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pmid:40455761

Identification of potential biomarkers associated with immune cell infiltration patterns in Kawasaki disease via bioinformatics.

Guolian Wu · Hui Liu · Meiling Wang · Rong Wang

PloS one · 2025 doi:10.1371/journal.pone.0324337

Paper

complete

Experiment

complete

Data

partial

Analysis

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Execution

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title
Identification of potential biomarkers associated with immune cell infiltration patterns in Kawasaki disease via bioinformatics.
doi
10.1371/journal.pone.0324337
pmid
40455761
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[0]
Guolian Wu
[1]
Hui Liu
[2]
Meiling Wang
[3]
Rong Wang
journal
PloS one
year
2025
abstract
Inflammation and immune dysregulation play critical roles in Kawasaki disease (KD) pathogenesis, yet specific biomarkers and immune signatures remain elusive. This study aims to identify key biomarkers and characterize immune cell infiltration scores in KD using bioinformatic approaches. The GSE73461 dataset, downloaded from the Gene Expression Omnibus (GEO) database, includes 78 KD patients and 55 normal controls collected by Imperial College London from 2015 to 2023, and was analyzed to identify differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed significant involvement of these DEGs in acute inflammatory responses, plasma membrane components, PI3K-Akt signaling, and cytokine interactions. Protein-protein interaction (PPI) network was constructed, and five candidate hub genes (AURKB, BUB1, CCL2, IL-4, and TOP2A) were identified. Immune cell infiltration analysis gusing the XCell algorithm showed increased levels of Monocytes, neutrophils, and other immune cells in KD, while B cells and T cells were decreased. Correlation analysis indicated that these candidate hub genes are associated with immune dysregulation and inflammation in KD. These findings provide potential diagnostic biomarkers and therapeutic targets for KD, warranting further validation in larger studies.
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Limma packages in R program were employed to screen the DEGs between KD and NC (│ log2 fold change (FC)│ ≥ 2 and an adjusted false discovery rate (FDR) P < 0.05).
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Limma packages in R program were employed to screen the DEGs between KD and NC (│ log2 fold change (FC)│ ≥ 2 and an adjusted false discovery rate (FDR) P < 0.05).
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All relevant data are within the paper and its Supporting information files.
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In this study, we identified 1229 DEGs with with P < 0.05 and |log2 FC| ≥ 2, including 720 up-regulated and 509 down-regulated genes.
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In this study, we identified 1229 DEGs with with P < 0.05 and |log2 FC| ≥ 2, including 720 up-regulated and 509 down-regulated genes.
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sections[9] "Identification of DEGs" paragraph 2
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In this study, we identified 1229 DEGs with with P < 0.05 and |log2 FC| ≥ 2, including 720 up-regulated and 509 down-regulated genes.
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In this study, we identified 1229 DEGs with with P < 0.05 and |log2 FC| ≥ 2, including 720 up-regulated and 509 down-regulated genes.
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Limma packages in R program were employed to screen the DEGs between KD and NC (│ log2 fold change (FC)│ ≥ 2 and an adjusted false discovery rate (FDR) P < 0.05).
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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.
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Automated Assays Laravel application and simulated adapters
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All relevant data are within the paper and its Supporting information files.
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The paper names GSE73461 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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PMID 40455761 full text
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Limma packages in R program were employed to screen the DEGs between KD and NC (│ log2 fold change (FC)│ ≥ 2 and an adjusted false discovery rate (FDR) P < 0.05).
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40455761", "kind": "science-record", "revision": 4, "content_hash": "sha256:1dbd99926fba695098a5dc415c73889e73da172848ff05743e7f17a896623fec", "created_at": "2026-11-01T20:30:35Z", "updated_at": "2026-11-01T22:56:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of potential biomarkers associated with immune cell infiltration patterns in Kawasaki disease via bioinformatics.", "doi": "10.1371/journal.pone.0324337", "pmid": "40455761", "authors": ["Guolian Wu", "Hui Liu", "Meiling Wang", "Rong Wang"], "journal": "PloS one", "year": 2025, "abstract": "Inflammation and immune dysregulation play critical roles in Kawasaki disease (KD) pathogenesis, yet specific biomarkers and immune signatures remain elusive. This study aims to identify key biomarkers and characterize immune cell infiltration scores in KD using bioinformatic approaches. The GSE73461 dataset, downloaded from the Gene Expression Omnibus (GEO) database, includes 78 KD patients and 55 normal controls collected by Imperial College London from 2015 to 2023, and was analyzed to identify differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed significant involvement of these DEGs in acute inflammatory responses, plasma membrane components, PI3K-Akt signaling, and cytokine interactions. Protein-protein interaction (PPI) network was constructed, and five candidate hub genes (AURKB, BUB1, CCL2, IL-4, and TOP2A) were identified. Immune cell infiltration analysis gusing the XCell algorithm showed increased levels of Monocytes, neutrophils, and other immune cells in KD, while B cells and T cells were decreased. 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