Corpus Engine Towards a reproducible .science file.
← All .science files

pmid:35903347

Comprehensive Analysis of the Aberrance and Functional Significance of Ferroptosis in Gastric Cancer.

Jun Xiao · Lingyan Zheng · Jingfeng Liu

Frontiers in pharmacology · 2022 doi:10.3389/fphar.2022.919490

Paper

complete

Experiment

partial

Data

partial

Analysis

complete

Execution

absent

Execution evidence has not yet been added to this .science file. Explore its methods, data and open questions to plan the next step.
Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
id
pmid:35903347
kind
science-record
content_hash
sha256:32fe48b4c978c57efb91ca4265eba4f55d44d4090cb046517e30c81a4b1d3a07
schema
https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T18:14:59Z
updated_at
2026-11-01T21:28:30Z
generator object · 2 fields
name
corpus-assay-engine
run_id
reading-sci-2026-11-01
paper object · 10 fields
state
complete
title
Comprehensive Analysis of the Aberrance and Functional Significance of Ferroptosis in Gastric Cancer.
doi
10.3389/fphar.2022.919490
pmid
35903347
authors array · 3 items
[0]
Jun Xiao
[1]
Lingyan Zheng
[2]
Jingfeng Liu
journal
Frontiers in pharmacology
year
2022
abstract
Objective: Ferroptosis is a type of iron-dependent necrosis related to cancer. Nevertheless, the features of ferroptosis in gastric cancer (GC) remain poorly understood. This study conducted a systematic analysis of ferroptosis regulators in GC. Methods: We gathered five GC cohorts, namely, TCGA-STAD, GSE84437, GSE62254, GSE26901, and GSE15459. Unsupervised clustering analysis was adopted to cluster GC patients into different ferroptosis subtypes based on ferroptosis regulators. Immune cell infiltration and hallmark pathway activity were estimated via ssGSEA. The ferroptosis index was developed with the PCA computational method. Response to chemotherapy agents and small molecular compounds was inferred via GDSC, CTRP, and PRISM projects. Two anti-PD-1 therapy cohorts were gathered and the potential of FPI in predicting immune response was assessed. Results: Expression profiles, genetic mutations, DNA methylation, prognostic implications, and drug sensitivity of ferroptosis regulators were characterized in GC. Three ferroptosis subtypes were clustered with distinct prognosis, hallmark pathway activity, and tumor-infiltrating immune cells. Ferroptosis levels were quantified based on the expression of prognostic ferroptosis-related signatures. The significant relationships between FPI and clinicopathological characteristics were observed. Furthermore, high FPI was in relation to poor prognosis, inflamed tumor microenvironment (TME) as well as high sensitivity to chemotherapy agents (docetaxel and cisplatin), and CTRP- and PRISM-derived compounds. Also, FPI acted as a promising predictor of immune response. Conclusion: Collectively, our findings identified a novel ferroptosis-based subtype classification of GC, and revealed the potential of ferroptosis in forming TME diversity and complexity, and guiding individualized treatment.
claims array · 3 items
[0] object · 6 fields
id
claim-method
statement
Unsupervised clustering analysis was adopted to cluster GC patients into different ferroptosis subtypes based on ferroptosis regulators.
kind
methodological
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:35903347
locator
sections[0] "Abstract" paragraph 2
quote
Unsupervised clustering analysis was adopted to cluster GC patients into different ferroptosis subtypes based on ferroptosis regulators.
state
complete
[1] object · 6 fields
id
claim-data-access
statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.
kind
methodological
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:35903347
locator
sections[26] "Data Availability Statement" paragraph 2
quote
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.
state
complete
[2] object · 6 fields
id
claim-target-result
statement
Patients with high FPI had higher therapeutic response than those with low FPI (75% vs. 36%; Figure 7H).
kind
qualitative
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:35903347
locator
sections[22] "Ferroptosis Index Score Serves as a Predictor of Immune Response" paragraph 2
quote
Patients with high FPI had higher therapeutic response than those with low FPI (75% vs. 36%; Figure 7H).
state
complete
gaps array · 0 items
experiment object · 8 fields
state
partial
objective
Patients with high FPI had higher therapeutic response than those with low FPI (75% vs. 36%; Figure 7H).
steps array · 3 items
[0] object · 13 fields
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.
parameters object · 0 fields
inputs array · 1 item
[0]
source:pmid:35903347
outputs array · 1 item
[0]
the published estimate and its evidence
instruments array · 0 items
reagents array · 0 items
[1] object · 13 fields
id
obtain_source_data
order
2
name
Obtain the source data from the named repository
kind
data-preparation
state
blocked
blocked_on
data:accession_absent
