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Multicenter Prospective Study On Risk Factors And Predictive Biomarkers Of Esophagogastric Junction Adenocarcinoma(Gastric Cardia Adenocarcinoma)in High-risk Areas Of Upper Gastrointestinal Cancer In China

Posted on:2021-10-16Degree:DoctorType:Dissertation
Country:ChinaCandidate:S H XieFull Text:PDF
GTID:1484306308488354Subject:Epidemiology and Health Statistics
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ObjectivesBased on a population-based multicenter endoscopic screening cohort of upper gastrointestinal(UGI)cancer established in high-risk areas in China,the current study aims to investigate and identify the epidemiological risk factors,plasma protein biomarkers of gastric cardia adenocarcinoma(GCA),and to develop risk prediction models for GCA.So as to provide theoretical basis for comprehensive prevention and control of GCA in China.Material and Methods1.Cross-sectional exploration study on risk factors and predictive biomarkers of GCA,which included four parts as follows:(1)From 2005 to 2009,a population-based multicenter endoscopic screening of UGI was conducted in three high-risk areas(Linzhou,Cixian and Feicheng).The relationship between exposure factors and the occurrence of GCA and precursors was analyzed by unconditional logistic regression model.(2)A thermal exposure assessment protocol of hot foods and beverages were developed.Drinking temperatures and sip sizes were measured in a multi-cultural group(n=20)of Europe-based hot beverage drinkers and a group of hot porridge consumers(n=52)in a high UGI incidence region of China.How the following four thermal exposure metrics discriminate two distinct drinking habits were compared:i)temperature at first sip;ii)average temperature;iii)sip-weighted average temperature and iv)predicted intra-esophageal temperature(IET).(3)A population-based cross-sectional study was conducted in Linzhou County,Henan Province,China.Two thousand and three(2,003)randomly selected participants with data on current H.pylori infection,assayed by 13C-UBT,and a sequence of histological diagnoses of the gastric cardia mucosa were analyzed.(4)Plasma were collected from subjects diagnosed with GCA,high-grade intraepithelial neoplasia(HIN),low-grade intraepithelial neoplasia(LIN)and normal gastric cardia mucosa(10 cases per group,matched by age and gender).Orbitrap data independent acquisition(DIA)liquid chromatography tandem mass spectrometry(LC-MS/MS)was used to screen the differential proteins for GCA and precursors.Candidate proteins were selected according to the following criteria:the protein expression in normal cardia is at a low level,while at a high level(>5 times)in GCA or precursors.2.Multicenter prospective study on risk factors and risk prediction models of GCA:A population-based multicenter screening subcohort of GCA was established.The local cancer registration systems covering the entire population was used for the surveillance and follow-up of the cohort.Cox proportional hazard model was used to analyze the epidemiological risk factors,histological precursors of GCA.Logistic regression model was used to construct risk prediction models for GCA.C-statistic was used to evaluate the model's differentiation,and Hosmer-Lemeshow goodness of fit test was used to evaluate the model's calibration.Results1.A total of 21,592 subjects were recruited and endoscopically screened during 2005-2009,including 18,356 cases of normal cardiac mucosa or non-atrophic carditis(Normal/NAC),1,094 cases of atrophic carditis or intestinal metaplasia(AC/IM),1,117 cases of LIN,150 cases of HIN,and 118 cases of GCA.Male,age 50 or more,smoking,a no tea drinking habit,lower consumption(<1 time/week)of vegetables or fruits,higher consumption(?1 time/week)of onions or garlic,pickled foods,fried foods and hot foods,a family history of cancer and higher body mass index(BMI)were positively correlated with GCA and precursors(all OR values>1.0,all P values<0.05).2.Chinese porridge consumers took larger(mean difference+17 g)sips of hotter(mean difference+9.5?)liquid sooner after serving,whereas I ARC hot beverage drinkers waited longer(mean+5.6 