| BackgroundEsophageal Cancer（EC）is a common upper digestive tract malignant tumor.In 2020,the incident and death cases of EC in China were 324,422 cases and 301,135 cases,respectively,which accounted for more than 50%of the global incident and death cases.In China,the disease burden of EC is serious,and there is severe prevention and control situation.The incidence of EC in China has obvious regional heterogeneity.The incidence in Taizhou,Jiangsu,is higher than the national average incidence.Owing to the nonspecific clinical symptoms at the early stage,patients with EC are generally late in the discovery,which leads to the poor prognosis and low five-year survival rate.Therefore,the study on the risk factors of EC can provide the theoretical basis for local prevention and control strategies for EC.EC is a kind of multifactorial disease which could be caused by environmental and genetic factors.At present,many potential risk factors of EC have been reported,such as smoking,drinking,obesity,family history and oral hygiene.However,these traditional risk factors cannot completely clarify the pathogenesis of EC.With the advancement of the second-generation sequencing technology and the decrease in costs,the relationship between human microbiome and risk of EC has gradually become a hot research spot.Recently,there is evidence that oral flora disorders are related to the risk of EC.However,the function of oral microbiota to a large extent relies on its complex metabolic characteristics,and it is unable to conduct in-depth analysis of metabolic functions by the micobiome.The metabolome is in the lowest downstream of system biology.It is the closest omics to phenotype.It can provide the characteristics of small molecular metabolites that directly affect the system function.Therefore,the joint application of oral microbiome and metabolome could provide a good technical platform for the impact of oral microbiota and metabolites on the risk of EC.At present,some problems in the study of the oral microbiome and metabolome still need to be resolved urgently.First,the types of previous research are mainly based on cross-section design,and the sample sizes are usually small.The research results still need to be verified.Second,the number of research on the relationship between oral metabolism and EC is small,and there is still a lack of the joint application of oral microbiome and metabolome.The multi-omics research needs to be conducted to analyze the mechanism of EC at the species-and metabolite-levels.Therefore,based on the baseline saliva samples of the Taizhou Cohort,we use the microbiome and metabolome to analyze the impact of the changes in the oral microbita and metabolites on the risk of EC,in order to provide theoretical basis for early EC screening in the high-risk area.Objectives1 To clarify the relationship between the changes in oral flora and risk of EC through the microbiome sequencing technology,and find out the differential oral flora,so as to analyse the influence of changes in oral flora on risk of EC.2 To explore the changes in oral metabolism before the onset of EC through the untargeted metabolomics,and identify the specific metabolites which have effects on risk of EC,and further explore the changes in metabolism pathways.3 Based on the information of the oral microbiome and the metabolome,to explore the joint effect of oral flora and metabolites on the risk of EC from the multi-omics perspective.Methods1 Association between oral microbiota and risk of ECA case cohort study was conducted based on the Taizhou Cohort from 2009 to 2014.New EC cases were collected during the follow-up till December 31,among which cases diagnosed within half a year from the start of follow-up were excluded.A total of 1500 subjects were randomly selected from the core cohort.Finally,208 EC cases and 860 subcohort subjects were included in this study because of the adequate saliva samples.During the baseline survey,Taizhou Cohort collected the general information（including age,gender,marriage status and education levels）,smoking,alcohol drinking,tea drinking,dietary,family history of EC,oral hygiene and physical examination information,as well as the saliva samples.Basd on the Illumina Novaseq PE250 platform,the saliva samples were sequensed by 16S rDNA.The microbial characteristics were described,including Alpha diversity,Beta diversity,and the relative abundance of taxa at the phylum,class,order,family and genus leves.Principal Coordinates Analysis（PCoA）and Nonmetric Multidimensional Scaling（NMDS）analysis were used to explored the differences of the community structures between two groups.Then Analysis of similarities（Anosim）was conducted to test the significance.Linear Discriminant Analysis Effect Size（LEfSe）was used to identify differentially abundant microbes.Finally,Prentice-weighted Cox Proportional-Hazards Model was conducted to explore the role of oral microbiota in risk of EC,and the