
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-24-03980
00056
10.1097/MD.0000000000039596
3
5600
Research Article
Observational Study
Analysis of the correlation between gut microbiome imbalance and the development of endometrial cancer based on metagenomics
Xing Wenying MM wenyingdrxing@21cn.com
a
Yu Jie MM jiedryu@21cn.com
b
https://orcid.org/0009-0009-9710-3455
Cui Shihong MM a*
Liu Ling MD lingdrliu@21cn.com
a
Zhi Yunxiao MM yunxiaommzhi@21cn.com
a
Zhang Ting MM tingmmzhang@21cn.com
a
Zhou Junjie MM junjiemmzhou@21cn.com
a
a Department of Gynaecology and Obstetrics, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
b Department of Gynaecology and Obstetrics, Hefei Hospital Affiliated to Anhui Medical University, Anhui, China.
* Correspondence: Shihong Cui, Department of Gynecology and Obstetrics, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China (e-mail: shihongmmcui@outlook.com).
13 9 2024
13 9 2024
103 37 e3959612 4 2024
16 5 2024
15 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Endometrial cancer (EC) is the most prevalent gynecologic malignancy, with a higher risk in obese women, suggesting the potential involvement of gut microbiota in the progression of EC. However, there is no direct evidence of a connection between EC and the human gut microbiota. Using metagenomic sequencing, we investigated the relationship between gut microbiome imbalance and cancer development in patients with EC. In this prospective case–control study, we included 15 patients with EC based on endometrial biopsy in the case group and 15 women admitted to the hospital for female pelvic floor issues during the same time who did not have endometrial lesions from January 2023 to June 2023 in control group. The microbiota structure of EC cases and controls without benign or malignant endometrial lesions during the same time period was analyzed using metagenomic sequencing technology. We employed Alpha diversity analysis to reflect the richness and diversity of microbial communities. Statistical algorithm Bray-Curtis was utilized to calculate pairwise distances between samples, obtaining a beta diversity distance matrix. Subsequently, hierarchical clustering analysis was conducted based on the distance matrix. The results showed that the composition of bacterial colonies in both groups was dominated by Firmicutes, which had a higher proportion in the control group, followed by Bacteroidetes in the control group and Proteobacteria and Bacteroidetes in the case group. The abundance of Klebsiella (P = .02) was significantly higher, and the abundance of Alistipes (P = .04), Anearobutyricum (P = .01), and bacteria in Firmicutes such as Oscillospira and Catenibacterium was markedly lower in the case group than in the control group. These results demonstrated conclusively that a gut microbiome imbalance was associated with the development of EC.

Alistipes
Anearobutyricum
endometrial cancer
intestinal microecology
Klebsiella
metagenomics
OPEN-ACCESSTRUE
==== Body
pmc1. Introduction

Endometrioid cancer (EC; also referred to as corpus uterine cancer) is a malignant epithelial tumor that originates in the endometrium. It is 1 of the 3 most common malignant tumors of the female reproductive tract and primarily affects perimenopausal and postmenopausal women. The incidence of EC has steadily increased over the past 2 decades and has become more prevalent among young women due to rising life expectancy, obesity rates, and changes in lifestyle.[1] In China, EC ranks second among malignant tumors of the female reproductive system, while in Western countries, its incidence ranks first among malignant tumors of the female reproductive system. EC is classified into 2 types based on its pathogenesis and characteristics of biological behavior: estrogen-dependent (type I) and nonestrogen-dependent (type II).[2] Endometrioid adenocarcinoma is the most malignant form of estrogen-dependent EC, whereas mucinous adenocarcinoma is relatively uncommon. The pathologic types of nonestrogen-dependent EC include serous carcinoma, clear cell carcinoma, and carcinosarcoma. The majority of EC is type I. The occurrence of type I EC is a direct result of the continuous stimulation of estrogen in the absence of progesterone antagonism. Specifically, prolonged endometrial proliferation in the absence of progesterone antagonism leads to the development of EC.[3] Conversely, the mechanism of type II EC remains incompletely understood.

