Serum metabolism biomarker combination for ovarian cancer diagnosis and screening method thereof

Abstract

The invention discloses a serum metabolism biomarker combination for ovarian cancer diagnosis and a screening method thereof, and relates to the field of molecular diagnosis, and the biomarker combination comprises pyrrole-2-carboxylic acid, dihydrothymine, histidine and glucose. The screening method of the metabolic biomarker combination comprises the following steps: acquiring and processing serum metabolic fingerprint data by using a nano-particle enhanced laser desorption ionization time-of-flight mass spectrometry technology to obtain metabolic characteristics, and screening out the metabolic biomarker combination with significant difference through a machine learning and statistical analysis method. The combination can be accurately, rapidly and conveniently used for ovarian cancer diagnosis at low cost, industrialization is easy, after the combination with the ROMA index, the diagnosis performance is further improved, and a more accurate technical means is provided for clinical diagnosis of ovarian cancer.

Description

Technical Field

[0001] The present invention relates to the field of molecular diagnosis, and in particular to a serum metabolic biomarker combination for ovarian cancer diagnosis and a screening method thereof. Background Art

[0002] Early diagnosis of ovarian cancer is crucial to improving patient outcomes. However, existing diagnostic methods for ovarian cancer, such as transvaginal ultrasound and biopsy, rely on experienced clinicians, have limited performance (the sensitivity or specificity of transvaginal ultrasound is approximately 55-85%), or require invasive procedures (such as tissue sampling for biopsy, which may have potential risks). In addition, existing blood biomarkers for ovarian cancer diagnosis, such as cancer antigen 125 (CA-125) and human epididymal protein 4 (HE-4), are limited in diagnostic performance due to insufficient sensitivity or specificity (approximately 50-70%), especially for early-stage patients (approximately 50-60%). Therefore, it is of great significance to further explore blood-based biomarkers for early diagnosis of ovarian cancer in clinical settings.

[0003] The potential of blood-based biomarkers in the early diagnosis of cancer has received increasing attention, mainly focusing on genes, proteins and metabolites. It is worth noting that metabolic biomarkers can more directly reflect the phenotype of the disease and have the potential to predict the occurrence of cancers characterized by metabolic reprogramming. However, current research on blood metabolic biomarkers in ovarian cancer diagnosis is limited by relatively small cohort studies (sample size of approximately 200-500) and lack of biological function validation. Therefore, it is of great research value to identify blood metabolic biomarkers based on large-scale ovarian cancer cohorts and validate their biological functions for potential diagnostic purposes.

[0004] Mass spectrometry, as the main tool for metabolite analysis, is favored due to its high sensitivity and ability to achieve label-free identification by accurately measuring the mass-to-charge ratio (m / z). However, the analysis of metabolites in biological fluids using liquid / gas chromatography-mass spectrometry has limited analysis speed and throughput and requires sample processing such as deproteinization and metabolite purification. Notably, nanoparticle-enhanced laser desorption / ionization mass spectrometry using designed iron oxide nanoparticles can directly detect metabolites in biological fluids containing high concentrations of salts and proteins, significantly improving the analysis speed and throughput. Serum metabolic fingerprints recorded by nanoparticle-enhanced laser desorption / ionization mass spectrometry have shown potential in a variety of diseases and show good prospects in the diagnosis of ovarian cancer. In addition, machine learning has played an important role in associating massive data in serum metabolic fingerprints with clinical outcomes.

[0005] Therefore, technicians in this field are committed to developing an accurate, rapid, convenient, low-cost, and easily industrialized diagnostic marker and method for ovarian cancer. Summary of the invention

[0006] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is to provide an accurate, rapid, convenient, low-cost and easily industrialized diagnostic marker for ovarian cancer and a method thereof.

[0007] To achieve the above objectives, the present invention provides a serum metabolic biomarker combination for ovarian cancer diagnosis, wherein the biomarker combination is pyrrole-2-carboxylic acid, dihydrothymidine, histidine and glucose.

