Protein detection markers for screening early gastric cancer

Abstract

This invention relates to the fields of early tumor screening and molecular diagnostics, and particularly to protein biomarkers for screening early gastric cancer, especially a mass spectrometry-based method for detecting multiple protein biomarkers. The protein combinations described in this invention exhibit excellent diagnostic performance in early gastric cancer detection, achieving an AUC value of over 0.95 in a clinical cohort, demonstrating potential for clinical application.

Description

Technical Field

[0001] This invention relates to the field of early tumor screening and molecular diagnostics, and particularly to a protein detection method for screening early gastric cancer, especially a mass spectrometry-based multi-protein biomarker detection method. Background Technology

[0002] Stomach cancer is the fourth most common malignant tumor worldwide and the second leading cause of cancer-related death. Because early-stage stomach cancer often presents with no obvious symptoms, most patients are diagnosed at an advanced stage, missing the optimal window for surgical intervention. However, early diagnosis is crucial for the treatment of stomach cancer, with a five-year survival rate exceeding 90%. Although gastroscopy is considered the "gold standard" for early stomach cancer diagnosis, its invasiveness, high cost, and reliance on specialized procedures limit its feasibility and acceptance for large-scale screening in the general population.

[0003] Currently, commonly used serum tumor markers such as carcinoembryonic antigen (CEA), carbohydrate antigen 72-4 (CA72-4), and carbohydrate antigen 19-9 (CA19-9) generally have low sensitivity and specificity in early gastric cancer detection, failing to meet the needs of early screening in the general population or high-risk groups. Furthermore, these traditional markers are often affected by age, inflammation, and other non-tumor factors, further reducing their clinical value. Therefore, developing a non-invasive, convenient, highly sensitive detection method that can be widely applied to population screening has become an urgent need in the field of early gastric cancer screening.

[0004] The rapid development of proteomics has provided a new technological pathway for early non-invasive cancer screening. Blood, as a readily available and abundant clinical sample, contains a large number of proteins reflecting an individual's pathological and physiological state, making it an important carrier for the development of tumor biomarkers. Compared with single biomarkers, multi-biomarker detection strategies based on protein combinations can more effectively reflect systemic physiological changes during tumor development and progression, improving detection sensitivity and specificity while enhancing the ability to screen for cancer heterogeneity. Mass spectrometry (MS), as a core tool in proteomics research, possesses high-throughput, high-sensitivity, and high-precision qualitative and quantitative capabilities, and has been widely used in the screening, validation, and clinical translation of disease-related protein biomarkers.

[0005] Currently, there is still a lack of high-performance detection methods based on blood protein combinations for gastric cancer, especially early-stage gastric cancer. Therefore, developing an early gastric cancer screening method that uses blood samples and combines multi-protein biomarker combinations with high-throughput mass spectrometry detection technology has significant scientific importance and broad clinical application prospects. It is expected to improve the early detection rate of gastric cancer and also provides a practical basis for building a molecular screening platform based on mass spectrometry detection. Summary of the Invention

[0006] The use of a composition comprising at least seven proteins in the preparation of a kit or composition for in vitro auxiliary diagnosis of cancer, characterized in that the at least seven proteins comprise: CD14, APMAP, IGLL1, IGHV2-5, IGF2, IGLV4-69, and GP1BA.

[0007] The composition further comprises COMP.

[0008] The composition further comprises APOA4.

[0009] The composition further comprises VWF.

[0010] The composition further comprises LDHB.

[0011] The composition further comprises at least one protein selected from FN1, ACTB, and ACTG1.

[0012] A kit for in vitro cancer-aided diagnosis, comprising reagents for detecting at least seven of the following proteins:

[0013] CD14, APMAP, IGLL1, IGHV2-5, IGF2, IGLV4-69, and GP1BA.

[0014] The reagent is also used to detect combinations of proteins as defined in any one of claims 2-6.

[0015] The beneficial effects of this invention are:

[0016] 1. High sensitivity and high specificity: The protein combination described in this invention exhibits excellent diagnostic performance in the early detection of gastric cancer, achieving an AUC value of over 0.95 in a clinical cohort, and has potential for clinical application.

[0017] 2. Precise detection method based on mass spectrometry: This invention constructs a standardized proteomics detection process with advantages such as high throughput, good repeatability, and high specificity, and is suitable for clinical testing laboratories and translational medicine platforms.

[0018] 3. Non-invasive, convenient, and easy to promote: This invention uses plasma samples for testing, which is a non-invasive sampling method that is easy for test subjects to accept and is conducive to widespread screening in high-risk groups.

