Plasma metabolism marker for kidney cancer diagnosis and application thereof

Abstract

The invention provides a plasma metabolism marker for kidney cancer diagnosis and application thereof, and belongs to the technical field of disease diagnosis. According to the invention, through comprehensive analysis and comparison of metabolic profiles of healthy people, kidney benign disease patients and kidney cancer patients, a group of metabolic marker combination with high specificity and significant difference is screened out, and the metabolic marker combination comprises 14 metabolic markers. As an end product of cell metabolic activity, the metabolic marker can more directly and sensitively reflect the abnormal metabolic state of tumor cells, so that early accurate recognition of kidney cancer and precancerous lesions is realized, the sensitivity of early diagnosis is remarkably improved, and the current clinical dilemma of'late stage once found 'is fundamentally reversed. The strategy is expected to greatly improve the survival rate and life quality of patients with kidney cancer, and a brand new path is provided for promoting precise medical treatment of kidney cancer.

Description

Technical Field

[0001] This application belongs to the field of disease diagnostic technology, specifically relating to a plasma metabolic marker for the diagnosis of renal cell carcinoma and its application. Background Technology

[0002] Renal cell carcinoma (RCC) is one of the most common malignant tumors of the urinary system. Currently, the main clinical diagnostic methods for RCC include imaging examinations (such as ultrasound, CT, and MRI) and liquid biopsy techniques. Although imaging techniques can incidentally detect approximately 60% of cases, the sensitivity of existing biomarkers is insufficient to meet the needs of early diagnosis. Common clinical methods face significant challenges in identifying small tumors and differentiating between benign and malignant cases, often requiring time-consuming examination procedures. Furthermore, the lack of effective biomarkers and prognostic assessment tools also hinders the development of precision treatment strategies. Summary of the Invention

[0003] The purpose of this invention is to provide a plasma metabolic biomarker for the diagnosis of renal cell carcinoma, enabling early and accurate differentiation between renal cell carcinoma and precancerous lesions, significantly improving the sensitivity of early diagnosis, and providing a new pathway for promoting precision medicine for renal cell carcinoma.

[0004] This invention provides a plasma metabolic marker for the diagnosis of renal cell carcinoma, comprising at least three of the following metabolic markers: L-glutamyl-L-aspartic acid, L-piperidinic acid, 1-methylnicotinamide, uridine, arginine, glycyl-L-asparagine, ceramide d34:1, phosphatidylethanolamine 38:6e, D-glucuronic acid, glucose, 3-hydroxy-3-methylglutaric acid, urea, lysophosphatidylcholine 15:0, and lysophosphatidylcholine 20:1.

[0005] Preferably, it includes a combination of at least one of the following three metabolic markers: a first marker consisting of L-glutamyl-L-aspartate, L-piperidinic acid, and 1-methylnicotinamide; a second marker consisting of L-glutamyl-L-aspartate, L-piperidinic acid, and uridine; a third marker consisting of L-glutamyl-L-aspartate, L-piperidinic acid, and arginine; a fourth marker consisting of L-glutamyl-L-aspartate, L-piperidinic acid, and glycyl-L-asparagine; a fifth marker consisting of L-glutamyl-L-aspartate, L-piperidinic acid, and ceramide d34:1; and a third marker consisting of L-glutamyl-L-aspartate, L-piperidinic acid, and phosphatidylethanolamine. The sixth biomarker is composed of 8:6e; the seventh biomarker is composed of L-glutamyl-L-aspartic acid, L-piperidine acid and D-glucuronic acid; the eighth biomarker is composed of L-glutamyl-L-aspartic acid, L-piperidine acid and glucose; the ninth biomarker is composed of L-glutamyl-L-aspartic acid, L-piperidine acid and 3-hydroxy-3-methylglutaric acid; the tenth biomarker is composed of L-glutamyl-L-aspartic acid, L-piperidine acid and urea; the eleventh biomarker is composed of L-glutamyl-L-aspartic acid, L-piperidine acid and lysophosphatidylcholine in a 20:1 ratio; and the twelfth biomarker is composed of L-glutamyl-L-aspartic acid, 1-methylnicotinamide and uridine.

[0006] Preferably, it includes a combination of at least six metabolic markers: L-glutamyl-L ... The thirteenth biomarker consists of aspartic acid, L-piperidinic acid, uridine, arginine, D-glucuronic acid, and urea; the fourteenth biomarker consists of L-piperidinic acid, 1-methylnicotinamide, uridine, glucose, 3-hydroxy-3-methylglutaric acid, and lysophosphatidylcholine 15:0; the fifteenth biomarker consists of 1-methylnicotinamide, arginine, glycyl-L-asparagine, ceramide d34:1, glucose, and 3-hydroxy-3-methylglutaric acid; the sixteenth biomarker consists of L-glutyl-L-aspartic acid, 1-methylnicotinamide, arginine, ceramide d34:1, D-glucuronic acid, and lysophosphatidylcholine 15:0; and the seventeenth biomarker consists of glycyl-L-asparagine, ceramide d34:1, phosphatidylethanolamine 38:6e, urea, lysophosphatidylcholine 15:0, and lysophosphatidylcholine 20:1.

[0007] Preferably, the combination includes at least one of the following nine metabolic markers: an eighteenth marker consisting of L-glutoyl-L-aspartic acid, L-piperidinic acid, uridine, arginine, glycyl-L-asparagine, phosphatidylethanolamine 38:6e, D-glucuronic acid, urea, and lysophosphatidylcholine 15:0; a nineteenth marker consisting of L-piperidinic acid, 1-methylnicotinamide, glycyl-L-asparagine, ceramide d34:1, D-glucuronic acid, glucose, urea, lysophosphatidylcholine 15:0, and lysophosphatidylcholine 20:1; and a twentieth marker consisting of 1-methylnicotinamide, uridine, glycyl-L-asparagine, ceramide d34:1, D-glucuronic acid, glucose, 3-hydroxy-3-methylglutaric acid, urea, and lysophosphatidylcholine 15:0.

