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pmid:37727764

Identification of the shared mechanisms and common biomarkers between Sjögren's syndrome and atherosclerosis using integrated bioinformatics analysis.

Xiaoyi Qi · Qianwen Huang · Shijia Wang · Liangxian Qiu · Xiongbiao Chen · Kunfu Ouyang · Yanjun Chen

Frontiers in medicine · 2023 doi:10.3389/fmed.2023.1185303

Paper

complete

Experiment

complete

Data

partial

Analysis

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Execution

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pmid:37727764
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2026-11-01T19:02:33Z
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title
Identification of the shared mechanisms and common biomarkers between Sjögren's syndrome and atherosclerosis using integrated bioinformatics analysis.
doi
10.3389/fmed.2023.1185303
pmid
37727764
authors array · 7 items
[0]
Xiaoyi Qi
[1]
Qianwen Huang
[2]
Shijia Wang
[3]
Liangxian Qiu
[4]
Xiongbiao Chen
[5]
Kunfu Ouyang
[6]
Yanjun Chen
journal
Frontiers in medicine
year
2023
abstract
Background: Sjögren's syndrome (SS) is a chronic autoimmune disease characterized by exocrine and extra-glandular symptoms. The literature indicates that SS is an independent risk factor for atherosclerosis (AS); however, its pathophysiological mechanism remains undetermined. This investigation aimed to elucidate the crosstalk genes and pathways influencing the pathophysiology of SS and AS via bioinformatic analysis of microarray data. Methods: Microarray datasets of SS (GSE40611) and AS (GSE28829) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were acquired using R software's "limma" packages, and the functions of common DEGs were determined using Gene Ontology and Kyoto Encyclopedia analyses. The protein-protein interaction (PPI) was established using the STRING database. The hub genes were assessed via cytoHubba plug-in and validated by external validation datasets (GSE84844 for SS; GSE43292 for AS). Gene set enrichment analysis (GSEA) and immune infiltration of hub genes were also conducted. Results: Eight 8 hub genes were identified using the intersection of four topological algorithms in the PPI network. Four genes (CTSS, IRF8, CYBB, and PTPRC) were then verified as important cross-talk genes between AS and SS with an area under the curve (AUC) ≥0.7. Furthermore, the immune infiltration analysis revealed that lymphocytes and macrophages are essentially linked with the pathogenesis of AS and SS. Moreover, the shared genes were enriched in multiple metabolisms and autoimmune disease-related pathways, as evidenced by GSEA analyses. Conclusion: This is the first study to explore the common mechanism between SS and AS. Four key genes, including CTSS, CYBB, IRF8, and PTPRC, were associated with the pathogenesis of SS and AS. These hub genes and their correlation with immune cells could be a potential diagnostic and therapeutic target.
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The “limma” package was used for selecting the DEGs in GSE40611 and GSE28829, with the following criteria: |log2 Fold change (FC)| > 0.5 and value of p <0.01.
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sections[4] "Identification of differentially expressed genes" paragraph 2
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The “limma” package was used for selecting the DEGs in GSE40611 and GSE28829, with the following criteria: |log2 Fold change (FC)| > 0.5 and value of p <0.01.
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The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
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Four genes (CTSS, IRF8, CYBB, and PTPRC) were then verified as important cross-talk genes between AS and SS with an area under the curve (AUC) ≥0.7.
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Four genes (CTSS, IRF8, CYBB, and PTPRC) were then verified as important cross-talk genes between AS and SS with an area under the curve (AUC) ≥0.7.
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Four genes (CTSS, IRF8, CYBB, and PTPRC) were then verified as important cross-talk genes between AS and SS with an area under the curve (AUC) ≥0.7.
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Gene expression data of parotid tissue from Primary Sjogren’s Syndrome and controls
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[9]
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[10]
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[11]
asset:data:geo:GSE84844 licence not read
[12]
asset:data:geo:GSE84884 not downloaded
[13]
asset:data:geo:GSE84884 licence not read
analysis object · 4 fields
state
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method-1
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label
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normalise_counts
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differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
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count_matrix_with_condition
evidence object · 3 fields
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The “limma” package was used for selecting the DEGs in GSE40611 and GSE28829, with the following criteria: |log2 Fold change (FC)| > 0.5 and value of p <0.01.
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group_comparison
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Group comparison
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missing_operations array · 1 item
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group_comparison_wilcoxon
expressible
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data_shapes array · 1 item
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evidence object · 3 fields
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id
method-3
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network_analysis
specific
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label
Network / pathway analysis
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[0]
build_graph
[1]
centrality_scores
[2]
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missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
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evidence object · 3 fields
score
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quote
protein-protein interaction
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gaps array · 1 item
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method.reader_differs_from_extractor
capability object · 3 fields
state
complete
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[0] object · 6 fields
capability_id
capability:computational_analysis
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[0]
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[3]
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[4]
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[5]
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[6]
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[8]
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candidate
Automated Assays Laravel application and simulated adapters
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readiness object · 8 fields
state
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[2]
fdr_bh
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basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom.
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tier
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availability_state
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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
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[0]
