Published online Sep 27, 2026. doi: 10.4240/wjgs.118081
Revised: May 6, 2026
Accepted: July 20, 2026
Published online: September 27, 2026
Processing time: 181 Days and 23.9 Hours
The intestinal microbial imbalance and metabolic disorders that occur in patients with esophageal cancer after surgery can hinder recovery. Probiotics have been proven to be effective in improving clinical outcomes, but the impact of probiotics on the intestinal microbiome and metabolic profile of this population has not been fully elucidated.
To investigate the effects of the combined application of probiotics and enteral nutrition on the intestinal microbiome and metabolic profile of patients with eso
A total of 26 patients with esophageal squamous cell carcinoma were enrolled and equally divided into a probiotic group (PRO, n = 13) receiving enteral nutrition plus live combined Bifidobacterium and Lactobacillus tablets and a control group (NC, n = 13) receiving enteral nutrition alone. Fecal samples were collected on postoperative day 7. 16S rRNA gene sequencing and untargeted LC-MS metabolomics were performed to characterize microbial and metabolic alterations. Independent t tests, χ2 tests, linear discriminant analysis effect size (LEfSe), Wilcoxon rank-sum tests, and Spearman correlations (adjusted by false discovery rate) were used for statistical analyses.
No significant differences in baseline clinical characteristics were observed betwe
Probiotics can affect the intestinal flora and metabolites of patients after surgery and may promote their rehabilitation.
Core Tip: In this study, analysis of the clinical outcomes of the postoperative rehabilitation of patients with esophageal squamous cell carcinoma (ESCC) receiving live combined Bifidobacterium and Lactobacillus tablets revealed the underlying mechanisms including changes in the gut microbiome and fecal metabolome. Our research offers substantial evidence regarding the role of the gut microbiota in patients with ESCC after surgery, suggesting novel avenues for targeted interventions.
- Citation: Chen XF, Zhang HY, Gao PQ, Zhuang FN, Chen WJ, Wang F. Multi-omics reveal the effects of probiotics on the postoperative rehabilitation of patients with esophageal cancer. World J Gastrointest Surg 2026; 18(9): 118081
- URL: https://www.wjgnet.com/1948-9366/full/v18/i9/118081.htm
- DOI: https://dx.doi.org/10.4240/wjgs.118081
Esophageal cancer is a common type of cancer worldwide, with particularly high incidence rates in Asia[1]. According to the Global Cancer Statistics 2022, in 2022 there were 510716 new cases and 445129 deaths worldwide[2]. Its mortality rate ranks seventh globally. In China, over 90% of esophageal cancer cases are esophageal squamous cell carcinoma (ESCC), which has a poor prognosis, with a 5-year survival rate of only 20%[3,4]. Due to the lack of specific early symptoms, esophageal cancer is often diagnosed at an advanced stage, which leads to poor treatment outcomes and highlights the urgent need for improved treatment strategies[5].
The human microbiome is composed of various microorganisms[6]. The composition of the gut microbiome and its bioactive derivatives play a crucial role in regulating immune balance, thereby modulating host predisposition to autoim
Probiotics are microbial preparations that have been activated. When consumed in sufficient quantities, they can bring health benefits to the host[12]; among them, Lactobacillus and Bifidobacterium are commonly used strains, which are helpful in preventing and improving allergic and respiratory diseases[13]. These bacteria can stably colonize the intestinal tract, alter the gut microbial composition, increase the level of microbial metabolites, and regulate the host’s immune system[14]. Preclinical studies have that probiotics may be able to prevent and assist in the treatment of colorectal cancer by regulating the intestinal microbial flora[15]. Specifically, Bifidobacterium longum has been demonstrated to enhance postoperative liver function recovery in patients with hepatocellular carcinoma[16]. Moreover, probiotics can decrease the incidence of gastrointestinal complications and improve the nutritional status of postoperative patients with cancer. They achieve this by regulating the gut microbiota, optimizing the microecological balance, and restoring the physiological function of the gastrointestinal tract[17]. However, clinical studies on the use of probiotics for postoperative esophageal cancer patients are very limited, and their benefit to this patient group remains controversial[17].