[0008] Preferably, it includes a combination of at least one of the following 12 metabolic markers: L-glutamyl-L-aspartic acid, L-piperidinic acid, 1-methylnicotinamide, uridine, arginine, glycyl-L-asparagine, ceramide d34:1, phosphatidylethanolamine 38:6e, D The twenty-first biomarker consists of glucuronic acid, glucose, urea, and lysophosphatidylcholine in a 15:0 ratio; the twenty-second biomarker consists of L-glutamyl-L-aspartic acid, L-piperidinic acid, 1-methylnicotinamide, uridine, glycyl-L-asparagine, ceramide d34:1, phosphatidylethanolamine 38:6e, D-glucuronic acid, glucose, 3-hydroxy-3-methylglutaric acid, lysophosphatidylcholine 15:0, and lysophosphatidylcholine 20:1; and the twenty-third biomarker consists of L-glutamyl-L-aspartic acid, L-piperidinic acid, 1-methylnicotinamide, uridine, arginine, ceramide d34:1, phosphatidylethanolamine 38:6e, D-glucuronic acid, glucose, 3-hydroxy-3-methylglutaric acid, urea, and lysophosphatidylcholine 20:1.

[0009] This invention provides the application of the plasma metabolic biomarkers in constructing a predictive model for the diagnosis of renal cell carcinoma.

[0010] Preferably, the prediction model is constructed using the support vector machine algorithm.

[0011] This invention provides the application of the detection reagent for the plasma metabolic marker in the preparation of diagnostic kits that differentiate between healthy individuals, benign kidney diseases, and kidney cancer.

[0012] Preferably, the detection reagents include formic acid, acetonitrile, methyl tert-butyl ether, acetic acid, and methanol.

[0013] This invention provides a plasma metabolic biomarker for the diagnosis of renal cell carcinoma. Leveraging metabolomics technology, this invention offers an innovative solution for disease diagnosis and exhibits several significant advantages: First, it employs a non-invasive detection method, requiring only plasma sample collection, making it safe, reliable, and with high patient compliance; second, it possesses high sensitivity and specificity, accurately capturing subtle changes in metabolites for precise prediction and assessment; third, it has high-throughput analysis capabilities, simultaneously detecting multiple metabolites, suitable for efficiently processing large-scale clinical samples; fourth, it is cost-effective, effectively reducing resource consumption and significantly improving detection efficiency; fifth, it offers early diagnostic advantages, enabling accurate assessment in the early stages of diseases such as cancer. Furthermore, the changing trends of the metabolic biomarker reflect the body's current pathophysiological state, continuously tracking disease progression and treatment effectiveness, providing crucial evidence for timely adjustment of treatment strategies and reducing the risk of recurrence. Overall, this non-invasive detection method based on metabolomics has unique advantages, overcoming the invasiveness and limitations of traditional biopsies, enabling real-time monitoring of metabolite changes. At the same time, metabolomics analysis has high sensitivity, capable of detecting minute changes that traditional methods cannot detect, providing possibilities for early diagnosis and providing key references for clinicians to develop and optimize treatment plans. Attached Figure Description

[0014] Figure 1 The results of multivariate ROC curve analysis for 14 metabolic biomarkers in the modeling group; Figure 2 To validate the results of multivariate ROC curve analysis of 14 metabolic markers in the group. Detailed Implementation

[0015] This invention provides a plasma metabolic marker for the diagnosis of renal cell carcinoma, comprising at least three of the following metabolic markers: L-glutamyl-L-aspartic acid, L-piperidinic acid, 1-methylnicotinamide, uridine, arginine, glycyl-L-asparagine, ceramide d34:1 (d18:1 / 16:0), phosphatidylethanolamine 38:6e, D-glucuronic acid, glucose, 3-hydroxy-3-methylglutaric acid, urea, lysophosphatidylcholine 15:0, and lysophosphatidylcholine 20:1.

[0016] In this invention, the method for screening plasma metabolic markers preferably involves detecting metabolites in plasma samples from healthy individuals, patients with benign kidney disease, and patients with kidney cancer, and screening for differentially expressed metabolites to obtain a combination of plasma metabolic markers.

[0017] In this invention, the number of patients with benign renal diseases is no less than 50, and the number of patients with renal cancer is no less than 100. In this embodiment of the invention, the number of patients with benign renal diseases is 64, and the number of patients with renal cancer is 107. The patients with benign renal diseases and patients with renal cancer are qualified as subjects through strict screening criteria to ensure the accuracy of the test results. The screening criteria preferably include the following exclusion criteria: 1) pregnancy or lactation; 2) emergency or requiring resuscitation; 3) history of blood transfusion within 7 days prior to sampling; 4) individuals who have received organ transplants or have previously received non-autologous (allogeneic) bone marrow or stem cell transplants; 5) history of malignant tumors within 5 years or having undergone any anti-tumor treatment before sampling; 6) concurrent multiple primary malignant tumors.

