A biomarker combination for assessing the responsiveness of polycystic ovary syndrome treatment and applications thereof

Abstract

The present application relates to the technical field of biomedical detection technology. The present application provides a biomarker combination for evaluating the treatment responsiveness of polycystic ovary syndrome and an application thereof, wherein the biomarker combination comprises a choline sulfatase gene and a serum metabolite; and the serum metabolite is one or more of succinic acid, lysyl hydroxyproline, Hepoxilin A3 or hippuric acid. The biomarker combination of the present application is from two completely independent technical platforms (sequencing vs mass spectrometry) of metagenomics and metabolomics, which accurately point to the same metabolic pathway. The coincidence probability of multi-omics cross-platform locking is in the order of 10 ‑14 to 10 ‑18 , which ensures the robustness and prediction accuracy of the combined markers.

Description

Technical Field

[0001] This invention relates to the field of biomedical detection technology, and in particular to a combination of biomarkers for evaluating the treatment response of polycystic ovary syndrome and their applications. Background Technology

[0002] Polycystic ovary syndrome (PCOS) is one of the most common endocrine and metabolic disorders in women of reproductive age, with a prevalence of approximately 6%-20%. Clinical manifestations include hyperandrogenemia, ovulation disorders, and polycystic ovarian morphology. Currently, the clinical management of PCOS faces a long-standing but unresolved core challenge: under the same treatment regimen, some patients can restore ovulation and menstrual regularity, while others show no improvement. In clinical practice, doctors cannot predict whether a patient will respond to treatment beforehand, leading to a prevalent "trial-and-error" approach to treatment.

[0003] Current research on biomarkers for PCOS diagnosis and subtyping mainly focuses on the following areas: (a) Single hormone biomarkers: such as testosterone (T), free testosterone index (FAI), anti-Müllerian hormone (AMH), etc. These biomarkers reflect the downstream phenotype of the disease, cannot predict treatment response, and are greatly affected by factors such as menstrual cycle and age.

[0004] (ii) Single microbial species biomarkers: Previous studies have reported changes in the species composition of the gut microbiota in PCOS patients. However, biomarkers based on microbial species classification (genus / species level) have significant limitations—results at the species level are poorly reproducible across different study cohorts, and changes in species abundance are difficult to directly map to host pathophysiological functions.

[0005] (iii) Single metabolite biomarkers: Studies have reported changes in the serum metabolic profile of PCOS patients. However, single metabolites are greatly affected by confounding factors such as diet and circadian rhythm, and their robustness as clinical predictive biomarkers is insufficient.

[0006] (iv) Predictive Models: There are currently no PCOS treatment response prediction models based on the cross-omics combination of "gut microbiota functional genes + host metabolites".

[0007] In summary, existing technologies lack a combination of biomarkers and predictive products that can effectively assess the treatment response of PCOS patients before treatment. This invention addresses this technological gap. Summary of the Invention

[0008] The purpose of this invention is to provide a combination of biomarkers for evaluating the treatment response of polycystic ovary syndrome (PCOS) and their application, in order to address the clinical pain point of the prior art being unable to predict the treatment effect of PCOS patients.

[0009] To achieve the above-mentioned objectives, the present invention provides the following technical solution: This invention provides a combination of biomarkers for evaluating treatment response to polycystic ovary syndrome, the combination of biomarkers including choline sulfatase gene and serum metabolites; The serum metabolite is one or more of succinic acid, lysine hydroxyproline, Hepoxilin A3, or hippuric acid.

[0010] Preferably, the nucleotide sequence of the choline sulfatase gene is shown in SEQ ID NO.1 or SEQ ID NO.2; Or it has more than 90% homology with SEQ ID NO.1 or SEQ ID NO.2.

[0011] Preferably, the biomarker combination also includes choline dehydrogenase.

[0012] The present invention also provides a method for detecting the combination of the biomarkers, characterized in that the abundance of the choline sulfatase gene is detected by metagenomic sequencing or qPCR absolute quantification. The serum metabolite levels were detected by liquid chromatography-tandem mass spectrometry.

