Background:

There is currently no optimal sampling method for upper gastrointestinal (UGI) tract microbiota. We compared biopsies and mucosal swab specimens for microbial sampling from patients with UGI carcinoma.

Methods:

A total of 67 patients with esophageal squamous cell carcinoma (ESCC) and 36 patients with gastric cardia adenocarcinoma (GCA) were recruited in the Linxian Cancer Hospital (Henan, China). Sterile biopsies and swabs were used to collect paired samples from the resection specimens from carcinoma and adjacent normal tissue. Data from 16S rRNA gene sequencing were processed using QIIME2 to evaluate differences in alpha and beta diversity and taxonomic relative abundances between specimen types.

Results:

Alpha diversity was not significantly different between swab specimens and biopsies, both for ESCC and GCA. Paired specimens were correlated for both sample types from ESCC (ρ > 0.6, P < 0.001) but not GCA (ρ < 0.4, P > 0.05). For beta diversity, distinct clustering by sampling method was not observed for adjacent normal or tumor tissue from ESCC or GCA. There was a high correlation for weighted UniFrac and Bray–Curtis distance only in ESCC paired specimens (ρ > 0.6, P = 0.001). The 10 dominant bacterial genera were similar between swab and biopsy specimens. However, higher levels of Veillonella (P = 0.0002) and Streptococcus (P = 0.0002) were detected in ESCC adjacent normal and GCA carcinoma swabs, respectively, compared with the biopsies.

Conclusions:

Mucosal swab specimens and biopsies could yield similar microbial profiles from ESCC but not GCA. Both can be used to characterize UGI microbiota; one sampling method should be selected for future studies.

Impact:

This study provides insight for planning microbiota collections from the UGI tract.

Upper gastrointestinal (UGI) carcinoma significantly contributes to the global cancer burden (1), with gastric and esophageal cancer ranking as the third and sixth leading causes of global cancer-related death, respectively (2). In China, gastric cardia adenocarcinoma (GCA; cancer occurring in the gastroesophageal junction) and esophageal squamous cell carcinoma (ESCC) are the most common types of UGI carcinoma in some regions (3, 4). Both ESCC and GCA have a poor prognosis, with overall 5-year survival rates of less than 30%, which is mainly due to late stage diagnoses and distant metastases (1, 5–8). Although ESCC and GCA have similar geographic distributions in China (9) and share some risk factors, the etiology of ESCC and GCA are still poorly understood.

Increasing evidence indicates a key role for bacterial microbiota in carcinogenesis (10, 11). There are millions of microorganisms colonizing the gastrointestinal tract, which may interact with genetic and environmental factors to metabolize dietary constituents and xenobiotics, among other functions (12). When the microbial balance is disturbed, the microbiota could alter host cell proliferation and death, manipulate the immune system, and influence host metabolism, giving rise to carcinoma (13). Several studies have reported an important role of the human microbiota in UGI carcinoma (14, 15) and found associations between the microbiota and some diseases of the UGI tract, such as esophagitis and Barrett esophagus (16, 17), and with squamous dysplasia of the esophagus and gastric atrophy (18).

The method of specimen collection may affect the microbial composition obtained from the UGI tract. Tissue biopsy (19, 20) and mucosal specimens (21–23) are the primary sampling methods used for tissues. A tissue biopsy appears to currently be the “gold standard” method for sample collection, because most studies used upper endoscopy to collect biopsy specimens from the UGI tract for microbiota detection (19, 20, 24–26). Some researchers have used esophageal brushes, which might have improved performance, especially on glandular tissue, to collect microbiota samples from the UGI tract (27, 28). However, a systematic comparison of mucosal specimens and tissue biopsies has not been conducted.

To compare tissue biopsy and mucosal swab specimens for assessment of the UGI microbiota, paired tissue biopsy and mucosal swab specimens were collected from patients diagnosed with incident ESCC or GCA from the Linxian Cancer Hospital (Henan Province, China) a high risk area for ESCC in China (29), who were scheduled for surgery with organ removal. Without a true gold-standard method, we rigorously compared the two methods to assess differences to provide insight for planning future microbiota collections from the UGI tract.

Study participants

A total of 103 inpatients with newly diagnosed ESCC or GCA were enrolled at the Linxian Cancer Hospital (Henan Province, China) during the period of October 2015 to January 2016. All the patients underwent surgical resection and had a histologic diagnosis of primary ESCC or GCA. Ethical approval was obtained from Cancer Hospital, Chinese Academy of Medical Sciences. Written informed consent was obtained from all participants before specimen collection.