[0018] In this invention, the method for metabolite detection preferably includes pretreatment of plasma samples and detection of small molecule metabolites using liquid chromatography-mass spectrometry (LC-MS / MS). The plasma sample pretreatment method preferably includes mixing plasma with a first extract, treating the resulting mixture with a methanol-water solution, centrifuging to obtain a lower aqueous phase and an upper organic phase; treating the aqueous phase with ice-cold methanol, drying the supernatant, and resolving the resulting solid phase in water to obtain the aqueous analyte; removing the solvent from the upper organic phase, extracting with a second extract, separating, and collecting the supernatant to obtain the organic analyte. The first extract is preferably a mixture of methyl tert-butyl ether and methanol at a volume ratio of 3:1. The second extract is preferably a mixture of acetonitrile and isopropanol at a volume ratio of 3:1. The mixing is preferably performed using a vortex ultrasonic method. The volume ratio of methanol-water solution to the mixture is preferably 1:1 to 2. The volume ratio of methanol-water solution to the aqueous phase is preferably 3:1. The organic analyte is analyzed using Waters ACQUITY UPLC. ® A BEH C8 1.7 µm 2.1 × 100 mm column was used, and the aqueous phase analyte was analyzed using a Waters ACQUITY UPLC. ® Small molecule separation was performed using an HSS T3 1.8 µm 2.1 × 100 mm column. The liquid chromatography system preferably included an ACQUITY UPLC I-Class system (Waters). The mass spectrometer preferably included a Q-Exactive mass spectrometry system (Thermo Fisher Scientific). The mobile phase parameters for the organic analyte were as follows: Mobile phase A was an aqueous solution containing 0.1% acetic acid and 10 mmol ammonium acetate; mobile phase B was a mixed solution of acetonitrile and isopropanol containing 0.1% acetic acid and 10 mmol ammonium acetate (acetonitrile to isopropanol volume ratio of 7:3, acetic acid mass content of 0.1%, ammonium acetate mass content of 0.1%), with the following elution gradient: 55%-89% mobile phase B for 0-12 min, and 100% mobile phase B for 12-19.5 min.

[0019] The preferred mobile phase parameters for liquid chromatography detection of the aqueous sample solution are as follows: mobile phase A is an aqueous solution containing 0.1% formic acid, and mobile phase B is an acetonitrile solution containing 0.1% formic acid, with a flow rate set at 0.4 mL / min. The separation elution gradient is as follows: 0 min, 1% mobile phase B; 13 min, 70% mobile phase B; 13.01 min, 99% mobile phase B; 18 min, maintaining 99% mobile phase B; 18.01 min, returning to 1% mobile phase B; 22 min, maintaining 1% mobile phase B. The sample injection volume is 3 μL, and the autosampler temperature is set to 10 ℃. The preferred mass spectrometry detection parameters are: the analysis of polar metabolites uses a combination of full scan and data-dependent acquisition (DDA) to obtain spectral information from mass spectrometry (MS1) and mass spectrometry (MS2). The full scan mass spectrometry range was set to 100–1500 Da, while the second-stage scan mode (Full MS / dd-MS2) had three scan ranges: 100–300 Da, 300–710 Da, and 700–1500 Da. Specific parameter settings were as follows: Automatic Gain Control (AGC) was set to 3E+6, Maximum Ion Implantation Time (IT) was 200 ms, and the resolution of the first-stage full scan was 70,000 FWHM (Full Width at Half Maximum) (@200 m / z). In the second-stage scan mode (Full MS / dd-MS2), the resolution of the second-stage mass spectrometry was adjusted to 17,500, the quadrupole isolation window width was 1.5 m / z, the AGC value was set to 1E+5, the maximum ion implantation time was 50 ms, and the Higher-energy Collisional Dissociation (HCD) was 30%. Furthermore, the ion spray voltage is 3,500 V in positive mode and 3,000 V in negative mode; the nebulizer gas pressure is 20 psi, the sheath gas temperature is 400 °C, and the sheath gas flow rate is 10 L / min. After obtaining the metabolomics data, data preprocessing and metabolite identification are preferably performed. This invention does not impose any particular limitations on the data preprocessing method; conventional methods for metabolomics data preprocessing well-known in the art can be used. The metabolite identification method preferably involves comparing and matching the spectral information of the primary precursor ion (MS1) and secondary fragment ions (MS2) of the compound obtained after resolution with spectral information in a public database, and then verifying the mass spectrometry information using matched standards. The spectral information preferably includes the mass-to-charge ratio (m / z) of the primary mass spectrometer and the fragmentation of the secondary ions.The public databases preferably include the Human Metabolites Database (HMDB, www.hmdb.ca), the Metabolomics Database (Metlin, metlin.scripps.edu), and the Mass Spectrometry Database (www.massbank.jp).

[0020] In this invention, the plasma metabolic marker preferably comprises a combination of at least three of the following metabolic markers: a first marker composed of L-glutamyl-L-aspartate, L-piperidinic acid, and 1-methylnicotinamide; a second marker composed of L-glutamyl-L-aspartate, L-piperidinic acid, and uridine; a third marker composed of L-glutamyl-L-aspartate, L-piperidinic acid, and arginine; a fourth marker composed of L-glutamyl-L-aspartate, L-piperidinic acid, and glycyl-L-asparagine; a fifth marker composed of L-glutamyl-L-aspartate, L-piperidinic acid, and ceramide d34:1; and a sixth marker composed of L-glutamyl-L-aspartate, L-piperidinic acid, and phosphoric acid. The sixth biomarker is composed of acylethanolamine 38:6e; the seventh biomarker is composed of L-glutamyl-L-aspartic acid, L-piperidine acid and D-glucuronic acid; the eighth biomarker is composed of L-glutamyl-L-aspartic acid, L-piperidine acid and glucose; the ninth biomarker is composed of L-glutamyl-L-aspartic acid, L-piperidine acid and 3-hydroxy-3-methylglutaric acid; the tenth biomarker is composed of L-glutamyl-L-aspartic acid, L-piperidine acid and urea; the eleventh biomarker is composed of L-glutamyl-L-aspartic acid, L-piperidine acid and lysophosphatidylcholine 20:1; and the twelfth biomarker is composed of L-glutamyl-L-aspartic acid, 1-methylnicotinamide and uridine.