[0013] The present invention also provides the application of the aforementioned biomarker combination in the preparation of products for detecting treatment response in PCOS patients.

[0014] The present invention also provides a kit for preparing a treatment response test for PCOS patients, the kit comprising a qPCR primer pair for specifically amplifying the choline sulfatase gene and a reagent for detecting the level of at least one metabolite among succinate, lysine hydroxyproline, Hepoxilin A3 or hippuric acid.

[0015] Preferably, the reagent is an internal standard and / or chemical reference for LC-MS / MS detection; The kit also includes control standards for establishing a standard curve, the standards being selected from one or more of the following: Succinate isotope internal standard, Succinate chemical reference, Lysylhydroxyproline chemical reference, Hepoxilin A3 chemical reference, and Hippurate chemical reference.

[0016] This invention also provides SUCNR1 inhibitors in the preparation of drugs for treating PCOS ovarian ECM remodeling or ovarian fibrosis.

[0017] This invention also provides the use of LOX inhibitors in the preparation of drugs for treating ovarian collagen deposition or ovarian fibrosis in patients with PCOS.

[0018] This invention also provides the application of reagents that regulate any node in the Succinate-SUCNR1-ROS-HIF1α-LOX signal axis in the treatment of PCOS.

[0019] Beneficial effects: (1) Significantly improved prediction accuracy: K01133 (choline sulfatase gene) and Succinate come from two completely independent technology platforms (sequencing vs. mass spectrometry), namely metagenomics and metabolomics, and both precisely point to the same metabolic pathway. The probability of coincidence in multi-omics cross-platform targeting is 10. - ¹ 4 Up to 10 - ¹ 8 The magnitude ensures the robustness and predictive accuracy of the joint markers.

[0020] (2) High data support strength: Succinate: AB_p=0.0049, Interaction FDR=0.036 Lysylhydroxyproline: AB_p=1.58×10 -6 (The strongest signal in the entire table) Hepoxilin A3: Interaction FDR = 0.001 (strongest interaction signal in the entire table) All of the above metabolites decreased consistently in the treatment-effective group. (3) Excellent prediction performance: The prediction model based on the joint markers K01133 and Succinate, after repeated 5-fold cross-validation, has an AUC of 0.871, an optimal cutoff value of 0.579, a sensitivity of 79.55%, and a specificity of 80.63%.

[0021] (4) Filling a clinical gap: There are currently no detection products or predictive models that can predict the treatment response of PCOS patients before treatment. This invention fills this technical gap.

[0022] (5) The test samples are easy to obtain: the present invention uses peripheral blood and fecal samples, the sample acquisition is non-invasive or minimally invasive, the patient compliance is good, and it is suitable for clinical promotion.

[0023] (6) Reserved multi-omics expansion interface: The biomarker combination described in this invention can be further combined with other clinical indicators (such as BMI, hormone levels, etc.) to construct a multi-factor prediction model and further improve the prediction accuracy. Detailed Implementation

[0024] This invention provides a combination of biomarkers for evaluating treatment response to polycystic ovary syndrome, the combination of biomarkers including choline sulfatase gene and serum metabolites; The serum metabolite is one or more of succinic acid, lysine hydroxyproline, Hepoxilin A3, or hippuric acid.

[0025] In this invention, the nucleotide sequence of the choline sulfatase gene is shown in SEQ ID NO.1 or SEQ ID NO.2; Or it has more than 90% homology with SEQ ID NO.1 or SEQ ID NO.2.

[0026] In this invention, the biomarker combination also includes choline dehydrogenase as an auxiliary biomarker.

[0027] The choline dehydrogenase (K00108, EC 1.1.99.1) is another key enzyme in the choline metabolism pathway, which, together with the choline sulfatase gene (K01133), participates in the choline metabolism process of the gut microbiota.

[0028] The present invention also provides a method for detecting the combination of the biomarkers, wherein the abundance of the choline sulfatase gene is detected by metagenomic sequencing or qPCR absolute quantification. The serum metabolite levels were detected by liquid chromatography-tandem mass spectrometry.

[0029] The present invention also provides the application of the aforementioned biomarker combination in the preparation of products for detecting treatment response in PCOS patients.

