The relationship between occupational asbestos exposure and esophagus cancer (EC) is not fully understood. We performed a meta-analysis to quantitatively assess the association. We systematically searched databases of PubMed, EMBASE, and Web of Science for studies with quantitative estimates of asbestos exposure and EC mortality. Pooled standardized mortality ratios (SMRs) and their corresponding 95% confidence intervals (CIs) were calculated. Twenty cohort studies on EC and asbestos exposure were included in this meta-analysis. Overall, occupational exposure to asbestos was associated with an excess risk of EC (SMR = 1.24, 95% CI: 1.13-1.38, P < 0.001), with little evidence of heterogeneity among studies (I(2) = 0.0%, P = 0.682). Being male, exposure to chrysotile or mixed asbestos, working at textile industry, long study follow-up (≥20 years), Asia, Europe and America cohorts with larger cohort size (>500), and high-exposure group all contribute to significantly higher SMR. Publication bias was not detected (Egger's test P-value = 0.374). This meta-analysis suggested that occupational asbestos exposure might be associated with an increased risk of EC in male. High-exposure level of asbestos could contribute to significantly higher risk of EC mortality.

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.