GSE40611
[1]
GSE28829
[2]
GSE28892
[3]
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[4]
GSE84884
[5]
GSE43292
[6]
GSE49332
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[0]
GSE40611
[1]
GSE28829
[2]
GSE28892
[3]
GSE84844
[4]
GSE84884
[5]
GSE43292
[6]
GSE49332
statements array · 1 item
[0]
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
basis
The paper names GSE40611, GSE28829, GSE28892, GSE84844, GSE84884, GSE43292, GSE49332 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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source:pmid:37727764
[1]
data:geo:GSE28829
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[0]
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data:geo:GSE28829
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review object · 5 fields
state
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kind
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reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-11-01T18:53:57Z
independence
single_reader
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method
verbatim
availability
verbatim
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prompt_tokens
9873
completion_tokens
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cached_tokens
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[0] object · 4 fields
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source:pmid:37727764
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manuscript
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PMID 37727764 full text
[2] object · 3 fields
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data:geo:GSE28829
type
dataset
label
GEO GSE28829
activities array · 3 items
[0] object · 4 fields
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type
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used array · 3 items
[0] object · 3 fields
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type
paper
label
pmid:37727764
[1] object · 3 fields
id
source:pmid:37727764
type
manuscript
label
PMID 37727764 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-37727764
type
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used array · 1 item
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agent:corpus-assay-engine
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dsl_catalogue_fingerprint
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lineage object · 4 fields
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[1]
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[2]
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[3]
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[4]
asset:data:geo:GSE40611 licence not read
[5]
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[6]
asset:data:geo:GSE43292 licence not read
[7]
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[8]
asset:data:geo:GSE49332 licence not read
[9]
asset:data:geo:GSE49332 not downloaded
[10]
asset:data:geo:GSE84844 licence not read
[11]
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[12]
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[13]
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[14]
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evidence array · 2 items
[0]
y of SS and AS via bioinformatic analysis of microarray data. Methods: Microarray datasets of SS (GS
[1]
rmatic analysis of microarray data. Methods: Microarray datasets of SS (GSE40611) and AS (GSE28829)
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The “limma” package was used for selecting the DEGs in GSE40611 and GSE28829, with the following criteria: |log2 Fold change (FC)| > 0.5 and value of p <0.01.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37727764", "kind": "science-record", "revision": 5, "content_hash": "sha256:dff3f09d997968f96eb102ffc94ec7abe0a61b6d989b7fb03bd235f5c3877f4f", "created_at": "2026-11-01T19:02:33Z", "updated_at": "2026-11-01T22:52:45Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of the shared mechanisms and common biomarkers between Sjögren's syndrome and atherosclerosis using integrated bioinformatics analysis.", "doi": "10.3389/fmed.2023.1185303", "pmid": "37727764", "authors": ["Xiaoyi Qi", "Qianwen Huang", "Shijia Wang", "Liangxian Qiu", "Xiongbiao Chen", "Kunfu Ouyang", "Yanjun Chen"], "journal": "Frontiers in medicine", "year": 2023, "abstract": "Background: Sjögren's syndrome (SS) is a chronic autoimmune disease characterized by exocrine and extra-glandular symptoms. The literature indicates that SS is an independent risk factor for atherosclerosis (AS); however, its pathophysiological mechanism remains undetermined. This investigation aimed to elucidate the crosstalk genes and pathways influencing the pathophysiology of SS and AS via bioinformatic analysis of microarray data.\nMethods: Microarray datasets of SS (GSE40611) and AS (GSE28829) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were acquired using R software's \"limma\" packages, and the functions of common DEGs were determined using Gene Ontology and Kyoto Encyclopedia analyses. The protein-protein interaction (PPI) was established using the STRING database. The hub genes were assessed via cytoHubba plug-in and validated by external validation datasets (GSE84844 for SS; GSE43292 for AS). Gene set enrichment analysis (GSEA) and immune infiltration of hub genes were also conducted.\nResults: Eight 8 hub genes were identified using the intersection of four topological algorithms in the PPI network. Four genes (CTSS, IRF8, CYBB, and PTPRC) were then verified as important cross-talk genes between AS and SS with an area under the curve (AUC) ≥0.7. Furthermore, the immune infiltration analysis revealed that lymphocytes and macrophages are essentially linked with the pathogenesis of AS and SS. Moreover, the shared genes were enriched in multiple metabolisms and autoimmune disease-related pathways, as evidenced by GSEA analyses.\nConclusion: This is the first study to explore the common mechanism between SS and AS. Four key genes, including CTSS, CYBB, IRF8, and PTPRC, were associated with the pathogenesis of SS and AS. These hub genes and their correlation with immune cells could be a potential diagnostic and therapeutic target.", "claims": [{"id": "claim-method", "statement": "The “limma” package was used for selecting the DEGs in GSE40611 and GSE28829, with the following criteria: |log2 Fold change (FC)| > 0.5 and value of p <0.01.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37727764", "locator": "sections[4] \"Identification of differentially expressed genes\" paragraph 2", "quote": "The “limma” package was used for selecting the DEGs in GSE40611 and GSE28829, with the following criteria: |log2 Fold change (FC)| > 0.5 and value of p <0.01."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37727764", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Four genes (CTSS, IRF8, CYBB, and PTPRC) were then verified as important cross-talk genes between AS and SS with an area under the curve (AUC) ≥0.7.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37727764", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Four genes (CTSS, IRF8, CYBB, and PTPRC) were then verified as important cross-talk genes between AS and SS with an area under the curve (AUC) ≥0.7."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Four genes (CTSS, IRF8, CYBB, and PTPRC) were then verified as important cross-talk genes between AS and SS with an area under the curve (AUC) ≥0.7.", "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:37727764"], "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:37727764", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fmed.2023.1185303", "sha256": 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