Biomarker for diagnosing renal cell carcinoma and use thereof

Abstract

The present invention relates to a biomarker for diagnosing renal cell carcinoma and a use thereof. The biomarker specifically increases or decreases in expression in renal cell carcinoma patients compared to a normal group and can thus be used for monitoring, diagnosing, and predicting the prognosis of renal cell carcinoma. In particular, the biomarker can be effectively used for the early diagnosis of renal cell carcinoma.

Description

Biomarkers for the diagnosis of renal cancer and their uses

[0001] The present invention relates to a biomarker for diagnosing renal cell carcinoma and its use.

[0002] Tumors that occur in the kidney are classified into tumors that occur in the renal parenchyma and renal pelvic carcinomas that occur in the renal pelvis, depending on where they occur. Tumors in the renal parenchyma are further classified into primary tumors that occur in the kidney itself and renal metastases that occur in other organs and have metastasized to the kidney. Most tumors that occur in the kidney are primary tumors, and among them, more than 85-90% are renal cell carcinomas, which are malignant tumors. Therefore, when we generally say kidney cancer, we mean renal cell carcinoma, a malignant tumor that occurs in the renal parenchyma.

[0003] The cause of renal cell carcinoma is not clearly known, but risk factors can be broadly divided into environmental factors, lifestyle habits, existing kidney diseases, and genetic factors. Environmental and lifestyle-related factors include smoking, obesity, and high blood pressure. In addition, dietary habits such as excessive intake of animal fat and high-energy foods, and occupational exposure to organic solvents, leather, petroleum products, and heavy metals such as cadmium are also mentioned as risk factors. Pre-existing diseases such as polycystic kidney disease, kidney stones, and long-term hemodialysis are also risk factors.

[0004] Existing renal diseases thought to be related to the development of renal cell carcinoma include patients with chronic renal failure undergoing long-term hemodialysis. In particular, 4-9% of patients with acquired renal cysts are known to develop renal cell carcinoma, with a risk 30-100 times higher than that of the general population. Furthermore, genetic factors have been found to play a significant role in the development of renal cell carcinoma, with a family history of the disease increasing the risk by 4-5 times.

[0005] Renal cell carcinoma (RCC) has no specific symptoms or findings, and like most other cancers, there are no specific symptoms in the early stages. Furthermore, symptoms often remain until the tumor has progressed to a certain extent, often only being discovered after it has metastasized to other organs. Traditionally, the three classic symptoms of RCC were pain in the flank, blood in the urine (hematuria), and a palpable mass in the flank or upper abdomen. However, in reality, only 10-15% of cases present with all three symptoms.

[0006] The progression of renal cell carcinoma (RCC) is classified according to the TNM staging system, which is divided into stages I, II, III, and IV. This staging allows for the selection of the most appropriate treatment method and prediction of prognosis. However, RCC has no specific symptoms, and symptoms often appear late, often leading to an advanced stage. It also does not respond well to non-surgical treatments such as radiation therapy or chemotherapy. Therefore, early diagnosis is crucial for a cure. With increasing health awareness and the widespread use of imaging diagnostic techniques such as abdominal ultrasound in health screenings, early detection of cancer is on the rise. In these cases, the tumor size is small and the stage is low, often leading to a good prognosis. However, 10-30% of patients are still diagnosed after metastasis to other organs. Therefore, early diagnosis of RCC is a crucial preventive measure.

[0007] Accordingly, the inventors of the present invention completed the present invention by discovering a metabolite that can be used as a biomarker for early diagnosis while researching a method for early diagnosis of renal cancer.

[0008] The purpose of the present invention is to provide a biomarker composition for diagnosing renal cell carcinoma.

[0009] In addition, the present invention aims to provide a kit for diagnosing renal cancer comprising the biomarker composition for diagnosing renal cancer.

[0010] In addition, the present invention aims to provide a method for providing information for diagnosing renal cancer using the biomarker composition for diagnosing renal cancer.

