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Summary Epigallocatechin‐3‐gallate (EGCG), a major polyphenolic constituent of green tea, possesses remarkable chemopreventive and therapeutic potential against various types of cancer, including leukaemia. However, the molecular mechanism involved in chronic myeloid leukaemia (CML), especially imatinib‐resistant CML cells, is not completely understood. In the present study, we investigated the effect of EGCG on the growth of Bcr/Abl+ CML cell lines, including imatinib‐resistant cell lines and primary CML cells. The results revealed that EGCG could inhibit cell growth and induce apoptosis in CML cells. The mechanisms involved inhibition of the Bcr/Abl oncoprotein and regulation of its downstream p38‐MAPK/JNK and JAK2/STAT3/AKT pathways. In conclusion, we documented the anti‐CML effects of EGCG in imatinib‐sensitive and imatinib‐resistant Bcr/Abl+ cells, especially T315I‐mutated cells.

Gastric cancer is a malignant tumor with high morbidity and mortality worldwide. However, increasing evidences have revealed the correlation between the glycolysis process and tumorigenesis. This study is aim to develop a list of glycolysis-related genes for risk stratification in gastric cancer patients. We included 500 patients’ sample data from GSE62254 and GSE26942 datasets, and classified patients into training (n = 350) and testing sets (n = 150) at a ratio of 7: 3. Univariate and multivariate Cox regression analysis were performed to screen genes having prognostic value. Based on HALLMARK gene sets, we identified 9 glycolysis-related genes (BPNT1, DCN, FUT8, GMPPA, GPC3, LDHC, ME2, PLOD2, and UGP2). On the basis of risk score developed by the 9 genes, patients were classified into high- and low-risk groups. The survival analysis showed that the high-risk patients had a worse prognosis ( p < 0.001). Similar finding was observed in the testing cohort and 2 independent cohorts (GSE13861 and TCGA-STAD, all p < 0.001). The multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for overall survival ( p < 0.001). Furthermore, we constructed a nomogram that integrated the risk score and tumor stage, age, and adjuvant chemotherapy. Through comparing the results of the receiver operating characteristic curves and decision curve analysis, we found that the nomogram had a superior predictive accuracy than conventional TNM staging system, suggesting that the risk score combined with other clinical factors (age, tumor stage, and adjuvant chemotherapy) can develop a robust prediction for survival and improve the individualized clinical decision making of the patient. In conclusion, we identified 9 glycolysis-related genes from hallmark glycolysis pathway. Based on the 9 genes, gastric cancer patients were separated into different risk groups related to survival.