Integrative Framework for Functional analysis of multiple Traditional Chinese Medicines based on transcriptome-driven systems biology and supervised learning strategy

Traditional Chinese Medicines (TCMs) contain a wide variety of ingredients and are rich in bioactive chemical sources. However, their complex and unknown effects on the human body prove a challenge in TCM research. Multiple genes are involved in a biological system and disease progression; therefore, transcriptome-driven systems biology approach directs our attention to perturbed pathways or enriched gene sets from whole gene expression profile, allowing more explanatory power and less analytical complexity than solely considering differentially expressed genes (DEGs). This framework integrated differential expression analysis (DEA), pathway-based expression analysis and supervised learning approaches to explore TCM functions and extract signature genes of high importance. Supervised learning approaches were employed to extract signature genes that effectively discriminate between four TCMs (92% accuracy). These signature genes were further examined through database annotations and literature survey, where some were found to be TCM compound targets, such as fatty acid synthase (FASN) and thioredoxin reductase 1 (TXNRD1). Based on network pharmacology concept, the proposed TCM-compound-target-pathway network can link TCM compounds to signature genes, DEGs and perturbed pathways, revealing the rationale behind TCM’s mechanism of action in treatment. Integrating a supervised learning strategy in identifying TCM-associated genes can offer a new perspective for discovering potential drug targets.