Application of metabolic marker in preparation of head and neck squamous cell carcinoma diagnosis product, kit, screening method of head and neck squamous cell carcinoma metabolic marker, diagnosis model and construction method and application of diagnosis model

Abstract

The invention relates to the technical field of metabonomics analysis, in particular to application of a metabolic marker in preparation of a head and neck squamous cell carcinoma diagnostic product, a kit, a screening method of the metabolic marker of the head and neck squamous cell carcinoma, a diagnostic model and a construction method and application of the diagnostic model. The metabolic marker comprises at least one of lactic acid, sphingosine, cadaverine, uridine diphosphate, allantoic acid, hydroxyproline, sphingosine-1-phosphoric acid, indole-3-acetaldehyde, pantothenic acid, fumaric acid, malic acid, prostaglandin or ornithine in red blood cells and / or plasma. The screened red blood cells and plasma metabolism markers can be used for predicting or diagnosing the head and neck squamous cell carcinoma respectively or independently, particularly, the red blood cell metabolism markers can provide more stable tumor microenvironment information, and the metabolism stability is high and is not easily influenced by diet and circadian rhythm.

Description

Technical Field

[0001] This invention relates to the field of metabolomics analysis technology, and in particular to the application of a metabolic biomarker in the preparation of diagnostic products for head and neck squamous cell carcinoma, a reagent kit, a method for screening metabolic biomarkers for head and neck squamous cell carcinoma, a diagnostic model and its construction method and application. Background Technology

[0002] Cancer, the second leading cause of death worldwide, poses a significant public health threat due to its continuously rising incidence and mortality rates. According to GLOBOCAN data, there were 19.3 million new cancer cases and 10 million cancer deaths globally in 2020. Head and neck squamous cell carcinoma (HNSCC), a malignant tumor originating from the epithelium of the nasal cavity, oral cavity, larynx, and pharynx, ranks as the seventh most common cancer globally, with nearly 900,000 new cases and over 450,000 deaths annually. Despite continuous advancements in treatment methods (including surgery, radiotherapy, chemotherapy, and emerging immunotherapies), the disease's early symptoms are often subtle, and the lack of effective screening strategies means that a high proportion of patients are diagnosed at an advanced stage (stage III / IV) or with metastasis. This results in a persistently low 5-year overall survival rate of around 50%, further exacerbated by high local recurrence rates and frequent lymph node metastases.

[0003] In recent years, metabolomics diagnosis has gradually become a new way to screen for early cancer symptoms. Compared with other early screening methods, metabolomics analysis has a shorter screening time and higher accuracy, and can largely rule out misdiagnosis caused by age, individual differences, and the influence of multiple diseases. Against this backdrop, developing novel biomarkers to achieve early diagnosis has become a key path to overcome the current clinical challenges of head and neck squamous cell carcinoma. However, existing biomarker technologies still have the following shortcomings: I. Primarily relying on a single category of biomarkers, such as: 1) Although circulating RNA markers or miRNA combinations can achieve non-invasive detection, their diagnostic accuracy is limited when used alone due to RNA stability, individual expression differences, and technical sensitivity.

[0004] 2) While protein biomarkers or metabolic enzymes are associated with tumor progression, they are easily affected by non-tumor factors such as inflammation and infection, leading to a high false-positive rate. These single-dimensional biomarkers are insufficient to fully capture the complex pathological features of HNSCC and cannot meet the needs of high-precision diagnosis.

[0005] II. Lack of Multidimensional Integration and Synergistic Analysis: Although numerous studies have proposed different categories of biomarkers (such as RNA, proteins, and metabolites), current technologies lack integrated models for multi-source biological information. Plasma biomarkers (such as ctDNA and proteome) mainly reflect short-term changes in the tumor microenvironment, while tissue biomarkers require invasive acquisition, making dynamic correlation between the two difficult. The systematic value of biomarker combinations has not been explored, hindering the establishment of reliable diagnostic decision-making models.

[0006] Third, the stability and reproducibility of biomarkers are severely affected by external factors: existing liquid biopsy biomarkers rely only on liquid biopsy signals from a single source, lacking a stable reference system that can reflect the body's long-term pathological adaptation state, making it difficult to distinguish between short-term physiological fluctuations and real disease signals, and easily affected by temporary external factors: plasma metabolites are significantly affected by diet and diurnal rhythm fluctuations; circulating RNA is easily degraded during sample processing.

[0007] IV. Limited value in prognostic prediction and treatment guidance: Existing biomarkers (such as PD-L1 and tumor mutation burden) are mainly used to predict the survival of patients with advanced disease, but cannot guide the optimization of early treatment: The lack of dynamic monitoring models for biomarkers leads to a lag in efficacy assessment.