[0030] The present invention also provides a kit for preparing a treatment response test for PCOS patients, the kit comprising a qPCR primer pair for specifically amplifying the choline sulfatase gene and a reagent for detecting the level of at least one metabolite among succinate, lysine hydroxyproline, Hepoxilin A3 or hippuric acid.

[0031] In this invention, the reagent is an internal standard and / or chemical reference for LC-MS / MS detection; The kit also includes control standards for establishing a standard curve, the standards being selected from one or more of the following: Succinate isotope internal standard, Succinate chemical reference, Lysylhydroxyproline chemical reference, Hepoxilin A3 chemical reference, and Hippurate chemical reference.

[0032] This invention also provides SUCNR1 inhibitors in the preparation of drugs for treating PCOS ovarian ECM remodeling or ovarian fibrosis.

[0033] This invention also provides the use of LOX inhibitors in the preparation of drugs for treating ovarian collagen deposition or ovarian fibrosis in patients with PCOS.

[0034] This invention also provides the application of reagents that regulate any node in the Succinate-SUCNR1-ROS-HIF1α-LOX signal axis in the treatment of PCOS. The technical solutions provided by this invention are described in detail below with reference to embodiments, but these should not be construed as limiting the scope of protection of this invention.

[0035] Example 1 Experimental Design

[0036] Through prior metagenomic sequencing and untargeted metabolomics (LC-MS / MS) analysis of paired samples from PCOS patients before and after treatment, the inventors made the following discovery for the first time: (1) The gut microbiota functional gene K01133 (choline-sulfatase, EC 3.1.6.6) showed significant differences between the treatment-responsive group and the non-responder group. K01133 encodes choline-sulfatase, whose natural function is to catalyze the hydrolysis of choline-O-sulfate into choline and inorganic sulfate, anchoring the metabolic entry point of "choline → one-carbon metabolism → energy metabolism".

[0037] The representative DNA sequence of the K01133 gene described in this invention includes, but is not limited to: SEQ ID NO.1: A nucleotide sequence of 1371 bp (derived from geneset001061903), the DNA sequence of which is shown in SEQ ID NO.1 of the sequence listing; SEQ ID NO.1:

[0038] SEQ ID NO.2: A nucleotide sequence of 1533 bp (derived from geneset002695191), whose DNA sequence is shown in the sequence listing SEQ ID NO.2; SEQ ID NO.2:

[0039] Both sequences were verified by the Pfam domain and contain both the PF00884 (Sulfatase) and PF12411 (Choline_sulf_C) domains, which are consistent with the gene characteristics of the K01133 functional protein.

[0040] (2) Serum metabolite Succinate showed significant differences between the effective and ineffective treatment groups. Succinate is an intermediate metabolite of the tricarboxylic acid cycle (TCA cycle).

[0041] (3) There is a cross-omics covariate relationship between K01133 and Succinate—the two indicators come from two completely independent technology platforms, metagenomic sequencing and liquid chromatography-tandem mass spectrometry (LC-MS / MS), but they precisely point to the same metabolic pathway. This cross-platform locking makes the robustness of the combined biomarker significantly better than that of the single-platform biomarker.

[0042] (4) In addition to Succinate, the following three serum metabolites also showed significant differences between the effective and ineffective treatment groups, and all decreased consistently in the effective treatment group: Lysylhydroxyproline: a marker of collagen degradation / ECM turnover, AB_p = 1.58 × 10⁻⁶. -6 ; Hepoxilin A3: an oxidized lipid inflammatory mediator, with an interaction FDR of 0.001; Hippurate: a co-metabolite of gut bacteria and host, which interacts upstream and downstream with the functional genes K01133 / K00108.

[0043] This invention is based on the following biological mechanisms: In the pathological state of PCOS, the expression of the gut microbiota functional gene K01133 is abnormal, leading to a disruption in the hydrolysis of choline sulfate into choline and inorganic sulfate, which in turn affects downstream one-carbon metabolism and energy metabolism pathways. This abnormal gut microbiota function results in the accumulation of succinate, an intermediate product of the tricarboxylic acid cycle, in the serum and local microenvironment of the ovary.