[0011] To achieve the above purpose, the present invention provides a biomarker composition for diagnosing renal cell carcinoma, comprising at least one metabolite selected from the group consisting of L-glutamic acid, decanoylcarnitine, L-tryptophan, and lysophosphatidylcholine.

[0012] In addition, the present invention provides a kit for diagnosing renal cancer, comprising a composition for diagnosing renal cancer, comprising at least one metabolite selected from the group consisting of L-glutamic acid, decanoylcarnitine, L-tryptophan, and lysophosphatidylcholine.

[0013] The above lysophosphatidylcholine may be at least one selected from the group consisting of LysoPC(16:0), LysoPC(18:0), LysoPC(18:1), and LysoPC(18:2).

[0014] The L-glutamic acid may exhibit an AUC value of 0.833, the decanoylcarnitine may exhibit an AUC value of 0.842, the L-tryptophan may exhibit an AUC value of 0.848, the LysoPC(16:0) may exhibit an AUC value of 0.853, the LysoPC(18:0) may exhibit an AUC value of 0.833, the LysoPC(18:1) may exhibit an AUC value of 0.811, and the LysoPC(18:2) may exhibit an AUC value of 0.841.

[0015] The above composition for diagnosing kidney cancer may be used to diagnose kidney cancer at an early stage and to distinguish stage 1 of kidney cancer.

[0016] In addition, the present invention provides a biomarker composition for diagnosing renal cancer comprising CPT1 (carnitine palmitoyltransferase I).

[0017] In addition, the present invention provides a kit for diagnosing renal cancer, comprising a biomarker composition for diagnosing renal cancer including CPT1.

[0018] The above composition for diagnosing kidney cancer may be used to diagnose kidney cancer at an early stage and to distinguish stage 1 of kidney cancer.

[0019] In addition, the present invention provides a method for providing information for diagnosing renal cancer, comprising the step of measuring the expression level of at least one metabolite selected from the group consisting of L-glutamic acid, decanoylcarnitine, L-tryptophan, and lysophosphatidylcholine in a biological sample isolated from a test subject.

[0020] The above information providing method may further include a step of comparing the measurement results of the above step with a normal control sample, and if the expression level of L-glutamic acid in the measurement results increases compared to the normal control sample, or the expression level of decanoylcarnitine, L-tryptophan, or lysophosphatidylcholine decreases compared to the normal control sample, the method may further include a step of determining the subject as a renal cancer patient.

[0021] The above lysophosphatidylcholine may be at least one selected from the group consisting of LysoPC(16:0), LysoPC(18:0), LysoPC(18:1), and LysoPC(18:2).

[0022] The above biological sample may be blood or plasma.

[0023] The above method of providing information may be for early diagnosis of kidney cancer and for distinguishing stage 1 of kidney cancer.

[0024] In addition, the present invention provides a method for providing information for diagnosing renal cancer, comprising a step of measuring the expression level of CPT1 in a biological sample isolated from a test subject.

[0025] The above information providing method may further include a step of comparing the measurement results of the above step with a normal control sample, and may further include a step of determining the subject as a renal cancer patient if the expression level of CPT1 in the measurement results is decreased compared to the normal control sample.

[0026] The above biological sample may be blood or plasma.

[0027] The above method of providing information may be for early diagnosis of kidney cancer and for distinguishing stage 1 of kidney cancer.

[0028] The biomarker according to the present invention can be used for monitoring, diagnosing, and predicting the prognosis of renal cancer by specifically increasing or decreasing its expression in renal cancer patients compared to a normal group, and can be particularly useful for the early diagnosis of renal cancer.

[0029] In addition, the biomarker according to the present invention has the advantage of being able to be more easily used in the diagnosis of kidney cancer, as its expression level can be measured through a blood sample.

[0030] Figure 1 shows a flow chart of metabolomics analysis.