[0008] Given the above, red blood cells, as a stable and readily available biological sample in the circulatory system, have attracted the attention of researchers. However, the role of red blood cell metabolites in the diagnosis of head and neck squamous cell carcinoma has not yet been reported, nor is there a multidimensional synergistic model based on red blood cells and other metabolic markers. There is an urgent need in this field to develop diagnostic products and models for the differential diagnosis of head and neck squamous cell carcinoma based on red blood cell and plasma metabolites, to overcome technical bottlenecks, and to achieve early and accurate identification and screening. Summary of the Invention

[0009] This invention provides the application of metabolic biomarkers in the preparation of diagnostic products for head and neck squamous cell carcinoma, a reagent kit, a method for screening metabolic biomarkers for head and neck squamous cell carcinoma, a diagnostic model and its construction method and application. Its purpose is to fill the gap in the existing technology for the differential diagnosis of head and neck squamous cell carcinoma based on erythrocyte and plasma metabolites, by using metabolic biomarkers and diagnostic models for predicting and screening head and neck squamous cell carcinoma.

[0010] To achieve the above objectives, the present invention provides an application of metabolic markers in the preparation of diagnostic products for head and neck squamous cell carcinoma, wherein the metabolic markers include at least one of lactic acid, sphingosine, cadaverine, uridine diphosphate, allantoic acid, hydroxyproline, sphingosine-1-phosphate, indole-3-acetaldehyde, pantothenic acid, fumaric acid, malic acid, prostaglandin, or ornithine from red blood cells and / or plasma.

[0011] Preferably, the diagnostic product assesses the risk of head and neck squamous cell carcinoma by detecting a significant increase or decrease in the relative levels of metabolic markers in erythrocytes and / or plasma, wherein the significance P-value of the significant increase or decrease is <0.05.

[0012] Preferably, the metabolic markers are used independently or jointly in erythrocytes or plasma to assess the risk of head and neck squamous cell carcinoma. In erythrocytes, the relative content of one or more of lactic acid, pantothenic acid, fumaric acid, malic acid, hydroxyproline or prostaglandins decreased significantly, while the relative content of one or more of sphingosine-1-phosphate, sphingosine, cadaverine, uridine diphosphate, allantoic acid, indole-3-acetaldehyde or ornithine increased significantly. In plasma, the relative levels of one or more of sphingosine-1-phosphate, sphingosine, hydroxyproline, fumaric acid, malic acid, lactic acid, ornithine, allantoic acid, or prostaglandins decreased significantly, while the relative levels of one or more of uridine diphosphate, cadaverine, pantothenic acid, or indole-3-acetaldehyde increased significantly.

[0013] Preferably, among the erythrocyte metabolic markers, any one of lactate, sphingosine, cadaverine, uridine diphosphate, allantoic acid, hydroxyproline, or sphingosine-1-phosphate is used as a single metabolic marker for independently determining the risk of head and neck squamous cell carcinoma. Indole-3-acetaldehyde, pantothenic acid, fumaric acid, malic acid, prostaglandin, or ornithine can be used as auxiliary discriminant metabolic markers for assessing the risk of head and neck squamous cell carcinoma. Among the plasma metabolic markers, any one of lactic acid, allantoic acid, hydroxyproline, sphingosine-1-phosphate, fumaric acid, malic acid, ornithine, cadaverine, uridine diphosphate, pantothenic acid, or prostaglandin is used as a single metabolic marker for independently assessing the risk of head and neck squamous cell carcinoma. Indole-3-acetaldehyde is used as an auxiliary metabolic marker for assessing the risk of head and neck squamous cell carcinoma.

[0014] Under the same technical concept, the present invention also provides a kit for the preparation of a diagnostic product for head and neck squamous cell carcinoma using metabolic biomarkers, the kit comprising the diagnostic product.

[0015] Under the same technical concept, the present invention also provides a method for screening metabolic biomarkers for head and neck squamous cell carcinoma, comprising the following steps: (1) Collect red blood cell and plasma samples from patients with head and neck squamous cell carcinoma and healthy controls, and perform LC-MS non-targeted metabolomics analysis on the red blood cell and plasma samples; (2) The results of the LC-MS non-targeted metabolomics analysis were subjected to partial least squares discriminant analysis and univariate analysis. Substances with VIP>1.0 and P<0.05 were selected as significantly different metabolites. ROC curve analysis was then performed on the selected significantly different metabolites, and metabolites with AUC>0.65 were selected as metabolic markers.

[0016] Under the same technical concept, the present invention also provides a method for constructing a diagnostic model for head and neck squamous cell carcinoma, comprising the following steps: S1. Collect red blood cell and plasma samples from patients with head and neck squamous cell carcinoma and healthy controls, and perform LC-MS targeted or non-targeted metabolomics analysis on the red blood cell and plasma samples to screen for metabolic biomarkers; S2. Preprocess and analyze the data, construct a diagnostic model for metabolic biomarkers based on the Logistic regression model, and evaluate the diagnostic performance of single or different combinations of metabolic biomarkers through cross-validation and independent validation sets.

