Objective:Exploring gene-age interactions associated with breast cancer prognosis based on epigenomic data.Methods:Differential expression analysis of DNA methylation was conducted using multiple independent epigenomic datasets of breast cancer from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The false discovery rate (FDR) method was used for multiple corrections, retaining differentially methylated sites with q-FDR≤0.05. A three-stage analytic strategy was implemented, using a multivariable Cox proportional hazards regression model to examine gene-age interactions. In the discovery phase, signals with q-FDR ≤ 0.05 were screened out using TCGA-BRCA database. In validation phaseⅠ, the interaction was validated using GSE72245 data, with criteria of P≤0.05 and consistent effect direction. In validation phaseⅡ, the signals were further validated using GSE37754 and GSE75067 data. A prognostic prediction model was constructed by incorporating clinical indicators and interaction signals. Results:The three-stage analytic strategy identified a methylation site (cg16126280 EBF1), which interacted with age to jointly affect the overall survival time of patients (interaction HR= 1.001 1,95% CI:1.000 7-1.001 5, P<0.001). Stratified analysis by age showed that the effect of hypermethylation of cg16126280 EBF1 was completely opposite in younger patients ( HR=0.550 5, 95% CI: 0.383 8-0.789 6, P=0.001) and older patients ( HR=2.166 5, 95% CI: 1.285 2-3.652 2, P=0.004). Conclusions:The DNA methylation site cg16126280 EBF1 exhibits an interaction with age, jointly influencing the prognosis of breast cancer in a complex association pattern. This finding contributes new population-based evidence for the development of age-specific targeted drugs.

This study describes the development of a wireless and wearable ECG monitoring system with ultra-low power consumption. The system is mainly composed of a connection part of an ECG electrode sticker, an electrocardiogram collecting part, a data storage part, a Bluetooth main control unit, a charging module, a voltage regulator and a lithium battery. The low-power ECG acquisition chip ADS1292R and the ultra-low-power Bluetooth microcontroller nRF51822 together constitute the ECG signal acquisition and wireless data communication part. The collected ECG signals can be sent to the mobile APP through the Bluetooth function provided by the MCU, and can completly display and analysis to achieve low power system. After testing, the system power consumption is only (3.7 V×2.87 mA)10.619 mW, and if it is optimized, it can further reduce power consumption, therefore, the system design can have good applicability.
Electric Power Supplies ; Electrocardiography ; Equipment Design ; Monitoring, Physiologic/instrumentation* ; Signal Processing, Computer-Assisted ; Wearable Electronic Devices ; Wireless Technology