16S rRNA sequencing has the advantages of high cost-effectiveness and mature application, and is particularly suitable for large-scale screening studies. The main goal of such studies is to analyze the taxonomic characteristics of bacterial and archaeal communities[18]. While 16S sequencing reveals the identity of the microbes, metabolomics addresses the complementary question of their functions by capturing the downstream functional output of the host-microbiota ecosystem[19]. Metabolomics is the comprehensive profiling of small molecules, including carbohydrates, lipids, amino acids, and other metabolites, within a biological sample. It provides a direct readout of the metabolic state that integrates signals from both the host and its microbial inhabitants[20]. Microbial metabolites serve as key molecular hubs that connect the gut microbiota composition to host physiology and disease progression, including cancer[21]. Therefore, comb
This was a retrospective study, and a primary aim of this research was to examine the clinical outcomes of live combi
A total of 26 patients with ESCC were enrolled after tumor resection. The control group (NC, n = 13) received conventional treatment and enteral nutrition therapy. The experimental group (PRO, n = 13) received probiotics via nasal feeding in addition to the NC regimen. Patients with ESCC aged 40-80 years with a histologically confirmed diagnosis who provided informed consent, and whose biological samples (blood and feces), and complete clinical records were available were included in the study. The exclusion criteria were: Antibiotic exposure within 3 months prior to enrollment; comorbid malignancies or gastrointestinal disorders; and a history of gastroesophageal surgery. The probiotic (a compound tablet containing active Bifidobacterium and Lactobacillus) was administered through a nasogastric tube starting from the first postoperative day (POD1). The dosage and frequency were as follows: 2 tablets (each containing 0.5 g, with a total viable count of ≥ 1.0 × 107 CFU) every 12 hours. Both groups received the same standardized enteral nutrition formula. The NC received an equivalent volume of 0.9% normal saline via the nasogastric tube to ensure that the total fluid volume was consistent between the two groups, thereby eliminating fluid balance as a confounding factor. Fecal and serum samples were collected 7 days after the administration. Fresh samples were stored at -80 °C until further use. All participants or their families provided informed consent. The Ethics Committee of the Fujian Cancer Hospital approved the study on November 20, 2024 (No. K2024-524-01).
A clean test tube was used to collect 500 mg of fresh feces, ensuring that it was not contaminated by other substances. Within two hours of collection, the samples were stored at -80 °C. For 16S rRNA sequencing, the processing of the fecal samples was as follows: A 200 mg sample was taken, cut into smaller fragments, and mix it evenly with sterile phosphate buffered saline (PBS). This mixture was centrifuged, following which the supernatant was discarded. The sediment was subsequently resuspended in PBS, stirred thoroughly, and centrifuged again to eliminate any remaining supernatant.
Fecal samples were subjected to 16S rRNA sequencing (Illumina NovaSeq platform, Tgene Bio, China) following microbial genomic DNA extraction with a modified CTAB/SDS protocol. Before library construction, the DNA integrity was validated by 1% agarose gel electrophoresis. In this study, the V3-V4 region of the 16S rRNA gene was amplified with the specific primers 338F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’) for sequencing. Equivalent volumes of 1X loading buffer (with SYBR Green) were added to all the PCR amplicons. Then, these amplification products were electrophoresed on a 2% agarose gel for detection. Next, the purified amplification products were purified using the Qiagen Gel Extraction Kit® (Qiagen™, Germany). Finally, the purified amplicons were sequenced via the Illumina™ MiSeq PE300 platform to generate 250-bp paired-end reads in sequence.
Fecal aliquots (200 mg) were homogenized in prechilled 2 mL tubes with 600 μL of methanol containing 2-amino-3-(2-chloro-phenyl)-propionic acid (4 ppm internal standard). The suspension was successively subjected to the following treatments: Vortexing (30 seconds), ultrasonic extraction (10 minutes, 25 °C), centrifugation (12000 × g, 10 minutes, 4 °C), and membrane filtration (0.22 μm). Equal volumes of all filtered supernatants were pooled to prepare the quality control samples.