[0017] Under the same technical concept, the present invention also provides a diagnostic model for head and neck squamous cell carcinoma, which is constructed using the aforementioned construction method.

[0018] Preferably, the diagnostic model uses a combination of metabolic markers, namely sphingosine-1-phosphate, hydroxyproline, malic acid in plasma and lactate in erythrocytes, for the diagnosis of head and neck squamous cell carcinoma, to form the diagnostic model. The original detection value of the metabolic biomarker is the relative signal intensity value obtained by mass spectrometry detection, and its unit is: mass spectrometry peak intensity value (arbitrary units, au).

[0019] Perform a Z-score transformation on it, and the Z-score is calculated according to Z=(x-μ) / σ.

[0020] Where: x represents the measured value of a certain metabolic marker in a single sample; μ represents the average value of the metabolic biomarker in the overall training sample; σ represents the standard deviation of the metabolic biomarker in the training sample population; Z is a dimensionless standardized value; The concentrations of the metabolic biomarkers were converted to Z-scores, and the Z-scores were calculated according to (x-μ) / σ. The resulting diagnostic model scoring formula is as follows: Diagnostic score = Z[sphingosine-1-phosphate (plasma)] × 12.94 Z[hydroxyproline (plasma)]×5.47+Z[malic acid (plasma)]×11.39+Z[lactic acid (erythrocytes)]×1.65+4.92; The meanings of each parameter are explained below: Z[sphingosine-1-phosphate (plasma)] represents the Z-score-normalized value of sphingosine-1-phosphate concentration in a plasma sample; Z[hydroxyproline (plasma)] represents the Z-score-normalized value of hydroxyproline concentration in a plasma sample; Z[malic acid (plasma)] represents the Z-score-normalized value of malic acid concentration in a plasma sample; Z[Lactate (Red Blood Cells)] represents the Z-score-normalized value of lactate concentration in a red blood cell sample; 12.94 5.47, 11.39, and 1.65 are the regression coefficients of the above metabolic markers in the diagnostic model, reflecting the relative weight and contribution direction of each metabolic marker to the diagnostic score. All of them are dimensionless coefficients. 4.92 is the constant term (intercept term) of the model, used to correct the overall scoring baseline, and the unit is dimensionless; When the diagnostic score exceeds the preset cut-off value of 0.37, a high risk of developing head and neck squamous cell carcinoma is determined.

[0021] Under the same technical concept, the present invention also provides an application of a head and neck squamous cell carcinoma diagnostic model, wherein the diagnostic model or the diagnostic model constructed by the construction method is used to construct a clinical screening or auxiliary diagnostic system for head and neck squamous cell carcinoma.

[0022] The above-described solution of the present invention has the following beneficial effects: (1) This invention provides a set of red blood cell and plasma metabolic markers for the diagnosis of head and neck squamous cell carcinoma. The selected red blood cell and plasma metabolic markers can be used independently or as an adjunct to predict or diagnose the disease of head and neck squamous cell carcinoma. In particular, red blood cell metabolic markers can provide more stable tumor microenvironment information, have high metabolic stability, and are not easily affected by diet and diurnal rhythm. (2) The metabolic markers provided by the present invention have significantly improved stability, sensitivity and integrity. The metabolic markers can be used alone or in combination based on diagnostic needs. The combination of screened red blood cell and plasma metabolic markers and analysis of both plasma and red blood cells can systematically reflect short-term changes in the human tumor microenvironment, eliminate interference from the body, and provide an effective method and scientific basis for early detection and intervention treatment of head and neck squamous cell carcinoma.

[0023] (3) Based on multiple cross-comparisons of different machine learning models, this invention constructs a comprehensive model for diagnosing head and neck squamous cell carcinoma, and the comprehensive model provided has high accuracy.

[0024] (4) The present invention also provides a mass spectrometry method for rapid detection and screening of target metabolic small molecules in red blood cells or plasma. Attached Figure Description

[0025] Figure 1 These are 13 metabolic markers coexisting in erythrocytes and plasma, selected in Example 1 of this invention; Figure 2 The diagnostic efficacy (AUC≥0.7) of erythrocyte metabolic markers in ROC analysis in Example 2 of this invention; Figure 3 The diagnostic efficacy (AUC≥0.7) of plasma metabolic markers in ROC analysis in Example 2 of this invention; Figure 4 This is a flowchart illustrating the overall solution of an embodiment of the present invention; Figure 5 These are metabolites that show a decreasing trend in red blood cells of patients with squamous cell carcinoma of the neck; Figure 6 These are metabolites that show an increasing trend in red blood cells of patients with squamous cell carcinoma of the neck. Figure 7 These are metabolites that show a decreasing trend in plasma levels in patients with squamous cell carcinoma of the neck; Figure 8 These are metabolites that show an increasing trend in the plasma of patients with squamous cell carcinoma of the neck; Figure 9 This is the ROC curve of the training set of the Logistic regression joint diagnostic model in Embodiment 3 of the present invention; Figure 10 This is the ROC curve of the test set of the Logistic regression joint diagnostic model in Embodiment 3 of the present invention. Detailed Implementation