[0044] Succinate, as a signaling molecule, can activate its membrane receptor SUCNR1 (succinate receptor 1, also known as GPR91)—a G protein-coupled receptor. SUCNR1 is an autocrine and paracrine sensor of the metabolic stress signal Succinate. The Succinate-SUCNR1 signaling pathway connects metabolic stress, inflammation, and energy homeostasis. Activation of this pathway can trigger an intracellular ROS burst, thereby stabilizing HIF1α and activating the TGFβ / Smad pathway, ultimately upregulating LOX-mediated ECM collagen crosslinking and deposition, driving pathological remodeling of the ovarian microenvironment.

[0045] Therefore, the combined changes in K01133 and Succinate are not only a statistical association, but also reflect the abnormal state of the above-mentioned biological pathways, and have a clear pathophysiological basis.

[0046] Example 2 Sample Collection and Processing

[0047] 1. Research Subjects

[0048] Patients with PCOS who met the Rotterdam 2003 diagnostic criteria and were aged 18-40 years were included. Exclusion criteria included: use of hormone medications, antibiotics, probiotics, metformin, or traditional Chinese medicine within the past 3 months; other causes of hyperandrogenism; uncontrolled thyroid dysfunction; type 1 or type 2 diabetes; severe liver or kidney dysfunction; and pregnancy or lactation.

[0049] 2. Sample collection

[0050] Blood samples: Peripheral blood was collected in EDTA anticoagulant tubes before treatment (baseline, V0) and after treatment (endpoint, V1) on an empty stomach. The blood was centrifuged at 1600×g for 15 minutes at 4°C, and the plasma was separated, aliquoted and stored at -80°C for later use.

[0051] Stool samples: Fresh stool samples were collected before treatment (V0) and after treatment (V1), aliquoted into sterile cryovials (≥200mg per tube), and stored at -80℃ with 15% glycerol as a preservative.

[0052] Example 3: Metagenomic sequencing to detect the abundance of the K01133 gene

[0053] 1. DNA extraction

[0054] Take 200 mg of fecal sample and extract total DNA using the QIAamp PowerFecal Pro DNA Kit (Qiagen) or an equivalent kit, eluting in 100 μL. Determine DNA concentration and purity, requiring A260 / A280 = 1.8-2.0.

[0055] 2. Metagenomic sequencing

[0056] Metagenomic libraries were constructed from the extracted DNA, and paired-end sequencing (150bp reads) was performed using the Illumina NovaSeq 6000 platform, with each sample generating ≥10Gb of sequencing data.

[0057] 3. Bioinformatics analysis

[0058] After quality control (removal of low-quality reads and adapter contamination), the raw sequencing data were used for functional gene annotation using HUAnmN3 or equivalent software. The abundance of the K01133 gene was expressed as copies per million (CPM) or relative abundance. 16S rRNA was used as an internal reference for calibration.

[0059] 4. Representative sequence of the K01133 gene

[0060] The representative DNA sequence of the K01133 gene (choline-sulfatase, EC 3.1.6.6) described in this invention includes, but is not limited to: SEQ ID NO.1: A nucleotide sequence of 1371 bp (derived from geneset001061903), the DNA sequence of which is shown in SEQ ID NO.1 of the sequence listing; SEQ ID NO.2: A nucleotide sequence of 1533 bp (derived from geneset002695191), whose DNA sequence is shown in the sequence listing SEQ ID NO.2.

[0061] Both sequences were verified by the Pfam domain and contain both the PF00884 (Sulfatase) and PF12411 (Choline_sulf_C) domains, which are consistent with the gene characteristics of the K01133 functional protein.

[0062] 5. Test Results

[0063] The inventors used the above method to detect the abundance of K01133 in 40 PCOS patients. The results showed that the abundance of K01133 in the treatment-responsive group (Responder) was significantly higher than that in the ineffective group (Non-responder), and the difference was statistically significant.

[0064] Note: The criteria for determining the effective group is: ovulation is confirmed at least once during the treatment period (within three months).