[0021] In one embodiment of the present invention, the efficacy of three metabolic biomarkers for the diagnosis of renal cell carcinoma is illustrated using the seventh biomarker as an example. After modeling, ROC curve analysis was performed on the diagnostic efficacy of the combination of metabolic biomarkers. The results showed that the diagnostic model constructed in the modeling group had an AUC of 0.935 (sensitivity = 0.763, specificity = 0.891, accuracy = 0.776, precision = 0.778); the diagnostic model constructed in the validation group had an AUC of 0.946 (sensitivity = 0.799, specificity = 0.910, accuracy = 0.812, precision = 0.797). This indicates that the diagnostic model constructed using the three metabolic biomarkers can stably and reliably diagnose renal cell carcinoma samples and has good clinical application potential.

[0022] In this invention, the plasma metabolic markers preferably comprise a combination of at least six of the following metabolic markers: L-glutamyl-L ... The thirteenth biomarker consists of aspartic acid, L-piperidinic acid, uridine, arginine, D-glucuronic acid, and urea; the fourteenth biomarker consists of L-piperidinic acid, 1-methylnicotinamide, uridine, glucose, 3-hydroxy-3-methylglutaric acid, and lysophosphatidylcholine 15:0; the fifteenth biomarker consists of 1-methylnicotinamide, arginine, glycyl-L-asparagine, ceramide d34:1, glucose, and 3-hydroxy-3-methylglutaric acid; the sixteenth biomarker consists of L-glutyl-L-aspartic acid, 1-methylnicotinamide, arginine, ceramide d34:1, D-glucuronic acid, and lysophosphatidylcholine 15:0; and the seventeenth biomarker consists of glycyl-L-asparagine, ceramide d34:1, phosphatidylethanolamine 38:6e, urea, lysophosphatidylcholine 15:0, and lysophosphatidylcholine 20:1.

[0023] In another embodiment of the present invention, the thirteenth biomarker is used as an example to illustrate the effect of six metabolic biomarkers in the diagnosis of renal cell carcinoma. After modeling, ROC curve analysis was performed on the diagnostic efficacy of the combination of metabolic biomarkers. The results showed that the diagnostic model constructed by the modeling group had an AUC of 0.918 (sensitivity = 0.830, specificity = 0.915, accuracy = 0.816, precision = 0.817); the diagnostic model constructed by the validation group had an AUC of 0.963 (sensitivity = 837, specificity = 0.930, accuracy = 0.859, precision = 0.845). This indicates that the diagnostic model constructed by the six metabolic biomarkers can stably and reliably diagnose renal cell carcinoma samples and has good clinical application potential.

[0024] In this invention, the plasma metabolic markers preferably include a combination of at least one of the following nine metabolic markers: an eighteenth marker consisting of L-glutoyl-L-aspartic acid, L-piperidinic acid, uridine, arginine, glycyl-L-asparagine, phosphatidylethanolamine 38:6e, D-glucuronic acid, urea, and lysophosphatidylcholine 15:0; a nineteenth marker consisting of L-piperidinic acid, 1-methylnicotinamide, glycyl-L-asparagine, ceramide d34:1, D-glucuronic acid, glucose, urea, lysophosphatidylcholine 15:0, and lysophosphatidylcholine 20:1; and a twentieth marker consisting of 1-methylnicotinamide, uridine, glycyl-L-asparagine, ceramide d34:1, D-glucuronic acid, glucose, 3-hydroxy-3-methylglutaric acid, urea, and lysophosphatidylcholine 15:0.

[0025] In another embodiment of the present invention, the eighteenth biomarker is used as an example to illustrate the effect of nine metabolic biomarkers in the diagnosis of renal cell carcinoma. After modeling, ROC curve analysis was performed on the diagnostic efficacy of the combination of metabolic biomarkers. The results showed that the diagnostic model constructed by the modeling group had an AUC of 0.906 (sensitivity = 0.778, specificity = 0.889, accuracy = 0.796, precision = 0.785); the diagnostic model constructed by the validation group had an AUC of 0.964 (sensitivity = 0.862, specificity = 0.943, accuracy = 0.891, precision = 0.894). This indicates that the diagnostic model constructed with nine metabolic biomarkers can stably and reliably diagnose renal cell carcinoma samples and has good clinical application potential.

[0026] In this invention, the plasma metabolic markers preferably comprise a combination of at least one of the following 12 metabolic markers: L-glutamyl-L-aspartate, L-piperidinic acid, 1-methylnicotinamide, uridine, arginine, glycyl-L-asparagine, ceramide d34:1, phosphatidylethanolamine 38:6e, D... The twenty-first biomarker consists of glucuronic acid, glucose, urea, and lysophosphatidylcholine in a 15:0 ratio; the twenty-second biomarker consists of L-glutamyl-L-aspartic acid, L-piperidinic acid, 1-methylnicotinamide, uridine, glycyl-L-asparagine, ceramide d34:1, phosphatidylethanolamine 38:6e, D-glucuronic acid, glucose, 3-hydroxy-3-methylglutaric acid, lysophosphatidylcholine 15:0, and lysophosphatidylcholine 20:1; and the twenty-third biomarker consists of L-glutamyl-L-aspartic acid, L-piperidinic acid, 1-methylnicotinamide, uridine, arginine, ceramide d34:1, phosphatidylethanolamine 38:6e, D-glucuronic acid, glucose, 3-hydroxy-3-methylglutaric acid, urea, and lysophosphatidylcholine 20:1.