[0031] Figure 2 shows Volcano plots of renal cell carcinoma (RCC) patients and controls (HC). Each point represents a metabolite; negative log2 (FC) values, shown in blue, indicate lower concentrations of the metabolite in RCC than in HC, while positive values, shown in red, indicate higher concentrations of the metabolite in RCC than in HC.

[0032] Figure 3 shows the results of quantitative analysis of LysoPC(16:0), L-Tryptophan, LysoPC(18:2), Decanoylcarnitine, LysoPC(18:0), L-Glutamic acid, and LysoPC(18:1) between renal cell carcinoma (RCC) patients and control (HC) using the AbsoluteIDQ p400HR kit (***p < 0.001).

[0033] Figure 4 shows individual univariate receiver operating characteristic (ROC) curves (biomarker analysis results) of LysoPC(16:0), L-tryptophan, LysoPC(18:2), Decanoylcarnitine, LysoPC(18:0), L-glutamic acid, and LysoPC(18:1) between renal cell carcinoma (RCC) patients and control (HC) patients.

[0034] Figure 5 shows the multivariate ROC curves of the combination of LysoPC(16:0), L-Tryptophan, LysoPC(18:2), Decanoylcarnitine, LysoPC(18:0), L-Glutamic acid, and LysoPC(18:1).

[0035] Figure 6 shows the multivariate receiver operating characteristic (ROC) curves according to the combination of metabolites between the control group (HC) and renal cell carcinoma (RCC) stage 1 (T stage 1) and between the control group (HC) and renal cell carcinoma (RCC) stages 2 and 3 (T stage 2&3). Seven (m=7) metabolites were analyzed, including LysoPC(16:0), L-tryptophan, LysoPC(18:2), decanoylcarnitine, LysoPC(18:0), L-glutamic acid, and LysoPC(18:1), six (m=6) metabolites were analyzed excluding L-glutamic acid, and five (m=5) metabolite analyses were analyzed excluding L-glutamic acid and LysoPC(18:2).

[0036] Figure 7 shows the results of CPT family gene expression analysis between renal cell carcinoma (RCC) patients and controls. CPT1A, CPT1B, CPT2, CACT, and CrAT were analyzed in the GEO database, and CPT1 was evaluated using an ELISA kit (**p<0.01; ***p<0.001).

[0037] Figure 8 shows the results of the internal validation model using LGBM evaluation, showing the average score of the 5-fold model. The AUC curve, accuracy, sensitivity, specificity, F1-score, and precision are each displayed.

[0038] Hereinafter, with reference to the attached drawings, embodiments and examples of the present invention will be described in detail so that those skilled in the art can easily implement the present invention. However, the present invention may be implemented in various forms and is not limited to the embodiments and examples described herein.

[0039] Throughout this specification, whenever a part is said to "include" a component, this means that it may include other components, but not to the exclusion of other components, unless otherwise stated.

[0040] The present invention provides a biomarker composition for diagnosing renal cell carcinoma, comprising at least one metabolite selected from the group consisting of L-glutamic acid, decanoylcarnitine, L-tryptophan, and lysophosphatidylcholine, or carnitine palmitoyltransferase I (CPT1).

[0041] In addition, the present invention provides a kit for diagnosing renal cancer, comprising a biomarker composition for diagnosing renal cancer, comprising CPT1 or at least one metabolite selected from the group consisting of L-glutamic acid, decanoylcarnitine, L-tryptophan, and lysophosphatidylcholine.

[0042] In addition, the present invention provides a method for providing information for diagnosing renal cancer, comprising the step of measuring the expression level of CPT1 or any one or more metabolites selected from the group consisting of L-glutamic acid, decanoylcarnitine, L-tryptophan, and lysophosphatidylcholine in a biological sample isolated from a test subject.

[0043] In addition, the present invention provides a method for providing information for diagnosing renal cancer, comprising the steps of: measuring the expression level of the biomarker for diagnosing renal cancer in a biological sample isolated from a test subject; and comparing the measurement result of the step with a normal control sample.

