Objective To explore the influence of honokiol on growth,cell cycle and radiosensitivity in nasopharyngeal carcinoma cells,CNE-1 and CNE-2.Methods MTT assay and clonogenic assay were used to detect cell growth and survival respectively.Flow cytometry was employed to analyze cell cycle progression.Annexin V-FITC kit was used to detect cell apoptosis.Western blot assay was applied to examine protein expression.Results Honokiol signficantly inhibited proliferation of CNE-1 and CNE-2 cells in a dose and time dependent manner,the IC50 value was 2.84 and 2.68 μmol/L(24 h)and 2.50 and 2.20 μmol/L (48 h),respectively.After being treated with 2.5 μmol/L honokiol for 24 h,the ratios of early apoptosis,late apoptosis and necrosis were 24.53％,23.05％ and 7.13％ in CNE-1 cells compared with the control group(t =-41.17,-8.18,-6.08,P ＜0.05).The expression levels of pro-apoptotic proteins Caspase-3 and Bax were significantly increased to 2.31 and 1.89 times (t =-15.92,-17.15,P ＜ 0.05),4.43 and 1.85 times (t =-29.39,-13.47,P ＜ 0.05).Simultaneously the expression level of anti-apoptotic protein Bcl-2 was reduced by 2.22 and 2.74 times(t =26.94,66.14,P ＜ 0.05) as compared with controls after being treated with 4 and 3 μmol/L honokiol.Additionally,honokiol at lower doses signicantly enhanced the senstivity of CNE-1 and CNE-2 cells to X-ray irradiation.The SER was 1.41 and 1.88 in CNE-1 and CNE-2 cells.3 Gy irradiation of X-rays increased the proportion at G2/M state in both cell lines (t =-14.96,-19.26,P ＜ 0.05).Honokiol reduced the G2/M cell cycle arrest induced by irradiation significantly (t =7.65,4.98,P ＜ 0.05).Simultaneously,cyclin B1 protein expression obviously elevated (t =-33.07,-73.49,P ＜ 0.05).Conclusions Honokiol is a potent inhibitor of nasopharyngeal carcinoma cell growth by inducing cell apoptosis and necrosis and works as a radiosensitizer by disrupting G2/M cell cycle checkpoint.

Objective:To construct a signature for identifying active tuberculosis (TB) based on the relative expression orderings (REOs) of gene expression within a single sample.Methods:Using peripheral whole blood samples from 75 active TB and 69 latently infected individuals from four datasets as the training set, and highly stable REO patterns were extracted from the gene expression profile of the two groups of samples. Then, the gene pairs that reversed the REO pattern between the two groups were selected, and each gene pair was ranked in descending order based on their reversal degree. Finally, the top k gene pairs with the highest classification accuracy were selected as the signature for independent dataset validation. Results:A signature composed of seven gene pairs, denoted as 7-GPS, was constructed from the training set. The accuracy rate for 7-GPS to distinguish active TB from latently infected samples was 88.89%, and the accuracy rate for distinguishing active TB from normal samples was 90.09%. In the mixed validation data from different detection platforms, the AUC value for distinguishing active TB from latently infected samples was 0.914 (95% CI: 0.881-0.948), and the AUC value for distinguishing active TB from normal samples was 0.934 (95% CI: 0.904-0.964). In addition, the four genes ETV7, BATF2, ANKRD22 and CARD17P from this signature tended to be highly expressed in peripheral blood samples of active TB, and their expression values were significantly related to the duration of anti-tuberculosis treatment in clinical. Conclusion:The 7-GPS signature is robust and suitable for individualized analysis of a single peripheral blood sample. It has certain clinical application potential.

Objective:To construct a signature for identifying active tuberculosis (TB) based on the relative expression orderings (REOs) of gene expression within a single sample.Methods:Using peripheral whole blood samples from 75 active TB and 69 latently infected individuals from four datasets as the training set, and highly stable REO patterns were extracted from the gene expression profile of the two groups of samples. Then, the gene pairs that reversed the REO pattern between the two groups were selected, and each gene pair was ranked in descending order based on their reversal degree. Finally, the top k gene pairs with the highest classification accuracy were selected as the signature for independent dataset validation. Results:A signature composed of seven gene pairs, denoted as 7-GPS, was constructed from the training set. The accuracy rate for 7-GPS to distinguish active TB from latently infected samples was 88.89%, and the accuracy rate for distinguishing active TB from normal samples was 90.09%. In the mixed validation data from different detection platforms, the AUC value for distinguishing active TB from latently infected samples was 0.914 (95% CI: 0.881-0.948), and the AUC value for distinguishing active TB from normal samples was 0.934 (95% CI: 0.904-0.964). In addition, the four genes ETV7, BATF2, ANKRD22 and CARD17P from this signature tended to be highly expressed in peripheral blood samples of active TB, and their expression values were significantly related to the duration of anti-tuberculosis treatment in clinical. Conclusion:The 7-GPS signature is robust and suitable for individualized analysis of a single peripheral blood sample. It has certain clinical application potential.