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

pmid:39095691

Identification and verification of immune-related genes for diagnosing the progression of atherosclerosis and metabolic syndrome.

Qian Xie · Xuehe Zhang · Fen Liu · Junyi Luo · Chang Liu · Zhiyang Zhang · Yining Yang · Xiaomei Li

BMC cardiovascular disorders · 2024 doi:10.1186/s12872-024-04026-3

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

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:39095691
kind
science-record
content_hash
sha256:a0023c927d1ff59a4efe5590d2efc670d898a641f8c6d657d696f589c3b7654f
schema
https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T20:29:10Z
updated_at
2026-11-01T22:12:22Z
generator object · 2 fields
name
corpus-assay-engine
run_id
reading-sci-2026-11-01
paper object · 10 fields
state
complete
title
Identification and verification of immune-related genes for diagnosing the progression of atherosclerosis and metabolic syndrome.
doi
10.1186/s12872-024-04026-3
pmid
39095691
authors array · 8 items
[0]
Qian Xie
[1]
Xuehe Zhang
[2]
Fen Liu
[3]
Junyi Luo
[4]
Chang Liu
[5]
Zhiyang Zhang
[6]
Yining Yang
[7]
Xiaomei Li
journal
BMC cardiovascular disorders
year
2024
abstract
BACKGROUND: Atherosclerosis and metabolic syndrome are the main causes of cardiovascular events, but their underlying mechanisms are not clear. In this study, we focused on identifying genes associated with diagnostic biomarkers and effective therapeutic targets associated with these two diseases. METHODS: Transcriptional data sets of atherosclerosis and metabolic syndrome were obtained from GEO database. The differentially expressed genes were analyzed by RStudio software, and the function-rich and protein-protein interactions of the common differentially expressed genes were analyzed.Furthermore, the hub gene was screened by Cytoscape software, and the immune infiltration of hub gens was analyzed. Finally, relevant clinical blood samples were collected for qRT-PCR verification of the three most important hub genes. RESULTS: A total of 1242 differential genes (778 up-regulated genes and 464 down-regulated genes) were screened from GSE28829 data set. A total of 1021 differential genes (492 up-regulated genes and 529 down-regulated genes) were screened from the data set GSE98895. Then 23 up-regulated genes and 11 down-regulated genes were screened by venn diagram. Functional enrichment analysis showed that cytokines and immune activation were involved in the occurrence and development of these two diseases. Through the construction of the Protein-Protein Interaction(PPI) network and Cytoscape software analysis, we finally screened 10 hub genes. The immune infiltration analysis was further improved. The results showed that the infiltration scores of 7 kinds of immune cells in GSE28829 were significantly different among groups (Wilcoxon Test < 0.05), while in GSE98895, the infiltration scores of 4 kinds of immune cells were significantly different between groups (Wilcoxon Test < 0.05). Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets. The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (|Cor| > 0.3 & P < 0.05). There were 41 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE98895 (|Cor| > 0.3 & P < 0.05). Finally, our results identified 10 small molecules with the highest absolute enrichment value, and the three most significant key genes (CX3CR1, TLR5, IL32) were further verified in the data expression matrix and clinical blood samples. CONCLUSION: We have established a co-expression network between atherosclerotic progression and metabolic syndrome, and identified key genes between the two diseases. Through the method of bioinformatics, we finally obtained 10 hub genes in As and MS, and selected 3 of the most significant genes (CX3CR1, IL32, TLR5) for blood PCR verification. This may be helpful to provide new research ideas for the diagnosis and treatment of AS complicated with MS.
claims array · 3 items
[0] object · 6 fields
id
claim-method
statement
Based on the results of hub genes identification and immune infiltration analysis, Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets.
kind
methodological
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:39095691
locator
sections[7] "Immune infiltration and immune correlation analysis of HUB genes" paragraph 2
quote
Based on the results of hub genes identification and immune infiltration analysis, Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets.
state
complete
[1] object · 6 fields
id
claim-data-access
statement
The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895.
kind
methodological
target
null
evidence array · 1 item
[0] object · 3 fields
asset
source:pmid:39095691
locator
sections[23] "notes" paragraph 1
quote
The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895.
state
complete
[2] object · 6 fields
id
claim-target-result
statement
Spearman correlation analysis of the GEO datasets identified significant correlations between 10 hub genes and 22 immune-cell infiltration scores in both GSE28829 and GSE98895.
kind
quantitative
target object · 4 fields
value
0.05
unit
p-value
tolerance
null
source
The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (|Cor| > 0.3 & P < 0.05).
evidence array · 3 items
[0] object · 3 fields
asset
source:pmid:39095691
locator
sections[0] "Abstract" paragraph 3
quote
Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets.