[0027] In another embodiment of the present invention, the twenty-first biomarker is used as an example to illustrate the effect of 12 metabolic biomarkers in the diagnosis of renal cell carcinoma. After modeling, ROC curve analysis was performed on the diagnostic efficacy of the combination of metabolic biomarkers. The results showed that the diagnostic model constructed in the modeling group had an AUC of 0.897 (sensitivity = 0.791, specificity = 0.897, accuracy = 0.796, precision = 0.789); the diagnostic model constructed in the validation group had an AUC of 0.956 (sensitivity = 0.837, specificity = 0.929, accuracy = 0.859, precision = 0.845). This indicates that the diagnostic model constructed with 12 metabolic biomarkers can stably and reliably diagnose renal cell carcinoma samples and has good clinical application potential.

[0028] In another embodiment of the present invention, the efficacy of a combination of 14 metabolic biomarkers for the diagnosis of renal cell carcinoma was investigated. ROC curve analysis was performed on the diagnostic efficacy of the combination of metabolic biomarkers after modeling. The results showed that the diagnostic model constructed in the modeling group had an AUC of 0.918 (sensitivity = 0.854, specificity = 0.928, accuracy = 0.837, precision = 0.867); the diagnostic model constructed in the validation group had an AUC of 0.945 (sensitivity = 0.857, specificity = 0.936, accuracy = 0.875, precision = 0.865). This indicates that the diagnostic model constructed with 14 metabolic biomarkers can stably and reliably diagnose renal cell carcinoma samples and has good potential for clinical application.

[0029] This invention provides the application of the plasma metabolic biomarkers in constructing a predictive model for the diagnosis of renal cell carcinoma.

[0030] In this invention, the prediction model is a machine learning classification model, preferably constructed using the support vector machine algorithm. The prediction model is constructed using training set samples and validated using a test set. The preferred ratio of the number of samples in the training set to the test set is 3:1. After the prediction model is constructed, it is preferably further improved in terms of stability and reliability through 1000 rounds of random cyclic cross-validation.

[0031] In this invention, after the predictive model is constructed, its accuracy is evaluated using ROC curves, and the predictive model is assessed using the area under the curve (AUC). When the AUC value is greater than 0.5, the closer the AUC is to 1, the better the model's performance and the stronger its diagnostic ability; conversely, if the AUC is less than 0.5, it indicates that the model's diagnostic ability is weak. Predictive models constructed based on different combinations of biomarkers according to this invention can all achieve stable and reliable classification, demonstrating good potential for clinical application.

[0032] This invention provides the application of the detection reagent for the plasma metabolic marker in the preparation of diagnostic kits that differentiate between healthy individuals, benign kidney diseases, and kidney cancer.

[0033] In this invention, the benign kidney disease includes at least one of the following: renal cyst, eosinophilic renal tumor, angiomyolipoma, mesenchymal tumor juxtaglomerular cell tumor, renal atrophy, adrenocortical sebaceous adenoma, renal leiomyolipoma, renal tuberculosis, renal nodule, hydronephrosis, and renal hamartoma.

[0034] The following detailed description, in conjunction with embodiments, illustrates a plasma metabolic biomarker for the diagnosis of renal cell carcinoma and its application, but these should not be construed as limiting the scope of protection of this invention.

[0035] Example 1 A method for screening plasma metabolic biomarkers for the diagnosis of renal cell carcinoma Subject conditions and sample collection The inclusion and exclusion criteria for patients with benign kidney disease and kidney cancer are as follows: 1) Inclusion criteria: (1) All participants signed a written informed consent form before participating in the study; (2) Males and females aged 18 years and older; (3) Patients diagnosed by biopsy or postoperative pathology, or by clinicians based on comprehensive assessment as having benign kidney disease or renal cancer; 2) Exclusion criteria: (1) During pregnancy or lactation; (2) Emergency room visit or resuscitation required; (3) History of blood transfusion within 7 days prior to sampling; (4) People who have received organ transplants or have previously received non-autologous (allogeneic) bone marrow or stem cell transplants; (5) History of malignant tumors within 5 years or any anti-tumor treatment prior to sampling; (6) Simultaneous co-occurrence of multiple primary malignant tumors.

[0036] 3) Subject information: This study included plasma samples from 88 healthy controls (HC), 64 patients with benign kidney disease (BRD), and 107 patients with renal cell carcinoma (RCC). The plasma samples used for the modeling group consisted of 66 healthy controls, 48 ​​patients with BRD, and 81 patients with RCC; the plasma samples used for the validation group consisted of 22 healthy controls, 16 patients with BRD, and 26 patients with RCC (Table 1).

[0037] Table 1 Subject Information

[0038] 2. Detection, identification, and screening of small molecule metabolites in plasma samples as well as metabolic biomarkers. (1) Reagents: Methanol, acetonitrile, acetic acid, water, isopropanol, and methyl tert-butyl ether with mass spectrometry grade purity, and formic acid and ammonium acetate with chromatographic (HPLC) grade purity were all purchased from Sigma-Aldrich, USA. (2) Sample preparation: Take 100 μL of plasma and add 1000 μL of pre-cooled methyl tert-butyl ether and methanol mixture (volume ratio 3:1). Vortex to mix and obtain the sample extract. Then add 500 μL of methanol and water mixture (volume ratio 3:1) to the sample extract, sonicate, let stand, vortex and centrifuge to separate the layers. Organic phase: After sample separation, 500 μL of the upper layer was transferred to a centrifuge tube as the organic phase. The organic phase was dried, and 200 μL of a mixture of acetonitrile and isopropanol (3:1 v / v) was added. The mixture was incubated at room temperature for 15 min. After incubation, the mixture was vortexed, sonicated for 5 min, and then centrifuged at 12000 rpm for 5 min at room temperature. Finally, 180 μL of the supernatant was transferred to a 2 mL glass vial as the organic phase for LC-MS detection.

