Pathway analysis based on Monte Carlo Cross-Validation in polyarticular juvenile idiopathic arthritis.

INTRODUCTION

Juvenile idiopathic arthritis (JIA) is a common chronic disease with onset before the 16 years old in a child. Polyarticular JIA has been reported as the main form of JIA in several locations. Until now, understanding of the genetic basis of JIA is incomplete. The purpose of this study was to identify pathway pairs of great potential functional relevance in the progression of polyarticular JIA.

MATERIALS AND

METHODS

Microarray data of 59 peripheral blood samples from healthy children and 61 samples from polyarticular JIA were transformed to gene expression data. Differential expressed genes (DEG) between patients and normal controls were identified using Linear Models for Microarray Analysis. After performed enrichment of DEG, differential pathways were identified with Fisher's test and false discovery rate. Differential pathway pairs were constructed with random two differential pathways, and were evaluated by Random Forest classification. Monte Carlo Cross-Validation was introduced to screen the best pathway pair.

RESULTS

42 DEG with P-values<0.01 were identified. 19 differential pathways with P-values<0.01 were identified. Area under the curve (AUC) of pathway pairs was generated with RF classification. After 50 bootstraps of Monte Carlo Cross-Validation, the best pathway pair with the highest AUC value was identified, and it was the pair of tumoricidal function of hepatic natural killer cells pathway and erythropoietin signaling pathway.

CONCLUSION

These identified pathway pairs may play pivotal roles in the progress of polyarticular JIA and can be applied for diagnosis. Particular attention can be focused on them for further research.