Identification of significant pathway cross-talk in rheumatoid arthritis by the Monte Carlo cross-validation method

Abstract

We attempted to identify significant pathway cross-talk in rheumatoid arthritis (RA) by the Monte Carlo cross-validation (MCCV) method. We therefore obtained and preprocessed the gene expression profile of RA. MCCV involves identifying differentially expressed genes (DEGs), identifying differential pathways (DPs), calculating the discriminating score (DS) of the pathway cross-talk, and random forest (RF) classification. We carried out 50 bootstrap iterations of MCCV to identify the key instances of pathway cross-talk involved in RA. We identified a total of 17 significant DEGs and 15 significant DPs by comparing RA samples and normal controls. We found the most significant difference between RA and the normal controls in the eIF4 and p70S6K signaling regulation pathway. Furthermore, we identified 10 instances of pathway cross-talk with the best classification performance for RA and normal controls, using the RF classification model. All of the top 10 pathway pairs involved cross-talk with eIF4 and p70S6K signaling regulation, and the other 10 pathways were immune-related. By MCCV, we identified one critical DP and 10 significant instances of pathway cross-talk in RA. We propose that the eIF4 and p70S6K signaling regulation pathway and the other significant instances of pathway cross-talk play key roles in the occurrence and development of RA, and are potential predictive and prognostic markers for RA.