description
The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.
parameters object · 0 fields
inputs array · 0 items
outputs array · 1 item
[0]
the source data with its repository provenance
instruments array · 0 items
reagents array · 0 items
[2] object · 13 fields
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.
parameters object · 0 fields
inputs array · 0 items
outputs array · 1 item
[0]
the replicated result and its comparison
instruments array · 0 items
reagents array · 0 items
materials array · 0 items
instruments array · 0 items
reagents array · 0 items
conditions object · 0 fields
gaps array · 1 item
[0]
step:obtain_source_data blocked on data:accession_absent
data object · 3 fields
state
partial
assets array · 1 item
[0] object · 15 fields
id
source:pmid:35903347
role
manuscript
format
xml
media_type
application/xml
uri
https://doi.org/10.3389/fphar.2022.919490
sha256
sha256:38924e819ad1367a45bc5b53d570ff5a6d7423c4932626a6da4b271381e3a15b
bytes
54880
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
10.3389/fphar.2022.919490
version
null
note
Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.
structure object · 4 fields
pmcid
PMC9315307
source
europe_pmc_fulltext
coverage
whole_body
text_hash_scope
rendered_section_body
gaps array · 0 items
analysis object · 4 fields
state
complete
methods array · 1 item
[0] object · 10 fields
id
method-1
family
principal_component_analysis
specific
pca
label
Principal component analysis
dsl_operations array · 1 item
[0]
pca_fit
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
wide_numeric_matrix
evidence object · 3 fields
score
1
band
verbatim
quote
Unsupervised clustering analysis was adopted to cluster GC patients into different ferroptosis subtypes based on ferroptosis regulators.
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
blocked
tier
R4
blocked_on
data:accession_absent
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 1 item
[0]
pca_fit
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: pca.
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
R4
reachable
false
accessions array · 0 items
resolved_accessions array · 0 items
statements array · 1 item
[0]
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.
basis
The paper names no accession to its source data, so the data has to come from the authors.
can_run object · 3 fields
dsl_operations array · 1 item
[0]
pca_fit
assets array · 1 item
[0]
source:pmid:35903347
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:None
gaps array · 1 item
[0]
data:accession_absent
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
2026-11-01T17:57:51Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
16004
completion_tokens
3048
cached_tokens
264
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:35903347
type
paper
label
pmid:35903347
[1] object · 3 fields
id
source:pmid:35903347
type
manuscript
label
PMID 35903347 full text
[2] object · 3 fields
id
data:geo:None
type
dataset
label
GEO None
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:35903347
type
paper
label
pmid:35903347
[1] object · 3 fields
id
source:pmid:35903347
type
manuscript
label
PMID 35903347 full text
[2] object · 3 fields
id
data:geo:None
type
dataset
label
GEO None
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-35903347
type
extraction
used array · 1 item
[0]
source:pmid:35903347
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-None
type
accession_resolution
used array · 1 item
[0]
data:geo:None
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:35903347
parent_hash
sha256:c9a70550934d00cc1e6f2ea0ca9b70d769deebbf37a9ed9364c7e065ae8cd6c2
fork_of
null
gaps array · 5 items
[0]
execution.not_run
[1]
review.human_critique
[2]
review.independent_reader
[3]
review.machine_critique
[4]
step:obtain_source_data blocked on data:accession_absent
assays array · 1 item
[0] object · 11 fields
role
analysis
definition_id
principal_component_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 1 item
[0]
pca_fit
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
Unsupervised clustering analysis was adopted to cluster GC patients into different ferroptosis subtypes based on ferroptosis regulators.
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:35903347", "kind": "science-record", "revision": 3, "content_hash": "sha256:32fe48b4c978c57efb91ca4265eba4f55d44d4090cb046517e30c81a4b1d3a07", "created_at": "2026-11-01T18:14:59Z", "updated_at": "2026-11-01T21:28:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive Analysis of the Aberrance and Functional Significance of Ferroptosis in Gastric Cancer.", "doi": "10.3389/fphar.2022.919490", "pmid": "35903347", "authors": ["Jun Xiao", "Lingyan Zheng", "Jingfeng Liu"], "journal": "Frontiers in pharmacology", "year": 2022, "abstract": "Objective: Ferroptosis is a type of iron-dependent necrosis related to cancer. Nevertheless, the features of ferroptosis in gastric cancer (GC) remain poorly understood. This study conducted a systematic analysis of