Plasma metabolism marker for kidney cancer diagnosis and application thereof

Abstract

The invention belongs to the technical field of biomedicine, and particularly relates to a plasma metabolism marker for kidney cancer diagnosis and application of the plasma metabolism marker. The application provides a group of brand new plasma metabolism marker combination, the combination can synergistically and accurately distinguish healthy people and early and late patients with kidney cancer, a new and reliable biomarker is provided for molecular diagnosis of kidney cancer, and by detecting the synergistic change of the 13 metabolites in the plasma, the clinical diagnosis of kidney cancer can be realized. Healthy people, early-stage kidney cancer patients and late-stage kidney cancer patients can be effectively distinguished. Wherein the occurrence and development of the kidney cancer are accompanied by deep reprogramming of a metabolic network of an organism, the metabolites relate to a plurality of key paths such as amino acid metabolism, glycometabolism and lipid metabolism, and the abundance change of the metabolites can sensitively reflect the proliferation state and invasiveness of the tumor and the systematic reaction of the organism to the tumor from the molecular level.

Description

Technical Field

[0001] This application belongs to the field of biomedical technology, specifically relating to plasma metabolic markers for the diagnosis of renal cell carcinoma and their applications. Background Technology

[0002] Kidney cancer is one of the deadliest malignant tumors of the urinary system, with a 5-year survival rate of approximately 75%, which drops sharply to 15% if metastasis occurs. Currently, more than 60% of kidney cancer patients are only diagnosed when they experience typical symptoms such as hematuria and lower back pain, and these patients are often already in advanced stages, losing the opportunity for radical treatment. Due to the lack of highly sensitive methods to detect asymptomatic small kidney cancers (≤4 cm) in their early stages, there is an urgent clinical need for a minimally invasive, repeatable, and cost-effective molecular diagnostic tool to achieve early screening and accurate staging of kidney cancer, thereby compensating for the shortcomings of imaging (CT / MRI) in identifying small metastases and the sampling bias inherent in biopsy.

[0003] Treatment strategies for renal cell carcinoma vary significantly across different stages: For localized (stage I–II) patients, partial or radical nephrectomy is the preferred treatment, with a 5-year disease-free survival rate exceeding 80%; locally advanced (stage III) patients require combined lymph node dissection and perioperative targeted / immunotherapy to reduce the risk of recurrence; while metastatic (stage IV) patients primarily receive systemic therapy, including tyrosine kinase inhibitors, immune checkpoint inhibitors, and emerging dual immunotherapy regimens, with surgery used only for tumor reduction or palliative purposes. Therefore, accurately distinguishing between stages I–II and III–IV within the "curable window" is crucial to avoiding overtreatment or delaying systemic therapy and improving overall survival. Currently, clinical staging of renal cell carcinoma mainly relies on imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI). However, these existing technologies have the following limitations: First, imaging methods have limited sensitivity to small metastatic lesions (especially those less than 1 cm in diameter), which may lead to underestimation of the stage and missing the best treatment opportunity; second, imaging examination results mainly reflect the morphological characteristics of the tumor and are difficult to provide direct information about the tumor's malignancy, proliferative activity, and other biological behaviors, thus failing to fully meet the needs of accurate staging; in addition, imaging examinations have disadvantages such as radiation exposure and high cost, making them unsuitable for frequent disease monitoring.

[0004] Therefore, developing a diagnostic tool that can accurately and non-invasively reflect the biological characteristics of renal cell carcinoma and effectively distinguish different stages has important clinical value. Summary of the Invention

[0005] Based on this, one embodiment of this application provides a plasma metabolic marker for the diagnosis of renal cell carcinoma and its application.

[0006] This application provides, on the one hand, the application of detection reagents for biomarkers of renal cell carcinoma in the preparation of renal cell carcinoma diagnostic kits;

[0007] The biomarkers for the diagnosis of renal cell carcinoma include L-valine-L-serine, mannose, L-glutamyl-L-serine, fucose, L-citrulline, β-hydroxypyruvate, aspartic acid, 5-hydroxy-L-tryptophan, 3-hydroxypropionic acid, 2-oxoglutarate, 2-hydroxyvalerate, lysophosphatidylcholine 18:0e, and lysophosphatidylcholine 16:0e.

[0008] In some embodiments, the renal cell carcinoma diagnostic biomarker is one or more of L-valine-L-serine, L-glutamyl-L-serine, fucose, L-citrulline, β-hydroxypyruvate, aspartic acid, 5-hydroxy-L-tryptophan, 3-hydroxypropionic acid, 2-oxoglutarate, lysophosphatidylcholine 18:0e, and lysophosphatidylcholine 16:0e.

[0009] In some embodiments, the renal cell carcinoma diagnostic biomarker is one or more of L-valine-L-serine, L-glutamyl-L-serine, fucose, L-citrulline, β-hydroxypyruvate, aspartic acid, 3-hydroxypropionic acid, 2-oxoglutarate, and lysophosphatidylcholine 18:Oe.

[0010] This application also provides the application of a detection reagent for biomarkers of renal cell carcinoma in the preparation of a renal cell carcinoma diagnostic kit;

[0011] The biomarkers for the diagnosis of renal cell carcinoma are one or more of L-valine-L-serine, L-glutamyl-L-serine, fucose, β-hydroxypyruvate, aspartic acid, 3-hydroxypropionic acid, and lysophosphatidylcholine 18:0e.

[0012] In some embodiments, the test reagent detects samples including plasma samples.

[0013] In some embodiments, the detection reagents are used to detect the renal cell carcinoma diagnostic biomarkers by high performance liquid chromatography-mass spectrometry.

[0014] In some embodiments, when the sample to be detected by the detection reagent is prepared as an organic phase analyte, the liquid chromatography uses the C8 column.