[0008] The present invention also provides a method for screening the above-mentioned serum metabolic biomarker combination for ovarian cancer diagnosis, characterized in that the method comprises the following steps:

[0009] Step 1: Use nanoparticle enhanced laser desorption ionization time-of-flight mass spectrometry to collect and process serum metabolic fingerprint data to obtain metabolic characteristics;

[0010] Step 2: Identify and screen metabolic biomarkers in serum to obtain a metabolic biomarker combination.

[0011] In a preferred embodiment of the present invention, step 1 further comprises:

[0012] Step 1.1: Instrument and reagent preparation: matrix-assisted laser desorption ionization time-of-flight mass spectrometry, serum sample, deionized water, matrix;

[0013] Step 1.2: Dilute the serum sample 10 times with deionized water;

[0014] Step 1.3: Prepare 1 mg / mL matrix solution with deionized water;

[0015] Step 1.4: Prepare the sample on the mass spectrometry target plate, spot 1.5 μL of each diluted serum sample and dry at room temperature;

[0016] Step 1.5: Prepare the matrix on the mass spectrometry target plate, spot 1.5 μL of each matrix solution and dry at room temperature;

[0017] Step 1.6: Collect serum metabolic fingerprint data in laser desorption ionization time-of-flight mass spectrometer;

[0018] Step 1.7: Construct a metabolic fingerprint database for all serum samples;

[0019] Step 1.8: Preprocess the metabolic fingerprint data of all serum samples to obtain metabolic features.

[0020] In another preferred embodiment of the present invention, the matrix in step 1.1 is inorganic nanoparticles.

[0021] In another preferred embodiment of the present invention, the serum samples in step 1.7 include ovarian cancer samples and non-ovarian cancer samples, wherein the non-ovarian cancer samples include benign ovarian disease samples.

[0022] In another preferred embodiment of the present invention, the preprocessing in step 1.8 includes data resampling, spectrum smoothing, baseline correction, feature extraction, spectrum peak matching and missing value filling.

[0023] In another preferred embodiment of the present invention, the step 2 further comprises:

[0024] Step 2.1: Preparation of instruments and reagents: matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry;

[0025] Step 2.2: Set standards to screen and obtain several metabolic features, and measure the precise molecular weight corresponding to the screened metabolic features in a matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometer to determine the corresponding molecular formula, which is the metabolic biomarker combination.

[0026] In another preferred embodiment of the present invention, in step 2.2, the metabolic signature is obtained by screening according to the following criteria: average intensity ≥ 500 or containing paired adducts, AUC ≥ 0.65 and LASSO score ≥ 0.19.

[0027] In another preferred embodiment of the present invention, the screened metabolic biomarker combination has a significant difference between the ovarian cancer group and the non-ovarian cancer group, p<0.05.

[0028] In another preferred embodiment of the present invention, the method further comprises combining the metabolic biomarkers obtained by screening with the ROMA index.

[0029] Technical Effects

[0030] 1. The present invention identified four metabolic biomarkers for ovarian cancer diagnosis in serum (including glucose, histidine, pyrrole-2-carboxylic acid and dihydrothymine), and found that their combination with the ROMA index can further improve the diagnostic performance. First, the metabolic fingerprints of 1432 ovarian cancer-related serum samples were recorded by nanoparticle enhanced laser desorption / ionization mass spectrometry, and a large-scale (1432 cases) ovarian cancer-related serum metabolic fingerprint database was constructed. Then, through machine learning and statistical analysis, metabolic biomarkers with significant differences (including glucose, histidine, pyrrole-2-carboxylic acid and dihydrothymine) were screened out. And the combination of the four metabolic biomarkers with the ROMA index further improves the diagnostic performance of ovarian cancer, which can achieve accurate diagnosis of ovarian cancer.

[0031] In terms of overall diagnosis of ovarian cancer: the metabolic biomarker combination developed by the present invention showed excellent diagnostic performance (AUC = 0.900), which was significantly better than the single markers CA-125 (AUC = 0.831) and HE-4 (AUC = 0.848) currently used in clinical practice. In particular, when combined with the existing ROMA index, the diagnostic performance was further improved to AUC = 0.972, providing a more accurate technical means for the clinical diagnosis of ovarian cancer.