Chromatographic separation was performed on a Vanquish UHPLC system (Thermo Fisher, MA, United States) equipped with an ACQUITY UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm; Waters). The operational parameters were as follows: Column temperature, 40 °C; flow rate, 0.3 mL/minute; and injection volume, 2 μL. The composition of the mobile phase is shown in Table 1. Gradient elution program (0-8 minutes): 0-1 minutes: 10% B; 1-5 minutes: 10%→98% B; 5-6.5 minutes: 98% B; 6.5-6.6 minutes: 98%→10% B; 6.6-8 minutes: 10% B.
| Ionization mode | Phase A | Phase B |
| ESI(+) | 0.1% HCOOH in H2O | 0.1% HCOOH in ACN |
| ESI(-) | 5 mM ammonium format | ACN |
Metabolite analysis was conducted using an Orbitrap Exploris 120 mass spectrometer (Thermo Fisher Scientific, MA, United States) equipped with a dual-polarity ESI source. MS1 and MS/MS data were acquired simultaneously with the following parameters: The sheath gas pressure was set at 40 arb, and the aux gas flow was 10 arb. The spray voltage was 3.50 kV for ESI(+) and -2.50 kV for ESI(-). The capillary temperature was maintained at 325 °C. For MS1, the m/z range was 100-1000 @60k (FWHM), whereas for MS/MS, it was Top4 @15k (FWHM). The normalized collision energy was 30%, and dynamic exclusion was set to automatic.
The raw data were first converted to mzXML format by MSConvert in the ProteoWizard software package (v3.0.8789) and processed using R XCMS (v3.12.0) for feature detection, retention time correction, and alignment. The key parameters were set as follows: Ppm = 15, peak width = c(5, 30), mzdiff = 0.01, and method = centWave. The batch effect was then eliminated by correcting the data based on the QC samples. Metabolites with RSDs > 30% in the QC samples were filtered and then used for subsequent data analysis. The metabolites were identified by accurate mass and MS/MS data, which were matched with those in HMDB (http://www.hmdb.ca), MassBank (http://www.massbank.jp/), Kyoto Encyclo
GraphPad Prism 8.0 was used to analyze the results of the epidemiological questionnaire survey and clinical tests. The results are expressed as the mean ± SD. Continuous variables were analyzed using independent t tests, and categorical variables were analyzed using χ2 tests. P < 0.05 was considered to indicate statistical significance.
Alpha diversity was assessed using the Kruskal-Wallis test, followed by Dunn’s post hoc test. Beta diversity differences were measured through nonmetric multidimensional scaling (NMDS) analysis. We selected a total of 12 genera whose abundance significantly differed between groups (P < 0.05) according to the Wilcoxon rank-sum test for subsequent correlation analysis.
Linear discriminant analysis (LDA) effect size was utilized to identify gut microbiota markers among the groups. Based on the 16S rRNA sequencing results and the results of the PICRUSt2 and LDA effect size (LEfSe) tools, we predicted microbial metabolite functions and identified differentially enriched pathways. LEfSe analysis revealed significantly abundant taxa (phylum, genus) in different bacterial groups, with an LDA score > 2 and P < 0.05.
The R package ropls (version 1.6.2) was employed to conduct orthogonal partial least squares discriminant analysis (OPLS-DA). The stability of the model was evaluated using an OPLS-DA permutation test diagram, while a permutation test was used to assess the model for overfitting. The explanatory power of the model for the X and Y matrices was represented by R2X and R2Y, respectively. The predictive ability of the model was indicated by Q2, and values closer to 1 suggested a better fit and more accurate classification of training set samples into their original categories.
Significantly different metabolites were selected based on the Variable Importance in Projection (VIP) score from the OPLS-DA model and the P value from Student’s t test, with VIP > 1 and P < 0.05 as the criteria. For pathway analysis, the MetaboAnalyst software was used to perform functional pathway enrichment and topology analyses on the differentially abundant metabolites. The enriched pathways were visualized using the KEGG Mapper. The levels of microbes and metabolites were compared, and Spearman correlation analysis was used for the correlation analyses, in which P < 0.05 indicated a statistically significant difference.