[0044] In the present invention, the biomarker composition for diagnosing renal cancer may preferably include all of L-glutamic acid, decanoylcarnitine, L-tryptophan, and lysophosphatidylcholine, but is not limited thereto.

[0045] The above lysophosphatidylcholine may be at least one selected from the group consisting of LysoPC(16:0), LysoPC(18:0), LysoPC(18:1) and LysoPC(18:2), but is not limited thereto, and preferably may include all of LysoPC(16:0), LysoPC(18:0), LysoPC(18:1) and LysoPC(18:2).

[0046] In the present invention, the biomarker composition for diagnosing renal cancer may more preferably include, but is not limited to, all of L-glutamic acid, decanoylcarnitine, L-tryptophan, LysoPC(16:0), LysoPC(18:0), LysoPC(18:1) and LysoPC(18:2).

[0047] The L-glutamic acid may exhibit an AUC value of 0.833, the decanoylcarnitine may exhibit an AUC value of 0.842, the L-tryptophan may exhibit an AUC value of 0.848, the LysoPC(16:0) may exhibit an AUC value of 0.853, the LysoPC(18:0) may exhibit an AUC value of 0.833, the LysoPC(18:1) may exhibit an AUC value of 0.811, and the LysoPC(18:2) may exhibit an AUC value of 0.841.

[0048] The kidney cancer diagnostic kit of the present invention means a kit including the kidney cancer diagnostic composition of the present invention, and therefore the expression “kit” can be used interchangeably or interchangeably with “composition”.

[0049] The kit may further include tools and / or reagents for collecting a biological sample from a subject or patient, as well as tools and / or reagents for isolating metabolites from the sample.

[0050] In the method for providing information for diagnosing renal cancer of the present invention, if the expression level of L-glutamic acid increases compared to a normal control sample, or the expression level of decanoylcarnitine, L-tryptophan, lysophosphatidylcholine or CPT1 decreases compared to a normal control sample as a result of measuring the expression level of the biomarker for diagnosing renal cancer, the subject can be determined to be a renal cancer patient. Preferably, if the expression level of L-glutamic acid increases compared to a normal control sample, and the expression levels of decanoylcarnitine, L-tryptophan and lysophosphatidylcholine decrease compared to a normal control sample, or if the expression level of CPT1 decreases compared to a normal control sample, the subject can be determined to be a renal cancer patient.

[0051] In the method for providing information for diagnosing renal cancer of the present invention, the biological sample may include, but is not limited to, samples such as whole blood, serum, plasma, urine, feces, sputum, saliva, tissue, cells, cell extracts, and in vitro cell cultures. Preferably, it may be blood or plasma.

[0052] The biomarker composition for diagnosing renal cancer of the present invention has a better ability to distinguish stage 1 than stage 2 and stage 3 of renal cancer compared to a normal group, and thus can diagnose renal cancer at an early stage.

[0053] The "renal cancer" of the present invention refers to a tumor that occurs in the kidney. Renal cancer generally refers to renal cell carcinoma, a malignant tumor that occurs in the renal parenchyma. Therefore, the term "renal cancer" may be used interchangeably with or interchangeably with "renal cell carcinoma."

[0054] In the present invention, a "biomarker" can be used as a diagnostic marker that can confirm the normal or pathological state of a living organism, and includes organic biomolecules such as polypeptides or nucleic acids (e.g., mRNA, etc.), lipids, glycolipids, glycoproteins, or sugars (e.g., monosaccharides, disaccharides, oligosaccharides, etc.), which show an increase or decrease in a biological sample obtained from an individual exposed to a microbial substance compared to a biological sample obtained from a normal individual.

[0055] In the present invention, "diagnosis" includes determining the susceptibility of an object to a particular disease or condition, determining whether an object currently has a particular disease or condition, determining the prognosis of an object with a particular disease or condition, or therametrics (e.g., monitoring the condition of an object to provide information about the efficacy of a treatment).

