Integrating multi-omics and machine learning strategies to explore the “gene-protein-metabolite” network in ischemic heart failure with Qi deficiency and blood stasis syndrome - Wei, Jingjing; Wang, Aolong; Yu, Peng; Sun, Yang; Wu, Wenjun; Zhang, Yilin; Yu, Rui; Li, Bin; Zhu, Mingjun (Chinese medicine, 2025)
Background Ischemic heart failure (IHF) is a multifaceted syndrome associated with significant mortality and high hospitalization rates globally. According to traditional Chinese medicine (TCM) theory, Qi Deficiency and Blood Stasis (QXXY) Syndrome serves as the pathological basis of IHF. This study aims to investigate the biological basis of QXXY syndrome in IHF patients through an integrated multi-omics approach. Methods We enrolled 100 participants, comprising 40 IHF patients with QXXY syndrome (IHF-QXXY), 40 IHF patients without QXXY syndrome, and 20 healthy controls. Utilizing an integrated approach combining RNA sequencing (RNA-seq), data-independent acquisition (DIA) proteomics, and targeted metabolomics, we established a comprehensive “gene-protein-metabolite” network for IHF-QXXY syndrome. Candidate biomarkers were identified through machine learning algorithms and further validated using RT-qPCR and targeted proteomics via intelligent parallel reaction monitoring (iPRM). Results Patients with IHF-QXXY syndrome present with pronounced disruptions in energy metabolism, chronic inflammation, and coagulation abnormalities. The “gene-protein-metabolite” network of IHF-QXXY syndrome comprises six mRNAs, four proteins, and five metabolites. Key pathways involve the activation of neutrophil extracellular traps formation, platelet activation, the HIF-1 signaling pathway, and glycolysis/gluconeogenesis, alongside the suppression of the citrate cycle and oxidative phosphorylation. The key metabolites potentially associated with QXXY syndrome include 3-methylpentanoic acid, arachidonic acid, N-acetylaspartylglutamic acid, L-acetylcarnitine, and 12-hydroxystearic acid. We identified a panel of candidate biomarkers, including HIF-1α, IL10, PAD4, ACTG1, SOD2, GAPDH, FGA, FN1, F13A1, and ATP5PF. This biomarker combination significantly enhanced the diagnostic performance of IHF-QXXY syndrome (AUC > 0.863) and retained high diagnostic accuracy during validation (AUC > 0.75). Conclusion This study provides a comprehensive characterization of the molecular features of QXXY syndrome in IHF patients, highlighting key pathways and biomarkers linked to energy metabolism dysregulation, chronic inflammation, and coagulation abnormalities. These findings may provide novel insights and methods for further advancing this research field. Graphical Abstract