[0026] To make the technical problems, solutions, and advantages of this invention clearer, a detailed description will be provided below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0027] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0028] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a locking connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0029] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0030] Example 1: Screening of erythrocyte and plasma metabolic markers Figure 4 This is a flowchart illustrating the overall solution of an embodiment of the present invention; 1. Acquisition of human sample composition data This invention collected blood samples from 44 patients with head and neck squamous cell carcinoma, totaling 44 cases (HNSCC group). All patients included in the study were confirmed by histopathological diagnosis of head and neck squamous cell carcinoma and had not undergone local surgery, radiotherapy, chemotherapy or other anti-tumor treatments before the first treatment.

[0031] Collect patient clinicopathological characteristics, including age, gender, tumor TNM stage, lesion location, and depth of invasion.

[0032] Concurrently, 44 healthy volunteers matched for age and sex with the HNSCC group were recruited as the control group (NC group), excluding those with a history of cancer or other serious diseases.

[0033] All data used were derived from patient medical records and follow-up records with patient privacy information redacted. The implementation of the treatment plan had no impact on the patient's treatment or personal rights, and all participants signed informed consent forms, which were approved by the hospital's ethics committee.

[0034] 2. Blood sample collection, separation, and preservation Red blood cell samples include, but are not limited to: washed intact red blood cells, red blood cell lysates, and red blood cell extracts. Plasma samples include, but are not limited to: plasma separated from peripheral blood after anticoagulation treatment, plasma supernatant after further clarification, plasma lysate products or plasma extracts obtained by chemical or physical methods.

[0035] During sample collection, all subjects were required to have 3 mL of peripheral venous blood drawn in the morning on an empty stomach and placed into an EDTA anticoagulant tube. Within 1 hour of blood collection, the blood sample was centrifuged at 2000g for 5 minutes at 4°C to separate plasma, red blood cells, and the intermediate layer (white blood cells and platelets, etc.).

[0036] Extract the upper plasma layer, aliquot 200 μL into 1.5 ml EP tubes, flash freeze in liquid nitrogen, and store at -80°C.

[0037] Aspirate the middle layer (leukocytes and platelets) into 1.5 ml EP tubes, flash-freeze in liquid nitrogen, and store at -80°C. Aspirate the lower layer of red blood cells, aliquot 200 μL into 1.5 ml EP tubes, flash-freeze in liquid nitrogen, and store at -80°C.

[0038] 3. Rapid detection and analysis of non-targeted metabolomics based on LC-MS Characteristic metabolites in the erythrocyte and plasma components were detected separately.

[0039] 1) Sample pretreatment: The red blood cells and plasma samples to be tested were added to a lysis buffer pre-cooled at -20℃ (methanol:acetonitrile:water = 5:3:2, volume ratio) at a ratio of 1:10 and 1:25, respectively, for lysis treatment.

[0040] First, remove the red blood cell and plasma samples and thaw them on ice. After complete thawing, aspirate 20 μL of RBCs and plasma into 1.5 ml EP tubes respectively. Next, add 180 μL of pre-chilled lysis buffer (-20°C) to the red blood cell sample tube and 480 μL of pre-chilled lysis buffer (-20°C) to the plasma sample tube.

[0041] Shake the resulting mixture for a few seconds, then place it on a shaker at 4°C and shake for another 30 minutes. Centrifuge at 18300g for 10 minutes at 4°C. Take 100 μL of the supernatant and place it in the sample insertion tube. Take 20 μL of the supernatant from each sample and mix them to form a quality control (QC) sample for instrument testing.

[0042] 2) On-machine detection: Metabolomics detection was performed using a Thermo Fisher Dionex UltiMate 3000 UHPLC and Qexactive HF MS ultra-high performance liquid chromatography-mass spectrometry system.

[0043] The chromatographic column used was a Kinetex C18 column (2.1 x 150 mm, 1.7 μm), the flow rate was 0.45 m1 / min, the column oven temperature was 45°C, the sample temperature was 4°C, and the injection volume was 10 m.

[0044] The positive ion mode liquid chromatography section used mobile phase A (0.1% formic acid aqueous solution) and mobile phase D (0.1% formic acid acetonitrile solution). The elution gradient was as follows: 0-0.5 min, 5% D; 0.5-1.1 min, 5%-95% D; 1.1-2.75 min, 95% A; 2.75-3 min, 95%-5% A; 3-5 min, 5% A.