Specimen collection

All participants fasted for 12 hours prior to surgical resection. A mucosal specimen and tissue biopsy from both the carcinoma and the adjacent normal mucosa from each participant were collected for a total of 412 specimens from 103 participants immediately after resection. The mucosal specimen from the carcinoma and the adjacent normal mucosa were collected using sterile swabs (Puritan, sterile polyester tipped applicators) prior to the biopsies to prevent contamination of the mucosal specimens with blood. The adjacent normal specimens were collected from a location more than 4 cm from the carcinoma border. After mucosal sampling, the head of the swabs were broken off into sterile tubes (Cryovial, cryogenic tube 3.0 mL) including 1.5 mL of cell preserving fluid (Hologic, ThinPrep, PreservCyt Solution). Tissue biopsies measuring 5 × 5 × 5 mm3 were collected from carcinoma tissue and adjacent normal tissue by using sterile biopsy forceps and placed into a sterile tube without a preservative fluid. All specimens were stored at −80°C immediately after sampling and shipped to the laboratory at Promegene on dry ice.

DNA extraction, amplification, and sequencing

DNA was isolated from UGI tract biopsies and mucosal specimens by using the MOBIO PowerSoil DNA Isolation Kit 12888-100, and the extracted DNA was stored at −80°C in Tris-EDTA buffer solution prior to additional processing. To control for reagent contamination, we included water as a negative control without DNA template during specimen processing.

The V4 region of the 16S rRNA gene was amplified using the universal bacterial primer set of 515-Forward (5′-GTGYCAGCMGCCGCGGTAA-3′) and 806-Reverse (5′-GGACTACNVGGGTWTCTAAT-3′; refs. 30, 31). PCR mixtures contained 1 μL of each forward and reverse primer (10 μmol/L), 1 μL of template DNA, 4 μL of dNTPs (2.5 mmol/L), 5 μL of 10 × EasyPfu Buffer, 1 μL of Easy Pfu DNA Polymerase (2.5 U/μL), and 1 μL of double distilled water in a 50 μL total reaction volume. The PCR thermal cycling consisted of an initial denaturation step at 95°C for 5 minutes, followed by 30 cycles of denaturation at 94°C for 30 seconds, annealing at 60°C for 30 seconds, and extension at 72°C for 40 seconds, with a final extension step at 72°C for 4 minutes. Amplicons from each specimen were run on an agarose gel to ensure consistent sequencing length. Expected band size for 515-Forward to 806-Reverse is 300–350 bp. Amplicon quantification was performed using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific/Invitrogen catalog no. Q32854, following the manufacturer's instructions). The negative controls showed no 16S rRNA gene amplification. The amplicon library for high-throughput sequencing was combined at equal concentrations and volumes and subsequently quantified (KAPA Library Quantification Kit KK4824) according to the manufacturer's instructions.

Using Illumina V4 chemistry and paired-end 2 × 150 bp reads, sequencing was performed on the Illumina MiniSeq platform (Illumina). All sequencing was performed in a single MiniSeq run. Original sequence data processing was performed by the Illumina MiniSeq Reporter to remove adapter and primer sequences and then sequence data was exported in the FASTQ format.

Bioinformatics data processing and statistical analysis

Of the total 412 specimens collected, 396 (96.1%) specimens were successfully amplified and sequenced. The 16S rRNA gene sequencing data of 380 (92.2%) paired specimens were processed using the Quantitative Insights into Microbial Ecology (QIIME2, http://qiime2.org/) platform (32). All raw 16S rRNA gene sequences went through quality control and feature table construction using the DADA2 algorithm (33). Possible phiX reads and chimeric sequences were removed, and the remaining reads were truncated from 0 to 140 bp (for both forward and reverse reads) to avoid including sequencing errors at the ends of the reads. Paired-end reads were matched at a maximum mismatch parameter of six bases, which indicates a minimum similarity threshold of 90% for the overlap of the forward and reverse reads. The representative sequences (named “features” in QIIME2 nomenclature) were then generated by removing the redundant and low occurrence (n < 5 within all samples) sequences. We checked whether using only the forward reads generated different genus-level taxa, but observed generally consistent taxa and relative abundances as the paired-end data. The rarefaction curve was constructed for the Shannon index (Supplementary Fig. S1) and data from all samples was rarefied to 1,000 reads for both diversity and relative abundance to avoid bias due to differing sampling depths. We included 244 (59.2%) successfully paired specimens (swab and biopsy from the same participant) with at least 1,000 reads. The taxonomic assignment of the sequence variants (99% similarity) was assigned using the trained Naive Bayes classifier (trained on the Greengenes 13_8; ref. 34) through the q2-feature-classifier plugin, and the taxonomic composition at the phylum and genus level were generated based on operational taxonomic units (OTU) annotation. A total of 6,227,379 16S rRNA sequence reads were generated with identification of 1,739 OTUs which could be classified into 350 unique genera. Alpha diversity estimates were calculated including observed OTUs and the Shannon diversity index. The Wilcoxon signed-rank test was used to test the difference between the paired groups and the Spearman correlation analysis for alpha diversity was calculated for the paired specimens. Similarly, both taxonomic (Bray–Curtis distance) and phylogenetic (unweighted and weighted UniFrac distance) beta diversity matrices were calculated in addition to principal coordinates analysis of these matrices. The adonis statistical method was used (R package vegan 2.5.4) to determine differences between the independent beta diversity matrices, and the Mantel test (35) was used to compare the similarity of distance matrices from paired groups (R package vegan 2.5.4). The relative abundances were calculated at the phylum and genus level for each type of specimen and the microbiota was compared at the genus level in the two specimen types from both carcinoma and adjacent normal tissue by using the Wilcoxon signed-rank test. For comparing the top 10 genera, Bonferroni correction was used to adjust the significance level due to multiple testing (α = 0.05/10 = 0.005). All analyses were conducted using R (version 3.5.1).