[0015] In some embodiments, when the sample to be detected by the detection reagent is prepared as an organic phase analyte, the liquid chromatography mobile phase A is an aqueous solution containing 0.08 v / v%-0.12 v / v% acetic acid and 10 mmol / L-20 mmol / L ammonium acetate.

[0016] Mobile phase B: An acetonitrile-isopropanol solution containing 0.08 v / v%-0.12 v / v% acetic acid and 10 mmol / L-20 mmol / L ammonium acetate, wherein the volume ratio of acetonitrile to isopropanol is (6-8):(2-4).

[0017] In some embodiments, when the sample to be detected by the detection reagent is prepared as an organic phase test solution, the liquid chromatography elution method includes gradient elution.

[0018] In some embodiments, the gradient elution procedure includes:

[0019] From 0 to 12 minutes, the volume percentage of the mobile phase B increased from 55% to 89%.

[0020] Between 12 and 19.5 minutes, the volume percentage of mobile phase B increased from 89% to 100%.

[0021] In some embodiments, when the sample to be detected by the detection reagent is prepared as an aqueous test solution, the liquid chromatography uses the T3 column.

[0022] In some embodiments, mobile phase A is an aqueous solution containing 0.08 v / v% to 0.12 v / v % formic acid, and mobile phase B is an acetonitrile solution containing 0.08 v / v% to 0.12 v / v % formic acid.

[0023] In some embodiments, the elution method includes gradient elution.

[0024] In some embodiments, the gradient elution procedure includes: 0 minutes to 13 minutes, during which the volume percentage of the mobile phase B increases from 1% to 70%.

[0025] Between 13 and 18 minutes, the volume percentage of the mobile phase B increased from 70% to 99%.

[0026] In some embodiments, the mass spectrometry conditions of the high-performance liquid chromatography-mass spectrometry (HPLC-MS / MS) method include: acquisition in Full MS and Full MS / dd-MS2 modes; both Full MS and Full MS / dd-MS2 include both positive and negative modes.

[0027] In some of these embodiments, the resolution is 35,000 to 70,000.

[0028] In some of these embodiments, the scanning range is 100 m / z to 1500 m / z.

[0029] In some embodiments, the automatic gain control is 1.0 × 10⁻⁶. 6 -3.0×106 .

[0030] In some of these embodiments, the maximum IT is 180 ms–220 ms.

[0031] In some embodiments, the relative collision energy of HCD is 20%-80%.

[0032] In some embodiments, the maximum ion implantation time is 40ms-60ms.

[0033] In some embodiments, the kit also includes reagents for extracting the renal cell carcinoma diagnostic biomarkers from the sample.

[0034] In some embodiments, the reagents used to extract the renal cell carcinoma diagnostic biomarkers from the sample include one or both of methyl tert-butyl ether and methanol;

[0035] In some embodiments, the volume ratio of the methyl tert-butyl ether to the methanol is (2-4):1.

[0036] This application, in another aspect, provides a method for constructing a diagnostic model for renal cell carcinoma, comprising:

[0037] Small molecule metabolites were detected and identified in plasma samples from the healthy group and the renal cancer group in the modeling group, respectively, and data from the healthy group and the renal cancer group in the modeling group were obtained.

[0038] Significantly different metabolite analyses were performed on the healthy population data and the renal cell carcinoma population data at different stages in the modeling group to screen for small molecule metabolites with significant differences between groups and obtain combinations of metabolic biomarkers; and,

[0039] Multivariate ROC curve analysis was performed on the combination of metabolic biomarkers.

[0040] This application provides a novel combination of plasma metabolic biomarkers that can synergistically and accurately distinguish between healthy individuals and patients with early-stage and advanced renal cell carcinoma (RCC), offering new and reliable biomarkers for the molecular diagnosis of RCC. By detecting synergistic changes in these 13 metabolites in plasma, it is possible to effectively differentiate between healthy individuals, patients with early-stage (I-II) RCC, and patients with advanced-stage (III-IV) RCC. The occurrence and development of RCC are accompanied by a profound reprogramming of the body's metabolic network. These metabolites involve multiple key pathways, including amino acid metabolism, glucose metabolism, and lipid metabolism. Changes in their abundance can sensitively reflect the tumor's proliferative status, invasiveness, and the body's systemic response to the tumor at the molecular level.

[0041] Furthermore, this application reflects the biological activity of tumors and the metabolic state of the body at the molecular level, and has a higher potential sensitivity to micrometastases that are difficult to detect by imaging. It can effectively make up for the shortcomings of existing staging technologies, and provide a key basis for achieving precise individualized treatment of renal cell carcinoma, which is expected to improve patients' survival benefits and quality of life. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application and to more completely understand this application and its beneficial effects, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 Multivariate ROC curve analysis of 13 important biomarkers that distinguish between healthy individuals, early-stage renal cell carcinoma, and late-stage renal cell carcinoma in the modeling group;

[0044] Figure 2 Multivariate ROC curve analysis was performed on 11 important biomarkers that distinguish between healthy individuals, early-stage renal cell carcinoma, and late-stage renal cell carcinoma in the modeling group.

[0045] Figure 3 Multivariate ROC curve analysis was performed on nine key biomarkers that distinguish between healthy individuals, early-stage renal cell carcinoma, and late-stage renal cell carcinoma in the modeling group.

[0046] Figure 4 Multivariate ROC curve analysis was performed on seven key biomarkers that distinguish between healthy individuals, early-stage renal cell carcinoma, and late-stage renal cell carcinoma in the modeling group.