[0045] The negative ion mode liquid chromatography section used mobile phase B (95% acetonitrile aqueous solution / 1mM ammonium acetate solution) and mobile phase C (5% acetonitrile aqueous solution / 1mM ammonium acetate solution). The elution gradient was as follows: 0-0.5 min, 100% C; 0.5-1.1 min, 100% B; 1.1-2.75 min, 100% B; 2.75-3 min, 100%-0% B; 3-5 min, 100% C.

[0046] Mass spectrometry parameters were set to Resolution 60000, Scan Range 65-900 m / z, Maximum injection time 200 ms, Automatic gain control (AGC) 3 x 10⁶ ions, Sheath gas 45, Auxiliary gas 15, and Sweep gas 0. All samples were injected randomly, with a QC sample injected every 10 samples.

[0047] 4. Data Analysis and Screening The raw data was converted into .mzXML format files using RawConverter software, and then imported into EI-Maven software for peak extraction. The data underwent log10 transformation, normalization, and standardization.

[0048] Unsupervised principal component analysis (PCA) based on QC samples was used to assess data quality.

[0049] 5. Construction of Metabolic Biomarker Characteristics Univariate analysis of metabolic biomarkers: Partial least squares discriminant analysis (PLS-DA) and univariate analysis (t-test) were used to screen for metabolites that showed significant differences in erythrocytes and plasma between the head and neck squamous cell carcinoma group and the healthy control group (VIP>1.0, P<0.05).

[0050] Statistical analysis was performed on the data of metabolites in red blood cells and plasma to screen out metabolites with significant differences between the head and neck squamous cell carcinoma group and the control group (VIP>1, P<0.05).

[0051] The significantly different metabolites in red blood cells and plasma of the head and neck squamous cell carcinoma group and the control group were intersected to obtain a set of metabolic markers that coexist in red blood cells and plasma.

[0052] As Figure 1 shown, the specific metabolic markers are: Lactate, Sphingosine, Cadaverine, Uridine Diphosphate (UDP), Allantoate, 5-Oxoproline, Sphingosine-1-phosphate (S1P), Indole-3-acetaldehyde, Pantothenate, Fumarate, S-Malate, Prostaglandine, Ornithine.

[0053] The specific upward and downward trends of significantly different metabolites in red blood cells and plasma are as Figure 5-8 shown.

[0054] Figure 5 are metabolites showing a downward trend in the red blood cells of patients with cervical squamous cell carcinoma; Figure 6 are metabolites showing an upward trend in the red blood cells of patients with cervical squamous cell carcinoma; Figure 7 are metabolites showing a downward trend in the plasma of patients with cervical squamous cell carcinoma; Figure 8 are metabolites showing an upward trend in the plasma of patients with cervical squamous cell carcinoma.

[0055] Example 2: Analysis of the diagnostic ability of single metabolic markers ROC curve analysis of metabolic biomarkers ROC analysis was performed on the significantly different metabolites in red blood cells / plasma of the head and neck squamous cell carcinoma patient group and the healthy control group, and the judgment was made using the AUC (Area Under Curves) value. Among them, AUC = 0.5: It is considered that the classification ability is the same as random guessing; 0.5 < AUC < 0.7: The classification ability is weak; 0.7 < AUC < 0.9: The classification ability is good; AUC ≥ 0.9: The classification ability of the model is very strong. In this way, the diagnostic efficacy of single metabolic markers was clarified.

[0056] Biomarker ROC analysis (MetaboAnalyst 6.0) was performed on the non-targeted metabolic data of red blood cells and plasma. The results showed that the areas under the ROC curves (AUC) of 13 different metabolites in the red blood cells and plasma of head and neck squamous cell carcinoma patients are shown in Table 1 below, and the ROC curve analysis diagram is as Figure 2 , 3 As shown: Table 1. Area under the ROC curve for 13 metabolic markers in erythrocytes / plasma

[0057] Metabolomics analysis revealed stable and consistent trends in the changes of various metabolites between patients with head and neck squamous cell carcinoma and healthy controls. Erythrocyte and plasma metabolic markers, including lactate, sphingosine, cadaverine, uridine diphosphate (UDP), allantoate, 5-Oxoproline, and sphingosine-1-phosphate (S1P), all demonstrated good diagnostic performance (AUC ≥ 0.7).

[0058] 1) Tumor diagnosis based on erythrocyte metabolites The diagnostic ability of the above 13 metabolites in red blood cells was independently analyzed and evaluated by combining the tumor / normal fold change (FC) values ​​between patients with head and neck squamous cell carcinoma and healthy controls, and by incorporating receiver operating characteristic (ROC) curves. The details are as follows: ① AUC≥0.90: Metabolites with excellent diagnostic performance Lactate and sphingosine showed strong discriminative power between the cancer group and the healthy control group, with AUC values ​​exceeding 0.90.

[0059] Lactate levels were significantly lower in the cancer group, with a relative level significantly lower than that in the healthy control group; sphingosine levels were significantly higher in the cancer group.