Data availability

The data will be made available on the Sequence Read Archive (BioProject number: PRJNA561290).

Participants' overview

A total of 244 biopsies and mucosal specimens from 103 participants who had a pathologic diagnosis of ESCC (N = 50, 48.5%) and GCA (N = 27, 26.2%) were included. The average age for the ESCC and GCA participants was 62 years and 63 years, respectively. Males represented 69% of the ESCC participants and 93% of the GCA participants (Table 1).

Table 1.

Numbers of successfully sequenced esophageal and gastric cardia specimens from both the carcinoma and adjacent normal tissue and participant characteristics

TotalESCCGCA
Number of participants (N103 67 36 
Participants with paired specimens (N77 50 27 
Matched specimens (N244a 176 68 
Average age (SD) 62 (6.6) 62 (6.7) 63 (6.5) 
Male (%) 77 69 92 
TotalESCCGCA
Number of participants (N103 67 36 
Participants with paired specimens (N77 50 27 
Matched specimens (N244a 176 68 
Average age (SD) 62 (6.6) 62 (6.7) 63 (6.5) 
Male (%) 77 69 92 

aContains 88 paired specimens from ESCC and 34 paired specimens from GCA.

Alpha and beta diversity analysis

For the 50 ESCC participants, the analysis included 47 paired specimens from the carcinoma and 41 from the adjacent normal tissue (Table 1). For both observed OTUs (swab = 67 and biopsy = 66; P = 0.46) and the Shannon diversity index (swab = 4.4 and biopsy = 4.3; P = 0.41) of the carcinoma tissue, there were no significant differences between mucosal specimens and tissue biopsy specimens. Similar findings were observed in the adjacent normal tissue, such that neither measure of alpha diversity (observed OTUs: swab = 61 and biopsy = 63, P = 0.46; Shannon index: swab = 3.8 and biopsy = 3.9, P = 0.56) was statistically different (Fig. 1A and B). In addition, there was a high correlation (>0.60) in alpha diversity for the paired specimens from both carcinoma and adjacent normal tissue (Table 2).

Figure 1.

Microbial diversity in ESCC specimens. Alpha diversity of observed OTUs (A) and the Shannon index (B). Paired specimen principal coordinate analysis plot based on unweighted UniFrac (C), weighted UniFrac (D), and the Bray–Curtis distance (E) from all ESCC specimens. Samples from the same participant and tissue location are connected with a line. PCoA, principal coordinates analysis.

Figure 1.

Microbial diversity in ESCC specimens. Alpha diversity of observed OTUs (A) and the Shannon index (B). Paired specimen principal coordinate analysis plot based on unweighted UniFrac (C), weighted UniFrac (D), and the Bray–Curtis distance (E) from all ESCC specimens. Samples from the same participant and tissue location are connected with a line. PCoA, principal coordinates analysis.

Close modal
Table 2.

Spearman correlation test for alpha diversity of paired specimens from esophageal and gastric cardia carcinoma

Observed OTUsShannon index
Sample typeρPρP
ESCC 0.61 5.2E-06 0.59 1.6E-05 
ESCC adjacent normal tissue 0.66 2.8E-06 0.84 2.2E-16 
GCA 0.23 0.28 0.20 0.33 
GCA adjacent normal tissue 0.12 0.76 0.40 0.29 
Observed OTUsShannon index
Sample typeρPρP
ESCC 0.61 5.2E-06 0.59 1.6E-05 
ESCC adjacent normal tissue 0.66 2.8E-06 0.84 2.2E-16 
GCA 0.23 0.28 0.20 0.33 
GCA adjacent normal tissue 0.12 0.76 0.40 0.29 

For the 27 GCA participants, the analysis included 25 paired specimens from the carcinoma and nine from the adjacent normal tissue (Table 1). Similar to ESCC, observed OTUs (carcinoma: swab = 62 and biopsy = 66, P = 0.72; adjacent normal: swab = 43 and biopsy = 41, P = 0.95) and the Shannon diversity index (carcinoma: swab = 4.2 and biopsy = 4.0, P = 0.98; adjacent normal: swab = 2.4 and biopsy = 2.0, P = 0.36) showed no statistical differences in alpha diversity between the swabs and the biopsies (Fig. 2A and B). However, we did not find a significant Spearman correlation between the paired specimens (Table 2).