[0047] Figure 5 To validate the multivariate ROC curve analysis of 13 key biomarkers that distinguish between healthy individuals, early-stage renal cell carcinoma, and late-stage renal cell carcinoma in the validation group. Detailed Implementation

[0048] The present application will be further described in detail below with reference to the embodiments and examples. It should be understood that these embodiments and examples are for illustrative purposes only and are not intended to limit the scope of the present application. The purpose of providing these embodiments and examples is to enable a more thorough and comprehensive understanding of the disclosure of the present application. It should also be understood that the present application can be implemented in many different forms and is not limited to the embodiments and examples described herein. Those skilled in the art can make various modifications or alterations without departing from the spirit of the present application, and the equivalent forms obtained also fall within the protection scope of the present application. Furthermore, numerous specific details are set forth in the following description to provide a fuller understanding of the present application. It should be understood that the present application can be implemented without one or more of these details.

[0049] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0050] Unless otherwise stated or in case of contradiction, the terms or phrases used herein shall have the following meanings:

[0051] The terms "and / or," "or / and," and "and / or" as used herein include any one of two or more of the related listed items, as well as any and all combinations of the related listed items. These arbitrary and all combinations include any two related listed items, any more related listed items, or a combination of all related listed items. It should be noted that when at least three items are connected by at least two conjunctions selected from "and / or," "or / and," and "and / or," it should be understood that in this application, the technical solution undoubtedly includes technical solutions connected by "logical AND," and also undoubtedly includes technical solutions connected by "logical OR." For example, "A and / or B" includes three parallel solutions: A, B, and A+B. For example, the technical solution of "A, and / or, B, and / or, C, and / or, D" includes any one of A, B, C, and D (that is, a technical solution that is connected by "logical OR"), as well as any and all combinations of A, B, C, and D, that is, combinations of any two or three of A, B, C, and D, and also combinations of all four of A, B, C, and D (that is, a technical solution that is connected by "logical AND").

[0052] In this application, the terms "multiple", "various", "multiple times", "multi-dimensional", etc., unless otherwise specified, refer to a quantity greater than or equal to 2. For example, "one or more" means one or more than or equal to two.

[0053] The terms “combinations of,” “any combination of,” and “any combination of” used in this article include all suitable combinations of any two or more of the listed items.

[0054] In this document, the term "suitable" as used in phrases such as "suitable combination," "suitable method," and "any suitable method" refers to the ability to implement the technical solution of this application, solve the technical problem of this application, and achieve the expected technical effect of this application.

[0055] In this application, terms such as "further," "even further," and "particularly" are used to describe purposes and indicate differences in content, but should not be construed as limiting the scope of protection of this application.

[0056] In this application, "optionally," "optionally," and "optional" mean that something is optional, that is, it means that it is selected from either "with" or "without." If there are multiple "optional" entries in a technical solution, unless otherwise specified, and there are no contradictions or mutual constraints, each "optional" entry shall be independent.

[0057] In this application, the technical features described in an open-ended manner include both closed-ended technical solutions composed of the listed features and open-ended technical solutions containing the listed features.

[0058] In this application, numerical intervals (i.e., numerical ranges) are involved. Unless otherwise specified, the selected numerical distributions within the aforementioned numerical intervals are considered continuous and include the two endpoints (i.e., the minimum and maximum values) of the numerical range, as well as every value between these two endpoints. Unless otherwise specified, when a numerical interval refers only to integers within that interval, it includes the two endpoint integers of the numerical range, as well as every integer between the two endpoints. In this document, this is equivalent to directly listing every integer. For example, if t is an integer selected from 1 to 10, it means that t is any integer selected from the group of integers consisting of 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10. Furthermore, when multiple ranges are provided to describe features or characteristics, these ranges can be merged. In other words, unless otherwise specified, the ranges disclosed herein should be understood to include any and all subranges to which they are included.

[0059] Unless otherwise specified, the temperature parameters in this application are permitted to be either constant-temperature treatment or variations within a certain temperature range. It should be understood that the constant-temperature treatment allows temperature fluctuations within the precision range of the instrument control, such as ±5℃, ±4℃, ±3℃, ±2℃, or ±1℃.

[0060] In this application, % (w / w) and wt% both represent weight percentage, % (v / v) refers to volume percentage, and % (w / v) refers to mass-volume percentage.

[0061] All references to documents mentioned in this application are incorporated herein by reference as if each document were individually incorporated herein by reference. Unless they conflict with the inventive purpose and / or technical solution of this application, all cited documents are incorporated herein by reference in their entirety and for all purposes. When citing documents in this application, the definitions of relevant technical features, terms, nouns, phrases, etc., are also incorporated herein by reference. When citing documents in this application, examples and preferred embodiments of the cited technical features may also be incorporated herein by reference, but only to the extent that they enable the implementation of this application. It should be understood that when the cited content conflicts with the description in this application, this application shall prevail or modifications shall be made adaptably to the description in this application.

[0062] The technical problem to be solved by this application is to overcome the defects and deficiencies of the existing technology and provide a detection method based on a combination of plasma metabolic markers. This method constructs a diagnostic model that can distinguish between healthy individuals, early-stage (I–II) and late-stage (III–IV) renal cell carcinoma, and can accurately and sensitively reflect different stages of renal cell carcinoma development (early and late stages). This will compensate for the shortcomings of imaging staging and provide a new solution for achieving accurate and non-invasive staging and efficacy monitoring of renal cell carcinoma.

[0063] This application provides, on the one hand, the application of detection reagents for biomarkers of renal cell carcinoma in the preparation of renal cell carcinoma diagnostic kits;

[0064] The biomarkers for the diagnosis of renal cell carcinoma include L-valine-L-serine, mannose, L-glutamyl-L-serine, fucose, L-citrulline, β-hydroxypyruvate, aspartic acid, 5-hydroxy-L-tryptophan, 3-hydroxypropionic acid, 2-oxoglutarate, 2-hydroxyvalerate, lysophosphatidylcholine 18:0e, and lysophosphatidylcholine 16:0e.