[0056] In the present invention, "metabolites," also called metabolites or metabolites, are intermediate products or products of metabolism. These metabolites have diverse functions, including fuel, structure, signaling, enzyme promoting and inhibitory effects, their own catalytic activity (typically as cofactors for enzymes), defense, and interactions with other organisms (e.g., pigments, aromatic compounds, pheromones). Primary metabolites are directly involved in normal growth, development, and reproduction. Secondary metabolites are not directly involved in these processes, but often have important ecological functions.

[0057] The above metabolites refer to metabolites obtained from a sample of biological origin, i.e., a biological sample, and the biological sample refers to a biological fluid, tissue, or cell. In addition, the biological sample may be pretreated for the detection of metabolites, and this may include, for example, filtration, distillation, extraction, separation, concentration, inactivation of interfering components, addition of reagents, etc. In addition, the metabolites may include substances produced by metabolism and metabolic processes, or substances resulting from chemical metabolic reactions by biological enzymes and molecules.

[0058] In the present invention, "subject" or "patient" refers to any single organism requiring treatment, including humans, apes, monkeys, cows, dogs, guinea pigs, rabbits, chickens, insects, etc. Furthermore, any subject participating in a clinical research trial without exhibiting any clinical findings of a disease, a subject participating in an epidemiological study, or a subject used as a control group is also included. Furthermore, the present invention refers to a mammal, preferably a human.

[0059] In the present invention, "biological sample (sample)" means a biological sample obtained from a subject or patient, and encompasses various types of samples obtained from organisms that can be used for diagnostic or monitoring assays. The term encompasses blood and other liquid samples of biological origin, solid tissue samples such as biopsy specimens, or tissue cultures or cells derived therefrom and their progeny. The term specifically encompasses clinical samples, and further includes cells in cell culture, cell supernatants, cell lysates, serum, plasma, urine, amniotic fluid, biological fluids, and tissue samples. The term also encompasses samples that have been manipulated in any way after procurement, solubilization, or enrichment for specific components, such as treatment with reagents.

[0060] In the present invention, the expression level or concentration of a biomarker in a sample can be measured using any method known to those skilled in the art. Measurement methods include, but are not limited to, spectroscopic methods, including mass spectrometry, soft ionization techniques for mass spectrometry, such as electrospray ionization (ESI), and matrix-assisted laser desorption / ionization (MALDI).

[0061] In the present invention, "detection" or "measurement" means quantifying the concentration of a detected or measured object, and "measuring" or "measuring" means assessing the presence, absence, quantity or amount (which may be an effective amount) of a given substance within a sample, including deriving a qualitative or qualitative concentration level of such substance, or otherwise assessing the value or categorization of a clinical parameter of a subject.

[0062] In the present invention, determining the level of a biomarker can be performed on an unprocessed or unfractionated sample, and the level of a biomarker can be determined in an unprocessed or unfractionated sample obtained from a subject.

[0063] In the present invention, determining the level of a biomarker may be quantitative or semi-quantitative. In one embodiment, quantitative determination may involve determining the absolute amount or concentration (expression level) of one or more metabolites. Furthermore, quantitative determination may involve determining the relative amount or concentration of one or more metabolites relative to one or more other metabolites.

[0064] In the present invention, "increase in concentration" or "increase in expression level" refers to a case where the concentration or expression level of a metabolite in a biological sample is significantly higher than that of a control group (normal group). Specifically, it refers to a case where the concentration or expression level of a metabolite in a biological sample is increased by about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% or more compared to the normal control group, but is not limited thereto.

[0065] In the present invention, "decrease in concentration" or "decrease in expression level" refers to a case where the concentration or expression level of a metabolite in a biological sample is significantly lower than that of a control group (normal group). Specifically, it refers to a case where the concentration or expression level is reduced by about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% or more compared to the normal control group, but is not limited thereto.