[0039] Aqueous phase: After sample separation, 400 μL of the lower aqueous phase was transferred to a centrifuge tube, and 1100 μL of ice-cold methanol was added to precipitate proteins. After complete protein precipitation, the sample was centrifuged, and 1000 μL of the supernatant was transferred to a new centrifuge tube and dried overnight at room temperature. After drying, 200 μL of water was added to the centrifuge tube, and the sample was incubated at room temperature for 15 min. After incubation, the sample was vortexed, sonicated for 5 min, and then centrifuged at 12000 rpm for 5 min at room temperature. Finally, 180 μL of the supernatant was transferred to a 2 mL glass vial as the aqueous phase for LC-MS detection. (3) Detection of small molecule metabolites: Organic phases were separated using a Waters ACQUITY UPLC® BEH C8 1.7 µm 2.1 × 100 mm column, and aqueous phases were separated using a Waters ACQUITY UPLC® HSS T3 1.8 µm 2.1 × 100 mm column. Liquid chromatography and mass spectrometry analyses were performed using an ACQUITY UPLC I-Class liquid chromatography system (Waters) and a Q-Exactive mass spectrometry system (Thermo Fisher Scientific). The mobile phase parameters of the organic phase test liquid are as follows: Mobile phase A is an aqueous solution containing 0.1% acetic acid and 0.1% ammonium acetate; mobile phase B is a mixed solution of acetonitrile and isopropanol containing 0.1% acetic acid and 0.1% ammonium acetate (the volume ratio of acetonitrile to isopropanol is 7:3, the mass content of acetic acid is 0.1%, and the mass content of ammonium acetate is 0.1%). The separation elution gradient is as follows: 55%~89% mobile phase B from 0 to 12 min, and 100% mobile phase B from 12 to 19.5 min.

[0040] The mobile phase parameters of the aqueous test liquid are as follows: Mobile phase A was an aqueous solution containing 0.1% formic acid, and mobile phase B was an acetonitrile solution containing 0.1% formic acid. The flow rate was set to 0.4 mL / min. The separation and elution gradient was as follows: 0 min, 1% mobile phase B; 13 min, 70% mobile phase B; 13.01 min, 99% mobile phase B; 18 min, maintaining 99% mobile phase B; 18.01 min, returning to 1% mobile phase B; 22 min, maintaining 1% mobile phase B. The sample injection volume was 3 μL, and the autosampler temperature was set to 10 ℃. The mass spectrometry parameters are as follows: The analysis of polar metabolites employed a combination of full scan and data-dependent acquisition (DDA) methods to obtain spectral information from mass spectrometry (MS1) and mass spectrometry (MS2). The full scan mass spectrum range was set to 100–1500 Da, while the second-level scan mode (Full MS / dd-MS2) had three scanning ranges: 100–300 Da, 300–710 Da, and 700–1500 Da. The mass spectrometer used in the experiment was an Orbitrap high-resolution mass spectrometer equipped with an electrospray ionization (ESI) source, and data acquisition was performed in both positive and negative ionization modes.

[0041] The specific parameter settings are as follows: Automatic Gain Control (AGC) is set to 3E+6, Maximum Ion Implantation Time (IT) is 200 ms, and the resolution of the first-stage full scan is 70,000 FWHM (Full Width at Half Maximum) (@200 m / z). In the second-stage scan mode (Full MS / dd-MS2), the resolution of the second-stage mass spectrometer is adjusted to 17,500, the quadrupole isolation window width is 1.5 m / z, the AGC value is set to 1E+5, the maximum ion implantation time is 50 ms, and the Higher-energy Collisional Dissociation (HCD) is 30 eV. Furthermore, the ion spray voltage is 3,500 V in positive mode and 3,000 V in negative mode; the nebulizer gas pressure is 20 psi, the sheath gas temperature is 400 ℃, and the sheath gas flow rate is 10 L / min. (4) Metabolomics data processing First, detection peaks were extracted from all mass spectra, and background noise was removed through baseline correction while preserving the original signal peaks. The raw data was then converted into discrete center data. Next, the peak values ​​in individual samples were precisely matched with the retention times in the chromatograms. Finally, isotope peaks were removed from the dataset to obtain the final mass spectrometry matrix data. To reduce differences in metabolite concentrations among different samples and to make the data distribution more symmetrical, a normalization autoencoder (NormAE) was used to homogenize the data. (5) Identification of metabolites By analyzing the raw data using software, spectral information of the parent ion and its secondary fragment ions can be obtained, including the mass-to-charge ratio (m / z) of the primary mass spectrometer and the fragment characteristics of the secondary ions. Qualitative analysis of the metabolites can be achieved by matching this information with primary and secondary metabolite spectral data in databases. Commonly used metabolite databases include: HMDB (www.hmdb.ca), PubChem (https: / / pubchem.ncbi.nlm.nih.gov), MassBank (http: / / www.massbank.jp), North American MassBank (https: / / massbank.us), and LipidMaps (www.lipidmaps.org). Metabolites initially identified based on relevant databases can be finally confirmed by using retention times, MS1 and MS2 mass spectrometry information obtained when separated from standards under the same chromatographic column and mass spectrometry conditions. The criteria for metabolite identification are: retention time difference within 0.1 min and molecular weight error of less than 10 ppm. (6) Metabolomics data analysis 1) Screening of metabolic biomarkers for differentiating patients with renal cell carcinoma Based on the screening criterion of Variable Importance in the Projection (VIP) greater than 1, the samples of each pair were compared and analyzed. The intersection of the three screening results was taken to obtain the common differential metabolites. Non-endogenous metabolites were further eliminated, and finally 14 metabolites with significant differences among the three groups were screened out (see Table 2). These metabolites can be used as key metabolic markers to distinguish between HC, BRD and RCC groups.