[0065] The aforementioned biomarker composition, by detecting synergistic changes in these 13 metabolites in plasma, can effectively differentiate between healthy individuals, patients with early-stage (I-II) and advanced-stage (III-IV) renal cell carcinoma. The occurrence and development of renal cell carcinoma are accompanied by profound reprogramming of the body's metabolic network. These metabolites involve multiple key pathways, including amino acid metabolism, glucose metabolism, and lipid metabolism. Changes in their abundance can sensitively reflect the tumor's proliferative status, invasiveness, and the body's systemic response to the tumor at the molecular level. Through multivariate analysis, this biomarker composition constructs a complex metabolic "fingerprint," overcoming the limitations of single biomarkers in terms of limited information and low specificity, thereby achieving accurate differentiation of different stages of renal cell carcinoma development. The metabolites in this composition can be accurately quantified using liquid chromatography-mass spectrometry, providing a reliable technical basis for clinical applications.

[0066] In some embodiments, the renal cell carcinoma diagnostic biomarkers include one or more of L-valine-L-serine, L-glutamyl-L-serine, fucose, L-citrulline, β-hydroxypyruvate, aspartic acid, 5-hydroxy-L-tryptophan, 3-hydroxypropionic acid, 2-oxoglutarate, lysophosphatidylcholine 18:Oe, and lysophosphatidylcholine 16:Oe. This combination of 11 biomarkers simplifies biomarker selection while maintaining high diagnostic performance, helping to reduce testing costs and the complexity of data analysis, making it a more optimized option.

[0067] In some embodiments, the renal cell carcinoma diagnostic biomarkers include one or more of L-valine-L-serine, L-glutamyl-L-serine, fucose, L-citrulline, β-hydroxypyruvate, aspartic acid, 3-hydroxypropionic acid, 2-oxoglutarate, and lysophosphatidylcholine 18:Oe.

[0068] This combination of nine biomarkers simplifies biomarker pairing while maintaining high diagnostic performance, helping to reduce testing costs and the complexity of data analysis, making it a more optimized option.

[0069] In some embodiments, the renal cell carcinoma diagnostic biomarkers include one or more of L-valine-L-serine, L-glutamyl-L-serine, fucose, β-hydroxypyruvate, aspartic acid, 3-hydroxypropionic acid, and lysophosphatidylcholine 18:Oe.

[0070] In some embodiments, the test reagent detects samples including plasma samples.

[0071] In some embodiments, the detection reagents are used to detect the renal cell carcinoma diagnostic biomarkers by high performance liquid chromatography-mass spectrometry.

[0072] In some embodiments, the liquid chromatography conditions for the high-performance liquid chromatography-mass spectrometry (HPLC-MS / MS) method include:

[0073] Chromatographic columns include C8 columns or T3 columns;

[0074] For the organic phase analyte, the C8 column was used, with mobile phase A being an aqueous solution containing 0.08 v / v%-0.12 v / v% acetic acid and 10 mmol / L-20 mmol / L ammonium acetate, and mobile phase B being an acetonitrile-isopropanol solution containing 0.08 v / v%-0.12 v / v% acetic acid and 10 mmol / L-20 mmol / L ammonium acetate, wherein the volume ratio of acetonitrile to isopropanol was (6-8):(2-4).

[0075] Elution methods include gradient elution, and the gradient elution procedure includes:

[0076] From 0 min to 12 min, the volume percentage of the mobile phase B increased from 55% to 89%.

[0077] Between 12 and 19.5 minutes, the volume percentage of the mobile phase B increased from 89% to 100%.

[0078] For the aqueous phase test solution, the T3 chromatographic column was used, with mobile phase A being an aqueous solution containing 0.08 v / v% to 0.12 v / v % formic acid and mobile phase B being an acetonitrile solution containing 0.08 v / v% to 0.12 v / v % formic acid.

[0079] Elution methods include gradient elution, and the gradient elution procedure includes:

[0080] From 0 min to 13 min, the volume percentage of the mobile phase B increased from 1% to 70%.

[0081] Between 13 and 18 minutes, the volume percentage of the mobile phase B increased from 70% to 99%.

[0082] In some embodiments, data is acquired in Full MS and Full MS / dd-MS2 modes; both Full MS and Full MS / dd-MS2 include both positive and negative modes.

[0083] In some of these embodiments, the resolution is 35,000 to 70,000.

[0084] In some embodiments, the scan range is 100 m / z to 1500 m / z; for example, the scan range is 100 m / z, 200 m / z, 300 m / z, 400 m / z, 500 m / z, 600 m / z, 700 m / z, 800 m / z, 900 m / z, 1000 m / z, 1100 m / z, 1200 m / z, 1300 m / z, 1400 m / z, or 1500 m / z, and any value in between.

[0085] In some embodiments, the automatic gain control is 1.0 × 10⁻⁶. 6 -3.0×10 6 For example, the automatic gain control (AGC) is 1.0 × 10⁻⁶. 6 1.5×10 6 2.0×10 6 2.5×10 6 3.0×10 6 And any value in between.

[0086] In some embodiments, the Maximum IT is 180 ms to 220 ms. For example, the Maximum IT is 180 ms, 185 ms, 190 ms, 195 ms, 200 ms, 205 ms, 210 ms, 215 ms, or 220 ms, or any value in between.

[0087] In some embodiments, the relative collision energy of the HCD is 20%-80%. For example, the relative collision energy of the HCD is 20%, 30%, 40%, 50%, 60%, 70%, or 80%, or any value in between.

