Objective To study the expression of STIM 1 gene in human hypopharyngeal carcinoma cell line FaDu and its effect on FaDu cell apoptosis .Methods Lentivirus infection was used to knock STIM 1 down in FaDu cells .Group STIM1-siRNA: the expression of STIM1 in FaDu cell was inhibited by STIM 1-siRNA lentivirus .Group control:FaDu cells were infected by negative control siRNA lentivirus . Real-Time PCR was applied to identify the efficacy of lenticirus infection and the expression of STIM 1 in FaDu cells.Western blot was used to identify the expression of STIM 1 protein after lenticirus infection .Flow cytometry assay was performed to detect the apoptosis of FaDu cells in the two groups.The data were statistically analyzed with SPSS 17.0 software.Results Compared with GAPDH (Ct=12.08 ±0.05),the expression of STIM1 in FaDu cells was significant expressed (Ct=22.21 ±0.05,P<0.001).Real-Time PCR analysis the relative mRNA expression of STIM1 in FaDu cells of control group and STIM 1-siRNA group were (1.00 ±0.08) and (0.12 ±0.01) respectively (P<0.001). Western blot showed that the expression of STIM 1 gene and protein in FaDu cells were inhibited significantly after STIM 1-siRNA lentiviral in-fection,which was in accordance with the results of Real-Time PCR analysis.Flow cytometry assay showed that the siRNA-mRNA group had a higher apoptosis percentage (9.81 ±0.56)% compared to the control group (4.36 ±1.32)%,with statistically significant difference (P<0.05).Conclusion STIM1 gene correlated significantly with FaDu cell apoptosis .It inhibits apoptosis of FaDu cells ,and it may be a potential diagnostic and therapeutic target for the hypopharyngeal carcinoma .

OBJECTIVETo evaluate the inhibition effect of STIM1 gene silencing on tumor growth of human hypopharyngeal carcinoma cell lines FaDu in nude mice.

METHODSSTIM1 gene in FaDu was silenced by lentiviral infection, and the effect of inhibition was detected by Real-time PCR and Western blot after lentiviral infection. Nude mice were divided into 2 groups, 5 mice in each group. Inhibition group: subcutaneous inject FaDu cells which STIM1 expression was inhibited.

CONTROL GROUPsubcutaneous inject FaDu cells infected with negative control siRNA-expressing lentivirus. Tumor volumes were measured by calipers, and small animal imaging was detected by NightOWL system on the day 10, 14, 18 and 22 after tumor inoculated. Tumor weights were evaluated in the day 22 after tumor inoculated. Statistical analysis was performed using standard student test(P value threshold was 0.05).

RESULTSThe expressions of human STIM1 gene and protein in FaDu cells were suppressed effectively after STIM1-siRNA lentiviral infection. The mean tumor volumes of control group and inhibition group were (51±25) mm3 and (40±35) mm3, respectively, on the day 10, (262±107) and (106±41) mm3 on the day 14, (716±226) and (340±158) mm3 on the day, (1 682±592) mm3 and (917±252)mm3 on the day 22 (P<0.05). On the day 22, the tumor weight was (1.22±0.41) g in control group and (0.66±0.26) g in STIM1-siRNA group (P<0.05). Small animal imaging showed that the tumors had a smaller fluorescence range with lower signal intensity in STIM1-siRNA group than in control group on the day 14, 18 and 22.

CONCLUSIONThe expression of STIM1 in human hypopharyngeal carcinoma cell lines FaDu can be inhibited effectively by lentiviral infection, causing the inhibition of tumor formation and growth.

Animals ; Cell Line, Tumor ; Gene Silencing ; Humans ; Hypopharyngeal Neoplasms ; pathology ; Lentivirus ; Membrane Proteins ; genetics ; Mice ; Mice, Nude ; Neoplasm Proteins ; genetics ; Neoplasm Transplantation ; RNA, Small Interfering ; Real-Time Polymerase Chain Reaction ; Stromal Interaction Molecule 1

Objective:To explore the correlation between symptoms and their contribution to syndrome based on syndrome of lung damp-heat accumulation in coronavirus disease 2019 (COVID-19), thus to provide methodological basis for the syndrome diagnosis.Methods:Based on 654 clinical investigation questionnaires data of COVID-19 patients, a model based on syndrome of lung damp-heat accumulation was set. Using SPSS Modeler 14.1 software, association rules and Bayesian network were applied to explore the correlation between symptoms and their contribution to syndrome.Results:There were 121 questionnaires referring to syndrome of lung damp-heat accumulation in total 654 questionnaires. The symptoms with frequency > 40% were fever (53.72%), cough (47.93%), red tongue (45.45%), rapid pulse (43.80%), greasy fur (42.15%), yellow tongue (41.32%), fatigue (40.50%) and anorexia (40.50%). Association rule analysis showed that the symptom groups with strong binomial correlation included fever, thirst, chest tightness, shortness of breath, cough, yellow phlegm, etc. The symptom groups with strong trinomial correlation included cough, yellow phlegm, phlegm sticky, anorexia, vomiting, heavy head and body, fever, thirst, fatigue, etc. Based on SPSS Modeler 14.1 software, with syndrome of lung damp-heat accumulation (yes = 1, no = 0) as target variable, and the selected symptoms with frequency > 15.0% as input variables, the Bayesian network model was established to obtain the probability distribution table of symptoms (groups), in which there was only one parent node (the upper node of each input variable) of fever, and the conditional probability was 0.54. The parent node of cough had yellow phlegm and syndrome of lung damp-heat accumulation, indicating that there was a direct causal relationship between cough and yellow phlegm in syndrome of lung damp-heat accumulation, and the conditional probability of cough was 0.99 under the condition of yellow phlegm. The common symptom groups and their contribution to syndrome were as follows: fever and thirsty (0.47), cough and yellow phlegm (0.49), chest tightness and polypnea (0.46), anorexia and heavy cumbersome head and body (0.61), yellow greasy fur and slippery rapid pulse (0.95).Conclusions:It is feasible and objective to analyze the correlation between symptoms and their contribution to syndromes by association rules combined with Bayesian network. It could provide methodological basis for the syndrome diagnosis.