[0042] Table 2. 14 metabolic markers used to differentiate between HC, BRD, and RCC groups.

[0043] 2) Construct a diagnostic model to differentiate patients with renal cell carcinoma. To evaluate the discriminative ability of the selected metabolic biomarkers in distinguishing renal cell carcinoma, multivariate ROC analysis was performed on these 14 metabolic biomarkers. Specifically, three-quarters of the samples from the modeling group were randomly selected as the training set to build and optimize the machine learning classification model; the remaining one-quarter of the samples were used as the test set to evaluate the model's classification performance. A Support Vector Machine (SVM) algorithm was employed, and the stability and reliability of the model were further improved through 1000 runs of randomized cyclic cross-validation.

[0044] ROC curves are tools used to analyze the relationship between a model's sensitivity and specificity. They are plotted with sensitivity on the y-axis and 1-specificity on the x-axis. Model performance is typically evaluated using the area under the curve (AUC): an AUC greater than 0.5, closer to 1, indicates a better model performance and stronger diagnostic ability; conversely, an AUC below 0.5 indicates weaker diagnostic ability. Furthermore, ROC classification and diagnostic model systems not only encompass the basic ROC curve and AUC metrics but also involve several other key parameters such as sensitivity, specificity, accuracy, and precision.

[0045] Sensitivity is calculated according to Formula I: Formula I; Specificity is calculated according to Formula II: Formula II; Accuracy is calculated according to Formula III: Formula III Precision is calculated according to Formula IV: Formula IV; Among them, TP (True Positive): the number of samples that are actually positive but were correctly predicted as positive; TN (True Negative): The number of samples that are actually negative but were correctly predicted as negative. FP (False Positive): The number of samples that are actually negative but are incorrectly predicted as positive. FN (False Negative): The number of samples that are actually positive but are incorrectly predicted as negative. The results are as follows Figure 1As shown, the diagnostic model constructed based on a combination of 14 metabolic biomarkers had an AUC of 0.918 (sensitivity = 0.854, specificity = 0.928, accuracy = 0.837, precision = 0.867). This result indicates that the model has excellent discriminative ability in distinguishing different groups.

[0046] 3) Validation of diagnostic models for differentiating patients with renal cell carcinoma The diagnostic model constructed based on the above 14 metabolic biomarkers was subjected to ROC analysis in the validation group, and the results are as follows: Figure 2 As shown in the figure. The results show that AUC=0.945 (sensitivity=0.857, specificity=0.936, accuracy=0.875, precision=0.865).

[0047] Example 2 Modeling was performed using the method described in Example 1, and ROC curve analysis was conducted on the diagnostic efficacy of 12 combinations of metabolic biomarkers. These 12 combinations included L-glutamyl-L-aspartate, L-piperidinic acid, 1-methylnicotinamide, uridine, arginine, glycyl-L-asparagine, ceramide d34:1 (d18:1 / 16:0), phosphatidylethanolamine 38:6e, D-glucuronic acid, glucose, urea, and lysophosphatidylcholine 15:0. The results showed that in the modeling group, modeling based on the 12 metabolic biomarker combinations achieved an AUC of 0.897 (sensitivity = 0.791, specificity = 0.897, accuracy = 0.796, precision = 0.789).

[0048] The diagnostic model established based on the combination of 12 metabolic biomarkers was also subjected to multivariate ROC curve analysis in the validation group. The results showed that the AUC of the combination of 12 metabolic biomarkers was 0.956 (sensitivity = 0.837, specificity = 0.929, accuracy = 0.859, precision = 0.845).

[0049] Example 3 Modeling was performed using the method described in Example 1, and ROC curve analysis was conducted on the diagnostic efficacy of nine metabolic biomarker combinations. These nine combinations included L-glutamyl-L-aspartate, L-piperidinic acid, uridine, arginine, glycyl-L-asparagine, phosphatidylethanolamine 38:6e, D-glucuronic acid, urea, and lysophosphatidylcholine 15:0. The results showed that, in the modeling group, modeling based on these nine metabolic biomarker combinations yielded an AUC of 0.906 (sensitivity = 0.778, specificity = 0.889, accuracy = 0.796, precision = 0.785).

[0050] Multivariate ROC curve analysis was also performed on the diagnostic model established based on the combination of 9 metabolic biomarkers in the validation group. The results showed that the modeling based on the combination of 9 metabolic biomarkers had an AUC of 0.964 (sensitivity = 0.862, specificity = 0.943, accuracy = 0.891, precision = 0.894).

[0051] Example 4 The diagnostic efficacy of a combination of six metabolic biomarkers was analyzed using the method described in Example 1. These six biomarkers included L-glutamyl-L-aspartate, L-piperidinic acid, uridine, arginine, D-glucuronic acid, and urea. Results showed that in the modeling group, the AUC based on the six biomarker combinations was 0.918 (sensitivity = 0.830, specificity = 0.915, accuracy = 0.816, precision = 0.817).

[0052] Meanwhile, multivariate ROC curve analysis was also performed on the diagnostic model established based on the combination of six metabolic biomarkers in the validation group. The results showed that the modeling based on the combination of six metabolic biomarkers had an AUC of 0.963 (sensitivity = 837, specificity = 0.930, accuracy = 0.859, precision = 0.845).

