Identification of characteristic genes ofanddeficiency constitutions: an integrated analysis based on bioinformatics and machine learning.

OBJECTIVE

To utilize the Traditional Chinese Medicine constitution (TCMC) as a complementary and alternative approach for early disease detection and treatment, with a focus on and deficiency constitutions, which serve as key references for disease prevention and management.

METHODS

The dataset containing the data of and deficiency constitution was identified through the Gene Expression Omnibus database. This database was used for differential expression genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA), and the characteristic genes were then obtained in the dataset using a machine learning method. The hub genes of and deficiency constitution were obtained after analysis using the above three methods, and the hub genes were enriched and analyzed. Subsequently, the hub genes of and deficiency constitution were validated using external datasets. Receiver operating characteristic (ROC) analysis was used on each hub genes of the two groups to further understand their diagnostic performance. The miRNA-lncRNA-gene network was used to further analyze the hub genes. Immunoinfiltration and gene set enrichment analysis were performed on the shared hub genes.

RESULTS

The GSE87474 dataset was used for DEGs analysis and WGCNA. Using machine learning analyses, we identified 15 and 14 hub genes for and deficiency constitutions, respectively. The results of enrichment analyses showed that deficiency constitution was associated with interleukin-17 signaling pathway, whereas deficiency constitution was associated with glycosaminoglycan biosynthesis-keratan sulfate. The validation dataset GSE56116 showed statistically significant data for s-adenosylmethionine sensor upstream of MTORC1 (SAMTOR, also named C7orf60), cofilin 2 (CFL2), cytohesin 1 interacting protein (CYTIP), G protein-coupled receptor 183 (GPR183), hippocampus abundant transcript 1 (HIAT1), kelch like family member 15 (KLHL15), mitogen-activated protein kinase 6 (MAPK6), and prostaglandin-endoperoxide synthase 2 (PTGS2) in deficiency and fucosy-ltransferase 8 (FUT8), TATA-box binding protein associated factor, RNA polymerase I subunit D (TAF1D), zinc finger protein 24 (ZNF24), MAPK6, and leptin receptor overlapping transcript like 1 (LEPROTL1) in deficiency. The ROC results indicated that these genes have diagnostic value. MAPK6 is a shared hub gene for and deficiencies.

CONCLUSIONS

This study identified C7orf60, CFL2, CYTIP, GPR183, HIAT1, KLHL15, MAPK6, and PTGS2 in deficiency and FUT8, TAF1D, ZNF24, MAPK6, and LEPROTL1 in deficiency as potential biomarkers, providing insights into their pathogenesis. This theory not only guides the diagnostic approach in TCM but also extends its influence to various scientific research fields.