ferroptosis regulators in GC. Methods: We gathered five GC cohorts, namely, TCGA-STAD, GSE84437, GSE62254, GSE26901, and GSE15459. Unsupervised clustering analysis was adopted to cluster GC patients into different ferroptosis subtypes based on ferroptosis regulators. Immune cell infiltration and hallmark pathway activity were estimated via ssGSEA. The ferroptosis index was developed with the PCA computational method. Response to chemotherapy agents and small molecular compounds was inferred via GDSC, CTRP, and PRISM projects. Two anti-PD-1 therapy cohorts were gathered and the potential of FPI in predicting immune response was assessed. Results: Expression profiles, genetic mutations, DNA methylation, prognostic implications, and drug sensitivity of ferroptosis regulators were characterized in GC. Three ferroptosis subtypes were clustered with distinct prognosis, hallmark pathway activity, and tumor-infiltrating immune cells. Ferroptosis levels were quantified based on the expression of prognostic ferroptosis-related signatures. The significant relationships between FPI and clinicopathological characteristics were observed. Furthermore, high FPI was in relation to poor prognosis, inflamed tumor microenvironment (TME) as well as high sensitivity to chemotherapy agents (docetaxel and cisplatin), and CTRP- and PRISM-derived compounds. Also, FPI acted as a promising predictor of immune response. Conclusion: Collectively, our findings identified a novel ferroptosis-based subtype classification of GC, and revealed the potential of ferroptosis in forming TME diversity and complexity, and guiding individualized treatment.", "claims": [{"id": "claim-method", "statement": "Unsupervised clustering analysis was adopted to cluster GC patients into different ferroptosis subtypes based on ferroptosis regulators.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35903347", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Unsupervised clustering analysis was adopted to cluster GC patients into different ferroptosis subtypes based on ferroptosis regulators."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35903347", "locator": "sections[26] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Patients with high FPI had higher therapeutic response than those with low FPI (75% vs. 36%; Figure 7H).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35903347", "locator": "sections[22] \"Ferroptosis Index Score Serves as a Predictor of Immune Response\" paragraph 2", "quote": "Patients with high FPI had higher therapeutic response than those with low FPI (75% vs. 36%; Figure 7H)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Patients with high FPI had higher therapeutic response than those with low FPI (75% vs. 36%; Figure 7H).", "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:35903347"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35903347", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fphar.2022.919490", "sha256": "sha256:38924e819ad1367a45bc5b53d570ff5a6d7423c4932626a6da4b271381e3a15b", "bytes": 54880, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fphar.2022.919490", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9315307", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Unsupervised clustering analysis was adopted to cluster GC patients into different ferroptosis subtypes based on ferroptosis regulators."}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["pca_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pca."}, "capability": {"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": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["pca_fit"], "assets": ["source:pmid:35903347"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:57:51Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 16004, "completion_tokens": 3048, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:35903347", "type": "paper", "label": "pmid:35903347"}, {"id": "source:pmid:35903347", "type": "manuscript", "label": "PMID 35903347 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35903347", "type": "paper", "label": "pmid:35903347"}, {"id": "source:pmid:35903347", "type": "manuscript", "label": "PMID 35903347 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35903347", "type": "extraction", "used": ["source:pmid:35903347"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"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": {"parent_id": "pmid:35903347", "parent_hash": "sha256:c9a70550934d00cc1e6f2ea0ca9b70d769deebbf37a9ed9364c7e065ae8cd6c2", "fork_of": null, "generation": 2}, "gaps": ["execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "analysis", "definition_id": "principal_component_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["pca_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Unsupervised clustering analysis was adopted to cluster GC patients into different ferroptosis subtypes based on ferroptosis regulators."], "order_basis": "analysis_only", "id": "assay:1"}]}