[0088] In some embodiments, the maximum ion implantation time is 40 ms to 60 ms. For example, the maximum ion implantation time is 40 ms, 41 ms, 42 ms, 43 ms, 44 ms, 45 ms, 46 ms, 47 ms, 48 ​​ms, 49 ms, 50 ms, 51 ms, 52 ms, 53 ms, 54 ms, 55 ms, 56 ms, 57 ms, 58 ms, 59 ms, or 60 ms, or any value in between.

[0089] In some embodiments, the kit also includes reagents for extracting the renal cell carcinoma diagnostic biomarkers from the sample.

[0090] In some embodiments, the reagents used to extract the renal cell carcinoma diagnostic biomarkers from the sample include one or both of methyl tert-butyl ether and methanol;

[0091] In some embodiments, the volume ratio of the methyl tert-butyl ether to the methanol is (2-4):1.

[0092] This application, in another aspect, provides a method for constructing a diagnostic model for renal cell carcinoma, comprising:

[0093] Small molecule metabolites were detected and identified in plasma samples from the healthy group and the renal cancer group in the modeling group, respectively, and data from the healthy group and the renal cancer group in the modeling group were obtained.

[0094] Significantly different metabolite analyses were performed on the data of healthy groups and renal cancer groups at different stages (early renal cancer group and late renal cancer group) in the modeling group to screen small molecule metabolites with significant differences between groups and obtain a combination of metabolic biomarkers.

[0095] Multivariate ROC curve analysis was performed on the combination of metabolic biomarkers.

[0096] The embodiments of this application will be described in detail below with reference to examples. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of this application. For experimental methods in the following embodiments where specific conditions are not specified, please refer to the guidelines given in this application, or follow experimental manuals or conventional conditions in the art, or follow the conditions recommended by the manufacturer, or refer to experimental methods known in the art.

[0097] In the specific embodiments described below, the measurement parameters involving raw material components may have slight deviations within the weighing accuracy range unless otherwise specified. Temperature and time parameters are subject to acceptable deviations due to instrument testing accuracy or operational precision.

[0098] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0099] Example 1

[0100] 1. Subject Information

[0101] 1) Inclusion criteria:

[0102] Participants must meet all of the following inclusion criteria to be eligible to participate in this study:

[0103] (1) Male or female aged ≥18 years; (2) Read and fully understand the informed consent form and sign it, and be able to provide a blood sample for metabolomics testing; (3) Diagnosed by biopsy / postoperative pathology or clinically diagnosed by a clinician through comprehensive evaluation, and the renal cancer patients are divided into early renal cancer group (stage I + stage II) and late renal cancer group (stage III + stage IV) according to the pathological staging information.

[0104] 2) Exclusion criteria:

[0105] Subjects who meet any of the following exclusion criteria are ineligible to participate in this study:

[0106] (1) During pregnancy or lactation;

[0107] (2) Emergency room visit or resuscitation required;

[0108] (3) History of blood transfusion within 7 days prior to sampling;

[0109] (4) People who have received organ transplants or have previously received non-autologous (allogeneic) bone marrow or stem cell transplants;

[0110] (5) History of malignant tumor within 5 years or any anti-tumor treatment before sampling;

[0111] (6) Simultaneous co-occurrence of multiple primary malignant tumors.

[0112] 3) Subject information

[0113] This study collected plasma samples from 296 subjects across two medical centers, including 88 healthy controls (HC), 145 early-stage renal cell carcinoma (ERCC, comprising 100 stage I and 45 stage II) cases, and 63 late-stage renal cell carcinoma (LRCC, comprising 39 stage III and 24 stage IV) cases. The plasma samples used for the modeling group were: 66 healthy controls (HC), 109 patients with early-stage renal cell carcinoma (ERCC) (75 in stage I and 34 in stage II), and 47 patients with advanced-stage renal cell carcinoma (LRCC) (29 in stage III and 18 in stage IV). The plasma samples used for the validation group were: 22 healthy controls (HC), 36 patients with early-stage renal cell carcinoma (ERCC) (25 in stage I and 11 in stage II), and 16 patients with advanced-stage renal cell carcinoma (LRCC) (10 in stage III and 6 in stage IV). The results are shown in Table 1.

[0114] Table 1: Subject Information

[0115]

[0116] 2. Plasma metabolite detection

[0117] 1) Test reagents:

[0118] Methanol, acetonitrile, water, acetic acid, methyl tert-butyl ether of mass spectrometry grade, and formic acid of chromatographic (HPLC) grade were all purchased from Sigma-Aldrich, USA.

[0119] 2) Sample preparation:

[0120] Take 100 μL of plasma and place it in 1000 μL of pre-cooled solution (methyl tert-butyl ether: methanol, volume ratio 3:1). Vortex to mix the extracted blood sample and obtain the sample extract. Add 500 μL of solution (methanol: water, volume ratio 3:1) to the sample extract, sonicate, let stand, vortex and centrifuge to separate the layers. The upper layer is the organic phase and the lower layer is the aqueous phase.

[0121] Organic phase: After the sample is separated into layers, take 500 μL of the upper organic phase into a centrifuge tube, dry it, add 200 μL of solution (acetonitrile:isopropanol, volume ratio 3:1), and incubate at room temperature for 15 minutes; after incubation, vortex the centrifuge tube, sonicate for 5 minutes, and then centrifuge at room temperature for 5 minutes (12000 rpm); take 180 μL of supernatant from the centrifuge tube into a 2 mL glass vial, which is the organic phase test solution, and analyze it by LC-MS.

[0122] Aqueous phase: After sample separation, transfer the lower 400 μL aqueous phase to a centrifuge tube and add 1100 μL of ice-cold methanol to precipitate proteins. After protein precipitation, centrifuge the tube and transfer 1000 μL of the supernatant to a new centrifuge tube, then dry overnight. Add 200 μL of water to the dried centrifuge tube and incubate at room temperature for 15 minutes. After incubation, vortex the mixture, sonicate for 5 minutes, and then centrifuge at room temperature for 5 minutes (12000 rpm). Transfer 180 μL of the supernatant from the centrifuge tube to a 2 mL glass vial as the aqueous phase test solution. Analyze using LC-MS.

