Identifying ICAM-1 as a Therapeutic Target for Cytokine Storm in Human Macrophages Through Integrative Bioinformatics Approaches

Excessive macrophage activation is thought to be the primary cause of the cytokine storm that results in severe coronavirus disease 2019 (COVID-19) complications. The underlying mechanisms remain elusive, and more research is needed to find disease-critical genes and develop effective therapies. In this study, we used publicly accessible microarray datasets of cytokine storm in cultured human monocyte-derived macrophages challenged with cytokines, and employed bioinformatics, such as weighted gene co-expression network analysis (WGCNA) and differential expression analysis, to dissect gene expression profiles and identify putative disease-related molecules. Initially, three co-expression modules and related key genes were discovered, which highly correlated to macrophages challenged with cytokines. Then, a preliminary gene expression signature consisting of 203 upregulated and 24 downregulated genes was identified. Next, protein–protein interaction analysis and hub gene identification were used to identify 11 crucial hub genes, namely tripartite motif-containing 21 (TRIM21), interferon regulatory factor 1 (IRF1), guanylate binding protein 1 (GBP1), transporter associated with antigen processing 1 (TAP1), nuclear myosin I (NMI), interleukin 15 receptor subunit alpha (IL15RA), apolipoprotein L1 (APOL1), intercellular adhesion molecule 1 (ICAM-1), protein tyrosine phosphatase non-receptor type 1 (PTPN1), E74-like ETS transcription factor 4 (ELF4) and guanylate binding protein 2 (GBP2). Then, the LINCS L1000 characteristic direction signatures search engine (L1000CDS2) was employed for drug repurposing studies. Dasatinib was predicted to be the leading therapeutic compound to perturb the gene signature of cytokine storm in human macrophages. Connectivity Map results suggested that dasatinib may normalize ICAM-1 expression. In addition, the results of molecular docking studies and molecular dynamics simulation revealed that dasatinib may spontaneously interact with ICAM-1 via several key residues and form a relatively stable protein–ligand complex. Overall, this work, based on an analysis of co-expression correlation networks, gene expression signatures and pivotal genes in human macrophages challenged with cytokines, combined with drug repurposing studies, demonstrated that dasatinib may interact with ICAM-1 and could be a potential candidate for cytokine storm. However, due to the limitations of computational approaches, further experimental validation is necessary.

Authors

Institutions

Publication Details

Journal
Molecules
Published
2026-03-27
DOI
https://doi.org/10.3390/molecules31071111
Primary Topic
Immune cells in cancer
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Identifying ICAM-1 as a Therapeutic Target for Cytokine Storm in Human Macrophages Through Integrative Bioinformatics Approaches

Zhe Zheng, D. H. Wu, Shaojun Chen, Lihua Zhang et al.
Molecules
Immune cells in cancer
article

Identifying ICAM-1 as a Therapeutic Target for Cytokine Storm in Human Macrophages Through Integrative Bioinformatics Approaches

Zhe Zheng, D. H. Wu, Shaojun Chen, Lihua Zhang, Yiyuan Luo
article en

Abstract

Excessive macrophage activation is thought to be the primary cause of the cytokine storm that results in severe coronavirus disease 2019 (COVID-19) complications. The underlying mechanisms remain elusive, and more research is needed to find disease-critical genes and develop effective therapies. In this study, we used publicly accessible microarray datasets of cytokine storm in cultured human monocyte-derived macrophages challenged with cytokines, and employed bioinformatics, such as weighted gene co-expression network analysis (WGCNA) and differential expression analysis, to dissect gene expression profiles and identify putative disease-related molecules. Initially, three co-expression modules and related key genes were discovered, which highly correlated to macrophages challenged with cytokines. Then, a preliminary gene expression signature consisting of 203 upregulated and 24 downregulated genes was identified. Next, protein–protein interaction analysis and hub gene identification were used to identify 11 crucial hub genes, namely tripartite motif-containing 21 (TRIM21), interferon regulatory factor 1 (IRF1), guanylate binding protein 1 (GBP1), transporter associated with antigen processing 1 (TAP1), nuclear myosin I (NMI), interleukin 15 receptor subunit alpha (IL15RA), apolipoprotein L1 (APOL1), intercellular adhesion molecule 1 (ICAM-1), protein tyrosine phosphatase non-receptor type 1 (PTPN1), E74-like ETS transcription factor 4 (ELF4) and guanylate binding protein 2 (GBP2). Then, the LINCS L1000 characteristic direction signatures search engine (L1000CDS2) was employed for drug repurposing studies. Dasatinib was predicted to be the leading therapeutic compound to perturb the gene signature of cytokine storm in human macrophages. Connectivity Map results suggested that dasatinib may normalize ICAM-1 expression. In addition, the results of molecular docking studies and molecular dynamics simulation revealed that dasatinib may spontaneously interact with ICAM-1 via several key residues and form a relatively stable protein–ligand complex. Overall, this work, based on an analysis of co-expression correlation networks, gene expression signatures and pivotal genes in human macrophages challenged with cytokines, combined with drug repurposing studies, demonstrated that dasatinib may interact with ICAM-1 and could be a potential candidate for cytokine storm. However, due to the limitations of computational approaches, further experimental validation is necessary.

MoleculesVol. 31(7)
Ningbo University (CN), Nanjing University of Chinese Medicine (CN), Nanjing Traditional Chinese Medicine Hospital (CN), Zhejiang Pharmaceutical College (CN)
Good health and well-being
Openalex Percentile: Top 6%
Immune cells in cancer
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.