[0066] The advantages and features of the present invention, and the methods for achieving them, will become clearer with reference to the detailed examples described below. The present invention will now be described in detail through examples. However, these examples are intended to specifically illustrate the present invention and are not intended to limit the scope of the present invention.

[0067]

[0068] [Example 1] Experimental method

[0069] 1-1. Experimental subjects

[0070] Adults (aged 19 years or older) diagnosed with renal cell carcinoma (RCC) at the National Cancer Center (Korea) were included, along with a healthy control group (HC) without cancer. This study included 87 RCC patients and 241 healthy controls (HC); two RCC patients were excluded due to lung metastases. The questionnaire collected information on age, sex, BMI, and alcohol / smoking status. This study was approved by the Institutional Review Board (IRB No. 2019-0116).

[0071]

[0072] 1-2. Preparation of plasma samples

[0073] Subjects fasted for 12 h before blood collection. Blood was collected into K2 EDTA tubes (BD Vacutainer, NJ, USA) and centrifuged at 3,000 rpm for 20 min at 4°C. Plasma was stored at -80°C.

[0074]

[0075] 1-3. UPLC / Orbitrap MS and Metabolite Analysis

[0076] Targeted metabolic and lipid profiling was performed using a Vanquish Flex UHPLC (Thermo, MA, USA) coupled with a Q ExactiveTM Hybrid Quadrupole-Orbitrap MS (Thermo, MA, USA). 408 metabolites and lipids in blood were analyzed using the AbsoluteIDQ p400HR kit (Biocrates, Innsbruck, Austria) according to the kit instructions. Quantities were calculated using Quant Browser and MetIDQ software.

[0077]

[0078] 1-4. Ambassador Data Processing and Verification

[0079] MetaboAnalyst 5.0 (https: / www.metaboanalyst.ca / ) was used for the analysis. Below LoD or LLoQ ( <LoD 또는 <LLoQ)의 값을 갖는 91개의 대사산물은 제외되었고, 누락된 값은 LoD 값으로 대체되었다. 정규화에는 로그 변환 및 자동 크기 조정이 포함되어 284개의 대사산물을 분석하였다. Partial least squares discriminant analysis(PLS-DA)을 수행하여 FDR-adjustedp-value는 0.05 미만(<0.05)이고 variable importance in projection(VIP)은 1.0 초과(> 1.0) was selected as a differential metabolite.

[0080]

[0081] 1-5. ROC Curve Analysis (Biomarker Analysis)

[0082] Log2 fold change (FC) represents the difference in metabolites between groups. Enriched lipid / metabolite sets were analyzed using MetaboAnalys and Cytoscape (3.10.0, https: / / cytoscape.org / ), with a p < 0.05 cutoff; pathways were analyzed with a p < 0.05 false discovery rate (FDR) cutoff. Potential RCC-related markers were identified using receiver operating characteristic (ROC) curve analysis. A cutoff of area under the curve (AUC) > 0.80 was used for potential biomarkers with high sensitivity and specificity.

[0083]

[0084] 1-6. CPT family gene expression analysis and CPT1 measurement

[0085] CPT family genes (Carnitine palmitoyltransferase 1A (CPT1A), CPT1B, CPT2, Carnitine-acylcarnitine translocase (CACT), and carnitine acetyltransferase (CrAT)) were analyzed in human kidney using GEO microarray datasets (GSE781 and GSE6344) published through the GEO database (https: / www.ncbi.nlm.nih.gov / geo / ). CPT1 levels were measured using an ELISA Kit (mbs724213, MybioSource, Inc., San Diego, California, USA).

[0086]

[0087] 1-7. Evaluating Diagnostic Models Using Machine Learning

[0088] Machine learning was used to analyze the blood metabolome of renal cell carcinoma (RCC) patients using the GOSS algorithm combined with LightGBM. The model's performance was evaluated through 5-fold cross-validation on the training set (167 HC and 63 RCC samples). After trying extreme gradient boosting (XGBoost), light gradient boosted machine (LightGBM), gradient boosted machine (GBM), and random forest, LightGBM was selected as the best performing model. The model's performance was comprehensively evaluated using the 5-fold average scores for indicators such as accuracy, area under the curve (AUC), sensitivity, specificity, F-1 score, and precision.