Figure 2.

Microbial diversity in GCA specimens. Alpha diversity of observed OTUs (A) and the Shannon index (B). Paired specimen principal coordinate analysis plot based on unweighted UniFrac (C), weighted UniFrac (D), and the Bray–Curtis distance (E) from all GCA specimens. Samples from the same participant and tissue location are connected with a line. PCoA, principal coordinates analysis.

Figure 2.

Microbial diversity in GCA specimens. Alpha diversity of observed OTUs (A) and the Shannon index (B). Paired specimen principal coordinate analysis plot based on unweighted UniFrac (C), weighted UniFrac (D), and the Bray–Curtis distance (E) from all GCA specimens. Samples from the same participant and tissue location are connected with a line. PCoA, principal coordinates analysis.

Close modal

For ESCC, there was no distinct clustering in beta diversity by sample collection type, except for unweighted UniFrac, from the carcinoma (unweighted UniFrac: R2 = 0.033, P = 0.001; weighted UniFrac: R2 = 0.010, P = 0.444; Bray–Curtis: R2 = 0.005, P = 0.998, adonis test) and adjacent normal tissue (unweighted UniFrac: R2 = 0.094, P = 0.001; weighted UniFrac: R2 = 0.014, P = 0.308; Bray–Curtis: R2 = 0.008, P = 0.910, adonis test; Fig. 1C–E). For GCA, the same trend was observed for both the carcinoma tissue (unweighted UniFrac: R2 = 0.106, P = 0.001; weighted UniFrac: R2 = 0.027, P = 0.216; Bray–Curtis: R2 = 0.025, P = 0.174, adonis test) and the adjacent normal tissue (unweighted UniFrac: R2 = 0.146, P = 0.024; weighted UniFrac: R2 = 0.018, P = 0.762; Bray–Curtis: R2 = 0.021, P = 0.872, adonis test; Fig. 2C–E). The Mantel test showed a high correlation for both weighted UniFrac (>0.40) and Bray–Curtis (>0.50), particularly for the ESCC specimens (>0.60). The Mantel statistic ρ of unweighted UniFrac was low (<0.40) for both specimen types from both ESCC and GCA participants (Table 3).

Table 3.

Mantel test for beta diversity of paired specimens from esophageal and gastric cardia carcinoma

Unweighted UniFracWeighted UniFracBray–Curtis
Sample typeρPρPρP
ESCC 0.35 0.001 0.61 0.001 0.67 0.001 
ESCC adjacent normal tissue 0.25 0.001 0.73 0.001 0.67 0.001 
GCA 0.29 0.008 0.42 0.002 0.51 0.003 
GCA adjacent normal tissue 0.26 0.128 0.55 0.017 0.61 0.011 
Unweighted UniFracWeighted UniFracBray–Curtis
Sample typeρPρPρP
ESCC 0.35 0.001 0.61 0.001 0.67 0.001 
ESCC adjacent normal tissue 0.25 0.001 0.73 0.001 0.67 0.001 
GCA 0.29 0.008 0.42 0.002 0.51 0.003 
GCA adjacent normal tissue 0.26 0.128 0.55 0.017 0.61 0.011 

Taxonomic relative abundance

For the ESCC participants, at the phylum level, all specimens contained Bacteroidetes, Firmicutes, Proteobacteria, Fusobacteria, and Actinobacteria. Similarly, all specimens from the carcinoma and adjacent normal tissue had the same top 10 genera (Supplementary Figs. S2 and S3). In pairwise comparisons, there were three genera enriched in the swab sample compared with the biopsy specimen in the carcinoma: Prevotella, Streptococcus, and Veillonella. The mean relative abundances of these three genera were 35.2%, 12.6%, and 11.4%, respectively, in the swab specimen and 32.4%, 10.0%, and 8.9%, respectively, in the biopsy specimen. These three genera were also observed to be different in adjacent normal swabs and biopsies, with the relative abundances of 30.7%, 24.6%, and 11.5% in swabs and 27.1%, 26.9%, and 8.4% in biopsies, respectively. After adjusting for multiple comparisons for testing the top 10 genera, only the difference in Veillonella detected in the adjacent normal specimens was statistically significant between swabs and biopsies (P = 0.0002; Fig. 3A).

Figure 3.

Comparison of the relative abundance of microbial taxa between the two specimen collection types for ESCC and GCA. A, Mean relative abundance of the top 10 prevalent genera in swab and biopsy specimens of ESCC. B, Mean relative abundance of the top 10 prevalent genera in swab and biopsy specimens of GCA. Statistical differences were calculated between two specimen types using the Wilcoxon signed-rank test, and error bars are the SEM. An unclassified genus similar to Prevotella is indicated as [Prevotella]. An unclassified bacterium at the genus level was shown as f_Enterobacteriaceae.