[0060] The aforementioned metabolites can achieve highly efficient differentiation of head and neck squamous cell carcinoma when used alone, and have extremely high auxiliary diagnostic value. They can be used as core metabolic markers for independent risk assessment of head and neck squamous cell carcinoma.

[0061] ② 0.70≤AUC<0.90: Metabolites with good diagnostic performance and practical value. Figure 2The diagnostic efficacy of erythrocyte metabolic markers in ROC analysis (AUC≥0.7) was evaluated. Cadaverine and uridine diphosphate (UDP) were significantly elevated in the cancer group, while allantoate, 5-Oxoproline, sphingosine-1-phosphate, and pantothenate showed a decreasing trend in the cancer group.

[0062] These metabolites showed stable and reproducible differentiation effects and trends between the cancer group and the healthy control group, and can be used as a single metabolic biomarker for obtaining auxiliary diagnostic information of head and neck squamous cell carcinoma.

[0063] ③ 0.65≤AUC<0.70: Metabolites used for auxiliary discrimination Indole-3-acetaldehyde and ornithine showed an increasing trend in the cancer group; fumarate, S-malate, and prostaglandin showed a decreasing trend in the cancer group.

[0064] A certain degree of differentiation still exists between the cancer group and the healthy control group. Its diagnostic performance is limited when used alone, but its trends can reflect the overall perturbation of tumor-related metabolic pathways. When combined with high AUC metabolites for analysis, it can serve as a complementary feature to enhance the model's ability to characterize the overall metabolic state of tumors.

[0065] 2) Tumor diagnosis based on plasma metabolites The diagnostic ability of the above 13 metabolites in red blood cells was independently analyzed and evaluated by combining the tumor / normal fold change (FC) values ​​between patients with head and neck squamous cell carcinoma and healthy controls, and by incorporating receiver operating characteristic (ROC) curves. The details are as follows: ① AUC≥0.90: Metabolites with excellent diagnostic performance Specifically, lactate, allantoate, 5-Oxoproline, sphingosine-1-phosphate, fumarate, S-malate, and ornithine were all significantly downregulated in the plasma of the cancer group; while cadaverine showed a significant upregulation in the cancer group.

[0066] The aforementioned metabolites not only have extremely high diagnostic discrimination ability at the AUC level, but also exhibit the most significant variation. When used alone, they can achieve highly efficient discrimination of head and neck squamous cell carcinoma, possessing extremely high auxiliary diagnostic value. They can serve as core metabolic markers and be used independently for risk assessment of head and neck squamous cell carcinoma.

[0067] ② 0.70≤AUC<0.90: Metabolites with good diagnostic properties in plasma Figure 3 shows the diagnostic efficacy (AUC≥0.7) of plasma metabolic markers in ROC analysis; uridine diphosphate (UDP) and pantothenate were significantly upregulated in the cancer group plasma, while sphingosine and prostaglandin were significantly downregulated in the cancer group. These metabolites can be used to obtain auxiliary diagnostic information for head and neck squamous cell carcinoma under single-indicator conditions, and their combined analysis with high-AUC metabolites can further improve the stability of the overall diagnostic model.

[0068] ③ 0.65≤AUC<0.70: Auxiliary discriminant metabolite This stratification contains only indole-3-acetaldehyde, and its diagnostic performance is relatively limited when used alone. However, this metabolite shows an upregulated trend in the plasma of cancer patients and can serve as a complementary feature when analyzed in conjunction with metabolites of high discriminative power.

[0069] In summary, the discriminative abilities of different erythrocyte and plasma metabolites in the auxiliary diagnosis of head and neck squamous cell carcinoma exhibit a stratified distribution. Among them, metabolites with higher AUC values ​​can serve as core diagnostic markers for independent application, while metabolites with lower AUC values ​​can serve as supplementary markers with synergistic effects.

[0070] Example 3: Construction and Validation of a Logistics Regression Joint Diagnostic Model A combined diagnostic factor for head and neck squamous cell carcinoma was developed using erythrocyte and plasma markers. Feature selection and model construction were performed using logistic regression, which improved the accuracy of disease diagnosis. Metabolites showing significant differences (VIP>1.0, P<0.05) in erythrocytes and plasma between the head and neck squamous cell carcinoma group and healthy controls were screened using the aforementioned method. A combined diagnostic factor was constructed based on a 4-metabolite combination of four metabolites—sphingosine-1-phosphate (plasma), hydroxyproline (plasma), malate (plasma), and lactate (erythrocytes)—with individual AUC>0.95 found in both erythrocytes and plasma. Subsequently, logistic regression was used to jointly model these four metabolic biomarkers.

[0071] All samples were randomly divided into a training set and a validation set, with the training set accounting for approximately 70%-80% of the total sample size and the validation set accounting for approximately 20%-30%. The regression coefficients of each metabolite in the logistic regression model were estimated using the training set, and a joint diagnostic scoring function was constructed. The diagnostic performance of the model was independently evaluated using the validation set.