[0089]

[0090] 1-8. Statistical Analysis

[0091] Statistical analysis was performed using SPSS (version 26.0, Chicago, IL, USA) for general characteristic evaluation, and chi-squared, t-test, and Fisher's exact test were used. Results are expressed as mean (SD) or n (%), and p < 0.05 indicates significance. Python (version 3.9.12, Wilmington, DE, USA) was used to perform multivariate logistic regression and correlation analyses of metabolites and nutrients. Metabolomics data were analyzed using FDR-adjusted p-values ​​to identify significant compounds in the HC and RCC groups. Multivariate PLS-DA VIP scores >1.0 were evaluated using MetaboAnalyst 5.0.

[0092]

[0093] [Example 2] Experimental results

[0094] 2-1. Clinical and diagnostic characteristics of the HC and RCC groups

[0095] As shown in Table 1 below, of the 328 subjects, 87 were renal cell carcinoma (RCC) patients and 241 were healthy controls (HC). Both groups had a higher proportion of males and were similar in age. RCC patients had a slightly higher BMI, similar smoking rates, and a 21.3% higher alcohol consumption history in the healthy controls (HC).

[0096] Characteristics Healthy control (n=241) Renal cell carcinoma (n=87) Sex (Male) 165 (68.5) 55 (63.2) 0.425 Age (year) 60.2 (9.2) 62.3 (10.3) 0.078 BMI (kg / m) 2)24.6 (3.06)25.4 (3.61)0.030Low(<18.5)6 (2.50)2 (2.30)0.240Normal(18.5-22.9)60 (24.9)19 (21.8)Overweight(23.0-24.9)77 (32.0)20 (23.0)Obese(≥5)98 (40.7)46 (52.9)Experiences of smoking126 (54.3)42 (48.3)0.379Experiences of drinking184 (81.1)52 (59.8)1.50 x 10 -4 For continuous variables, data are expressed as mean (standard deviation), and for categorical variables, data are expressed as n (%).

[0097]

[0098] 2-2. Metabolite Profiling Verification

[0099] As shown in Fig. 1, targeted metabolomics analysis was performed on plasma samples from 241 HC and 87 RCC patients, and as a result, 99 statistically significant metabolites were identified, as shown in Table 2 below. Among them, as shown in Fig. 2, 46 metabolites showed lower concentrations and 4 metabolites showed higher concentrations in RCC patients compared to the control group (HC).

