The present study was conducted to verify the influence of luteolin on apoptosis of Eca109 cells and to further investigate the possible mechanisms underlying its effect on apoptosis.The cells were exposed to differen...The present study was conducted to verify the influence of luteolin on apoptosis of Eca109 cells and to further investigate the possible mechanisms underlying its effect on apoptosis.The cells were exposed to different concentrations of luteolin(0,40,80,120,160,200,240M)for 24,48,and 72 h respectively.The influence of luteolin on proliferation of Eca109 cells was detected using MTT assay.Eca109 cells were then treated with luteolin(0,40,160,240M)for 24 h.The effect of luteolin on cell cycle progression and apoptosis was assayed by using flow cytometry(FCM).Expression of caspase9 and caspase3 mRNA and protein was analyzed by real-time PCR and Western blot respectively.The results showed that luteolin could inhibit the proliferation of Eca109 cells at all concentrations in a time-dependent manner and the relative inhibition rate showed an inverted U-shaped association with the concentration of luteolin.Further,the cell cycle was arrested in the S phase following treatment with luteolin.Apoptosis analysis indicated that luteolin could induce the apoptosis of Eca109 cells across the three concentration groups,which exhibited a trend of first promotional and then inhibitory with the increases in luteolin concentration.The effect of luteolin on the mRNA and protein expression of caspase 9 and caspase3 first manifested as promotion,then inhibition.Therefore,luteolin may serve a role in promoting cell apoptosis by inducing Eca109 cell apoptosis that involves the expression of caspase3,caspase9 mRNA and protein.This study provides theoretical basis for further study and clinical application of luteolin.The specific mechanism has not yet been clarified and the other activation pathways inducing apoptosis need to be further studied.展开更多
Objective To screen antigen targets for immunotherapy by analyzing over-expressed genes,and to identify significant pathways and molecular mechanisms in esophageal cancer by using bioinformatic methods such as enrichm...Objective To screen antigen targets for immunotherapy by analyzing over-expressed genes,and to identify significant pathways and molecular mechanisms in esophageal cancer by using bioinformatic methods such as enrichment analysis,protein-protein interaction(PPI)network,and survival analysis based on the Gene Expression Omnibus(GEO)database.Methods By screening with highly expressed genes,we mainly analyzed proteins MUC13 and EPCAM with transmembrane domain and antigen epitope from TMHMM and IEDB websites.Significant genes and pathways associated with the pathogenesis of esophageal cancer were identified using enrichment analysis,PPI network,and survival analysis.Several software and platforms including Prism 8,R language,Cytoscape,DAVID,STRING,and GEPIA platform were used in the search and/or figure creation.Results Genes MUC13 and EPCAM were over-expressed with several antigen epitopes in esophageal squamous cell carcinoma(ESCC)tissue.Enrichment analysis revealed that the process of keratinization was focused and a series of genes were related with the development of esophageal cancer.Four genes including ALDH3A1,C2,SLC6A1,and ZBTB7C were screened with significant P value of survival curve.Conclusions Genes MUC13 and EPCAM may be promising antigen targets or biomarkers for esophageal cancer.Keratinization may greatly impact the pathogenesis of esophageal cancer.Genes ALDH3A1,C2,SLC6A1,and ZBTB7C may play important roles in the development of esophageal cancer.展开更多
Automatic segmentation of early esophagus cancer(EEC)in gastrointestinal endoscopy(GIE)images is a critical and challenging task in clinical settings,which relies primarily on labor-intensive and time-consuming routin...Automatic segmentation of early esophagus cancer(EEC)in gastrointestinal endoscopy(GIE)images is a critical and challenging task in clinical settings,which relies primarily on labor-intensive and time-consuming routines.EEC has often been diagnosed at the late stage since early signs of cancer are not obvious,resulting in low survival rates.This work proposes a deep learning approach based on the U-Net++method to segment EEC in GIE images.A total of 2690 GIE images collected from 617 patients at the Digestive Endoscopy Center,West China Hospital of Sichuan University,China,have been utilized.The experimental result shows that our proposed method achieved promising results.Furthermore,the comparison has been made between the proposed and other U-Net-related methods using the same dataset.The mean and standard deviation(SD)of the dice similarity coefficient(DSC),intersection over union(IoU),precision(Pre),and recall(Rec)achieved by the proposed framework were DSC(%)=94.62±0.02,IoU(%)=90.99±0.04,Pre(%)=94.61±0.04,and Rec(%)=95.00±0.02,respectively,outperforming the others.The proposed method has the potential to be applied in EEC automatic diagnoses.展开更多
基金Thanks for the support of the National Natural Science Foundation of China(https://isisn.nsfc.gov.cn/egrantindex/funcindex/prjsearch-list,grant nos.:81673147,81372985)he Fundamental Research Funds for the Central Universities(grant no.:2242017K40035)Postgraduate Research&Practice Innovation Program of Jiangsu Province(grant no.:SJCX170070).