[1] object · 3 fields
asset
source:pmid:39095691
locator
sections[0] "Abstract" paragraph 3
quote
The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (|Cor| > 0.3 & P < 0.05).
[2] object · 3 fields
asset
source:pmid:39095691
locator
sections[0] "Abstract" paragraph 3
quote
There were 41 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE98895 (|Cor| > 0.3 & P < 0.05).
state
complete
gaps array · 0 items
experiment object · 8 fields
state
complete
objective
Spearman correlation analysis of the GEO datasets identified significant correlations between 10 hub genes and 22 immune-cell infiltration scores in both GSE28829 and GSE98895.
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:39095691
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
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.
parameters object · 0 fields
inputs array · 1 item
[0]
data:geo:GSE28829
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 · 1 item
[0]
data:geo:GSE28829
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 · 0 items
data object · 3 fields
state
partial
assets array · 3 items
[0] object · 15 fields
id
source:pmid:39095691
role
manuscript
format
xml
media_type
application/xml
uri
https://doi.org/10.1186/s12872-024-04026-3
sha256
sha256:97666c9947cfb1eab8854774dcec9160f140bee2c2d1ed7c1e7ed571cce9b832
bytes
35127
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
10.1186/s12872-024-04026-3
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
PMC11295872
source
europe_pmc_fulltext
coverage
whole_body_without_references
text_hash_scope
rendered_section_body
[1] object · 15 fields
id
data:geo:GSE28829
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=GSE28829
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE28829
version
null
note
The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895.
access_statement_kind
data_availability_statement
sample_accessions array · 29 items
[0]
GSM714070
[1]
GSM714071
[2]
GSM714072
[3]
GSM714073
[4]
GSM714074
[5]
GSM714075
[6]
GSM714076
[7]
GSM714077
[8]
GSM714078
[9]
GSM714079
[10]
GSM714080
[11]
GSM714081
[12]
GSM714082
[13]
GSM714083
[14]
GSM714084
[15]
GSM714085
[16]
GSM714086
[17]
GSM714087
[18]
GSM714088
[19]
GSM714089
[20]
GSM714090
[21]
GSM714091
[22]
GSM714092
[23]
GSM714093
[24]
GSM714094
[25]
GSM714095
[26]
GSM714096
[27]
GSM714097
[28]
GSM714098
sample_count
29
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Gene Expression in early and advanced atherosclerotic plaque from human carotid
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:GSE98895
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=GSE98895
sha256
null
bytes
null
access
open
state
resolved
reader
null
reader_status
reader_required
identifier
GSE98895
version
null
note
The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895.
access_statement_kind
data_availability_statement
sample_accessions array · 40 items
[0]
GSM2627121
[1]
GSM2627122
[2]
GSM2627123
[3]
GSM2627124
[4]
GSM2627125
[5]
GSM2627126
[6]
GSM2627127
[7]
GSM2627128
[8]
GSM2627129
[9]
GSM2627130
[10]
GSM2627131
[11]
GSM2627132
[12]
GSM2627133
[13]
GSM2627134
[14]
GSM2627135
[15]
GSM2627136
[16]
GSM2627137
[17]
GSM2627138
[18]
GSM2627139
[19]
GSM2627140
[20]
GSM2627141
[21]
GSM2627142
[22]
GSM2627143
[23]
GSM2627144
[24]
GSM2627145
[25]
GSM2627146
[26]
GSM2627147
[27]
GSM2627148
[28]
GSM2627149
[29]
GSM2627150
[30]
GSM2627151
[31]
GSM2627152
[32]
GSM2627153
[33]
GSM2627154
[34]
GSM2627155
[35]
GSM2627156
[36]
GSM2627157
[37]
GSM2627158
[38]
GSM2627159
[39]
GSM2627160
sample_count
40
group_sizes
null
group_sizes_basis
null
platform
6947
taxon
Homo sapiens
title
Gene expression profiles of PBMCs in healthy subjects (CTRL) and patients with Metabolic Syndrome (MS) - PART 1 - Genes
resolution_checked_at
2026-11-01T18:46:34Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 4 items
[0]
asset:data:geo:GSE28829 not downloaded
[1]
asset:data:geo:GSE28829 licence not read
[2]
asset:data:geo:GSE98895 not downloaded
[3]
asset:data:geo:GSE98895 licence not read
analysis object · 4 fields
state
partial
methods array · 4 items
[0] object · 10 fields
id
method-1
family
correlation_analysis
specific
spearman
label
Correlation analysis
dsl_operations array · 1 item
[0]
spearman_rho
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
paired_numeric_columns
evidence object · 3 fields
score
1
band
verbatim
quote
Based on the results of hub genes identification and immune infiltration analysis, Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets.