[0032] In terms of early diagnosis: The metabolic biomarker combination of the present invention also showed excellent diagnostic ability for early ovarian cancer (AUC = 0.891), and when combined with the ROMA index, it can reach AUC = 0.968, which is significantly better than the performance of CA-125 (AUC = 0.764) and HE-4 (AUC = 0.818) in early diagnosis. This is of great significance for improving the prognosis of ovarian cancer patients, because the 5-year survival rate of early patients (stages I-II) can reach 70-90%.

[0033] 2. The present invention uses nanoparticle enhanced laser desorption / ionization mass spectrometry to record ovarian cancer-related serum metabolic fingerprints, which has the advantages of simple sample pretreatment, fast analysis speed and low cost. This high-throughput detection method is more suitable for large-scale clinical screening applications, which can significantly improve detection efficiency and reduce medical costs.

[0034] 3. Solid foundation for industrialization: The present invention is verified based on a large-scale clinical sample (1,432 cases), including 662 ovarian cancer patients and 770 control group samples. The research results have strong statistical significance and clinical application value. At the same time, the experimental method is standardized and the operation process is standardized, which is easy to transform into a clinical detection product.

[0035] 4. Broad market application prospects: Considering the high incidence and mortality of ovarian cancer and the limitations of existing diagnostic methods, the serum metabolic marker detection scheme of the present invention has a huge market demand. This technology can be used as an independent diagnostic product or integrated with the existing ROMA index detection system to expand the application scope of existing products.

[0036] In summary, the present invention has significant advantages in clinical application value, technological advancement, industrialization basis, etc., and has good industrialization prospects and market potential.

[0037] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a representative serum metabolic fingerprint of ovarian cancer and non-ovarian cancer in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following describes several preferred embodiments of the present invention with reference to the drawings in the specification, so that the technical content is clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0040] Example 1: Using nanoparticle enhanced laser desorption ionization time-of-flight mass spectrometry to collect serum metabolic fingerprint data:

[0041] Step 1: Instrument and reagent preparation: matrix-assisted laser desorption ionization time-of-flight mass spectrometry, serum sample, deionized water, matrix (inorganic nanoparticles);

[0042] Step 2: Dilute the serum sample 10 times with deionized water;

[0043] Step 3: Prepare the inorganic nanoparticles into a 1 mg / mL matrix solution with deionized water;

[0044] Step 4: Prepare the sample on the mass spectrometry target plate, spot 1.5 μL of each diluted serum sample and dry at room temperature;

[0045] Step 5: Prepare the matrix on the mass spectrometry target plate, spot 1.5 μL of each matrix solution and dry at room temperature;

[0046] Step 6: Serum metabolic fingerprint data acquisition in laser desorption ionization time-of-flight mass spectrometer;

[0047] Step 7: Through step 6, a metabolic fingerprint database was constructed for 1432 serum samples (662 ovarian cancer, 770 non-ovarian cancer (563 benign ovarian diseases and 207 healthy controls)). Figure 1 );

[0048] Step 8: Preprocess the metabolic fingerprint data of 1432 serum samples, including data resampling, spectral smoothing, baseline correction, feature extraction, spectral peak matching and missing value filling, to obtain 333 metabolic features.

[0049] Example 2: Identification of metabolic biomarkers in serum and combining them with the ROMA index to further improve the performance of ovarian cancer diagnosis:

[0050] Step 1: Preparation of instruments and reagents: matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry;

[0051] Step 2: Six metabolic features were screened by the following criteria: average intensity ≥ 500 or presence of paired adducts, AUC ≥ 0.65, and LASSO score ≥ 0.19, and the precise molecular weights corresponding to these six metabolic features were measured in a matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometer (m / z error < 2 ppm, Table 1);

[0052] Table 1. Six metabolic characteristics and corresponding information on four metabolic biomarkers

[0053]

[0054] Step 3: Based on the precise molecular weights of these six metabolic features, we determined the corresponding four molecular formulas (C 5 H 5 NO 2 ,C 5 H 8 N 2 O 2 ,C 6 H 9 N 3 O 2 and C 6 H 12 O 6 ), and were identified as pyrrole-2-carboxylic acid, dihydrothymine, histidine and glucose in the Human Metabolome Database (HMDB) (Table 1). There were significant differences in pyrrole-2-carboxylic acid, dihydrothymine, histidine and glucose between the ovarian cancer and non-ovarian cancer groups (p<0.05, Table 2).