Reproductive endocrine disorders, such as anovulatory menstrual abnormalities, anovulatory infertility, and polycystic ovary syndrome (PCOS), are common risk factors for the development of EC. Due to the absence of cyclic ovulation, the endometrium lacks progesterone antagonism, and the prolonged action of a single estrogen can result in endometrial hyperplasia and even cancer.[4] The triad of EC is obesity, diabetes, and hypertension. According to reports, the relative risk of EC increases by 9% for each unit increase in body mass index (BMI; kg/m2). The risk of EC is increased about 1.6 times in women with a BMI between 30 and 35 and 3.7 times in women with a BMI > 35 when compared with women with a BMI < 25. Patients with diabetes or those with impaired glucose tolerance have a 2.8-fold increased risk of EC, while patients with hypertension have a 1.8-fold increased risk compared to normal individuals. In addition, risk factors for EC include early menarche, late menopause, exogenous estrogen supplementation, and genetic factors. There is currently no recommended routine screening method for EC, and ultrasound screening is optional. Monitoring endometrial thickness and abnormalities through transvaginal or transabdominal ultrasound is the primary screening method for EC. In hematology, there are no specific serum markers for EC, so there are no routine monitoring and screening indicators for EC.

A metagenome is the sum of the genetic material of all organisms present in a specific environment.[5] Metagenomic sequencing is revolutionizing the detection and characterization of microbial species, and these data can be analyzed using a wide range of software and tools.[6] In this study, genomic DNA was extracted from fecal samples and subjected to high-throughput sequencing in order to analyze the relationship between microbial diversity, community structure, functional information, and the environment. Gut microbiotas are dynamic and complex entities with huge numbers. In addition, the total number of microorganisms parasitized in the human intestine is as high as 1014 levels, and the gut microbiota of healthy populations consists primarily of Gram-negative bacilli such as Bacteroidetes, Firmicutes, and Proteobacteria. The development of second-generation sequencing technology has enabled the possibility of studying the vast quantity of microbial data in the intestine. The effects of gut microbiome imbalance extend beyond the gastrointestinal tract. It has been reported that gut microbiome imbalance can alter the intestinal barrier and microbiota, thereby influencing estrogen metabolism, chronic inflammation, obesity, and tumorigenesis.[7,8]

The intestinal microecosystem is composed of the normal gut microbiota and the environment in which it resides, with the normal gut microbiota forming the core part; 78% of all microorganisms in the human body are found in the digestive system. One of the most notable features of the gut microbiome is its stability, and its imbalance can contribute to the development of a variety of intestinal and extraintestinal diseases. In recent years, evidence has accumulated that an altered composition of the gut microbiota is associated with the development of malignant tumors and other diseases,[9,10] such as inflammatory bowel disease, hypertension, PCOS, obesity, diabetes, colorectal cancer, and breast cancer. PCOS,[11] obesity, insulin resistance, hypertension, high estrogen levels, and age have been reported to be associated with the occurrence of EC. Disruption of gut microbiome homeostasis promotes the development of obesity, hypertension, PCOS, and elevated estrogen levels, indicating a link between gut microbiota and EC. Micrococcus was associated with dysregulation of the endometrial microbiota and inflammatory cytokines in patients with EC, according to a study on the composition of the endometrial microbiota and its relationship with inflammatory cytokines in EC.[12]

Analyzing the relationship between gut microbiome imbalance and the development of EC, we compared gut microbiota in fecal samples from patients and controls using metagenomic sequencing in this study.

2. Participants and methods

In this prospective case–control study, we included 15 patients with EC based on endometrial biopsy is the case group and 15 women admitted to the hospital for female pelvic floor issues during the same time who did not have endometrial lesions from January 2023 to June 2023 in the control group. Clinical data of participants in the 2 groups were collected, stool specimens were retained, and fecal samples from both groups were obtained and sequenced using metagenomic sequencing technology. To analyze the changes in the structure, abundance, and diversity of the gut microbiota, operational taxonomic unit clustering and species annotation were applied to the sequences.

2.1. Participants

2.1.1. Inclusion criteria

The inclusion criteria for the case group were as follows: cases meeting the diagnostic criteria for EC according to the Endometrial Cancer Diagnostic Guidelines, 2022 edition.

The inclusion criteria for the control group were as follows: women who were admitted to the hospital for female pelvic floor issues during the same time period and did not have complications of benign or malignant endometrial lesions; women aged 18 to 75 years; women with no history of smoking or alcohol abuse; women who had at least 6 years of education, agreed to participate in the study, and were able to complete relevant tests and examinations.

The exclusion criteria were as follows: women with a history of antibiotic treatment or probiotic use during the study or within 1 month prior to inclusion; women with severe liver or kidney disease, connective tissue disease, metabolic disease, and cardiac disease; women with severe psychiatric disorders; women with a history of drug or alcohol abuse.