Figure 3.

Comparison of the relative abundance of microbial taxa between the two specimen collection types for ESCC and GCA. A, Mean relative abundance of the top 10 prevalent genera in swab and biopsy specimens of ESCC. B, Mean relative abundance of the top 10 prevalent genera in swab and biopsy specimens of GCA. Statistical differences were calculated between two specimen types using the Wilcoxon signed-rank test, and error bars are the SEM. An unclassified genus similar to Prevotella is indicated as [Prevotella]. An unclassified bacterium at the genus level was shown as f_Enterobacteriaceae.

Close modal

For the GCA participants, all specimens contained Bacteroidetes, Firmicutes, Proteobacteria, Fusobacteria, and Actinobacteria and had the same top 10 genera (Supplementary Figs. S4 and S5). For the carcinoma specimens, four bacteria at the genus level Prevotella, Streptococcus, Veillonella, and Helicobacter varied between the sample types with a mean relative abundance of 25.6%, 18.3%, 12.4%, and 4.0% in the swabs, respectively, and 23.2%, 8.9%, 8.6%, and 13.4% in the biopsies, respectively. For the adjacent normal specimens, these four genera were observed with the relative abundances of 19.3%, 4.9%, 7.3%, and 48.9%, respectively, in the swabs and 15.6%, 2.8%, 5.1%, and 60.1%, respectively, in the biopsies. After adjusting for multiple comparisons for testing the top 10 genera, only Streptococcus detected in the carcinoma was significantly greater in the carcinoma swab samples compared with the biopsy samples (P = 0.0002; Fig. 3B).

Using the paired tissue biopsies and mucosal swab specimens from UGI carcinomas and adjacent normal tissues, overall, alpha diversity was not significantly different in the swab specimens compared with the biopsy specimens from both esophageal squamous tissue and gastric cardia glandular tissue. Paired specimens were correlated for both sample types from ESCC but not GCA. In addition, the community structure, as assessed in the beta diversity analyses, suggested that the swab and biopsy specimens had generally similar bacterial communities, except for unweighted UniFrac. The relative abundances of the top phyla and genera from the two types of specimens were generally similar for ESCC or GCA tissues except that the relative abundance of Veillonella was observed to be decreased in ESCC adjacent normal biopsies and Streptococcus was observed to be decreased in GCA carcinoma biopsies compared with the swab specimens.

Our results generally showed a similar alpha diversity for the swab specimens compared with the biopsies, which is not consistent with other studies (27, 36). Gall and colleagues (27) collected brush mucosal specimens from the esophagus and found higher species diversity in mucosal specimens compared with biopsies as measured by quadratic entropy analysis, but they did not compare other diversity metrics between sample types. Watt and colleagues (36) observed no statistical differences in alpha diversity as measured by the Shannon and inverse Simpson indices between colonic lavage mucosal specimens and biopsies from the sigmoid colon but Watt and colleagues (36) did observe higher numbers of OTUs in colonic lavage than those in biopsy, however, esophageal sampling may differ from that in the colon. Our calculation of alpha diversity by QIIME2 is different from other studies, which could influence the similarity to some extent. Moreover, the high correlation indicated good consistency between ESCC paired samples. Likely because of our limited sample size, we did not see the same correlation trend for GCA. Our beta diversity analysis showed similar community structures by specimen collection types from carcinoma and adjacent normal tissue for the beta diversity measures which take relative abundance into account (i.e., weighted UniFrac and Bray–Curtis). Elliott and colleagues found similar microbial communities in biopsies and mucosal brushes from esophageal adenocarcinoma (23), similar to our findings. In addition, the weighted UniFrac and Bray–Curtis were correlated between two sample types. The differences in specimen types for unweighted UniFrac suggests that these two methods may detect different rare taxa, because unweighted UniFrac takes into account the presence or absence of taxa and not their evenness.

The microbial communities in mucosal specimens from UGI carcinoma participants in our study were mainly composed of the phyla Bacteroidetes, Firmicutes, and Proteobacteria. The dominant genera of Prevotella and Veillonella in the UGI carcinomas were observed in our study, which were also detected in participants with precancerous lesions for esophageal adenocarcinoma of esophagitis or Barrett esophagus (25). The composition of the microbiota in mucosal specimens was similar to that in biopsies, except that there was a higher relative abundance of Veillonella in swab specimens from ESCC adjacent normal and Streptococcus in swab specimens from GCA carcinoma compared with the biopsy. We stratified the analysis by ESCC and GCA tissue because the histopathologic structure of GCA is columnar epithelium (37), which is distinct from the normal stratified squamous epithelium of the esophagus (38), and we hypothesized that the sampling method could vary by tissue type. In particular, we hypothesized that swabs might be a better sampling method for the esophagus and biopsy might be a better method for the gastric cardia, and we did observe similar microbial diversity trends for both esophageal and gastric cardia tissue. However, the high correlation was only detected in ESCC paired specimens, and we did not observe the same trend in GCA which could be influenced by the limited sample size.

