A perimenopausal woman endometrial polyp malignant transformation risk assessment method and system based on an endometrial microorganism-metabolic axis and a storage medium

Abstract

The present application relates to the field of gynecological disease risk assessment and multi-group biological information analysis, and discloses a perimenopausal female endometrial polyp malignant transformation risk assessment method and system based on endometrial microorganism-metabolic axis and a storage medium. The method obtains perimenopausal female uterine cavity content / endometrial tissue samples, obtains microorganism characteristics through 16S rRNA sequencing, and obtains metabolite characteristics through LC MS metabonomics; a malignant transformation risk prediction model is constructed in combination with clinical epidemiological variables, and risk stratification results are output, which are used for early identification, risk stratification and clinical decision support of perimenopausal endometrial polyp malignant transformation. The present application improves prediction efficiency through combined modeling of microbiome and metabolome, reduces the risk of misjudgment caused by the instability of a single index, and has the advantages of non-invasive / minimally invasive sampling, repeatability, scalability and ease of clinical transformation.

Description

Technical Field

[0001] This invention belongs to the field of early screening and risk assessment technology for obstetric and gynecological diseases, specifically involving a method, system and storage medium for assessing the risk of malignant transformation of endometrial polyps in perimenopausal women based on the endometrial microbiome-metabolic axis. Background Technology

[0002] Endometrial polyps are a common gynecological condition. Some patients may experience abnormal uterine bleeding, infertility, and other symptoms, and there is a certain risk of malignant transformation. Perimenopausal women have an increased risk of endometrial lesions and malignant transformation due to hormonal fluctuations and metabolic changes. Current clinical assessments of the malignant risk of endometrial polyps rely heavily on imaging, pathology, and traditional risk factors (such as age, obesity, diabetes, tamoxifen, and HRT). However, in the specific perimenopausal population, the predictive power of a single factor is limited.

[0003] Existing research suggests that dysbiosis of the uterine cavity / endometrial microbiota can trigger a chronic inflammatory microenvironment, potentially promoting polyp formation and malignant progression. Simultaneously, microbial-related metabolites can serve as functional "intermediate phenotypes" in disease development. Currently, there is a lack of a transferable risk assessment protocol that combines the endometrial microbiome and metabolome and is tailored to the perimenopausal population. Summary of the Invention

[0004] This invention provides a method for assessing the risk of malignant transformation of endometrial endothelial cells (EPs) in perimenopausal women based on the endometrial microbiota-metabolic axis, as well as a system / device / storage medium for implementing this method. This method enables early identification, early stratification, and early intervention of malignant transformation of EPs in perimenopausal women, thereby improving prediction accuracy and clinical usability.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for assessing the risk of malignant transformation of endometrial polyps in perimenopausal women based on the endometrial microbiome-metabolic axis includes the following steps:

[0007] Obtain uterine cavity samples from perimenopausal women;

[0008] The uterine cavity sample was subjected to 16S rRNA sequencing to obtain microbial characteristic data, which included at least the relative abundance of Lactobacillus genus;

[0009] The uterine cavity sample was subjected to liquid chromatography-mass spectrometry to obtain metabolite characteristic data, wherein the metabolite characteristics included at least the concentration of prostaglandin E2;

[0010] Obtain clinical variable data of the perimenopausal women, wherein the clinical variables include at least abnormal uterine bleeding;

[0011] The relative abundance of Lactobacillus, the concentration of prostaglandin E2, and the occurrence of abnormal uterine bleeding were used as feature variables and input into a pre-constructed Logistic regression model to output the probability of malignant transformation of endometrial polyps.

[0012] In some embodiments, the uterine cavity sample is the uterine cavity contents aspirated using a disposable uterine aspiration tube during hysteroscopic surgery, before the removal of lesion tissue.

[0013] In some embodiments, the 16S rRNA sequencing targets the V4-V5 region and is performed by PCR amplification using primers 515F and 907R.

[0014] In some embodiments, the metabolite characterization data may further include the concentrations of leukotriene B4 and / or glutathione.

[0015] In some embodiments, the clinical variable data may also include one or more of age, body mass index, history of diabetes, and polyp diameter.

[0016] In some embodiments, the formula for calculating the risk probability of the Logistic regression model is:

[0017] ;

[0018] Where σ is the Sigmoid function, For the intercept term, , , These are the weighting coefficients.

