TCM-HIN2Vec: A strategy for uncovering biological basis of heart qi deficiency pattern based on network embedding and transcriptomic experiment

Abstract

Objective: To elucidate the biological basis of the heart qi deficiency (HQD) pattern, an in-depth understanding of which is essential for improving clinical herbal therapy. Methods: We predicted and characterized HQD pattern genes using the new strategy, TCM-HIN2Vec, which involves heterogeneous network embedding and transcriptomic experiments. First, a heterogeneous network of traditional Chinese medicine (TCM) patterns was constructed using public databases. Next, we predicted HQD pattern genes using a heterogeneous network-embedding algorithm. We then analyzed the functional characteristics of HQD pattern genes using gene enrichment analysis and examined gene expression levels using RNA-seq. Finally, we identified TCM herbs that demonstrated enriched interactions with HQD pattern genes via herbal enrichment analysis. Results: Our TCM-HIN2Vec strategy revealed that candidate genes associated with HQD pattern were significantly enriched in energy metabolism, signal transduction pathways, and immune processes. Moreover, we found that these candidate genes were significantly differentially expressed in the transcriptional profile of mice model with heart failure with a qi deficiency pattern. Furthermore, herbal enrichment analysis identified TCM herbs that demonstrated enriched interactions with the top 10 candidate genes and could potentially serve as drug candidates for treating HQD. Conclusion: Our results suggested that TCM-HIN2Vec is capable of not only accurately identifying HQD pattern genes, but also deciphering the basis of HQD pattern. Furthermore our finding indicated that TCM-HIN2Vec may be further expanded to develop other patterns, leading to a new approach aimed at elucidating general TCM patterns and developing precision medicine.

Keywords

• Qi deficiency pattern
• Heart failure
• Biological basis
• Network embedding
• Transcriptome