2.2. Sample collection

Samples of blood were collected. Specifically, 5 mL of blood samples were collected from participants in the 2 groups, with whole blood collected in ethylenediamine tetraacetic acid-anticoagulant tubes and plasma separated immediately after. Blood samples were centrifuged at 3000 rpm for 10 minutes at room temperature, and the upper layer was collected, placed into 1.5 mL centrifuge tubes (0.2 mL/tube), and frozen at −80 °C.

Fecal samples were obtained. Participants opened the sampling package, wore disposable gloves, and used the small spoon affixed to the sampling tube to collect more than half a tube of feces. The feces were sent to the laboratory at a low temperature and then stored in a refrigerator with a temperature of −80 °C.

2.3. Detection method

After thawing fecal samples at −80 °C to room temperature, 0.1 g of feces was weighed, added to 0.9 mL of ultrapure water, thoroughly mixed, and stored overnight at 4 °C. The samples were centrifuged at 4 °C and 10,000×g for 30 minutes. The supernatant (1 mL) was mixed with 1 mL of ethanol, incubated at 4 °C for 60 minutes, and centrifuged at 4 °C and 10,000×g for 20 minutes. The supernatant was nitrogen-blown to remove ethanol, 50 mL of water was added, and then it was filtered through a 0.22 μm filter membrane for detection.

2.3.1. Sequencing experiment flow

Flow chart: DNA extraction from the samples, fragmentation of about 400 bp, construction of a paired-end (PE) library-bridge polymerase chain reaction (PCR), and sequencing.

Flow introduction:

DNA extraction from environmental samples: The genomic DNA was detected using 1% agarose gel electrophoresis after being extracted.

Fragmentation of approximately 400 bp: The instrument used was a Covaris M220.

Construction of a PE library:

The joint was connected.

Self-connecting fragments of the joint were removed using magnetic beads.

PCR amplification was conducted for the enrichment of library templates.

The PCR products were recovered using magnetic beads to obtain the final library.

The NEXTFLEX Rapid DNA-Seq Kit (Majorbio, Shanghai, China) was utilized.

2.3.2. Bridge PCR and sequencing

One end of the library molecule was complementary to the primer bases, and the template information was immobilized on the chip after a round of amplification.

The other end of the immobilized molecule on the chip was randomly complementary to the neighboring immobilized primer, forming a “bridge.”

PCR amplification produced DNA clusters.

The DNA amplicon was linearized into a single strand.

Modified DNA polymerase and deoxy-ribonucleoside triphosphate containing 4 fluorescent labels were added, and only one base per cycle was synthesized.

The surface of the reaction plate was scanned with a laser to determine the types of nucleotides that were polymerized during the initial round of reactions for each template sequence.

Chemical cleavage of the “fluorescent group” and the “termination group” reinstated the 3’ end adhesion, and the polymerization of the second nucleotide continued.

The sequence of the template DNA fragment was obtained by counting the fluorescence signals collected in each round.

NovaSeq Reagent Kits and HiSeq X Reagent Kits (Majorbio, Shanghai, China) were utilized.

2.3.3. Bioinformatic analysis process

Data analysis started with the downlinking of the original sequence. The unprocessed sequences were initially optimized through splitting, quality shearing, and contamination removal. The optimized sequences were then used for splicing assembly and gene prediction, and the obtained genes were annotated and classified according to species and function using nonredundant proteins, evolutionary genealogy of genes, nonsupervised Orthologous Groups, and the Kyoto Encyclopedia of Genes and Genomes. Based on the preceding analysis, multidirectional statistical analyses and explorations were conducted, including similar clustering, grouping, sorting, and difference comparison, and the results were visualized and displayed in order to extract the effective information from the data, reveal the hidden regular patterns, verify the experimental hypotheses, and identify new problems.

We employed Alpha diversity analysis to reflect the richness and diversity of microbial communities. Statistical algorithm Bray-Curtis was utilized to calculate pairwise distances between samples, obtaining a beta diversity distance matrix. Subsequently, hierarchical clustering analysis was conducted based on the distance matrix.

3. Results

Participants in both groups were menopausal and had not undergone hormone replacement therapy or had metabolic diseases such as diabetes. Age, BMI, alanine transaminase, aspartate transaminase, alkaline phosphatase, urea, creatinine, and uric acid were compared between the 2 groups using the 2 independent samples t-test. There was no statistically significant difference between the results (Table 1).

Table 1 Comparison of age, body mass index, alanine transaminase, aspartate transaminase, alkaline phosphatase, urea, creatinine, and uric acid between the 2 groups.