[0019] In some embodiments, the method further includes a step of stratifying the probability of malignant transformation based on a preset threshold, wherein a high risk is determined when P ≥ 0.35 and a low risk is determined when P < 0.35, thus dividing the subjects into a high-risk group and a low-risk group.

[0020] A system for assessing the risk of malignant transformation of endometrial polyps in perimenopausal women based on the endometrial microbiota-metabolic axis for implementing the method, characterized in that it comprises:

[0021] The sample acquisition module is used to acquire uterine cavity samples from perimenopausal women.

[0022] The data acquisition module is used to receive microbial characteristic data, metabolite characteristic data, and clinical variable data;

[0023] The risk assessment module contains the aforementioned Logistic regression model, which is used to output the probability of malignant transformation.

[0024] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for assessing the risk of malignant transformation of endometrial polyps in perimenopausal women based on the endometrial microbiome-metabolic axis.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. Combining endometrial microbiome and metabolome for the risk assessment of malignant transformation of endometrial epithelial cells (EPs) during perimenopause improves predictive efficacy and robustness;

[0027] 2. Risk alerts can be obtained in a short process before or during the operation, supporting tiered management (such as follow-up frequency, whether to strengthen pathological evaluation, and whether to take more aggressive measures).

[0028] 3. By leveraging the three-dimensional characteristics of "microbial metabolite clinical variables," interpretability is enhanced, facilitating the development of clinically feasible strategies;

[0029] 4. Applicable to real-world clinical cohorts, scalable to reagent kits / software systems / testing workflows, and has translational potential. Attached Figure Description

[0030] Figure 1 ROC curve for a model predicting the risk of malignant transformation of perimenopausal epithelial cells (EPs). Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention is further described below with reference to specific embodiments. Unless otherwise described in detail, the technical means used in the following embodiments are all conventional means well known to those skilled in the art, or are performed according to the kit and product instructions. Unless otherwise specified, the materials and reagents used in the following embodiments are commercially available.

[0032] Example 1

[0033] 1. Collection and preprocessing of uterine cavity samples from perimenopausal women

[0034] 1.1 Experimental Subjects

[0035] One hundred and twenty perimenopausal patients with endometrial polyps (EPs) who visited the Department of Gynecology at Yangzhou Maternal and Child Health Hospital between March 2023 and March 2025 were selected. All patients underwent hysteroscopy or surgical treatment, and postoperative pathology confirmed that they were either benign EPs or endometrial cancer (EC). Based on the pathological results, they were divided into two groups: a benign EP group of 100 patients, aged 42–54 (48.6±3.2) years; and an endometrial cancer (EC) group of 20 patients, aged 45–55 (50.2±2.8) years.

[0036] Inclusion criteria: Diagnostic criteria for EPs.

[0037] Exclusion criteria: Those who have received antibiotic treatment, hormone replacement therapy, or immunosuppressive therapy within the past 3 months.

[0038] This experiment was approved by the Ethics Committee of Yangzhou Maternal and Child Health Hospital (approval number: YZFY-2023-013), and all patients signed informed consent forms.

[0039] 1.2 Clinical Data Collection

[0040] Clinical data of the two groups of patients were collected and organized through the hospital's electronic medical record system, including:

[0041] (1) General information: age, height, weight, body mass index (BMI);

[0042] (2) Menstrual and reproductive history: age at menarche, parity, number of deliveries, number of induced abortions, menopausal status, and intrauterine device (IUD) usage;

[0043] (3) Relevant medication history: oral contraceptives, tamoxifen, etc.;

[0044] (4) Comorbidities: hypertension, diabetes, endometriosis, etc.;

[0045] (5) Clinical manifestations: abnormal uterine bleeding (such as increased menstrual flow, irregular bleeding), history of infertility;

[0046] (6) Imaging and pathological features: maximum diameter of polyp (measured by ultrasound), number of polyps (single / multiple).

[0047] Comparison of clinical data between the EP group and the EC group:

[0048] The age of the EC group was significantly greater than that of the EP group (P=0.031), the incidence of abnormal uterine bleeding in the EC group (85.0%) was significantly higher than that in the EP group (P<0.001), the proportion of comorbid diabetes in the EC group was higher than that in the EP group (P=0.047), and the diameter of EPs in the EC group was larger than that in the EP group (P<0.001), as shown in Table 1.