[0053] Example 5 Modeling was performed using the method described in Example 1, and ROC curve analysis was conducted on the diagnostic efficacy of the combination of three metabolic biomarkers: L-glutamyl-L-aspartate, L-piperidinic acid, and D-glucuronic acid. The results showed that, in the modeling group, modeling based on the combination of three metabolic biomarkers achieved an AUC of 0.935 (sensitivity = 0.763, specificity = 0.891, accuracy = 0.776, precision = 0.778).

[0054] Meanwhile, multivariate ROC curve analysis was also performed on the diagnostic model established by the combination of three metabolic biomarkers in the validation group. The results showed that the modeling of the combination of three metabolic biomarkers had an AUC of 0.946 (sensitivity = 0.799, specificity = 0.910, accuracy = 0.812, precision = 0.797).

[0055] The results of the examples show that the diagnostic models constructed based on any combination of the 14 metabolic biomarkers all exhibited high efficacy. All performance indicators of the metabolic biomarker combinations remained at high levels, indicating that the diagnostic model maintains stable and reliable classification ability under different numbers of biomarkers, demonstrating good potential for clinical application. The results of the above examples demonstrate that the constructed diagnostic model for distinguishing between HC, BRD, and RCC groups still exhibited good discriminative performance in the validation group.

[0056] In summary, this invention, based on plasma samples and utilizing advanced metabolomics technology, accurately detects small-molecule metabolites in plasma and successfully screens a set of specific metabolic biomarkers that can effectively distinguish between healthy individuals, benign kidney disease groups, and renal cell carcinoma groups. Building upon this foundation, we further employ machine learning algorithms to construct a highly efficient and accurate diagnostic model. This model can effectively identify patients in the renal cell carcinoma group, providing important reference value for disease prognosis assessment and personalized treatment.

[0057] 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 plasma metabolic marker for diagnosis of renal cancer, characterized by, comprises at least one of the following 3 metabolic markers: L-glutamyl-L-aspartate, L-piperidine acid, 1-methylnicotinamide, uridine, arginine, glycol-L-asparagine, ceramide d34:1, phosphatidyl ethanolamine 38:6e, D-glucuronate, glucose, 3-hydroxy-3-methylglutarate, urea, lysophosphatidylcholine 15:0, and lysophosphatidylcholine 20:

1.

2. The plasma metabolic marker of claim 1, wherein, comprises at least one of the following 3 metabolic markers: L-glutamyl-L-aspartate, L-piperidine acid, 1-methylnicotinamide, uridine, arginine, glycol-L-asparagine, ceramide d34:1, phosphatidyl ethanolamine 38:6e, D-glucuronate, glucose, 3-hydroxy-3-methylglutarate, urea, lysophosphatidylcholine 15:0, and lysophosphatidylcholine 20:

1.

3. The plasma metabolic marker of claim 1, wherein, comprises at least one of the following 3 metabolic markers: L-glutamyl-L-aspartate, L-piperidine acid, 1-methylnicotinamide, uridine, arginine, glycol-L-asparagine, ceramide d34:1, phosphatidyl ethanolamine 38:6e, D-glucuronate, glucose, 3-hydroxy-3-methylglutarate, urea, lysophosphatidylcholine 15:0, and lysophosphatidylcholine 20:

1.

4. The plasma metabolic marker of claim 1, wherein, The combination of at least one of the following 9 metabolic markers: the eighteenth marker consisting of L-glutamyl-L-aspartate, L-pipecolic acid, uridine, arginine, glycyl-L- asparagine, phosphatidyl ethanolamine 38:6 e, D-glucuronate, urea and lysophosphatidylcholine 15:0, the nineteenth marker consisting of L-pipecolic acid, 1-methylnicotinamide, glycyl-L-asparagine, ceramide d34:1, D-glucuronate, glucose, urea, lysophosphatidylcholine 15:0 and lysophosphatidylcholine 20:1 and the twentieth marker consisting of 1-methylnicotinamide, uridine, glycyl-L-asparagine, ceramide d34:1, D-glucuronate, glucose, 3-hydroxy-3-methylglutarate, urea, lysophosphatidylcholine 15:

0.

5. The plasma metabolic marker of claim 1, wherein, The combination of at least one of the following 12 metabolic markers: the twenty-first marker consisting of L-glutamyl-L-aspartate, L-pipecolic acid, 1-methylnicotinamide, uridine, arginine, glycyl-L-asparagine, ceramide d34:1, phosphatidyl ethanolamine 38:6 e, D-glucuronate, glucose, urea and lysophosphatidylcholine 15:0, the twenty-second marker consisting of L-glutamyl-L-aspartate, L-pipecolic acid, 1-methylnicotinamide, uridine, glycyl-L-asparagine, ceramide d34:1, phosphatidyl ethanolamine 38:6 e, D-glucuronate, glucose, 3-hydroxy-3-methylglutarate, lysophosphatidylcholine 15:0 and lysophosphatidylcholine 20:1 and the twenty-third marker consisting of L-glutamyl-L-aspartate, L-pipecolic acid, 1-methylnicotinamide, uridine, arginine, ceramide d34:1, phosphatidyl ethanolamine 38:6 e, D-glucuronate, glucose, 3-hydroxy-3-methylglutarate, urea and lysophosphatidylcholine 20:

1.

6. The use of the plasma metabolic markers of any one of claims 1-5 in constructing a prediction model for diagnosing renal cancer.

7. Use according to claim 6, characterized in that, The prediction model is constructed using a support vector machine algorithm.

8. The use of the detection reagents of the plasma metabolic markers of any one of claims 1-5 in preparing a diagnostic kit for distinguishing healthy people, benign diseases of the kidney and renal cancer.

9. Use according to claim 8, characterized in that, The detection reagents include formic acid, acetonitrile, methyl tert-butyl ether, acetic acid and methanol.

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