The cGAS-STING pathway in acute myocardial infarction: Biomarker discovery and mechanistic insights

Q: What is the clinical evidence from this study?

Study design: Observational. Population: Acute Myocardial Infarction (n=152). Intervention: PFKFB3 and TLR2 expression vs. Healthy controls. Primary outcome: Diagnostic performance (Area Under the Curve) for acute myocardial infarction (AUC > 0.7).

Q: What does this research mean for the field?

PFKFB3 and TLR2 serve as diagnostic biomarkers for acute myocardial infarction, distinguishing patients from controls with an AUC greater than 0.7. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

Hongkui Chen; Yihao Huang; Yupeng Zhi; Chun Chen; Yansong Guo

doi:10.1371/journal.pone.0355694

September 18, 2026PLoS ONEOpen Access

The cGAS-STING pathway in acute myocardial infarction: Biomarker discovery and mechanistic insights

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Key result

Novel biomarkers PFKFB3 and TLR2 identify acute myocardial infarction with an AUC > 0.7.

AUC > 0.7
n=152

Figures

Why the study?

Early diagnosis and treatment optimization of acute myocardial infarction remain problematic despite new therapies, motivating the search for novel biomarkers related to the cGAS-STING inflammatory pathway.

Can bioinformatics analysis of the cGAS-STING pathway identify novel diagnostic biomarkers for acute myocardial infarction?

Key Points

To identify novel diagnostic biomarkers for acute myocardial infarction by analyzing genes associated with the inflammation-related cGAS-STING pathway.
Screened transcriptomic datasets (GSE66360 training set, GSE48060 validation set) to identify differentially expressed genes (thresholds: P.Value < 0.05 and |log2FC| > 0.5).
Constructed weighted gene co-expression network analysis (WGCNA) to detect disease-correlated modules and intersected them with cGAS-STING pathway genes to find 11 candidate genes.
Applied Boruta, LASSO regression, and Random Forest machine learning algorithms to select core feature genes, followed by immune cell infiltration and ROC curve analyses.
WGCNA identified a key pink module of 438 genes significantly correlated with the disease phenotype (|r| = 0.63, p-value < 0.05).
Machine learning feature selection narrowed the 11 candidate genes to four overlapping hub genes, with PFKFB3 and TLR2 exhibiting diagnostic AUC values greater than 0.7 across cohorts.
Single-cell and immune infiltration analyses revealed significant associations between PFKFB3 and TLR2 expression and innate immune cells, primarily localized to macrophages, monocytes, and neutrophils.

Study Design

Type

Observational (n=152)

Structured PICO

Can bioinformatics analysis of the cGAS-STING pathway identify novel diagnostic biomarkers for acute myocardial infarction?

P

Population

152 individuals, comprising 81 acute myocardial infarction patients and 71 healthy controls, whose transcriptomic data were analyzed from public datasets to identify diagnostic biomarkers.

E

Exposure

Bioinformatics screening and machine learning (Boruta, LASSO, Random Forest) to identify cGAS-STING pathway-related diagnostic biomarkers.

C

Comparator

Healthy controls

O

Outcome

Identification of candidate diagnostic biomarker genes and diagnostic performance assessed by Area Under the Curve (AUC)surrogate

Main Result

Effect estimate: AUC > 0.7

PFKFB3 and TLR2 are promising novel diagnostic biomarkers for acute myocardial infarction linked to the cGAS-STING inflammatory pathway.

Limitations

Retrospective database analysis with inherent biases such as inconsistent data collection and lack of standardized follow-up
Moderate overall sample size without an independent prospective validation cohort
Reliance on peripheral blood transcriptome data rather than cardiac tissue
Lack of direct experimental validation of the cGAS-STING pathway through in vitro or in vivo functional experiments
Single-cell analysis results are of preliminary exploratory nature and unverified by wet lab experiments
Potential batch effects due to technical heterogeneity between different datasets
Validation in independent populations is needed

Implication

May support PFKFB3/TLR2 evaluation as AMI biomarkers; hypothesis-generating and requires prospective validation before clinical use.

Abstract

Acute Myocardial infarction (AMI) continues to be a severe deadly disease with a high case-fatality rate and great psychological and economic costs. Despite the development of new therapies, early diagnosis and treatment optimization still are problematical. Here we applied the integrated bioinformatics strategy to screen potential new diagnosis biomarkers for AMI based on the inflammation-related cGAS-STING pathway. By integrating differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), and cGAS-STING gene set from high-throughput RNA sequencing, we obtained a total of 11 candidate genes from 914 DEGs, which enriched mainly for inflammatory and immune response pathways. Further refining them in machine learning algorithms, we identified core genes with diagnostic values. Subsequent test shows the AUC values of PFKFB3 and TLR2 both were more than 0.7 and their promising diagnostic values were verified. Based on the immune infiltration analysis, expression of PFKFB3 and TLR2 were very significantly associated with a large number of immune cells, such as monocytes, neutrophils and dendritic cells. As for single cell sequencing analysis, they were mainly found expressed on macrophages. Overall, we show that PFKFB3 and TLR2 are possible biomarkers of AMI which have not been reported so far. Thus they may open a path for new diagnostics and personalized therapy. Validation in independent populations is needed.

Discussion

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Cite This Study

Chen et al. (2026) conducted an observational in Acute Myocardial Infarction (n=152). PFKFB3 and TLR2 expression vs. Healthy controls was evaluated on Diagnostic performance (Area Under the Curve) for acute myocardial infarction (AUC > 0.7). PFKFB3 and TLR2 were identified as novel diagnostic biomarkers for acute myocardial infarction, both demonstrating an area under the curve greater than 0.7 for distinguishing patients from controls.

synapsesocial.com/papers/6ad0fd1915427f6a840404ff https://doi.org/10.1371/journal.pone.0355694

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The cGAS-STING pathway in acute myocardial infarction: Biomarker discovery and mechanistic insights

Figures

Key Points

Study Design

Structured PICO

Main Result

Limitations

Implication

Abstract

Discussion

Cite This Study

The cGAS-STING pathway in acute myocardial infarction: Biomarker discovery and mechanistic insights

Figures

Key Points

Study Design

Structured PICO

Main Result

Limitations

Implication

Abstract

Discussion

Cite This Study

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