stratified analysis was further conducted by the frequency of tooth brushing and the number of missing teeth after 20 years old.2 Association between oral metabolites and risk of ECBased on the included subjects of the first part,two-thirds of the included cases were randomly selected,and controls were frequency-matched according to age,gender and the enrollment time.Using the nested case-control study design,oral metabolomics study was conducted.The untarget metabolomics for saliva samples by Liquid chromatography-Mass spectrometry（LC-MS）was relying on the Vanquish Flex UHPLC system and QExactive Mass Spectrometer.Principal component analysis（PCA）and Partial least squares-discriminant analysis（PLS-DA）were used to explore the separation trend between two groups.Variable Importance for the projection（VIP）was extracted from PLS-DA.The filter was set as VIP>1.0 and P<0.05,which was calculated from Student’s t-test.Pathway analysis was conducted to uncover the fluctuated metabolic pathways.Finally,Logistic regression models were applied to evaluate the association of oral metabolites with risk of EC.3 The correlation analysis of oral differential microflora and metabolitesFirst,using the co-intertia analysis（CoIA）,the whole correlation between oral differential microflora and differential metabolites were explored in EC cases and controls,respectively.Then,Spearman correlation analysis method was used to test the specific correlation coefficient between oral differential microflora and differential metabolites in EC cases and controls,respectively.Finally,the network diagrams were plotted to show the correlation among oral differential microflora,metabolites and esophageal cancer.Results1 The association between oral microbiota and risk of EC1.1 There was no significant difference in the richness and diversity of the oral flora between EC cases and the subcohort.The PCoA and NMDS analysis based on the weighted Unifrac distance found that the two groups had a certain degree of heterogeneity in the community structure of oral flora（R=0.0437,P=0.012）.1.2 According to the relative abundance of the flora,the top five phylum were Firmicutes,Bacteroidota,Proteobacteria,Actinobacteriota and Fusobacteriota,and the top ten genus were Neisseria,Streptococcus,Prevotella7,Veillonella,Prevotella,Rothia,Alloprevotella,Haemophilus,Porphyromonas and Fusobacterium.1.3 LEfSe analysis found that the relative abundance of Firmicutes and Actinobacteriota in the EC cases were significantly higher than that of the subcohort,and the relative abundance of Proteobacteria was significantly lower than that of the subcohort.At the genus level,the relative abundance of Prevotella7,Leptotrichia,Rothia,Veillonella,Actinomyces,and Atopobium were significantly higher in EC cases,while the relative abundance of Neisseria,Haemophilus,Fusobacterium,Moraxella,Porphyromonas,Aggregatibacter,and Lautropia were significantly higher in subcohort subjects.1.4 After adjusting age,gender,marital status,education levels,annual family income,smoking,alcohol drinking,tea drinking,frequency of fruits intake,family history of first-degree relatives with EC,frequency of tooth brushing,and the number of miss teeth after 20 years old,it was found that when the relative abundance of Leptotrichia increased by 1.00%,the risk of EC would become 1.05 times（P=0.047）.And in subjects who brushed their teeth less than 2 times per day,with every 1.00%increase in the relative abundance of Leptotrichia（P=0.022）,Campylobacter（P=0.028）and Lachnoanaerobaculum（P=0.024）,the risk of EC would become 1.07 times,1.43 times and 2.19 times,respectively.2 The association between oral metabolites and risk of EC2.1 PC A analysis showed that the samples in the QC（quality control）group showed a good aggregation,which indicated the system stability of the LC-MS detection.PLS-DA analysis showed that there was a certain degree of heterogeneity in the sample distribution between EC cases and controls（permutation test Q2<0）.2.2 According to the screening conditions,866 differential metabolic ions were identified among 13,632 metabolic ions,of which 494 were significantly up-regulated in the EC cases,and 372 were significantly down-regulated.According to the results of metabolite annotation,64 differential metabolites were identified,of which 22 metabolites belonged to Lipids and lipid-like molecules,and 14 metabolites belonged to Organic acids and derivatives,9 belong to Organoheterocyclic compounds,and 8 belong to Benzenoids.2.3 The metabolic pathway analysis based on 64 differential metabolites showed that Linoleic acid metabolism was the most important differential metabolic pathway（Impact=1,P=0.002）,while Sphingolipid metabolism and Citrate cycle（TCA cycle）showed potential influence（Impact>0.1,P<0.05）.2.4 After adjusting education levels,annual family income,alcohol drinking,frequency of fruits intake,and family history of esophageal cancer in first-degree