Samples of a total of 26 postoperative patients with ESCC were collected from both the PRO group (13 patients receiving enteral nutrition with probiotics) and the NC group (13 patients receiving enteral nutrition alone). According to the results of independent t tests and χ2 tests, no significant differences were observed between patients in the PRO and NC groups in terms of age, sex, height, body mass index, smoking and drinking habits, consumption of pickled foods, or distance of the tumor from the incisors (P > 0.05), indicating a well-matched sample (Table 2).
| Characteristics | NC group | PRO group | P value |
| Age (years) | 60.9 ± 8.6 | 61.5 ± 6.9 | 0.838 |
| Sex | 1 | ||
| Male | 11 (84.6) | 11 (84.6) | |
| Female | 2 (15.4) | 2 (15.4) | |
| Height (cm) | 163.7 ± 8.3 | 163.5 ± 6.1 | 0.968 |
| Weight (kg) | 57.9 ± 13.7 | 63.9 ± 10.6 | 0.225 |
| BMI | 21.6 ± 5.0 | 23.9 ± 3.1 | 0.171 |
| Smoking | 1 | ||
| Yes | 2 (15.4) | 2 (15.4) | |
| No | 11 (84.6) | 11 (84.6) | |
| Drinking | 1 | ||
| Yes | 0 | 0 | |
| No | 13 | 13 | |
| Consumption of pickled food | 1 | ||
| Yes | 0 | 0 | |
| No | 13 | 13 | |
| Distance from incisor (cm) | 29.4 ± 4.0 | 30.58 ± 4.5 | 0.478 |
16S rRNA gene sequencing was conducted on fecal samples to evaluate the impact of probiotics on postoperative patients with esophageal cancer. After denoising, high-quality operational taxonomic units (OTUs) were identified and visualized in a Venn diagram, as shown in Figure 1A. The results revealed 93 unique OTUs for samples in the NC group, 22 unique OTUs for samples in the PRO group, and 330 shared OTUs among samples from both the groups (Figure 1A). Taxonomic annotation was subsequently performed. At the phylum level, both groups were predominantly composed of Firmicutes, Proteobacteria, Bacteroidetes, and Actinobacteria; Firmicutes was the most abundant, accounting for 73.08% and 78.32% of the microbiota in the samples collected from patients in the PRO and NC groups, respectively (Figure 1B). As shown in Figure 1C, at the genus level, while the dominant species were the same in samples acquired from patients in both the groups, their proportions differed. The abundances of Enterococcus, Escherichia-Shigella, Blautia, Parvimonas, Faeca
To delve deeper into the intestinal microbiota variations among patients in the PRO and NC groups, we employed LEfSe to pinpoint distinct bacterial phenotypes across phylogenetic levels. The criteria for significant differences were set at P < 0.05 and an LDA score exceeding 2.0 in the screening rank-sum test. As shown in Figure 2A and B, 18 bacteria were identified, which were categorized into two groups: 15 specific bacteria in the NC group and 3 in the PRO group, including Clostridium innocuum and Bifidobacterium. Then, the Wilcoxon rank-sum test was used to compare the characteristic abundance of bacteria in the samples collected from patients in the two groups at the genus level. Clostridium innocuum was more abundant in samples acquired from patients in the PRO group than in those acquired from patients in the NC group, while the abundances of CAG-352, Olsenella, Prevotella_9, Peptoniphilus, Pyramidobacter, and Dialister were significantly lower in samples acquired from patients in the PRO group (Figure 2C). In addition, PICRUSt2 and LEfSe tools were used to predict the potential functions of the distinct gut microbiota, revealing 16 notably enriched KEGG metabolic pathways (Figure 2D). Eight pathways were activated in patients in the PRO group, including fructose and mannose metabolism, pentose and glucuronate interconversions, ascorbate and aldarate metabolism, phosphonate and phosphinate metabolism, starch and sucrose metabolism, mismatch repair, chlorocyclohexane and chlorobenzene degradation, and selenocompound metabolism. Most of these pathways are related to carbohydrate metabolism. In addition, eight pathways - namely, D-arginine and D-ornithine metabolism; drug metabolism-other enzymes; histidine metabolism; chloroalkane and chloroalkene degradation; the citrate cycle (tricarboxylic acid cycle); glycine, serine, and threonine metabolism; linoleic acid metabolism; and inositol phosphate metabolism - were notably inhibited in patients in the PRO group.
