Establishment of a discriminant mathematical model for diagnosis of deficiency-cold syndrome using gene expression profiling.

Objective: To screen diagnostic markers of Deficiency-Cold syndrome by gene expression profile and to establish a discriminant mathematical milliliters model for the clinical diagnosis of this syndrome based on a support vector machine (SVM).

Methods: A family suffering from Deficiency-Cold syndrome is chosen for this study. This family has 5 patients with Deficiency-Cold syndrome and 10 normal members. The peripheral blood samples for these 5 patients and 5 normal members are tested by using cDNA microarray with 18,816 clones to get their differential expression genes. These genes are further explored to understand their biological functions and pathways through existing databases. A SVM model for clinical diagnosis is then developed based on these differential expression genes.

Results: A total of 83 differential expression genes were identified between patients and normal members, in which 21 genes were recorded in the FATIGO database and 16 genes were related to metabolism. Eight (8) pathways were sorted out in the KEGG database, and half pathways were associated with human metabolism. A discriminant mathematical model based on a support vector machine successfully predicted a normal person and a patient with heavy Deficiency-Cold syndrome based on their gene differential expression profiles. Thus, this model may classify the Deficiency-Cold syndrome.

Conclusion: This work demonstrates that the differential expression genes can be used to identify normal persons and patients with Deficiency-Cold syndrome. Deficiency-Cold syndrome is mainly associated with the metabolism-related gene regulations. In addition, the discriminant mathematical model based on a support vector machine is applicable to the clinical diagnosis for Deficiency-Cold syndrome.