[0123] 3) Detection of small molecule metabolites:

[0124] For small molecule separation, a Waters ACQUTTY UPLC® BEH C8 1.7µm 2.1*100mm column was used for the organic phase, and a Waters ACQUTTY UPLC® HSS T3 1.8µm 2.1*100mm column was used for the aqueous phase. The liquid chromatography and mass spectrometry systems used were the ACQUITY UPLC I-Class liquid chromatography system (Waters) and the Q-Exactive mass spectrometry system (Thermo Fisher Scientific).

[0125] The mobile phase parameters are as follows:

[0126] Organic phase test solution mobile phase parameters - Mobile phase A is an aqueous solution containing 0.1% acetic acid and 10 mmol / L ammonium acetate; Mobile phase B is an acetonitrile-isopropanol (7:3 v / v) solution containing 0.1% acetic acid and 10 mmol / L ammonium acetate. The separation elution gradient is as follows: 0 min - 12 min is 55% - 89% mobile phase B, 12 min - 19.5 min is 100% mobile phase B.

[0127] The mobile phase parameters of the aqueous test solution are as follows: mobile phase A is an aqueous solution containing 0.1% formic acid; mobile phase B is an acetonitrile solution containing 0.1% formic acid. The separation and elution gradient is as follows: 0 min-13 min is 1%-70% mobile phase B, and 13 min-18 min is 99% mobile phase B.

[0128] The mass spectrometry parameters are as follows:

[0129] Mass spectrometry data were acquired using Full MS and Full MS / dd-MS2 (each with both positive and negative modes). The parameters used by QExactive were as follows: Full MS mode had a resolution of 70,000 m / z, a scan range of 100 m / z - 1500 m / z, an Automatic Gain Control (AGC) of 3E+6, and a Maximum IT of 200 ms; in Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer was 17,500 m / z, the quadrupole window was 1.5 m / z, the AGC was 1E+5, the maximum ion implantation time was 50 ms, and the Higher Energy Collisional Dissociation (HCD) was 30%.

[0130] 3. Metabolomics data preprocessing and metabolite identification

[0131] 1) Metabolomics data processing:

[0132] (1) Extract peaks from the RAW format file of the mass spectrometer and convert it into a FeatureXML format file to reduce the dimensionality of the original mass spectrometry data and improve the signal-to-noise ratio; (2) Use the peak alignment algorithm of OpenMS software to correct and align the retention time of the extracted peak format data between samples, thereby converting the mass spectrometry data into a data matrix; (3) Match and filter the isotope peaks in the data matrix obtained in step 2, and replace abnormal data (0, negative values, background noise, etc.) with missing values; (4) Remove the characteristic peaks with a detection rate of <80% from all the characteristic peaks obtained in step 3, fill the median value of the characteristic peak with the characteristic peaks with a detection rate of >80%, and add 5% random noise (following a standard normal distribution); (5) In order to reduce the difference in metabolite concentration between samples and make the data distribution more symmetrical, use Normalization Autoencoder (NormAE) to perform normalization processing to remove systematic errors such as batch effects.

[0133] 2) Identification of metabolites:

[0134] After analyzing the raw data using software, the spectral information of the primary precursor ion (MS1) and secondary fragment ion (MS2) of the compound is obtained. This information, such as the mass-to-charge ratio (m / z) of the primary mass spectrometer and the fragment ion data, is matched with the spectral information of primary and secondary metabolites in public databases to qualitatively identify the metabolites. Commonly used metabolite databases include the Human Metabolite Database (HMDB, www.hmdb.ca), the Metabolomics Database (Metlin, metlin.scripps.edu), the Mass Spectrometry Database (www.massbank.jp), and the Lipid Map Database (Lipidmap, www.lipidmaps.org). Metabolites identified based on these databases are then finally validated using retention times, MS1, and MS2 mass spectrometry data obtained when separated from standards under the same chromatographic column and mass spectrometry conditions. The criteria for metabolite identification are a retention time difference within 0.1 minutes and a theoretical and measured molecular weight difference of less than 10 ppm.

[0135] 4. Data Analysis

[0136] 1) Biomarker screening

[0137] Metabolite detection was performed on the above samples, and a total of 571 metabolites were obtained after annotation. LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis was performed on the data of the modeling group. The average error corresponding to each regularization parameter alpha was calculated using 5-fold cross-validation. The optimal alpha with the smallest error was found to be 0.0483. Metabolites with non-zero regression coefficients in their corresponding models were retained. Finally, 13 differential metabolites were selected, as shown in Table 2, which serve as important metabolic markers for distinguishing between healthy individuals, early-stage renal cell carcinoma, and late-stage renal cell carcinoma.

[0138] Table 2: 13 Important Metabolic Markers for Differentiating HC, ERCC, and LRCC

[0139]

[0140] 2) Construction of a diagnostic model to differentiate between healthy individuals, early-stage renal cell carcinoma, and late-stage renal cell carcinoma.

[0141] To verify the discriminative effect of the 13 selected metabolic biomarkers in distinguishing between healthy individuals, early-stage renal cell carcinoma, and late-stage renal cell carcinoma, multivariate ROC curve analysis was performed on the 13 metabolic biomarkers in the modeling group. Three-quarters of the sample data from the HC, ERCC, and LRCC groups in the modeling group were randomly used as the training set and one-quarter as the test set for training. The model was then iterated 1000 times using a support vector machine (SVM) machine learning method. By statistically analyzing the average accuracy of the final model, a diagnostic model that distinguishes between healthy individuals, early-stage renal cell carcinoma, and late-stage renal cell carcinoma was constructed.