[0072] The raw detection values ​​of metabolic biomarkers are relative signal intensity values ​​obtained from mass spectrometry detection, with units of arbitrary units (au). These values ​​are then converted to Z-scores, calculated as Z = (x - μ) / σ, where: x represents the measured value of a metabolic biomarker in a single sample; μ represents the mean value of that metabolic biomarker in the training sample population; σ represents the standard deviation of that metabolic biomarker in the training sample population; and Z is a dimensionless standardized value. The concentrations of metabolic biomarkers were converted using Z-score conversion, with the Z-score calculated as (x-μ) / σ. The joint factor regression equation for these four metabolites is as follows: Diagnostic score = Z[sphingosine-1-phosphate (plasma)] × 12.94 Z[hydroxyproline (plasma)]×5.47+Z[malate (plasma)]×11.39+Z[lactic acid (erythrocytes)]×1.65+4.92 The meanings of each parameter are explained below: Z[Sphingosine-1-phosphate (plasma)] represents the Z-score-normalized concentration of sphingosine-1-phosphate in the plasma sample; Z[Hydroxyproline (plasma)] represents the Z-score-normalized concentration of hydroxyproline in the plasma sample; Z[Malate (plasma)] represents the Z-score-normalized concentration of malate in the plasma sample; Z[Lactate (erythrocytes)] represents the Z-score-normalized concentration of lactate in the erythrocyte sample; 12.94, 5.47, 11.39, and 1.65 are the regression coefficients of the aforementioned metabolic biomarkers in the diagnostic model, reflecting the relative weight and contribution direction of each metabolic biomarker to the diagnostic score; all are dimensionless coefficients. 4.92 is the constant term (intercept term) of the model, used to correct the overall score baseline; the unit is dimensionless. The preset threshold (cut-off value) is 0.37; when the combined factor value is greater than or equal to the preset threshold, the sample is identified as a tumor. As shown in Table 2 below, the combined factor regression model constructed based on the four metabolites exhibits extremely high discriminative performance in both the training and independent test sets. The ROC curves of the training and independent test sets are shown below. Figure 9 , 10As shown in the figure. ROC curve analysis shows that the model's AUC in the training set is 0.9948 (95% CI: 0.9838–1.0000), and the AUC in the test set is 1.0000, indicating that the model can stably and accurately distinguish between tumor samples and non-tumor samples. The optimal interpretation threshold remains consistent in both the training and test sets. Under this threshold condition, the model achieves 100% sensitivity and specificity in both datasets, and the overall interpretation accuracy in the test set reaches 100%. These results demonstrate that this combined metabolite model has excellent discriminative ability and good reproducibility, making it suitable for auxiliary discrimination of tumor samples.

[0073] Table 2. ROC analysis results of the four metabolite combined factor model

[0074] To further evaluate the stability of the combined metabolite model, 10-fold cross-validation was used to validate the model's performance, and the results are shown in Table 3. The results show that, across different cross-validation tradeoffs, the model's mean AUC was 0.9675, and the mean sensitivity and specificity were 0.98 and 0.955, respectively. Even in the most unfavorable validation tradeoff, the model maintained high discriminative performance.

[0075] Table 3. Results of 10-fold cross-validation

[0076] The above results indicate that the combined metabolite model has good stability and generalization ability under different sample partitioning conditions, and its discrimination effect does not depend on the result of a single data partitioning.

Claims

1. The application of a metabolic biomarker in the preparation of diagnostic products for head and neck squamous cell carcinoma, characterized in that, The metabolic markers include at least one of the following in erythrocytes and / or plasma: lactate, sphingosine, cadaverine, uridine diphosphate, allantoic acid, hydroxyproline, sphingosine-1-phosphate, indole-3-acetaldehyde, pantothenic acid, fumaric acid, malic acid, prostaglandin, or ornithine.

2. The application as described in claim 1, characterized in that, The diagnostic product assesses the risk of head and neck squamous cell carcinoma by detecting a significant increase or decrease in the relative levels of metabolic markers in erythrocytes and / or plasma, wherein the significance P-value for the significant increase or decrease is <0.

05.

3. The application as described in claim 1, characterized in that, The aforementioned metabolic markers, independently or jointly, assess the risk of head and neck squamous cell carcinoma in erythrocytes or plasma. In erythrocytes, the relative content of one or more of lactic acid, pantothenic acid, fumaric acid, malic acid, hydroxyproline or prostaglandins decreased significantly, while the relative content of one or more of sphingosine-1-phosphate, sphingosine, cadaverine, uridine diphosphate, allantoic acid, indole-3-acetaldehyde or ornithine increased significantly. In plasma, the relative levels of one or more of sphingosine-1-phosphate, sphingosine, hydroxyproline, fumaric acid, malic acid, lactic acid, ornithine, allantoic acid, or prostaglandins decreased significantly, while the relative levels of one or more of uridine diphosphate, cadaverine, pantothenic acid, or indole-3-acetaldehyde increased significantly.