Few studies have evaluated the microbiota from the UGI tract likely due to difficulty in sampling. Unlike the relatively easy sampling of oral and fecal specimens, human studies of the UGI tract rely principally on specimens collected through mucosal biopsies using endoscopy, which is invasive and requires a skilled endoscopist. Several studies used the string test (22, 39) or a Cytologic device (18, 23) as a less invasive method to collect the esophageal microbiota and have been observed to collect high levels of microbial DNA. But this type of sampling includes microbial communities from the entire length of the esophagus and the oral cavity (22, 23). For that reason, we compared a potentially lower contamination approach to collect the microbiota of the UGI tract and found that the mucosal swab and biopsy had relatively similar microbial communities. However, we did not evaluate these methods using endoscopic sampling and therefore, future studies should evaluate any contamination by oral microbial communities or other endoscope-related contamination. Recent studies have shown that specimens with relatively small amounts of microbial biomass can produce inaccurate results on sequencing, due in part to DNA contamination of the reagents used (40, 41), but our reagent controls did not show any contamination.

There are limitations to this study. We had a limited sample size for all of the comparisons that were included in this study, so we may be underpowered to detect significant differences between the sampling methods. In addition, only individuals with cancer were included, and although similar findings were seen in the adjacent normal tissue, it is unclear whether the sampling methods would perform as well in nondiseased individuals. We also did not extract DNA from the cell-preserving fluid to examine contamination. Because this solution was used for only the mucosal swab specimens, some of the differences between the mucosal swab and the biopsy may be due to the cell-preserving fluid. However, we did not find any nonhuman-associated bacterial taxa with a relative abundance of more than 1% in the samples. Also, the majority of specimens that had to be dropped because of insufficient read counts were from swabs of the GCA adjacent normal tissue, which may indicate that swabs were not appropriate for GCA sampling. Finally, we did not test mucosal brushes and instead used a swab to simulate mucosal sampling. Future studies may wish to evaluate mucosal brushes and swabs to evaluate comparability.

In conclusion, mucosal specimens from ESCC participants appear to yield similar microbial profiles as tissue biopsies. Because a strong correlation between the two types of specimens in GCA was not observed, it is even more important to use a consistent collection method in any study of the gastric cardia. The collection method for a new study should be determined on the basis of feasibility and sampling invasiveness, but all study comparisons should be made within one sample type due to the differences between the two collection methods. Additional studies of samples collected during an upper endoscopy are needed to confirm our findings.

No potential conflicts of interest were disclosed.

Conception and design: D.-T. Shao, C.C. Abnet, W.-Q. Wei, W. Chen

Development of methodology: D.-T. Shao, W.-Q. Wei

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): A.-Q. Liu, D.-T. Shao, Z. Su, W. Chen

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A.-Q. Liu, E. Vogtmann, C.C. Abnet, H.-Y. Dou, Y. Qin

Writing, review, and/or revision of the manuscript: A.-Q. Liu, E. Vogtmann, C.C. Abnet, W.-Q. Wei, W. Chen

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): A.-Q. Liu, W.-Q. Wei, W. Chen