[0055] Table 2. Differential expression of 4 metabolic biomarkers in ovarian cancer and non-ovarian cancer

[0056]

[0057] Step 4: The diagnostic performance of the biomarker combination in the diagnosis and early diagnosis of ovarian cancer is AUCs = 0.891-0.900 (Table 3). Combining metabolic biomarkers with the ROMA index can further improve the diagnostic performance of ovarian cancer (AUCs = 0.968-0.972, Table 3). The ROMA index (ovarian malignancy risk index) is an algorithm based on serum markers and menopausal status to assess the malignant risk of ovarian tumors in women. The ROMA index mainly combines CA-125 and HE-4, as well as the patient's menopausal status (premenopausal or postmenopausal), and calculates a risk score for the diagnosis of ovarian cancer.

[0058] Table 3. Performance of 4 metabolic biomarker combinations and their combination with ROMA index in diagnosing ovarian cancer

[0059]

[0060] The preferred specific embodiments of the present invention are described in detail above. It should be understood that ordinary technicians in the field can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by technicians in the technical field based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A serum metabolic biomarker combination for ovarian cancer diagnosis, characterized in that: The biomarker combination is pyrrole-2-carboxylic acid, dihydrothymidine, histidine and glucose.

2. A method for screening a combination of serum metabolic biomarkers for ovarian cancer diagnosis according to claim 1, characterized in that: The method comprises the following steps: Step 1: Use nanoparticle enhanced laser desorption ionization time-of-flight mass spectrometry to collect and process serum metabolic fingerprint data to obtain metabolic characteristics; Step 2: Identify and screen metabolic biomarkers in serum to obtain a metabolic biomarker combination.

3. The method according to claim 2, characterized in that The step 1 also includes: Step 1.1: Instrument and reagent preparation: matrix-assisted laser desorption ionization time-of-flight mass spectrometry, serum sample, deionized water, matrix; Step 1.2: Dilute the serum sample 10 times with deionized water; Step 1.3: Prepare 1 mg / mL matrix solution with deionized water; Step 1.4: Prepare the sample on the mass spectrometry target plate, spot 1.5 μL of each diluted serum sample and dry at room temperature; Step 1.5: Prepare the matrix on the mass spectrometry target plate, spot 1.5 μL of each matrix solution and dry it at room temperature; Step 1.6: Collect serum metabolic fingerprint data in laser desorption ionization time-of-flight mass spectrometer; Step 1.7: Construct a metabolic fingerprint database for all serum samples; Step 1.8: Preprocess the metabolic fingerprint data of all serum samples to obtain metabolic features.

4. The method according to claim 3, characterized in that The matrix in step 1.1 is inorganic nanoparticles.

5. The method according to claim 3, characterized in that The serum samples in step 1.7 include ovarian cancer samples and non-ovarian cancer samples, wherein the non-ovarian cancer samples include benign ovarian disease samples.

6. The method according to claim 3, characterized in that The preprocessing in step 1.8 includes data resampling, spectrum smoothing, baseline correction, feature extraction, spectrum peak matching and missing value filling.

7. The method according to claim 2, characterized in that The step 2 also includes: Step 2.1: Preparation of instruments and reagents: matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry; Step 2.2: Set standards to screen and obtain several metabolic features, and measure the precise molecular weight corresponding to the screened metabolic features in a matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometer to determine the corresponding molecular formula, which is the metabolic biomarker combination.

8. The method according to claim 7, characterized in that In step 2.2, metabolic features were screened by the following criteria: average intensity ≥ 500 or containing paired adducts, AUC ≥ 0.65, and LASSO score ≥ 0.

19.

9. The method according to claim 7, characterized in that The screened metabolic biomarker combinations showed significant differences between the ovarian cancer and non-ovarian cancer groups, p<0.

05.

10. The method according to claim 2, characterized in that The method further includes combining the metabolic biomarkers obtained by screening with the ROMA index.

Images