[0142] ROC curves are a method for studying the relationship between model sensitivity and specificity. Sensitivity is plotted on the ordinate, and 1-specificity on the x-axis. The evaluation criterion is the area under the curve (AUC). An AUC greater than 0.5, and closer to 1, indicates better model performance and better discrimination. An AUC less than 0.5 indicates poor model accuracy. ROC classification prediction models, in addition to common parameters such as the receiver operating characteristic (ROC) curve and AUC, also include sensitivity and specificity.

[0143] Sensitivity calculation is shown in Formula I:

[0144] Formula I.

[0145] Specificity is calculated using Formula II:

[0146] Formula II.

[0147] Among them, TP (True Positive): True Positive, the number of samples that are actually positive but were correctly predicted as positive.

[0148] TN (True Negative): The number of samples that were actually negative but were correctly predicted as negative.

[0149] FP (False Positive): The number of samples that are actually negative but are incorrectly predicted as positive.

[0150] FN (False Negative): The number of samples that are actually positive but are incorrectly predicted as negative.

[0151] The results are as follows Figure 1As shown, AUC=0.851 (sensitivity=0.747, specificity=0.848), indicating that the constructed diagnostic model has high discriminative power.

[0152] In addition, ROC curve analysis was performed on diagnostic models with different combinations of metabolic markers in the modeling group. Eleven metabolic markers were used: L-valine-L-serine, L-glutamyl-L-serine, fucose, L-citrulline, β-hydroxypyruvate, aspartic acid, 5-hydroxy-L-tryptophan, 3-hydroxypropionic acid, 2-oxoglutarate, lysophosphatidylcholine 18:0e and lysophosphatidylcholine 16:0e.

[0153] Nine metabolic markers were used: L-valine-L-serine, L-glutamyl-L-serine, fucose, L-citrulline, β-hydroxypyruvate, aspartic acid, 3-hydroxypropionic acid, 2-oxoglutarate, and lysophosphatidylcholine 18:0e combination.

[0154] The combination of seven metabolic markers was used: L-valine-L-serine, L-glutamyl-L-serine, fucose, β-hydroxypyruvate, aspartic acid, 3-hydroxypropionic acid, and lysophosphatidylcholine 18:0e.

[0155] The results showed that when using 11 metabolic markers, the AUC was 0.854 (sensitivity = 0.703, specificity = 0.843), as follows: Figure 2 As shown; when using 9 metabolic markers, AUC=0.861 (sensitivity=0.734, specificity=0.865), the results are as follows. Figure 3 As shown; when using 7 metabolic markers, AUC=0.843 (sensitivity=0.748, specificity=0.871), the results are as follows. Figure 4 As shown, they all have stable discrimination capabilities.

[0156] 3) Validation of diagnostic models used to differentiate between healthy individuals, early-stage renal cell carcinoma, and late-stage renal cell carcinoma.

[0157] To further validate the effectiveness of the diagnostic model for distinguishing between healthy individuals, early-stage renal cell carcinoma, and late-stage renal cell carcinoma, built based on the modeling set data, the model was validated using the validation set data. Multivariate ROC curve analysis was performed to assess the model's independent validation performance on unknown datasets outside the modeling set dataset. The results are as follows: Figure 5 The AUC was 0.806 (sensitivity = 0.718, specificity = 0.855). These results indicate that the established diagnostic model for distinguishing between healthy individuals, early-stage renal cell carcinoma, and late-stage renal cell carcinoma also demonstrated good discriminative performance in the validation group.

[0158] In addition, diagnostic models with different combinations of metabolic biomarkers were validated in the validation group. Multivariate ROC curve analysis showed that the combination of the 11 metabolic biomarkers—L-valine-L-serine, L-glutamyl-L-serine, fucose, L-citrulline, β-hydroxypyruvate, aspartic acid, 5-hydroxy-L-tryptophan, 3-hydroxypropionic acid, 2-oxoglutarate, lysophosphatidylcholine 18:0e, and lysophosphatidylcholine 16:0e—had an AUC of 0.809 (sensitivity = 0.703, specificity = 0.849) in the validation group. The combination of the 9 metabolic biomarkers—L-valine-L-serine, L-glutamyl-L-serine, fucose, L-citrulline, β-hydroxypyruvate, aspartic acid, 5-hydroxy-L-tryptophan, 3-hydroxypropionic acid, 2-oxoglutarate, lysophosphatidylcholine 18:0e, and lysophosphatidylcholine 16:0e—had an AUC of 0.809 (sensitivity = 0.703, specificity = 0.849). The combination of L-valine-L-serine, L-glutamyl-L-serine, fucose, L-citrulline, β-hydroxypyruvate, aspartic acid, 3-hydroxypropionic acid, 2-oxoglutarate, and lysophosphatidylcholine 18:0e showed an AUC of 0.830 in the validation group (sensitivity = 0.706, specificity = 0.846); the combination of the above seven metabolic markers, L-valine-L-serine, L-glutamyl-L-serine, fucose, β-hydroxypyruvate, aspartic acid, 3-hydroxypropionic acid, and lysophosphatidylcholine 18:0e, showed an AUC of 0.835 in the validation group (sensitivity = 0.703, specificity = 0.853).

[0159] The results above indicate that the established diagnostic model also has good discriminative performance in the validation group.

[0160] The embodiments described above are merely illustrative of several implementation methods of this application, intended to facilitate a detailed understanding of the technical solutions of this application, but should not be construed as limiting the scope of protection of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Furthermore, it should be understood that after reading the above teachings of this application, those skilled in the art can make various alterations or modifications to this application, and the equivalent forms obtained also fall within the scope of protection of this application. It should also be understood that technical solutions obtained by those skilled in the art based on the technical solutions provided in this application through logical analysis, reasoning, or limited experimentation are all within the scope of protection of the appended claims. Therefore, the scope of protection of this patent application should be determined by the content of the appended claims, and the specification can be used to interpret the content of the claims.