	Age (yr)	BMI	ALT (U/L)	AST (U/L)	ALP (U/L)	Albumin (g/L)	Urea (μmol/L)	Creatinine (μmol/L)	Uric acid (μmol/L)	
Case group	56.20 ± 8.56	26.84 ± 4.83	23.60 ± 15.32	28.07 ± 14.52	78.24 ± 21.56	40.87 ± 5.28	4.94 ± 1.71	55.88 ± 9.31	299.25 ± 64.13	
Control group	54.60 ± 4.50	27.83 ± 4.39	23.03 ± 12.74	25.68 ± 14.50	75.72 ± 20.18	42.92 ± 3.36	4.87 ± 1.31	53.12 ± 6.41	287.91 ± 74.88	
t value	0.523	−0.480	0.09	0.368	0.270	−1.034	0.104	0.774	0.364	
P value	.340	.675	.963	.997	.985	.231	.503	.184	.221	
ALP = alkaline phosphatase, ALT = alanine aminotransferase, AST = aspartate aminotransferase, BMI = body mass index.

At the phylum level, the composition of bacterial colonies was dominated by Firmicutes in both groups, but the proportion was higher in the control group than in the case group (59.6% vs 44.8%). Second in the control group was Bacteroidetes (29.1%). In the case group, Proteobacteria was second (28.8%), followed by Bacteroidetes (19.3%). The proportion of Proteobacteria was significantly higher in the control group than in the case group (29.1% vs 19.3%) (Figs. 1 and 2).

Figure 1. Analysis of the composition of communities in the 2 groups. The horizontal coordinates represent the identity of the sample, while the vertical coordinates represent the proportion of species within the sample. Different colored bars represent different species, and the length of the bar represents the proportion of the species.

Figure 2. Heatmap of species clustering at the phylum level. The lower and right sides of the heatmap contain the sample name and species name, respectively, and the shade of color in the heatmap represents the abundance of the species.

The comparison of gut microbiota between the 2 groups revealed that 68% of the microbiotas were identical, 13% were exclusive to the case group, and 13% were unique to the control group. Differential bacteria between the 2 groups were mainly concentrated in Alistipes and Klebsiella (Fig. 3).

Figure 3. Composition of microbiota between the 2 groups. The distribution of each species within a given labeled grouping is displayed. Different colors indicate different species, and the pie chart area represents the percentage of that species among all species.

At the genus level, Alistipes in Bacteroidetes, Klebsiella in Proteobacteria, and Anearobutyricum in Firmicutes showed the biggest differences between the 2 groups. The abundance of Klebsiella was substantially higher in the case group than in the control group (P = .02), whereas the abundance of Alistipes (P = .04) and Anearobutyricum (P = .01) was significantly lower in the case group. In the case group, the number of other species such as Oscillospira and Catenibacterium in Firmicutes was markedly reduced (Fig. 4).

Figure 4. Comparison of gut microbiota between 2 groups at the genus level. Vertical coordinates denote species names at various levels of classification, whereas horizontal coordinates represent the percentage value of the abundance of a species for that sample. Different colors indicate different groups. *.01 < P ≤ .05; **.001 < P ≤ .01; ***P ≤ .001.

4. Discussion

It has been reported that a normal gut microbiome plays an important role in maintaining human function and the stability of the intestinal environment. Gut microbiotas promote vitamin synthesis, contribute to the metabolism of endogenous proteins, and facilitate the digestion and absorption of nutrients. In addition to the aforementioned risk factors, dietary habits, exercise, alcohol consumption, and smoking all influence the development of EC. Numerous studies have demonstrated a connection between gut microbiota, obesity, and cancer.[13] A large number of symbiotic bacteria inhabit the intestinal tract and are vital for maintaining normal intestinal function and regulating host immunity and metabolism. Numerous physiological processes within the gastrointestinal tract and at distant tissue sites, including cancer, are regulated by the intestine. The correlation of the host microbiota with cancer and antitumor immune responses has been investigated.[14] There are complex interactions between the gut microbiota, the intestinal epithelium, and the local mucosal immune system. In addition to the local mucosal immune response in the intestine, it is becoming increasingly apparent that gut microbiota influences systemic immunity.[15] As intestinal microecology has been studied more thoroughly, cancer immunotherapy has emerged as a promising treatment option for patients with cancer. The microbiota influences the efficacy of cancer immunotherapy, especially immune checkpoint inhibitors, according to accumulating evidence.[16] Gut microbiota has been observed to be associated with the development of ocular diseases, pulmonary diseases, and psychiatric disorders as intestinal microecological research has progressed. The results of an earlier study revealed that gut microbiota and their metabolites can alter the immune homeostasis of the body or regulate a variety of metabolic pathways under different circumstances to induce ocular autoimmune responses and promote chronic inflammation.[17] Short-chain fatty acids, bile acids, and urotoxin, which are metabolites of the gut microbiota, may be implicated in the development of heart failure.[18] However, the relationship between gut microbiota and the development of EC in humans has not been adequately investigated.