[0049] Table 1 Comparison of clinical data between the EP group and the EC group

[0050]

[0051] 1.3 Specimen Collection and Processing

[0052] Uterine contents were collected during hysteroscopic surgery. Disposable hysteroscopic aspiration tubes (4-6 gauge soft tubes, negative pressure 80 mmHg) were used to aspirate the contents before removing the lesion tissue to avoid microbial contamination during the procedure. Specimens were divided into two portions: one portion was placed in a sterile cryovial and stored at -80 °C for subsequent microbiological and metabolomics testing; the other portion was sent to the pathology department for routine histological examination.

[0053] 2. 16S rRNA sequencing and microbiome analysis

[0054] DNA Extraction and Amplification: Total DNA was extracted from the samples using the QIAamp DNA Stool Mini Kit. PCR amplification was performed on the V4-V5 region of the bacterial 16S rRNA gene using primers 515F (5'-GTGCCAGCMGCCGCGGTAA-3') and 907R (5'-CCGTCAATTCMTTTRAGTTT-3'). Sequencing and Data Analysis: After purification, the amplified products were sequenced using the Illumina HiSeq platform. After quality control and assembly, the raw data were clustered into OTUs with 97% similarity using QIIME2 software, and species annotation was completed based on the Silva database. Analysis indicators included α-diversity (Chao1 index for species richness, Shannon index for diversity), β-diversity, and comparisons of relative abundance at the phylum and genus levels to clarify the differences in the composition of the two bacterial communities. After purification, the amplified products were sequenced using the Illumina HiSeq platform.

[0055] The endometrial microbial community diversity (Chao1 index and Shannon index) in the EC group was significantly lower than that in the EP group (P < 0.05), and PCoA analysis showed a clear separation of the microbial community structure between the two groups. At the phylum level, the relative abundance of Firmicutes in the EC group was significantly lower than that in the EP group (P = 0.012), while the abundance of Proteobacteria was significantly higher in the EC group (P = 0.008). At the genus level, the abundance of Lactobacillus was lower in the EC group (P < 0.001), while the abundance of pro-inflammatory genera such as Escherichia coli and Fusobacterium was higher (P < 0.01), as shown in Table 2.

[0056] Table 2. Differences in endometrial microbiota between the two groups of patients

[0057]

[0058] 3. Metabolomics detection and analysis

[0059] Sample pretreatment: 50 mg of tissue was homogenized in methanol-acetonitrile-water (2:2:1 v / v), extracted by sonication, and centrifuged at 12,000 r / min for 15 min at 4 ℃. The supernatant was lyophilized and reconstituted in acetonitrile-water (1:1 v / v) for analysis. LC-MS detection: A Waters ACQUITY UPLC system was used with a BEH C18 column (2.1 mm × 100 mm). Mobile phase A was 0.1% formic acid aqueous solution, and mobile phase B was 0.1% formic acid-acetonitrile solution. Gradient elution was performed for 15 min at a flow rate of 0.3 mL / min, a column temperature of 40 ℃, and an injection volume of 5 μL. Mass spectrometry was performed using an ESI ion source in both positive and negative ion modes, with a scan range of 50–1000 m / z. Data analysis: Metabolites were identified using the HMDB and KEGG databases. Differential metabolites were screened using PCA and OPLS-DA (VIP > 1.0 and P < 0.05), and metabolic pathway enrichment analysis was performed.

[0060] Metabolomics analysis identified 28 differentially expressed metabolites (VIP > 1.0, P < 0.05), with 15 upregulated and 13 downregulated in the EC group. These differentially expressed metabolites primarily involved amino acid metabolism, lipid metabolism, and inflammation-related pathways. For example, arachidonic acid metabolites prostaglandin E2 (PGE2) and leukotriene B4 (LTB4) were significantly elevated in the EC group (P < 0.01), while glutathione and its derivatives, involved in antioxidant defense, were significantly decreased in the EC group (P < 0.05). KEGG pathway enrichment analysis showed that the differentially expressed metabolites were mainly enriched in pathways such as arachidonic acid metabolism, glycine, serine, and threonine metabolism, and sphingolipid metabolism.