[0019] 4. Suitable for translational applications and commercial development: The protein combination and detection process can be further developed into early cancer screening kits, LDT products or IVD products, and are compatible with multi-platform detection systems, with strong prospects for industrial transformation. Attached Figure Description

[0020] Figure 1 Volcano plot of differentially expressed proteins between gastric cancer patients (right) and healthy controls (left). The horizontal axis represents the log2 (fold change) of protein expression, and the vertical axis represents –log2. 10 (Adjusted P value), black dots represent proteins with significant differences (fold change > 2, P < 0.05), and gray dots represent proteins with no significant differences.

[0021] Figure 2 The receiver operating characteristic (ROC) curves of models built using LASSO regression, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) on the validation set.

[0022] Figure 3 : Screening process diagram. Detailed Implementation

[0023] The steps of the protein biomarker screening process for early gastric cancer detection in this patent are as follows:

[0024] 1. Sample collection:

[0025] Collect plasma or serum samples and extract a mixture of total proteins using solvent precipitation or magnetic bead enrichment.

[0026] 2. Enzymatic digestion of proteins:

[0027] The protein mixture is then subjected to enzymatic digestion, with trypsin being a commonly used digestive enzyme. This step is used to obtain the corresponding peptides of the proteins.

[0028] 3. Peptide separation:

[0029] Liquid chromatography (LC) was used to separate the peptides after enzymatic digestion in order to improve the coverage of mass spectrometry analysis.

[0030] 4. Mass spectrometry analysis:

[0031] The separated peptides were introduced into a mass spectrometer for detection and analysis. The mass-to-charge ratio (m / z) of the primary precursor ion and secondary fragment ions of the peptides was detected to obtain the peptide mass spectra of each protein.

[0032] 5. Data Processing:

[0033] Mass spectrometry results were used to analyze characteristic peptides of each protein. Based on the primary precursor ion spectrum and the m / z values ​​and abundance of the resolved secondary fragment peaks, the relative expression levels of each protein were determined. Differential proteins between cancer patients and healthy individuals were analyzed using methods such as t-tests, and a model was constructed for screening and differentiating multiple cancer types.

[0034] In the above steps, the separation conditions for liquid chromatography are as follows:

[0035] The chromatographic column is a C18 column;

[0036] Mobile phase A is an aqueous solution containing 0.05%-0.5% formic acid, and mobile phase B is a 70%-90% acetonitrile solution containing 0.05%-0.5% formic acid;

[0037] The gradient separation time is 30-120 minutes.

[0038] The parameters for mass spectrometry detection are as follows:

[0039] MS1 has a scan range of 375-1550 m / z, a resolution of 60,000-240,000, an AGC of 200%-500%, and an ion implantation time of 20-100 ms;

[0040] MS2 scan has an isolation window of 0.5-5 m / z, a resolution of 15,000-60,000, an AGC of 50%-2000%, an ion implantation time of 50-300 ms, and an HCD collision energy of 25%-35%.

[0041] In the following embodiments, protein biomarker detection and analysis are performed through the following steps:

[0042] 1. Sample collection:

[0043] A total of 167 plasma samples were collected from cancer patients and healthy volunteers (discovery set: 22 gastric cancer patients and 94 healthy individuals; validation set: 10 gastric cancer patients and 41 healthy individuals). Protein mixtures were obtained using direct dilution, solvent precipitation, or magnetic bead enrichment methods. This embodiment uses the solvent precipitation method. The specific steps are as follows: A certain volume of plasma was diluted and added to a 50-200mM ammonium bicarbonate solution at a plasma:ammonium bicarbonate solution ratio of 1:10. Pre-cooled methanol was then added to the plasma diluent at a plasma diluent:methanol ratio of 2:1. After vortexing and mixing, the mixture was centrifuged, the supernatant was discarded, and the precipitate was vacuum dried and stored at -80°C for later use.

[0044] 2. Enzymatic digestion of proteins:

[0045] The protein mixture is enzymatically digested, with trypsin being a commonly used digestive enzyme. This step is used to obtain the corresponding peptides of the protein. The specific steps are as follows: Dissolve the dried precipitated protein in 8M urea, add 50mM MTCEP solution as a reducing agent to a final concentration of 5mM, heat to denature and reduce, then add 100mM IAA solution as an alkylating agent to a final concentration of 10mM, and react at room temperature in the dark for 30 minutes. Dilute the urea to below 1M with 50-200mM ammonium bicarbonate solution, then add Lys-C enzyme and Trypsin enzyme sequentially at an enzyme / substrate mass ratio of 1:50-1:500. After mixing and reacting overnight, add 10% TFA to a final concentration of 0.1% to terminate the reaction. After column desalting, vacuum dry the mixture and reconstitute with 0.1% FA before use.