Gut microbiotas are significantly affected by geography and diet, and healthy individuals possess normal microbiota. The life cycle of the gut microbiota varies depending on geographic location and diet.[19] In the present study, neither group had a history of smoking or alcohol abuse, and all participants had lived in the central plains for an extended period of time. In this context, the conclusion about the differences in intestinal microecology was more meaningful when as many confounding variables as possible were eliminated. Our results revealed that gut microbiotas are associated with the development of EC or that gut microbiotas were involved in the development of EC, providing new ideas for the diagnosis and treatment of EC.

Comparing the intestinal microecology of patients with EC and healthy controls of the same age revealed an imbalance. Firmicutes dominated gut microbiota in both groups, the proportion of Firmicutes was significantly higher in the control group. The majority of Firmicutes, such as Lactobacillus, are probiotics. In addition to Lactobacillus, other probiotic members of the Firmicutes, including Coprobacillus, Eubacterium, and Lactobacillus reuteri, are known for their production of butyrate. Anearobutyricum in Firmicutes was statistically significantly different between the 2 groups. Proteobacteria was second only to Firmicutes in the case group. Proteobacteria include many pathogenic bacteria, such as Escherichia coli, Escherichia, Salmonella, Vibrio, Helicobacterium, Shigella, Pseudomonas aeruginosa, Vibrio cholerae, Yersinia pestis, Neisseria meningitidis, Neisseria gonorrhoeae, Campylobacter jejuni, Helicobacter pylori, and other well-known genera. The Proteobacteria class includes the genus Klebsiella, which in our study differed significantly between the 2 groups. Klebsiella parasitica in the animal respiratory tract or intestinal tract, as a conditional pathogen, is one of the pathogens causing human pneumonia. It is highly pathogenic to humans and animals and can cause pneumonia, metritis, mastitis, and other suppurative inflammation in humans and animals, and even septicemia. The correlation between Klebsiella and cancer has not been reported in the past, and the sample size can be expanded to observe the relationship between both.

The gut microbiome is affected by a variety of factors, such as geographic differences, dietary differences, exercise, and antibacterial medications. Due to the complexity of the ecological characteristics of the gut microbiota, its function and structure remain unclear.

5. Conclusion

Our findings revealed that a gut microbiome imbalance was associated with the development of EC. Although the development of metagenomic sequencing technology has allowed for a greater and more comprehensive understanding of gut microbiota, its function and structure remain largely unknown due to its complex ecological characteristics. There is a need for more in-depth and specific research into the specific genera and mechanisms involved in the development of EC. Follow-up investigations can be combined with metagenomics, metabolomics, and proteomics to search for EC-related biomarkers and identify new targets for clinical treatment. Modulation of the microbiota through probiotics or microbiota transplantation may enhance responsiveness to cancer treatment and quality of life and may even reduce tumor incidence.

Author contributions

Conceptualization: Wenying Xing, Jie Yu, Shihong Cui, Ling Liu.

Data curation: Wenying Xing, Jie Yu, Ting Zhang.

Formal analysis: Wenying Xing, Jie Yu, Shihong Cui, Ling Liu, Yunxiao Zhi, Junjie Zhou.

Writing – original draft: Wenying Xing, Ting Zhang.

Writing – review & editing: Wenying Xing, Junjie Zhou.

Abbreviations:

ALP alkaline phosphatase

ALT alanine aminotransferase

AST aspartate aminotransferase

BMI body mass index

EC endometrioid carcinoma

PCOS polycystic ovarian syndrome.

Written informed consent was obtained from all participants.

This study was conducted with approval from the Ethics Committee of The Third Affiliated Hospital of Zhengzhou University (No.2024-143-01). This study was conducted in accordance with the declaration of Helsinki.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

How to cite this article: Xing W, Yu J, Cui S, Liu L, Zhi Y, Zhang T, Zhou J. Analysis of the correlation between gut microbiome imbalance and the development of endometrial cancer based on metagenomics. Medicine 2024;103:37(e39596).

WX and JY contributed equally to this study.
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