Fecal samples were analyzed by LC-MS/MS to evaluate metabolic status. In the samples obtained from patients in the two groups, 418 and 358 differentially abundant primary metabolites were identified under positive and negative ion modes, respectively (Figure 3A and B). Based on these findings, OPLS-DA was used to distinguish the PRO group from the NC group (Figure 3C and D), with permutation tests confirming the reliability of these results (Figure 3E and F). In addition, when P < 0.05 and VIP > 1.0 were applied, 109 differentially abundant second-level (MS/MS, MS2) metabolites were detected in samples obtained from patients in the two groups (Figure 4A). Among the metabolites, 19 were upregulated and 90 were downregulated in samples obtained from patients in the PRO group compared with those obtained from patients in the NC group. The Z score map reveals the relative content of the differentially abundant metabolites in the samples obtained from patients in the two groups.
Compared with those obtained from patients in the control group, the relative contents of 2,5-dihydro-2,4,5-trimethyloxazole, beta-D-Glcp-(1-4)-alpha-L-Rhap-(1-3)-D-Glcp, and dehydroferreirin were the highest in samples obtained from patients in the PRO group (Supplementary Figure 1). The correlations between the various pairs of metabolites were determined by calculating the Pearson correlation coefficient for each pair of metabolites. The results revealed that most metabolites were significantly positively correlated with each other (Supplementary Figure 2). To analyze the metabolic pathways associated with the differentially abundant metabolites between the two groups, the MetaboAnalyst database was utilized. A total of 109 differentially abundant metabolites were assigned to 49 distinct KEGG metabolic pathways. The findings indicated that 3 pathways identified in samples obtained from patients in the PRO group were notably activated, including aldosterone synthesis and secretion and cortisol synthesis and secretion. Seventeen pathways were significantly inhibited, mainly related to amino acid metabolism (Figure 4B).
Furthermore, we identified 12 differential bacteria via the Wilcoxon rank-sum test with a significance threshold of P < 0.05 between the PRO and NC groups at the genus level. In addition, we obtained the top 20 differentially abundant metabo
Probiotic adjuvant therapy has been demonstrated to be clinically effective in treating various conditions, such as irritable bowel syndrome[22] and cancer[15,23]. The gut microbiota plays a crucial role in human health and immune function. Disruptions in the gut microbial balance, known as gut dysbiosis, can contribute to multiple diseases. One potential way in which probiotics enhance treatment outcomes is by modulating the gut microbiota[24]. Whether probiotics are beneficial for postoperative patients with esophageal cancer is not fully understood. In this study, we employed microbiological and metabolomic approaches to investigate alterations in the gut microbiota structure and fecal metabolites among patients with ESCC after surgery. Additionally, we examined the function and variations of the gut microbiota and metabolites in relation to the administration of probiotic therapy.