[0046] 3. Peptide separation:

[0047] Liquid chromatography (LC) was used to separate the reconstituted peptides to improve the coverage of mass spectrometry analysis. The LC system was a Vanquish Neo UHPLC system, and the mass spectrometer was an Orbitrap Exploris 480 (ThermoFisher Scientific). The column specifications were 2 μm × 75 μm × 15 cm C18 column. Mobile phase A was water (containing 0.1% formic acid), and mobile phase B was 80% acetonitrile (containing 0.1% formic acid). The separation gradient time was 102 min.

[0048] 4. Mass spectrometry analysis:

[0049] The separated peptides were introduced into a mass spectrometer for detection and analysis. Mass spectra of different proteins were obtained by detecting the mass-to-charge ratio (m / z) of the primary precursor ion and secondary fragment ions. This example uses a non-targeted detection method, but it is not limited to non-targeted detection analysis and is also applicable to targeted detection methods. The mass spectrometer was an Orbitrap Exploris 480 (Thermo Fisher Scientific). The MS1 scan range was 380-1000 m / z, with a resolution of 120,000, an AGC of 300%, and an IT of 50 ms. The MS2 scan isolation window was 4 m / z, with a resolution of 30,000, an AGC of 1000%, an ion implantation time of 100 ms, and an HCD collision energy of 27%.

[0050] 5. Data Processing:

[0051] Mass spectrometry data of characteristic peptides of target proteins were compared with those generated from a database (UniProt Humandatabase, downloaded in Aug 2024). Based on the m / z (mass-to-charge ratio) and relative abundance of the primary precursor ion (MS1) and the dissociated secondary fragment ions (MS / MS), the relative expression levels of each protein in the samples were identified and quantified. Subsequently, a t-test was used to assess the differences in protein expression between cancer patients and healthy individuals, and statistically significant differentially expressed proteins were screened (see [link to t-test]). Figure 1 (As shown). The steps are as follows: Figure 3 .

[0052] A total of 447 proteins were identified, of which 258 proteins had an identification rate of >50% in the gastric cancer group and the healthy control group, and were identified in at least 3 samples in each group; further differentially expressed proteins were identified as 14 proteins (log2FC>1, P<0.05).

[0053] Based on the differentially expressed proteins identified above, a model was further constructed using LASSO regression, random forest (RF), support vector machine (SVM), and gradient boosting decision tree (XGBoost) for early gastric cancer screening analysis. Validation results showed that the model built using this protein combination had an area under the receiver operating characteristic (AUC) close to 1 on the validation set (see [link to validation dataset]). Figure 2 As shown in Tables 1 and 2, this demonstrates its extremely high screening and diagnostic efficacy. After comprehensive comparison, the random forest model achieved an AUC, sensitivity, specificity, and positive predictive value of 1, demonstrating the best performance in distinguishing between gastric cancer and healthy individuals.

[0054] It is evident that some combinations are ineffective. For example, the sensitivity of the first six groups is only 0.1, which is not suitable for clinical use. However, the sensitivity jumped from 0.1 to 0.5 after adding GP1BA, indicating a significant improvement in detection performance for the 7-protein combination containing GP1BA. The 8-protein combination, formed by adding the new COMP protein, has an even higher AUC. The 9-protein combination, with the addition of APOA4, further improved both its sensitivity (0.7) and Youden index (0.7). The 10-protein combination, obtained by adding VWF, achieved an AUC of 1. The protein combination with the addition of LDHB showed an AUC / sensitivity / specificity of 1, and had the fewest number of combinations. The 11-protein combination containing LDHB was the best performing combination in the data. Furthermore, the detection performance remained stable after adding FN1, ACTB, and ACTG1.

[0055] Table 1. Area under the receiver operating characteristic (AUC), sensitivity, specificity, and Youden index of the model built on Random Forest (RF) using differentially expressed protein features in the validation set.

[0056] Table 2. Area under the receiver operating characteristic (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of various models built based on LASSO regression, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) in the validation set.

[0057]

Claims

1. Use of a composition comprising at least seven proteins for the manufacture of a kit or composition for the in vitro aided diagnosis of gastric cancer, characterized in that, The at least seven proteins include: CD14, APMAP, IGLL1, IGHV2-5, IGF2, IGLV4-69, and GP1BA.

2. Use according to claim 1, characterized in that, The composition further comprises COMP.

3. The use according to claim 2, characterized in that, The composition further comprises APOA4.

4. The use according to claim 3, characterized in that, The composition further comprises VWF.

5. The use according to claim 4, characterized in that, The composition further comprises LDHB.

6. The use according to claim 5, characterized in that, The composition further comprises FN1.

7. The use according to claim 6, characterized in that, The composition further comprises ACTB and ACTG1.

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