minutes)to start drinking and took smaller sips which increased in size as temperature decreased.This resulted in significantly higher predicted IETs(mean difference+18.6?)in Chinese porridge drinkers.Distinguishing exposure characteristics between the two groups was greatly enhanced by measuring sip sizes.3.Of2,003 subjects,41.34%(828)were currently infected with H.pylori.The prevalence of current H.pylori infection was 39.29%in Normal/NAC,62.16%in AC/IM,55.91%in LIN,and 52.17%in HIN/GCA.H.pylori-positive subjects had significantly elevated adjusted odds ratios for each of the histological lesions.The ORs(95%CI)of H.pylori infection associated with AC/IM,LIN and HIN/GCA were 2.78(1.70-4.53),2.14(1.47-3.10),and 2.29(0.98-5.33),respectively.4.A total of 1,212 proteins were identified.Compared with Normal group,149,170 and 89 differentially expressed proteins were identified in the LIN,HIN and GCA group respectively.These proteins were involved in cell signaling,epithelial cell proliferation,development,differentiation,apoptosis,migration and adhesion,as well as angiogenesis and immune regulation.Signaling pathways were mainly focused on focal adhesion,actin cytoskeleton regulation,cell adhesion molecules,platelet activation,PI3K-Akt signaling pathway,H.pylori infection epithelial cell signal transduction and gastric acid secretion.Eight proteins including RNH1,TERA,ATAD2,ROBO4,RAC1,ICAM1,GBP4 and REG3A could be candidate biomarkers of GCA or precursors.5.As of December 31,2017,a total of21,474 subjects were recruited in the GCA subcohort.Two hundred and two subjects were diagnosed with GCA,including 125 males and 77 females,with a total follow-up of 207,179 person-years and an average follow-up of 9.7 years.Male(HR=1.92,95%CI:1.36-2.70),age 50 or more(50-59 years old:HR=2.59,95%CI:1.75-3.85;60-69 years old:HR=4.50,95%CI:2.91-6.96),a no tea drinking habit(HR=1.80,95%CI:1.19-2.74),lower consumption of meat,egg or milk(HR=1.31,95%CI:0.98-1.76),higher consumption of onion or garlic(HR=1.67,95%CI:1.08-2.58),hot food(HR=1.37,95%CI:1.03-1.81),and a family history of cancer(HR=2.05,95%CI:0.83-5.06),high BMI(25.0?29.9 kg/m2,HR=1.28,95%CI:0.95-1.72)increased the risk of GCA.6.The crude cumulative GCA incidence of AC/IM,LIN and HIN were 44.30×10-5,227.07×10-5,380.95×10-5,100×10-5 and 4366.35×10-5.Compared with Normal/NAC,the long-term GCA risk of AC/IM,LIN and HIN were increased by 3.45 times(HR=4.45,95%CI:2.74-7.23),5.67 times(HR=6.67,95%CI:4.48-9.94)and 60.65 times(HR=61.65,95%CI:40.19-94.55),respectively.7.The C-statistic of the risk prediction model incorporating with epidemiological risk factors was 0.725(95%CI:0.690-0.761),and the goodness of fit of the model was qualified(?2=6.20,P=0.625).The C-statistic of the risk prediction model further incorporating with histological diagnoses was 0.834(95%CI:0.810-0.871),the goodness of fit of the model was also qualified(?2=6.83,P=0.555).Conclusions1.Male,age 50 or more,a no tea drinking habit,lower consumption of meat,egg or milk,higher consumption of onion or garlic,and hot food,a family history of cancer,high BMI,as well as H.pylori infection are risk factors for GCA.Temperature at first sip is suboptimal for assessing human exposure to hot foods and beverages,and future studies should include sip size measurements in exposure assessment protocols.This study provides a logistically feasible framework for doing so.2.RNH1,TERA,ATAD2,ROBO4,RAC1,ICAM1,GBP4 and REG3A are potential protein biomarkers of GCA or precursors.3.Atrophic carditis,intestinal metaplasia,low-and high-grade intraepithelial neoplasia in the gastric cardia are prospectively proved as significant precursors for GCA.4.The risk prediction models of GCA incorporating with epidemiological risk and histopathological factors have good predictive ability and potential application value in the assessment of high-risk populations and shunt of GCA screening in high incidence areas,respectively.
Keywords/Search Tags:Adenocarcinoma of esophagogastric junction, Gastric cardia adenocarcinoma, Precursors, Risk factors, Malignant risk, Tumor Biomarker, Risk prediction model
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