different immune infiltration in MMPS-H and MMPS-L groups. Potential signaling pathways activated in the MMPS-H groups by Metascape. Results: MMP1, MMP7, MMP11, MMP24 and MMP26 were selected by LASSO regression and established the MMPS predict signature. The MMPS showed the high prognostic value in TCGA-PRAD training cohort (AUC=0.714) and validation cohorts (GSE116918: AUC=0.976, GSE70769: AUC=0.738, MSKCC: AUC=0.793). Pid integrin1 pathway, G2M checkpoint, and response to growth factor signaling pathways were activated in MMPS-H group, patients with the high MMPS risk score and low M2 macrophage showed the worst recurrence-free survival (RFS). Conclusion: MMPs involved and played an essential role in the tumorigenesis and biochemical recurrence in PCa patients. The MMPS signature could accurately predict the recurrence of PCa patients and validated in several cohorts.
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The least absolute shrinkage and selection operator (LASSO) Cox regression was employed to generate the MMPS signature.
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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 · 3 items
[0]
MSKCC/GSE21032
[1]
GSE116918
[2]
GSE70769
resolved_accessions array · 2 items
[0]
GSE116918
[1]
GSE70769
statements array · 1 item
[0]
In the current study, we concerned the TCGA-PRAD cohort with the RNA sequence data from the platform based on the Illumina system and clinical characteristics download from UCSC (https://gdc.xenahubs.net/download/TCGA-PRAD.htseq_fpkm.tsv.gz)
basis
The paper names GSE116918, GSE70769 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 · 2 items
[0]
source:pmid:32669958
[1]
data:geo:GSE116918
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:GSE116918
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
2026-10-31T18:21:10Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
null
calls
3
usage object · 3 fields
prompt_tokens
18614
completion_tokens
2642
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:32669958
type
paper
label
pmid:32669958
[1] object · 3 fields
id
source:pmid:32669958
type
manuscript
label
PMID 32669958 full text
[2] object · 3 fields
id
data:geo:GSE116918
type
dataset
label
GEO GSE116918
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:32669958
type
paper
label
pmid:32669958
[1] object · 3 fields
id
source:pmid:32669958
type
manuscript
label
PMID 32669958 full text
[2] object · 3 fields
id
data:geo:GSE116918
type
dataset
label
GEO GSE116918
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-32669958
type
extraction
used array · 1 item
[0]
source:pmid:32669958
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE116918
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE116918
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:32669958
parent_hash
sha256:718727c6306979ae69119b81f4f0b21b71d9d7d7d80c750faeb98083f7aae013
fork_of
null
gaps array · 9 items
[0]
asset:data:geo:GSE116918 licence not read
[1]
asset:data:geo:GSE116918 not downloaded
[2]
asset:data:geo:GSE70769 licence not read
[3]
asset:data:geo:GSE70769 not downloaded
[4]
execution.not_run
[5]
method.reader_differs_from_extractor
[6]
review.human_critique
[7]
review.independent_reader
[8]
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 least absolute shrinkage and selection operator (LASSO) Cox regression was employed to generate the MMPS signature.
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:32669958", "kind": "science-record", "revision": 8, "content_hash": "sha256:9f997dbae61b4376c097c11dfeec51e2ac3b382b6f99231bf1c4979ac75fd836", "created_at": "2026-11-01T15:41:06Z", "updated_at": "2026-11-01T22:01:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The prognostic value and potential mechanism of Matrix Metalloproteinases among Prostate Cancer.", "doi": "10.7150/ijms.46780", "pmid": "32669958", "authors": ["Xinyu Geng", "Chunyang Chen", "Yuhua Huang", "Jianquan Hou"], "journal": "International journal of medical sciences", "year": 2020, "abstract": "Background: Matrix Metalloproteinases (MMPs) play an indispensable role in the initial alteration and development of PCa. We tried to generate an MMP-related prognostic signature (MMPS) in prostate cancer (PCa). Methods: TCGA-PRAD, MSKCC/GSE21032, GSE116918, GSE70769 cohorts were enrolled to assess the prognostic value of MMPs. The least absolute shrinkage and selection operator (LASSO) Cox regression was employed to generate the MMPS signature. The log-rank test and Kaplan-Meier (K-M) survival curve were applied to show the difference RFS, The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) was plotted to predict the accuracy of signature. CIBERSORT was conducted to analyze the different immune infiltration in MMPS-H and MMPS-L groups. Potential signaling pathways activated in the MMPS-H groups by Metascape. Results: MMP1, MMP7, MMP11, MMP24 and MMP26 were selected by LASSO regression and established the MMPS predict signature. The MMPS showed the high prognostic value in TCGA-PRAD training cohort (AUC=0.714) and validation cohorts (GSE116918: AUC=0.976, GSE70769: AUC=0.738, MSKCC: AUC=0.793). Pid integrin1 pathway, G2M checkpoint, and response to growth factor signaling pathways were activated in MMPS-H group, patients with the high MMPS risk score and low M2 macrophage showed the worst recurrence-free survival (RFS). Conclusion: MMPs involved and played an essential role in the tumorigenesis and biochemical recurrence in PCa patients. The MMPS signature could accurately predict the recurrence of PCa patients and validated in several cohorts.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) Cox regression was employed to generate the MMPS signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32669958", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) Cox regression was employed to generate the MMPS signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "In the current study, we concerned the TCGA-PRAD cohort with the RNA sequence data from the platform based on the Illumina system and clinical characteristics download from UCSC (https://gdc.xenahubs.net/download/TCGA-PRAD.htseq_fpkm.tsv.gz)", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32669958", "locator": "sections[3] \"Data acquirement from enrolled cohorts\" paragraph 2", "quote": "In the current study, we concerned the TCGA-PRAD cohort with the RNA sequence data from the platform based on the Illumina system and clinical characteristics download from UCSC (https://gdc.xenahubs.net/download/TCGA-PRAD.htseq_fpkm.tsv.gz)"}], "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:32669958"], "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:GSE116918"], "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:GSE116918"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32669958", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7150/ijms.46780", "sha256": 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