4. The application as described in claim 3, characterized in that, Among the erythrocyte metabolic markers, any one of lactate, sphingosine, cadaverine, uridine diphosphate, allantoic acid, hydroxyproline, or sphingosine-1-phosphate is used as a single metabolic marker for independently assessing the risk of head and neck squamous cell carcinoma. Indole-3-acetaldehyde, pantothenic acid, fumaric acid, malic acid, prostaglandin, or ornithine can be used as auxiliary discriminant metabolic markers for assessing the risk of head and neck squamous cell carcinoma. Among the plasma metabolic markers, any one of lactic acid, allantoic acid, hydroxyproline, sphingosine-1-phosphate, fumaric acid, malic acid, ornithine, cadaverine, uridine diphosphate, pantothenic acid, or prostaglandin is used as a single metabolic marker for independently assessing the risk of head and neck squamous cell carcinoma. Indole-3-acetaldehyde is used as an auxiliary metabolic marker for assessing the risk of head and neck squamous cell carcinoma.

5. A kit for the use of metabolic markers in the preparation of diagnostic products for head and neck squamous cell carcinoma, characterized in that, The kit contains the diagnostic product as described in any one of claims 1-4.

6. A method for screening metabolic biomarkers for head and neck squamous cell carcinoma, characterized in that, Includes the following steps: (1) Collect red blood cell and plasma samples from patients with head and neck squamous cell carcinoma and healthy controls, and perform LC-MS non-targeted metabolomics analysis on the red blood cell and plasma samples; (2) The results of the LC-MS non-targeted metabolomics analysis were subjected to partial least squares discriminant analysis and univariate analysis. Substances with VIP>1.0 and P<0.05 were selected as significantly different metabolites. ROC curve analysis was then performed on the selected significantly different metabolites, and metabolites with AUC>0.65 were selected as metabolic markers.

7. A method for constructing a diagnostic model for head and neck squamous cell carcinoma, characterized in that, Includes the following steps: S1. Collect red blood cell and plasma samples from patients with head and neck squamous cell carcinoma and healthy controls, and perform LC-MS targeted or non-targeted metabolomics analysis on the red blood cell and plasma samples to screen for metabolic biomarkers; S2. Preprocess and analyze the data, construct a diagnostic model for metabolic biomarkers based on the Logistic regression model, and evaluate the diagnostic performance of single or different combinations of metabolic biomarkers through cross-validation and independent validation sets.

8. A diagnostic model for head and neck squamous cell carcinoma, characterized in that, It is constructed using the construction method described in claim 7.

9. The diagnostic model as described in claim 8, characterized in that, The diagnostic model uses sphingosine-1-phosphate, hydroxyproline, malic acid in plasma and lactate in erythrocytes as a combination of metabolic markers for the diagnosis of head and neck squamous cell carcinoma. The original detection value of the metabolic biomarker is a relative signal intensity value obtained by mass spectrometry detection, and its unit is the mass spectrum peak intensity value. The Z-score is then converted, and the Z-score is calculated according to Z=(x-μ) / σ, where: x represents the measured value of a metabolic marker in a single sample; μ represents the mean of the metabolic marker in the training sample population; σ represents the standard deviation of the metabolic marker in the training sample population; and Z is a dimensionless standardized value. The obtained diagnostic model scoring formula is: Diagnostic score = Z[sphingosine-1-phosphate (plasma)] × 12.94 Z[hydroxyproline (plasma)]×5.47+Z[malic acid (plasma)]×11.39+Z[lactic acid (erythrocytes)]×1.65+4.92; The meanings of each parameter are explained below: Z[sphingosine-1-phosphate (plasma)] represents the Z-score-normalized value of sphingosine-1-phosphate concentration in a plasma sample; Z[hydroxyproline (plasma)] represents the Z-score-normalized value of hydroxyproline concentration in a plasma sample; Z[Malate (Plasma)] represents the Z-score normalized value of malate concentration in plasma sample; Z[Lactate (Red Blood Cell)] represents the Z-score normalized value of lactate concentration in red blood cell sample. 12.94 5.47, 11.39, and 1.65 are the regression coefficients of the above metabolic markers in the diagnostic model, reflecting the relative weight and contribution direction of each metabolic marker to the diagnostic score. All of them are dimensionless coefficients. 4.92 is a constant term in the model, used to correct the overall scoring baseline, and its unit is dimensionless; When the diagnostic score exceeds the preset cut-off value of 0.37, a high risk of developing head and neck squamous cell carcinoma is determined.

10. An application of a diagnostic model for head and neck squamous cell carcinoma, characterized in that, The diagnostic model as described in any one of claims 8-9 or the diagnostic model constructed by the construction method as described in claim 7 can be used to construct a clinical screening or auxiliary diagnostic system for head and neck squamous cell carcinoma.

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