relatives,Syringic acid（OR=1.57）,Guanine（OR=1.47）,and Hexylbenzene（OR=1.49）,Ergothioneine（OR=1.70）,Alanyl-Proline（OR=1.46）,（E,E）-2.4-Hexadienal（OR=1.40）,L-Histidine（OR=1.41）,Heptadecanoic acid（OR=1.55）,Myristic acid（OR=1.54）and Isopropyl hexadecanoate（OR=1.61）were positively correlated with the risk of EC（P<0.05）,N-Acetylmuramate（OR=0.63）,Tetradecanoylcarnitine（OR=0.61）,Arabinonic acid（OR=0.62）,Phenylalanylphenylalanine（OR=0.62）and 3Hydroxyflavone（OR=0.56）were negatively correlated with the risk of EC（P<0.05）.3 The correlation analysis of oral differential microflora and metabolites3.1 The results of CoIA analysis showed that there was a significant correlation between oral differential microflora and metabolites in EC cases（RV=0.111,P=0.017）.And the top ten contributions of differential microbiota were Actinomyces,Atopobium,Neisseria,Porphyromonas,Aggregatibacter,Haemophilus,Rothia,Campylobacter,Lachnoanaerobaculum,and Leptotrichia.The top ten contributions of differential metabolites were N-Acetylleucine、Pantothenic acid、UDP-N-acetylmuramoyl-Lalanine、Isopropyl hexadecanoate、Heptadecanoic acid、2-Hydroxy-3methylbutyric acid、Palmitic acid、Sphinganine、Sphingosine and 2-Methyl-4pentenoic acid.No significant correlation was found in controls（P=0.076）.3.2 In EC cases,Actinomyces,Atopobium,Lachnoanaerobaculum,Leptotrichia and Campylobacter were associated with many differential metabolites,which were mainly the positive correlations.For example,there was a significant positive correlation between Leptotrichia and metabolites,such as Heptadecanoic acid,Phosphorylcholine,Syringic acid,Urate D-ribonucleotide and Vanillic acid,while a siginifcant negative correlation was found with（E,E）-2,4-Hexadienal.There was a significant positive correlation between Campylobacter and Hexylbenzene.There was a significant negative correlation between Lachnoanaerobaculum and a variety of differential metabolites,such as（E,E）-2,4-Hexadienal,Alanyl-Proline and Guanine.3.3 Prevotella7,Atopobium was positively correlated with EC,while Neisseria,Haemophilus,and Lautropia was negatively correlated with EC.However,Actinomyces,Lachnoanaerobaculum,Campylobacter,Rothia and Leptotrichia were not directly correlated with EC.They were related to different differential metabolites,which had direct correlations with EC.For example,there were positive correlations between Heptadecanoic acid,Hexylbenzene,（E,E）-2,4-Hexadienal and EC.Conclusions1 Although there was no significant difference in the diversity of oral microflora between EC cases and the subcohort at baseline,the composition of the oral microbiota was different,and there were a variety of differential taxa.The oral Leptotrichia,Campylobacter,and Lachnoanaerobaculum were risk factors for risk of EC,among those who brushed the teeth less than twice per day.2 A total of 64 oral differential metabolites were identified between EC cases and controls,mainly involving in linoleic acid metabolism and sphingolipid metabolism.Ten metabolites,including（E,E）-2,4-Hexadienal,Alanyl-Proline,Ergothioneine,Guanine,Heptadecanoic acid,Hexylbenzene,L-Histidine,Isopropyl hexadecanoate,Myristic acid and Syringic acid,were associated with the subsequently increasing risk of EC.However,five metabolites,including 3-Hydroxyflavone,Arabinonic acid,NAcetylmuramate,Tetradecanoylcarnitine,and Phenylalanylphenylalanine,were negatively associated with the risk of EC.These metabolites might play the etiological roles in the pathogenesis of EC,and have the potential to become the biomarkers.3 It was found that the oral differential microflora and metabolites were only significantly associated in EC cases rather than controls,by the correlation analysis of oral microbiome and metabolome.Leptotrichia was positively associated with Heptadecanoic acid,Campylobacter was positively associated with Hexylbenzene,while Lachnoanaerobaculum were negatively associated with（E,E）-2,4-Hexadienal.It might indicate that these microbiota play important roles in the pathogenesis of EC by affecting the metabolism.Innovations1 Based on the Taizhou Cohort,a prospective Oral Microbiomics study was conducted.The characteristics and changes of oral microbiota occurred before the onset of EC,which would make sure the strong reasoning ability.2 In this study,Oral Untargeted Metabolomics was applied to the etiology of EC.This study prospectively explored the the impact of oral differential metabolites and metabolic pathways on the subsequent risk of EC,which would provide an important reference for the identification of potential EC biomarkers.3 This study explored the roles of not only oral microbiota but also oral metabolites,in risk of EC,by the joint application of microbiome and metabolomics,the results of which would explains the etiology of EC from the levels of species and metabolism,and provide more powerful scientific basis for pathogenesis and early screening of EC. |
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