Exploring the biological basis for the identification of different syndromes in ischemic heart failure based on joint multi-omics analysis

Abstract

Background: IHF is a major chronic disease that seriously threatens human health. Qi deficiency and blood stasis syndrome (QDBS), Yang deficiency with blood stasis syndrome (YDBS) and Yang deficiency and blood stasis with fluid retention syndrome (YDBSFR) are the basic syndromes of IHF in Chinese medicine. This study aims to explore the biological basis of the three IHF syndromes through integrated multi-omics research.

Methods: We analyzed and integrated transcriptomic, proteomic, and targeted metabolomic data from IHF patients and healthy persons to obtain the key biomarkers and enriched pathways of QDBS, YDBS and YDBSFR(Registration No.: ChiCTR2200058314). These biomarkers were combined with clinical indicators to construct the "Disease-Syndromes-Clinical phenotypes-Biomarkers-Pathways" network, and the obtained differential genes and proteins were externally validated.

Results: The potential biomarkers for QDBS included SDHD, IL10, ACTG1, VWF, MDH2, COX5A, Valeric acid, Succinic Acid and L-Histidine, which were predominantly enriched in TCA cycle, oxidative phosphorylation, platelet activation, and neutrophil extracellular trap formation pathways, demonstrating associations with energy metabolism, coagulation system, and immune-inflammatory responses.YDBS potential biomarkers included TSHR, PRKG1, ATP1A2, GNAI2, APOA2, PLTP, 3-Hydroxybutyrate, Hexadecanoic acid and Palmitelaidic acid, and the combined pathways were mainly enriched in thyroid hormone synthesis, regulation of lipolysis in adipocytes, cholesterol metabolism and PPAR signaling pathways, correlating with hormonal regulation and lipid metabolism. The potential biomarkers of YDBSFR were CNGB1, KCNMA1, PIK3R2, HSPA8, C3, FH, Oxamic acid, N-Acetyl-L-alanine, 4-Hydroxyhippuric acid, and the combined pathways were mainly enriched in aldosterone-regulated sodium reabsorption, cGMP-PKG signaling pathway, neutrophil extracellular trap formation and TCA cycle signaling pathways, which are related to hormone regulation, signal transduction, immune-inflammatory response and energy metabolism. Platelet activation was involved in the whole process of IHF. External validation demonstrated the above core targets.

Conclusion: This study investigated the biological basis of QDBS, YDBS and YDBSFR in IHF from a modern biomedical perspective, providing references for the objective research of TCM syndrome differentiation.

Keywords: biological basis; ischemic heart failure; multi-omics; syndrome differentiation; traditional Chinese medicine.

FIGURE 1

Transcriptomic characteristics of QDBS, YDBS and YDBSFR. (A–C) The DEGs volcano maps of HP, QDBS, YDBS and YDBSFR. (D) Enrichment pathway of specific DEGs to QDBS. (E) Enrichment pathway of specific DEGs to YDBS. (F) Enrichment pathway of specific DEGs to YDBSFR. (G) PPI analysis of specific DEGs and nodes of algorithm intersection to QDBS. (H) PPI analysis of specific DEGs and nodes of algorithm intersection to YDBS. (I) PPI analysis of specific DEGs and nodes of algorithm intersection to YDBSFR. (J) The ROC curves of SDHD, ACTG1 and IL10 in QDBS. (K) The ROC curves of PRKG1, TSHR and ATP1A2 in YDBS. (L) The ROC curves of CNGB1, KCNMA1 and PIK3R2 in YDBSFR.

FIGURE 2

Proteomic characteristics of QDBS, YDBS and YDBSFR. (A–C) The DEPs volcano maps of HP, QDBS, YDBS and YDBSFR. (D) Enrichment pathway of specific DEPs to QDBS. (E) Enrichment pathway of specific DEPs to YDBS. (F) Enrichment pathway of specific DEPs to YDBSFR. (G) PPI analysis of specific DEPs and nodes of algorithm intersection to QDBS. (H) PPI analysis of specific DEPs and nodes of algorithm intersection to YDBS. (I) PPI analysis of specific DEPs and nodes of algorithm intersection to YDBSFR. (J) The ROC curves of VWF, MDH2 and COX5A in QDBS. (K) The ROC curves of PLTP, APOA2 and GNAI2 in YDBS. (L) The ROC curves of HSPA8, C3 and FH in YDBSFR.

FIGURE 3

Metabolomic characteristics of QDBS, YDBS and YDBSFR. (A) Classification Bar Chart of DMs for QDBS, YDBS and YDBSFR. (B–D) The DMs volcano maps of HP, QDBS, YDBS and YDBSFR. (E) Enrichment pathway of specific DMs to QDBS. (F) Enrichment pathway of specific DMs to YDBS. (G) Enrichment pathway of specific DMs to YDBSFR. (H) The ROC curves of Valeric acid, Succinic Acid and L-Histidine in QDBS. (I) The ROC curves of 3-Hydroxybutyrate, Hexadecanoic acid and Palmitelaidic acid in YDBS. (J) The ROC curves of Oxamic acid, N-Acetyl-L-alanine and 4-Hydroxyhippuric acid in YDBSFR.

FIGURE 4

Joint network construction of clinical phenotypes and multi-omics. (A) Joint pathway analysis of DEGs, DEPs, and DMs in QDBS. (B) Joint pathway analysis of DEGs, DEPs, and DMs in YDBS. (C) Joint pathway analysis of DEGs, DEPs, and DMs in YDBSFR. (D) Correlation analysis of biomarkers and clinical phenotypes in QDBS. (E) Correlation analysis of biomarkers and clinical phenotypes in YDBS. (F) Correlation analysis of biomarkers and clinical phenotypes in YDBSFR. (G) “Disease-Syndromes-Clinical phenotypes-Biomarkers-Pathways” network of QDBS. (H) “Disease-Syndromes-Clinical phenotypes-Biomarkers-Pathways” network of YDBS. (I) “Disease-Syndromes-Clinical phenotypes-Biomarkers-Pathways” network of YDBSFR.

FIGURE 5

The RT-qPCR validation of candidate biomarkers for QDBS, YDBS and YDBSFR. (A–C) The mRNA expressions of ACTG1, IL10 and SDHD in QDBS. (D–F) The mRNA expressions of TSHR, PRKG1 and ATP1A2 in YDBS. (G–I) The mRNA expressions of KCNMA1, PIK3R2 and CNGB1 in YDBSFR.

FIGURE 6

The iPRM validation of candidate biomarkers for QDBS, YDBS and YDBSFR. (A–C) The differential expression of VWF, COX5A and MDH2 in QDBS. (D–F) The differential expression of APOA2, GNAI2 and PLTP in YDBS. (G–H) The differential expression of C3 and HSPA8 in YDBSFR.