[1] object · 10 fields
id
method-2
family
group_comparison
specific
wilcoxon
label
Group comparison
dsl_operations array · 0 items
missing_operations array · 1 item
[0]
group_comparison_wilcoxon
expressible
false
parameters object · 0 fields
data_shapes array · 1 item
[0]
two_or_more_groups_continuous
evidence object · 3 fields
score
0.7
band
medium
quote
Wilcoxon
[2] object · 10 fields
id
method-3
family
network_analysis
specific
ppi_network
label
Network / pathway analysis
dsl_operations array · 3 items
[0]
build_graph
[1]
centrality_scores
[2]
pathway_enrich
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
edge_list
evidence object · 3 fields
score
0.7
band
medium
quote
Protein-Protein Interaction
[3] object · 10 fields
id
method-4
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
GO enrichment
targets array · 1 item
[0] object · 8 fields
id
target-published-estimate
claim_id
claim-target-result
operation
build_graph
unit
p-value
tolerance
null
state
complete
note
The threshold the paper applied to its own result, as the paper states it. A run that reproduces the effect is judged against this threshold.
gaps array · 1 item
[0]
method.reader_differs_from_extractor
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]
spearman_rho
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: spearman.
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]
GSE28829
[1]
GSE98895
resolved_accessions array · 2 items
[0]
GSE28829
[1]
GSE98895
statements array · 1 item
[0]
The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895.
basis
The paper names GSE28829, GSE98895 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]
spearman_rho
assets array · 2 items
[0]
source:pmid:39095691
[1]
data:geo:GSE28829
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:GSE28829
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
2026-11-01T19:26:29Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
9799
completion_tokens
1950
cached_tokens
256
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:39095691
type
paper
label
pmid:39095691
[1] object · 3 fields
id
source:pmid:39095691
type
manuscript
label
PMID 39095691 full text
[2] object · 3 fields
id
data:geo:GSE28829
type
dataset
label
GEO GSE28829
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:39095691
type
paper
label
pmid:39095691
[1] object · 3 fields
id
source:pmid:39095691
type
manuscript
label
PMID 39095691 full text
[2] object · 3 fields
id
data:geo:GSE28829
type
dataset
label
GEO GSE28829
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-39095691
type
extraction
used array · 1 item
[0]
source:pmid:39095691
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE28829
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE28829
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:39095691
parent_hash
sha256:dea6f08e87c350df05645386c9c4bfc11653b9631558886ae5d36129570af370
fork_of
null
gaps array · 9 items
[0]
asset:data:geo:GSE28829 licence not read
[1]
asset:data:geo:GSE28829 not downloaded
[2]
asset:data:geo:GSE98895 licence not read
[3]
asset:data:geo:GSE98895 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
correlation_analysis
definition_version
f2132e96ae6c7a4e
required_operations array · 1 item
[0]
spearman_rho
support_state
supported
source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
[0]
Based on the results of hub genes identification and immune infiltration analysis, Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets.