Study supervision: E. Vogtmann, W.-Q. Wei, W. Chen

The authors would like to thank all of their study participants, and they also thank Dr. Wang Li from the Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Peking Union Medical College (Beijing, China), for her helpful comments and discussion. This work was supported by a grant from Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2016-I2M-3-001).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Crew
KD
,
Neugut
AI
. 
Epidemiology of upper gastrointestinal malignancies
.
Semin Oncol
2004
;
31
:
450
64
.
2.
Bray
F
,
Ferlay
J
,
Soerjomataram
I
,
Siegel
RL
,
Torre
LA
,
Jemal
A
, et al
. 
Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
.
CA Cancer J Clin
2018
;
68
:
394
424
.
3.
Colquhoun
A
,
Arnold
M
,
Ferlay
J
,
Goodman
KJ
,
Forman
D
,
Soerjomataram
I
, et al
. 
Global patterns of cardia and non-cardia gastric cancer incidence in 2012
.
Gut
2015
;
64
:
1881
8
.
4.
Abnet
CC
,
Arnold
M
,
Wei
WQ
. 
Epidemiology of esophageal squamous cell carcinoma
.
Gastroenterology
2018
;
154
:
360
73
.
5.
Pennathur
A
,
Farkas
A
,
Krasinskas
AM
,
Ferson
PF
,
Gooding
WE
,
Gibson
MK
, et al
Esophagectomy for T1 esophageal cancer: outcomes in 100 patients and implications for endoscopic therapy
.
Ann Thorac Surg
2009
;
87
:
1048
54
.
6.
Pennathur
A
,
Gibson
MK
,
Jobe
BA
,
Luketich
JD
. 
Oesophageal carcinoma
.
Lancet
2013
;
381
:
400
12
.
7.
Petrick
JL
,
Kelly
SP
,
Liao
LM
,
Freedman
ND
,
Graubard
BI
,
Cook
MB
, et al
. 
Body weight trajectories and risk of oesophageal and gastric cardia adenocarcinomas: a pooled analysis of NIH-AARP and PLCO studies
.
Br J Cancer
2017
;
116
:
951
9
.
8.
Zeng
H
,
Chen
W
,
Zheng
R
,
Zhang
S
,
Ji
JS
,
Zou
X
, et al
Changing cancer survival in China during 2003–15: a pooled analysis of 17 population-based cancer registries
.
Lancet Glob Health
2018
;
6
:
e555
e67
.
9.
Wang
LD
,
Zhou
FY
,
Li
XM
,
Sun
LD
,
Song
X
,
Jin
Y
, et al
Genome-wide association study of esophageal squamous cell carcinoma in Chinese subjects identifies susceptibility loci at PLCE1 and C20orf54
.
Nat Genet
2010
;
42
:
759
63
.
10.
Schwabe
RF
,
Jobin
C
. 
The microbiome and cancer
.
Nat Rev Cancer
2013
;
13
:
800
12
.
11.
Ohtani
N
. 
Microbiome and cancer
.
Semin Immunopathol
2015
;
37
:
65
72
.
12.
Walker
MM
,
Talley
NJ
. 
Review article: bacteria and pathogenesis of disease in the upper gastrointestinal tract–beyond the era of Helicobacter pylori
.
Aliment Pharmacol Ther
2014
;
39
:
767
79
.
13.
Garrett
WS
. 
Cancer and the microbiota
.
Science
2015
;
348
:
80
6
.
14.
Abreu
MT
,
Peek
RM
 Jr
. 
Gastrointestinal malignancy and the microbiome
.
Gastroenterology
2014
;
146
:
1534
46
.
15.
Zhang
C
,
Powell
SE
,
Betel
D
,
Shah
MA
. 
The gastric microbiome and its influence on gastric carcinogenesis: current knowledge and ongoing research
.
Hematol Oncol Clin North Am
2017
;
31
:
389
408
.
16.
Nardone
G
,
Compare
D
,
Rocco
A
. 
A microbiota-centric view of diseases of the upper gastrointestinal tract
.
Lancet Gastroenterol Hepatol
2017
;
2
:
298
312
.
17.
Baba Y
IM
,
Yoshida
N
,
Watanabe
M
,
Baba
H
. 
Review of the gut microbiome and esophageal cancer: pathogenesis and potential clinical implications
.
Ann Gastroenterol Surg
2017
;
1
:
99
104
.
18.
Yu
G
,
Gail
MH
,
Shi
J
,
Klepac-Ceraj
V
,
Paster
BJ
,
Dye
BA
, et al
Association between upper digestive tract microbiota and cancer-predisposing states in the esophagus and stomach
.
Cancer Epidemiol Biomarkers Prev
2014
;
23
:
735
41
.
19.
Pei
Z
,
Bini
EJ
,
Yang
L
,
Zhou
M
,
Francois
F
,
Blaser
MJ
. 
Bacterial biota in the human distal esophagus
.
Proc Natl Acad Sci U S A
2004
;
101
:
4250
5
.
20.
Pei
Z
,
Yang
L
,
Peek
RM
,
Levine
SM
 Jr
,
Pride
DT
,
Blaser
MJ
, et al
. 
Bacterial biota in reflux esophagitis and Barrett's esophagus
.
World J Gastroenterol
2005
;
11
:
7277
83
.
21.
Macfarlane
S
,
Furrie
E
,
Macfarlane
GT
,
Dillon
JF
. 