文摘The present study was conducted to verify the influence of luteolin on apoptosis of Eca109 cells and to further investigate the possible mechanisms underlying its effect on apoptosis.The cells were exposed to different concentrations of luteolin(0,40,80,120,160,200,240M)for 24,48,and 72 h respectively.The influence of luteolin on proliferation of Eca109 cells was detected using MTT assay.Eca109 cells were then treated with luteolin(0,40,160,240M)for 24 h.The effect of luteolin on cell cycle progression and apoptosis was assayed by using flow cytometry(FCM).Expression of caspase9 and caspase3 mRNA and protein was analyzed by real-time PCR and Western blot respectively.The results showed that luteolin could inhibit the proliferation of Eca109 cells at all concentrations in a time-dependent manner and the relative inhibition rate showed an inverted U-shaped association with the concentration of luteolin.Further,the cell cycle was arrested in the S phase following treatment with luteolin.Apoptosis analysis indicated that luteolin could induce the apoptosis of Eca109 cells across the three concentration groups,which exhibited a trend of first promotional and then inhibitory with the increases in luteolin concentration.The effect of luteolin on the mRNA and protein expression of caspase 9 and caspase3 first manifested as promotion,then inhibition.Therefore,luteolin may serve a role in promoting cell apoptosis by inducing Eca109 cell apoptosis that involves the expression of caspase3,caspase9 mRNA and protein.This study provides theoretical basis for further study and clinical application of luteolin.The specific mechanism has not yet been clarified and the other activation pathways inducing apoptosis need to be further studied.
文摘Objective To screen antigen targets for immunotherapy by analyzing over-expressed genes,and to identify significant pathways and molecular mechanisms in esophageal cancer by using bioinformatic methods such as enrichment analysis,protein-protein interaction(PPI)network,and survival analysis based on the Gene Expression Omnibus(GEO)database.Methods By screening with highly expressed genes,we mainly analyzed proteins MUC13 and EPCAM with transmembrane domain and antigen epitope from TMHMM and IEDB websites.Significant genes and pathways associated with the pathogenesis of esophageal cancer were identified using enrichment analysis,PPI network,and survival analysis.Several software and platforms including Prism 8,R language,Cytoscape,DAVID,STRING,and GEPIA platform were used in the search and/or figure creation.Results Genes MUC13 and EPCAM were over-expressed with several antigen epitopes in esophageal squamous cell carcinoma(ESCC)tissue.Enrichment analysis revealed that the process of keratinization was focused and a series of genes were related with the development of esophageal cancer.Four genes including ALDH3A1,C2,SLC6A1,and ZBTB7C were screened with significant P value of survival curve.Conclusions Genes MUC13 and EPCAM may be promising antigen targets or biomarkers for esophageal cancer.Keratinization may greatly impact the pathogenesis of esophageal cancer.Genes ALDH3A1,C2,SLC6A1,and ZBTB7C may play important roles in the development of esophageal cancer.
基金supported by the National Natural Science Foundation under Grants No.62271127,No.61872405,and No.81171411Natural Science Foundation of Sichuan Province,China under Grant No.23NSFSC0627Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China and West China Hospital of Sichuan University under Grants No.ZYGX2022YGRH011 and No.HXDZ22005.
文摘Automatic segmentation of early esophagus cancer(EEC)in gastrointestinal endoscopy(GIE)images is a critical and challenging task in clinical settings,which relies primarily on labor-intensive and time-consuming routines.EEC has often been diagnosed at the late stage since early signs of cancer are not obvious,resulting in low survival rates.This work proposes a deep learning approach based on the U-Net++method to segment EEC in GIE images.A total of 2690 GIE images collected from 617 patients at the Digestive Endoscopy Center,West China Hospital of Sichuan University,China,have been utilized.The experimental result shows that our proposed method achieved promising results.Furthermore,the comparison has been made between the proposed and other U-Net-related methods using the same dataset.The mean and standard deviation(SD)of the dice similarity coefficient(DSC),intersection over union(IoU),precision(Pre),and recall(Rec)achieved by the proposed framework were DSC(%)=94.62±0.02,IoU(%)=90.99±0.04,Pre(%)=94.61±0.04,and Rec(%)=95.00±0.02,respectively,outperforming the others.The proposed method has the potential to be applied in EEC automatic diagnoses.