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:39095691", "kind": "science-record", "revision": 3, "content_hash": "sha256:a0023c927d1ff59a4efe5590d2efc670d898a641f8c6d657d696f589c3b7654f", "created_at": "2026-11-01T20:29:10Z", "updated_at": "2026-11-01T22:12:22Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and verification of immune-related genes for diagnosing the progression of atherosclerosis and metabolic syndrome.", "doi": "10.1186/s12872-024-04026-3", "pmid": "39095691", "authors": ["Qian Xie", "Xuehe Zhang", "Fen Liu", "Junyi Luo", "Chang Liu", "Zhiyang Zhang", "Yining Yang", "Xiaomei Li"], "journal": "BMC cardiovascular disorders", "year": 2024, "abstract": "BACKGROUND: Atherosclerosis and metabolic syndrome are the main causes of cardiovascular events, but their underlying mechanisms are not clear. In this study, we focused on identifying genes associated with diagnostic biomarkers and effective therapeutic targets associated with these two diseases.\nMETHODS: Transcriptional data sets of atherosclerosis and metabolic syndrome were obtained from GEO database. The differentially expressed genes were analyzed by RStudio software, and the function-rich and protein-protein interactions of the common differentially expressed genes were analyzed.Furthermore, the hub gene was screened by Cytoscape software, and the immune infiltration of hub gens was analyzed. Finally, relevant clinical blood samples were collected for qRT-PCR verification of the three most important hub genes.\nRESULTS: A total of 1242 differential genes (778 up-regulated genes and 464 down-regulated genes) were screened from GSE28829 data set. A total of 1021 differential genes (492 up-regulated genes and 529 down-regulated genes) were screened from the data set GSE98895. Then 23 up-regulated genes and 11 down-regulated genes were screened by venn diagram. Functional enrichment analysis showed that cytokines and immune activation were involved in the occurrence and development of these two diseases. Through the construction of the Protein-Protein Interaction(PPI) network and Cytoscape software analysis, we finally screened 10 hub genes. The immune infiltration analysis was further improved. The results showed that the infiltration scores of 7 kinds of immune cells in GSE28829 were significantly different among groups (Wilcoxon Test < 0.05), while in GSE98895, the infiltration scores of 4 kinds of immune cells were significantly different between groups (Wilcoxon Test < 0.05). Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets. The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (|Cor| > 0.3 & P < 0.05). There were 41 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE98895 (|Cor| > 0.3 & P < 0.05). Finally, our results identified 10 small molecules with the highest absolute enrichment value, and the three most significant key genes (CX3CR1, TLR5, IL32) were further verified in the data expression matrix and clinical blood samples.\nCONCLUSION: We have established a co-expression network between atherosclerotic progression and metabolic syndrome, and identified key genes between the two diseases. Through the method of bioinformatics, we finally obtained 10 hub genes in As and MS, and selected 3 of the most significant genes (CX3CR1, IL32, TLR5) for blood PCR verification. This may be helpful to provide new research ideas for the diagnosis and treatment of AS complicated with MS.", "claims": [{"id": "claim-method", "statement": "Based on the results of hub genes identification and immune infiltration analysis, Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39095691", "locator": "sections[7] \"Immune infiltration and immune correlation analysis of HUB genes\" paragraph 2", "quote": "Based on the results of hub genes identification and immune infiltration analysis, Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39095691", "locator": "sections[23] \"notes\" paragraph 1", "quote": "The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Spearman correlation analysis of the GEO datasets identified significant correlations between 10 hub genes and 22 immune-cell infiltration scores in both GSE28829 and GSE98895.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (|Cor| > 0.3 & P < 0.05)."}, "evidence": [{"asset": "source:pmid:39095691", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets."}, {"asset": "source:pmid:39095691", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (|Cor| > 0.3 & P < 0.05)."}, {"asset": "source:pmid:39095691", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "There were 41 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE98895 (|Cor| > 0.3 & P < 0.05)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Spearman correlation analysis of the GEO datasets identified significant correlations between 10 hub genes and 22 immune-cell infiltration scores in both GSE28829 and GSE98895.", "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:39095691"], "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:GSE28829"], "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:GSE28829"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39095691", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12872-024-04026-3", "sha256": "sha256:97666c9947cfb1eab8854774dcec9160f140bee2c2d1ed7c1e7ed571cce9b832", "bytes": 