[Objective]To explore the clinical experience of professor SHAN Zhaowei treatment of chronic atrophic gastritis(CAG). [Method] To analyze SHAN Zhaowei professor from the perspective of four different pathogenesis of syndrome differentiation of CAG. To sum up professor SHAN Zhaowei 's clinical experience in the treatment of chronic atrophic gastritis, inheritance of Meng He YiPai essence. [Result] Professor SHAN Zhaowei treats CAG in qi, deficiency, blood stasis and toxin from four different pathogenesis to syndrome differentiation of traditional Chinese medicine treatment of CAG, mediation or insufficiency of qi activity, health, and remove stasis coating, detoxification cancer, the first emphasis on TCM four diagnosis, tongue mirror each other, differentiation is exquisite, precision, protecting stomach qi, along with the differentiation to add and subtract, insipid, but with magic effect. [Conclusion] The unique pathogenesis differentiation method in the treatment of CAG and the clinical experience for CAG of Professor SHAN Zhaowei is worth learning and promotion.

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.