Our results revealed that probiotic treatment did not significantly alter alpha diversity indices, such as the Shannon and Simpson indices, but did reduce the observed species, Chao1, and ACE indices, which was accompanied by distinct beta diversity separation between the PRO and NC groups. These findings suggest that probiotics may not simply increase overall microbial richness but rather induce a compositional shift toward a specific functional configuration. LEfSe and Wilcoxon rank-sum tests revealed that Clostridium innocuum and Bifidobacterium were significantly enriched in patients in the PRO group. Bifidobacterium is a well-recognized commensal with immunomodulatory properties that promotes regulatory T-cell differentiation and reduces intestinal inflammation[25]. Importantly, Bifidobacterium has been demonstrated to increase the efficacy of anticancer therapies and reduce chemotherapy-induced toxicity[26]. In contrast, Clostridium innocuum, which is generally considered a commensal with low pathogenic potential[27], is not a conventional probiotic. Its increased abundance in patients in the PRO group may reflect a probiotic-induced ecological niche shift that favors certain commensals. Conversely, potentially harmful bacteria such as Eggerthella lenta, which are associated with anaerobic bloodstream infections and high mortality[28], were reduced in patients in the PRO group. The reduction in the levels of such pathobionts, in addition to the enrichment of beneficial commensals, is likely beneficial for postoperative recovery[29].
PICRUSt2-based functional prediction revealed that receiving probiotics activated eight KEGG pathways, most of which are involved in carbohydrate metabolism, including fructose and mannose metabolism, pentose and glucuronate interconversions, ascorbate and aldarate metabolism, and starch and sucrose metabolism. Activation of these pathways is consistent with the observed increase in carbohydrate-related metabolites [e.g., beta-D-Glcp-(1-4)-alpha-L-Rhap-(1-3)-D-Glcp] in patients in the PRO group. Carbohydrates serve not only as carbon sources for microbial growth but also as adhesion motifs that enhance host-microbe interactions, potentially strengthening gut barrier function[30]. Furthermore, ascorbate (vitamin C) and aldarate metabolism, as well as selenocompound metabolism, were activated in patients in the PRO group. Ascorbate metabolism has been linked to reduced fatigue in patients with cancer[31], and selenium compounds possess antioxidant properties that help maintain microbial balance and reduce oxidative stress[32]. The elevation of flavonoids such as yatein and luteolinidin in patients in the PRO group is also noteworthy, as flavonoids are known to exert anticancer effects partly by modulating the gut microbiota[33].
It is important that we also need to address the issue of inhibition of the citric acid cycle (tricarboxylic acid cycle) in the bodies of patients in the PRO group. At the same time, we need to activate the upstream carbohydrate pathways and inhibit the tricarboxylic acid cycle. This approach may seem contradictory at first glance. However, this pattern may reflect the reprogramming of microbial metabolism in response to probiotic intervention. A reasonable explanation is that probiotics and their metabolites (such as short-chain fatty acids) may change the redox environment or substrate availability in the gut[34,35], causing the microbial energy metabolism to shift from complete oxidation (tricarboxylic acid cycle) to fermentation pathways that produce short-chain fatty acids (such as butyric acid and acetic acid). This transformation has been proven to be beneficial for intestinal health and systemic immunity[36]. In fact, the activated starch and sucrose metabolic pathways are usually combined with glycolytic fermentation. Therefore, inhibiting the tricarboxylic acid cycle may not be a harmful effect, but rather a functional shift to produce immunomodulatory metabolites[37]. This hypothesis requires further investigation through metatranscriptomics or targeted metabolomics for short-chain fatty acids.
We acknowledge that this study still has some significant limitations. Firstly, the clinical sample size is relatively small (13 patients in each group), which limits the statistical power and increases the risks of false positive and false negative results. This further restricts the general applicability of our conclusions. Therefore, our results should be regarded as preliminary and exploratory. Secondly, the enteral nutrition protocol did not use a placebo control (i.e., a control group receiving an equal amount of normal saline), which cannot completely rule out the non-specific effects of additional fluid supplementation or treatment. Thirdly, our functional predictions are based on 16S rRNA and PICRUSt2 analysis, which can only provide indirect inferences rather than direct measurements of microbial gene expression or metabolite flux. Future research needs to adopt methods such as metagenomics, transcriptomics, and targeted metabolomics to verify these findings. Fourthly, the single-center design and the specific probiotic formula used may limit its promotion in other environments or with different probiotic strains. Finally, observational correlation analysis cannot determine causality and requires animal experiments and in vitro mechanism studies to confirm the proposed pathways.
In conclusion, we conducted an analysis of fecal samples from patients with ESCC after surgery by combining micro
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