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Identifying OGN as a Biomarker Covering Multiple Pathogenic Pathways for Diagnosing Heart Failure: From Machine
Yihao Zhu1,2, Bin Chen3, Yao Zu1,2,4
1International Research Center for Marine Biosciences, Ministry of Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
Insights
Researchers identified OGN as a promising diagnostic biomarker for heart failure (HF). This finding advances understanding of HF pathogenesis and diagnosis, potentially improving patient outcomes.
Area of Science:
- Biomedical research
- Cardiovascular disease
- Genomics and bioinformatics
Background:
- Heart failure (HF) exhibits pathophysiologic heterogeneity.
- Accurate diagnostic biomarkers are needed to reflect diverse HF pathogenic pathways.
Purpose of the Study:
- To identify robust diagnostic biomarkers for heart failure.
- To elucidate the underlying pathogenic pathways and regulatory mechanisms.
- To investigate potential therapeutic targets and causal relationships.
Main Methods:
- Weighted gene co-expression network analysis and machine learning integration.
- Protein-protein interaction networks, gene set enrichment analysis (GSEA), and molecular docking.
- Quantitative polymerase chain reaction (qPCR) validation and Mendelian randomization analysis.
Main Results:
- Identified COL14A1, OGN, MFAP4, and SFRP4 as candidate HF biomarkers.
- Revealed OGN up-regulation in HF plasma, confirmed by qPCR and Mendelian randomization.
- Linked biomarkers to TFs (BNC2, MEOX2), pathways, and effector memory CD4+ T cell infiltration.
Conclusions:
- Propose OGN as a reliable diagnostic biomarker for heart failure.
- Suggest OGN can advance the understanding of HF diagnosis and pathogenesis.
Background:
The pathophysiologic heterogeneity of heart failure (HF) necessitates a more detailed identification of diagnostic biomarkers that can reflect its diverse pathogenic pathways.
Methods:
We conducted weighted gene and multiscale embedded gene co-expression network analysis on differentially expressed genes obtained from HF and non-HF specimens. We employed a machine learning integration framework and protein-protein interaction network to identify diagnostic biomarkers. Additionally, we integrated gene set variation analysis, gene set enrichment analysis (GSEA), and transcription factor (TF)-target analysis to unravel the biomarker-dominant pathways. Leveraging single-sample GSEA and molecular docking, we predicted immune cells and therapeutic drugs related to biomarkers. Quantitative polymerase chain reaction validated the expressions of biomarkers in the plasma of HF patients. A two-sample Mendelian randomization analysis was implemented to investigate the causal impact of biomarkers on HF.
Results:
We first identified COL14A1, OGN, MFAP4, and SFRP4 as candidate biomarkers with robust diagnostic performance. We revealed that regulating biomarkers in HF pathogenesis involves TFs (BNC2, MEOX2) and pathways (cell adhesion molecules, chemokine signaling pathway, cytokine-cytokine receptor interaction, oxidative phosphorylation). Moreover, we observed the elevated infiltration of effector memory CD4+ T cells in HF, which was highly related to biomarkers and could impact immune pathways. Captopril, aldosterone antagonist, cyclopenthiazide, estradiol, tolazoline, and genistein were predicted as therapeutic drugs alleviating HF via interactions with biomarkers. In vitro study confirmed the up-regulation of OGN as a plasma biomarker of HF. Mendelian randomization analysis suggested that genetic predisposition toward higher plasma OGN promoted the risk of HF.
Conclusions:
We propose OGN as a diagnostic biomarker for HF, which may advance our understanding of the diagnosis and pathogenesis of HF.