[0065] A confirmed spontaneous ovulation event is defined as one event when the following conditions are met: ① a biphasic basal body temperature (BBT) pattern, characterized by a basal body temperature increase of ≥0.3℃ for ≥10 days; ② serum progesterone >3 ng / mL (9.54 nmol / L) within the time window corresponding to the BBT change.

[0066] Example 4: Targeted detection of serum metabolites by LC-MS / MS

[0067] 1. Plasma pretreatment

[0068] Transfer 50 μL of plasma to a 1.5 mL centrifuge tube, add 10 μL of mixed internal standard working solution (containing Succinate-d4, Hippurate-d5, and Lysylhydroxyproline-d3), add 150 μL of pre-chilled acetonitrile (containing 1% formic acid), vortex for 2 minutes, incubate on ice for 10 minutes, and centrifuge at 14000×g for 15 minutes at 4°C. Transfer 150 μL of the supernatant to a vial, insert the tube, dry with nitrogen (40°C), redissolve with 100 μL of the initial mobile phase, vortex for 1 minute, centrifuge at 14000×g for 5 minutes at 4°C, and inject the supernatant.

[0069] 2. LC-MS / MS detection conditions

[0070] Column: ACQUITY UPLC HSS T3 (2.1×100mm, 1.8μm) or equivalent C18 column

[0071] Column temperature: 40℃

[0072] Mobile phase A: 0.1% formic acid aqueous solution

[0073] Mobile phase B: 0.1% formic acid acetonitrile solution

[0074] Flow rate: 0.3 mL / min

[0075] Injection volume: 5 μL

[0076] Running time: 12 minutes

[0077] Ion source: ESI

[0078] Detection mode: MRM (Multiple Response Monitoring)

[0079] Scanning mode: Segmented scan—negative ion mode (Succinate, Hippurate) + positive ion mode (Lysylhydroxyproline)

[0080] 3. Preparation of standard curve

[0081] Prepare a standard curve with 7 concentration points (including a blank point), covering the expected sample concentration range. The standard curve should have an R² ≥ 0.99.

[0082] 4. Test Results

[0083] The inventors used the above method to detect the levels of four metabolites in 40 PCOS patients. The results showed: Succinate: The effective treatment group had significantly lower efficacy than the ineffective group (AB_p=0.0049, interaction FDR=0.036). Lysylhydroxyproline: The effective treatment group had significantly lower efficacy than the ineffective group (AB_p=1.58×10). -6 ) Hepoxilin A3: The effective treatment group had significantly lower efficacy than the ineffective group (interaction FDR = 0.001). Hippurate: There was a significant difference between the effective and ineffective treatment groups. All four metabolites decreased consistently in the treatment-effective group.

[0084] Example 5: Construction and Performance Evaluation of the Prediction System

[0085] 1. Data Acquisition

[0086] Using the methods in Examples 3 and 4, the abundance data of the K01133 gene and the metabolite level data of the subjects were obtained.

[0087] 2. Calculation of the forecast index

[0088] The acquired data is input into a predictive model, which calculates the Therapeutic Response Index (TRI) based on the following formula: TRI Score = β1 × K01133 + β2 × Succinate + β3 × BMI Wherein, β1, β2, and β3 are regression coefficients determined by Logistic regression or LASSO regression.

[0089] 3. Prediction Results Output

[0090] The calculated TRI Score is compared with a preset threshold, and the prediction result is output: If the TRI Score is greater than or equal to the threshold, the subject is predicted to be a treatment responder. If the TRI score is less than the threshold, the subject is predicted to be a non-responder.

[0091] 4. Predictive performance evaluation

[0092] The inventors constructed the above-mentioned predictive model based on baseline data from 40 PCOS patients. Repeated 5-fold cross-validation was used to obtain the out-of-fold (OOF) prediction probability, and receiver operating characteristic (ROC) analysis was performed.

[0093] The results show: AUC (Area Under the Curve) = 0.871 The optimal cut-off value is 0.579 (determined using the Youden exponent). Sensitivity = 79.55% Specificity = 80.63% The above results demonstrate that the prediction model based on the combined biomarkers K01133 and Succinate described in this invention has good predictive performance and can effectively distinguish between PCOS treatment responders and non-responders before treatment.