[0061] 4. Logistic Regression Analysis of Risk Factors for Epsomyelitis Malignancy

[0062] Decreased abundance of Lactobacillus (OR=3.21, 95%CI: 1.56-6.62), abnormal uterine bleeding (OR=2.89, 95%CI: 1.34-6.23), and elevated PGE2 levels (OR=4.12, 95%CI: 1.87-9.08) were independent risk factors for malignant transformation of perimenopausal EPs (P<0.05), as shown in Table 3.

[0063] Table 3. Binary Logistic Regression Analysis of Risk Factors for Malignant Transformation of EPs

[0064]

[0065] 5. Construction and Evaluation of Risk Prediction Models

[0066] ROC curves were constructed based on three independent risk factors: abundance of Lactobacillus, abnormal uterine bleeding, and PGE2 level (see [link to ROC curve]). Figure 1 The results showed that the AUC was 0.849 (95% CI: 0.78-0.94), the sensitivity was 80.0%, and the specificity was 75.0%.

[0067] 6. Follow-up verification

[0068] A total of 112 patients were followed up, with 8 lost to follow-up, resulting in a follow-up rate of 93.3%. In the EP group, 12 patients experienced polyp recurrence, of which 3 developed malignancy during follow-up; in the EC group, 5 patients experienced tumor recurrence or metastasis. Further analysis revealed that patients with higher baseline risk prediction model scores had a significantly higher disease progression rate than those with lower scores (P < 0.05).

[0069] The above description is illustrative only and not restrictive of the present invention. Those skilled in the art will understand that many modifications, variations or equivalents can be made without departing from the spirit and scope defined by the appended claims, and all such modifications, variations or equivalents will fall within the protection scope of the present invention.

Claims

1. A method for assessing the risk of malignant transformation of endometrial polyps in perimenopausal women based on the endometrial microbiome-metabolic axis, characterized in that, Includes the following steps: Obtain uterine cavity samples from perimenopausal women; The uterine cavity sample was subjected to 16S rRNA sequencing to obtain microbial characteristic data, which included at least the relative abundance of Lactobacillus genus; The uterine cavity sample was subjected to liquid chromatography-mass spectrometry to obtain metabolite characteristic data, wherein the metabolite characteristics included at least the concentration of prostaglandin E2; Obtain clinical variable data of the perimenopausal women, wherein the clinical variables include at least abnormal uterine bleeding; The relative abundance of Lactobacillus, the concentration of prostaglandin E2, and the occurrence of abnormal uterine bleeding were used as feature variables and input into a pre-constructed Logistic regression model to output the probability of malignant transformation of endometrial polyps.

2. The method according to claim 1, characterized in that, The uterine cavity sample is the contents of the uterine cavity aspirated using a disposable uterine aspiration tube during hysteroscopic surgery, before the removal of the lesion tissue.

3. The method according to claim 1, characterized in that, The 16S rRNA sequencing targeted the V4-V5 region and used primers 515F and 907R for PCR amplification.

4. The method according to claim 1, characterized in that, The metabolite characterization data also includes the concentrations of leukotriene B4 and / or glutathione.

5. The method according to claim 1, characterized in that, The clinical variable data also include one or more of the following: age, body mass index, history of diabetes, and polyp diameter.

6. The method according to claim 1, characterized in that, The formula for calculating the risk probability of the Logistic regression model is as follows: ； Where σ is the Sigmoid function, For the intercept term, , , These are the weighting coefficients.

7. The method according to claim 1, characterized in that, It also includes a step of stratifying the malignancy risk probability according to a preset threshold, where P ≥ 0.35 is determined to be high risk and P < 0.35 is determined to be low risk.

8. A system for assessing the risk of malignant transformation of endometrial polyps in perimenopausal women based on the endometrial microbiome-metabolic axis for implementing the method of any one of claims 1-7, characterized in that, include: The sample acquisition module is used to acquire uterine cavity samples from perimenopausal women. The data acquisition module is used to receive microbial characteristic data, metabolite characteristic data, and clinical variable data; The risk assessment module contains the aforementioned Logistic regression model, which is used to output the probability of malignant transformation.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method for assessing the risk of malignant transformation of endometrial polyps in perimenopausal women based on the endometrial microbiome-metabolic axis as described in any one of claims 1-7.

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