Claims

1. Application of detection reagents for renal cell carcinoma diagnostic biomarkers in the preparation of renal cell carcinoma diagnostic kits; The biomarkers for the diagnosis of renal cell carcinoma include L-valine-L-serine, mannose, L-glutamyl-L-serine, fucose, L-citrulline, β-hydroxypyruvate, aspartic acid, 5-hydroxy-L-tryptophan, 3-hydroxypropionic acid, 2-oxoglutarate, 2-hydroxyvalerate, lysophosphatidylcholine 18:0e, and lysophosphatidylcholine 16:0e.

2. Application of detection reagents for renal cell carcinoma diagnostic biomarkers in the preparation of renal cell carcinoma diagnostic kits; The biomarkers for the diagnosis of renal cell carcinoma are one or more of the following: L-valine-L-serine, L-glutamyl-L-serine, fucose, L-citrulline, β-hydroxypyruvate, aspartic acid, 5-hydroxy-L-tryptophan, 3-hydroxypropionic acid, 2-oxoglutarate, lysophosphatidylcholine 18:0e, and lysophosphatidylcholine 16:0e.

3. Application of detection reagents for renal cell carcinoma diagnostic biomarkers in the preparation of renal cell carcinoma diagnostic kits; The biomarkers for the diagnosis of renal cell carcinoma are one or more of L-valine-L-serine, L-glutamyl-L-serine, fucose, L-citrulline, β-hydroxypyruvate, aspartic acid, 3-hydroxypropionic acid, 2-oxoglutarate, and lysophosphatidylcholine 18:0e.

4. Application of detection reagents for renal cell carcinoma diagnostic biomarkers in the preparation of renal cell carcinoma diagnostic kits; The biomarkers for the diagnosis of renal cell carcinoma are one or more of L-valine-L-serine, L-glutamyl-L-serine, fucose, β-hydroxypyruvate, aspartic acid, 3-hydroxypropionic acid, and lysophosphatidylcholine 18:0e.

5. The application according to any one of claims 1 to 4, characterized in that, The test reagent can be used to test plasma samples.

6. The application according to any one of claims 1 to 4, characterized in that, The detection reagent was used to detect the renal cell carcinoma diagnostic biomarkers by high performance liquid chromatography-mass spectrometry.

7. The application according to claim 6, characterized in that, When the sample to be detected by the detection reagent is prepared as an organic phase test solution, the liquid chromatography conditions satisfy one or more of the following conditions: (1) Using the C8 chromatographic column, (2) Mobile phase A: An aqueous solution containing 0.08 v / v%-0.12 v / v% acetic acid and 10 mmol / L-20 mmol / L ammonium acetate. Mobile phase B: An acetonitrile-isopropanol solution containing 0.08 v / v%-0.12 v / v% acetic acid and 10 mmol / L-20 mmol / L ammonium acetate, wherein the volume ratio of acetonitrile to isopropanol is (6-8):(2-4); (3) Elution methods include gradient elution, Optionally, the gradient elution procedure includes: From 0 to 12 minutes, the volume percentage of the mobile phase B increased from 55% to 89%. Between 12 and 19.5 minutes, the volume percentage of the mobile phase B increased from 89% to 100%. When the sample to be tested by the detection reagent is prepared as an aqueous test solution, the liquid chromatography conditions satisfy one or more of the following conditions: (1) Using the T3 chromatographic column, (2) Mobile phase A: An aqueous solution containing 0.08 v / v% - 0.12 v / v% formic acid. Mobile phase B: An acetonitrile solution containing 0.08 v / v% - 0.12 v / v% formic acid; (3) The elution method includes gradient elution. Optionally, the gradient elution procedure includes: From 0 to 13 minutes, the volume percentage of the mobile phase B increased from 1% to 70%. Between 13 and 18 minutes, the volume percentage of the mobile phase B increased from 70% to 99%.

8. The application according to claim 6, characterized in that, The mass spectrometry conditions for the high-performance liquid chromatography-mass spectrometry (HPLC-MS / MS) method meet one or more of the following conditions: (1) Data acquisition is performed in Full MS and Full MS / dd-MS2 modes; both Full MS and Full MS / dd-MS2 include positive and negative modes; (2) Resolution is 35,000-70,000; (3) The scanning range is 100 m / z to 1500 m / z; (4) Automatic gain control is 1.0 × 10 6 -3.0×10 6 ; (5) Maximum IT is 180 ms-220 ms; (6) The relative collision energy of HCD is 20%-80%; and (7) The maximum ion implantation time is 40ms-60ms.

9. The application according to any one of claims 1 to 3 and 7 to 8, characterized in that, The kit also includes reagents for extracting the kidney cancer diagnostic biomarkers from samples; Optionally, the reagents used to extract the renal cell carcinoma diagnostic biomarkers from the sample include one or both of methyl tert-butyl ether and methanol; Optionally, the volume ratio of the methyl tert-butyl ether to the methanol is (2-4):

1.

10. A method for constructing a diagnostic model for renal cell carcinoma, characterized in that, include: Small molecule metabolites were detected and identified in plasma samples from the healthy group and the renal cancer group in the modeling group, respectively, and data from the healthy group and the renal cancer group in the modeling group were obtained. Significantly different metabolite analyses were performed on the healthy population data and the renal cell carcinoma population data at different stages in the modeling group to screen for small molecule metabolites with significant differences between groups and obtain combinations of metabolic biomarkers; and, Multivariate ROC curve analysis was performed on the combination of metabolic biomarkers.

Images