[0094] Example 6: Composition and Usage of the Reagent Kit

[0095] 1. Kit Components

[0096] The kit described in this invention comprises the following components: (a) K01133 gene detection module: contains qPCR primer pairs for specific amplification of the K01133 gene, positive control, negative control, and SYBR Green qPCR premix; (b) Metabolite detection module: includes isotopic internal standards for Succinate, Lysylhydroxyproline, Hepoxilin A3, and Hippurate (such as Succinate-d4, Hippurate-d5, and Lysylhydroxyproline-d3) and corresponding chemical reference standards; (c) Standards module: Contains a series of concentration standards for establishing standard curves; (d) Instructions for use: This describes how to use the kit and the criteria for interpreting the results.

[0097] 2. Instructions for use

[0098] (1) Collect peripheral blood and stool samples from the subjects; (2) Extract DNA from fecal samples and use the qPCR primer pair in the kit to detect the abundance of the K01133 gene; (3) Extract metabolites from plasma samples and perform LC-MS / MS detection using the internal standard and control in the kit; (4) Substitute the detection results into the prediction model and calculate the TRI Score; (5) Based on the comparison between the TRI Score and the threshold, output the treatment response prediction results.

[0099] As can be seen from the above embodiments, the present invention provides a biomarker combination for evaluating the treatment response of polycystic ovary syndrome and its application. The biomarker combination includes the gut microbiota functional gene K01133 and serum metabolites; the serum metabolites are one or more of succinic acid, lysine hydroxyproline, Hepoxilin A3, or hippuric acid. The biomarker combination of the present invention originates from two completely independent technology platforms (sequencing vs. mass spectrometry)—metagenomics and metabolomics—both precisely targeting the same metabolic pathway. The probability of coincidence in multi-omics cross-platform targeting is 10%. -14 Up to 10 -18 The magnitude ensures the robustness and predictive accuracy of the joint markers.

[0100] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A combination of biomarkers for evaluating treatment response to polycystic ovary syndrome, characterized in that, The biomarker combination includes the choline sulfatase gene and serum metabolites; The serum metabolite is one or more of succinic acid, lysine hydroxyproline, Hepoxilin A3, or hippuric acid.

2. The biomarker combination according to claim 1, characterized in that, The nucleotide sequence of the choline sulfatase gene is shown in SEQ ID NO.1 or SEQ ID NO.2; Or it has more than 90% homology with SEQ ID NO.1 or SEQ ID NO.

2.

3. The biomarker combination according to claim 1 or 2, characterized in that, The biomarker combination also includes choline dehydrogenase.

4. The detection method of the combination of biomarkers according to any one of claims 1 to 3, characterized in that, The abundance of the choline sulfatase gene was detected by metagenomic sequencing or qPCR absolute quantification. The serum metabolite levels were detected by liquid chromatography-tandem mass spectrometry.

5. The use of the combination of biomarkers according to any one of claims 1 to 3 in the preparation of a product for detecting treatment response in PCOS patients.

6. A kit for preparing and detecting treatment response in PCOS patients, characterized in that, The kit includes qPCR primer pairs for specifically amplifying the choline sulfatase gene and reagents for detecting the levels of at least one metabolite among succinic acid, lysine hydroxyproline, Hepoxilin A3, or hippuric acid.

7. The reagent kit according to claim 6, characterized in that, The reagents are internal standards and / or chemical references for LC-MS / MS detection; The kit also includes control standards for establishing a standard curve, the standards being selected from one or more of the following: Succinate isotope internal standard, Succinate chemical reference, Lysylhydroxyproline chemical reference, Hepoxilin A3 chemical reference, and Hippurate chemical reference.

8. SUCNR1 inhibitors in the preparation of drugs for the treatment of PCOS ovarian ECM remodeling or ovarian fibrosis.

9. Application of LOX inhibitors in the preparation of drugs for treating ovarian collagen deposition or ovarian fibrosis in PCOS.

10. Application of reagents that regulate any node in the Succinate-SUCNR1-ROS-HIF1α-LOX signal axis in the treatment of PCOS.