Microbial colonization of the upper gastrointestinal tract in patients with Barrett's esophagus
.
Clin Infect Dis
2007
;
45
:
29
38
.
22.
Fillon
SA
,
Harris
JK
,
Wagner
BD
,
Kelly
CJ
,
Stevens
MJ
,
Moore
W
, et al
Novel device to sample the esophageal microbiome–the esophageal string test
.
PLoS One
2012
;
7
:
e42938
.
23.
Elliott
DRF
,
Walker
AW
,
O'Donovan
M
,
Parkhill
J
,
Fitzgerald
RC
. 
A non-endoscopic device to sample the oesophageal microbiota: a case-control study
.
Lancet Gastroenterol Hepatol
2017
;
2
:
32
42
.
24.
Yang
L
,
Lu
X
,
Nossa
CW
,
Francois
F
,
Peek
RM
,
Pei
Z
, et al
. 
Inflammation and intestinal metaplasia of the distal esophagus are associated with alterations in the microbiome
.
Gastroenterology
2009
;
137
:
588
97
.
25.
Liu
N
,
Ando
T
,
Ishiguro
K
,
Maeda
O
,
Watanabe
O
,
Funasaka
K
, et al
Characterization of bacterial biota in the distal esophagus of Japanese patients with reflux esophagitis and Barrett's esophagus
.
BMC Infect Dis
2013
;
13
:
130
.
26.
Amir
I
,
Konikoff
FM
,
Oppenheim
M
,
Gophna
U
,
Half
EE
. 
Gastric microbiota is altered in oesophagitis and Barrett's oesophagus and further modified by proton pump inhibitors
.
Environ Microbiol
2014
;
16
:
2905
14
.
27.
Gall
A
,
Fero
J
,
McCoy
C
,
Claywell
BC
,
Sanchez
CA
,
Blount
PL
, et al
Bacterial composition of the human upper gastrointestinal tract microbiome is dynamic and associated with genomic instability in a Barrett's esophagus cohort
.
PLoS One
2015
;
10
:
e0129055
.
28.
Norder Grusell
E
,
Dahlen
G
,
Ruth
M
,
Ny
L
,
Quiding-Jarbrink
M
,
Bergquist
H
, et al
Bacterial flora of the human oral cavity, and the upper and lower esophagus
.
Dis Esophagus
2013
;
26
:
84
90
.
29.
Lin
Y
,
Totsuka
Y
,
He
Y
,
Kikuchi
S
,
Qiao
Y
,
Ueda
J
, et al
Epidemiology of esophageal cancer in Japan and China
.
J Epidemiol
2013
;
23
:
233
42
.
30.
Walters
WA
,
Caporaso
JG
,
Lauber
CL
,
Berg-Lyons
D
,
Fierer
N
,
Knight
R
, et al
. 
PrimerProspector: de novo design and taxonomic analysis of barcoded polymerase chain reaction primers
.
Bioinformatics
2011
;
27
:
1159
61
.
31.
Caporaso
JG
,
Lauber
CL
,
Walters
WA
,
Berg-Lyons
D
,
Huntley
J
,
Fierer
N
, et al
Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms
.
ISME J
2012
;
6
:
1621
4
.
32.
Bolyen
E
,
Rideout
JR
,
Dillon
MR
,
Bokulich
NA
,
Abnet
C
,
Al-Ghalith
GA
, et al
QIIME 2: reproducible, interactive, scalable, and extensible microbiome data science
.
PeerJ Preprints
2018
;
6
:
e27295v2
.
33.
Callahan
BJ
,
McMurdie
PJ
,
Rosen
MJ
,
Han
AW
,
Johnson
AJ
,
Holmes
SP
, et al
. 
DADA2: high-resolution sample inference from Illumina amplicon data
.
Nat Methods
2016
;
13
:
581
3
.
34.
DeSantis
TZ
,
Hugenholtz
P
,
Larsen
N
,
Rojas
M
,
Brodie
EL
,
Keller
K
, et al
Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB
.
Appl Environ Microbiol
2006
;
72
:
5069
72
.
35.
Diniz-Filho
JA
,
Soares
TN
,
Lima
JS
,
Dobrovolski
R
,
Landeiro
VL
,
de Campos Telles
MP
, et al
Mantel test in population genetics
.
Genet Mol Biol
2013
;
36
:
475
85
.
36.
Watt
E
,
Gemmell
MR
,
Berry
S
,
Glaire
M
,
Farquharson
F
,
Louis
P
, et al
Extending colonic mucosal microbiome analysis-assessment of colonic lavage as a proxy for endoscopic colonic biopsies
.
Microbiome
2016
;
4
:
61
.
37.
McColl
KE
. 
Cancer of the gastric cardia
.
Best Pract Res Clin Gastroenterol
2006
;
20
:
687
96
.
38.
Enzinger
PC
,
Mayer
RJ
. 
Esophageal cancer
.
N Engl J Med
2003
;
349
:
2241
52
.
39.
Harris
JK
,
Fang
R
,
Wagner
BD
,
Choe
HN
,
Kelly
CJ
,
Schroeder
S
, et al
Esophageal microbiome in eosinophilic esophagitis
.
PLoS One
2015
;
10
:
e0128346
.
40.
Goodrich
JK
,
Di Rienzi
SC
,
Poole
AC
,
Koren
O
,
Walters
WA
,
Caporaso
JG
, et al
Conducting a microbiome study
.
Cell
2014
;
158
:
250
62
.
41.
Salter
SJ
,
Cox
MJ
,
Turek
EM
,
Calus
ST
,
Cookson
WO
,
Moffatt
MF
, et al
Reagent and laboratory contamination can critically impact sequence-based microbiome analyses
.
BMC Biol
2014
;
12
:
87
.