35127, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12872-024-04026-3", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11295872", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE28829", "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=GSE28829", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE28829", "version": null, "note": "The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM714070", "GSM714071", "GSM714072", "GSM714073", "GSM714074", "GSM714075", "GSM714076", "GSM714077", "GSM714078", "GSM714079", "GSM714080", "GSM714081", "GSM714082", "GSM714083", "GSM714084", "GSM714085", "GSM714086", "GSM714087", "GSM714088", "GSM714089", "GSM714090", "GSM714091", "GSM714092", "GSM714093", "GSM714094", "GSM714095", "GSM714096", "GSM714097", "GSM714098"], "sample_count": 29, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Gene Expression in early and advanced atherosclerotic plaque from human carotid", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE98895", "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=GSE98895", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE98895", "version": null, "note": "The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2627121", "GSM2627122", "GSM2627123", "GSM2627124", "GSM2627125", "GSM2627126", "GSM2627127", "GSM2627128", "GSM2627129", "GSM2627130", "GSM2627131", "GSM2627132", "GSM2627133", "GSM2627134", "GSM2627135", "GSM2627136", "GSM2627137", "GSM2627138", "GSM2627139", "GSM2627140", "GSM2627141", "GSM2627142", "GSM2627143", "GSM2627144", "GSM2627145", "GSM2627146", "GSM2627147", "GSM2627148", "GSM2627149", "GSM2627150", "GSM2627151", "GSM2627152", "GSM2627153", "GSM2627154", "GSM2627155", "GSM2627156", "GSM2627157", "GSM2627158", "GSM2627159", "GSM2627160"], "sample_count": 40, "group_sizes": null, "group_sizes_basis": null, "platform": "6947", "taxon": "Homo sapiens", "title": "Gene expression profiles of PBMCs in healthy subjects (CTRL) and patients with Metabolic Syndrome (MS)  - PART 1 - Genes", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE28829 not downloaded", "asset:data:geo:GSE28829 licence not read", "asset:data:geo:GSE98895 not downloaded", "asset:data:geo:GSE98895 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "correlation_analysis", "specific": "spearman", "label": "Correlation analysis", "dsl_operations": ["spearman_rho"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Based on the results of hub genes identification and immune infiltration analysis, Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.7, "band": "medium", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "Protein-Protein Interaction"}}, {"id": "method-4", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "GO enrichment"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "build_graph", "expected_value": 0.05, "unit": "p-value", "tolerance": null, "state": "complete", "note": "The threshold the paper applied to its own result, as the paper states it. A run that reproduces the effect is judged against this threshold."}], "gaps": ["method.reader_differs_from_extractor"]}, "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": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["spearman_rho"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: spearman."}, "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": "R1", "reachable": true, "accessions": ["GSE28829", "GSE98895"], "resolved_accessions": ["GSE28829", "GSE98895"], "statements": ["The datasets analysed during the current study are available in the GEO database: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE28829 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE98895."], "basis": "The paper names GSE28829, GSE98895 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["spearman_rho"], "assets": ["source:pmid:39095691", "data:geo:GSE28829"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE28829"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:26:29Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 9799, "completion_tokens": 1950, "cached_tokens": 256}}, "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:39095691", "type": "paper", "label": "pmid:39095691"}, {"id": "source:pmid:39095691", "type": "manuscript", "label": "PMID 39095691 full text"}, {"id": "data:geo:GSE28829", "type": "dataset", "label": "GEO GSE28829"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39095691", "type": "paper", "label": "pmid:39095691"}, {"id": "source:pmid:39095691", "type": "manuscript", "label": "PMID 39095691 full text"}, {"id": "data:geo:GSE28829", "type": "dataset", "label": "GEO GSE28829"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39095691", "type": "extraction", "used": ["source:pmid:39095691"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE28829", "type": "accession_resolution", "used": ["data:geo:GSE28829"], "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:39095691", "parent_hash": "sha256:dea6f08e87c350df05645386c9c4bfc11653b9631558886ae5d36129570af370", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE28829 licence not read", "asset:data:geo:GSE28829 not downloaded", "asset:data:geo:GSE98895 licence not read", "asset:data:geo:GSE98895 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "correlation_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["spearman_rho"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Based on the results of hub genes identification and immune infiltration analysis, Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets."], "order_basis": "analysis_only", "id": "assay:1"}]}