[0100] Compound name (Compound name) Abbreviation (Short name) VIPFDR adjusted P-value (FDR adjusted p-value) Fold change (Fold change) log2fold1 Decanoylcarnitine AC (10:0) 2.96 17 2.29 0 x 10 -26 0.2792-1.84042LysoPC(16:0)LPC(16:0)1.79614.045 x 10 -25 0.7302-0.45373L-TryptophanTrp1.98154.795 x 10 -230.7681-0.38064LysoPC(18:2(9Z,12Z))LPC(18:2)2.01274.795 x 10 -23 0.6395-0.64495LysoPC(18:0)LPC(18:0)1.65863.059 x 10 -22 0.7049-0.50456L-Glutamic acidGlu3.27342.812 x 10 -20 1.54580.62847LysoPC(18:1(9Z))LPC(18:1)1.70682.246 x 10 -18 0.7279-0.45838PC(16:0 / 20:2(11Z,14Z))PC(36:2)1.43112.267 x 10 -16 0.7544-0.406699-DecenoylcarnitineAC(10:1)1.93013.033 x 10 -15 0.0667-3.905410L-AlanineAla1.66972.427 x 10 -14 0.7892-0.341611L-AsparagineAsn1.93864.091 x 10 -14 0.8468-0.239912PC(16:0 / 22:5(4Z,7Z,10Z,13Z,16Z))PC(38:5)1.43868.538 x 10 -14 0.8124-0.299713LysoPC(P-16:0)LPC-O(16:1)1.49371.490 x 10 -13 0.4078-1.294014L-MethionineMet1.75841.499 x 10 -13 0.8292-0.270215PC(15:0 / 20:3(5Z,8Z,11Z))PC(35:3)1.33864.920 x 10 -13 0.8063-0.310716L-HistidineHis1.52901.665 x 10 -12 0.8758-0.191417L-LysineLys1.65312.790 x 10 -12 0.8589-0.219518PC(14:0 / 18:2(9Z,12Z))PC(32:2)1.29513.271 x 10 -120.7483-0.418419PC(16:0 / 18:2(9Z,12Z))PC(34:2)1.25813.707 x 10 -12 0.7959-0.329320L-OctanoylcarnitineAC(8:0)1.85604.849 x 10 -12 0.3040-1.718021PC(15:0 / 22:5(4Z,7Z,10Z,13Z,16Z))PC(37:5)1.27875.950 x 10 -12 0.8235-0.280322LysoPC(20:3(5Z,8Z,11Z))LPC(20:3)1.30606.558 x 10 -12 0.5996-0.737923PC(o-16:1(9Z) / 18:2(9Z,12Z))PC-O(34:3)1.22387.431 x 10 -12 0.7362-0.441824LysoPC(22:6(4Z,7Z,10Z,13Z,16Z,19Z))LPC(22:6)1.22981.187 x 10 -11 0.7452-0.424325PC(16:0 / 20:5(5Z,8Z,11Z,14Z,17Z))PC(36:5)1.30011.629 x 10 -11 0.8276-0.273126CE(18:2(9Z,12Z))CE(18:2)1.21273.327 x 10 -11 0.7573-0.401227L-TyrosineTyr1.46791.377 x 10 -10 0.8519-0.231328DG(18:0 / 24:1(15Z) / 0:0)DG(42:1)1.25212.734 x 10 -10 0.7112-0.4916294-Hydroxyprolinet4-OH-Pro1.54913.229 x 10 -10 0.6423-0.638830PC(o-18:1(11Z) / 18:2(9Z,12Z))PC-O(36:3)1.27019.161 x 10 -10 0.7649-0.386631PC(O-16:0 / 18:2(9Z,12Z))PC-O(34:2)1.14431.460 x 10 -090.7557-0.404032LysoPC(17:0)LPC(17:0)1.19021.501 x 10 -09 0.4653-1.1038332-OctenoylcarnitineAC(8:1)1.55091.551 x 10 -09 0.3143-1.669734TG(14:0 / 14:0 / 16:1(9Z))TG(44:1)1.35881.589 x 10 -09 0.4460-1.164935PC(P-16:0 / 20:4(5Z,8Z,11Z,14Z))PC-O(36:5)1.20185.158 x 10 -09 0.8075-0.308436SM(d17:1 / 24:0)SM(41:1)1.35725.561 x 10 -09 0.8223-0.282337LysoPC(16:1(9Z))LPC(16:1)1.11935.561 x 10 -09 0.7191-0.475738SarcosineSarcosine1.67405.687 x 10 -09 0.5850-0.773539PC(14:0 / 16:0)PC(30:0)1.13791.556 x 10 -08 0.7083-0.497640DG(14:0 / 0:0 / 18:1n9)DG(32:1)1.13872.099 x 10 -08 0.4098-1.286941PC(16:0 / 20:3(5Z,8Z,11Z))PC(36:3)1.22642.705 x 10 -08 0.8536-0.228442DG(18:1(11Z) / 24:1(15Z) / 0:0)DG(42:2)1.20013.902 x 10 -08 0.5715-0.807343L-PhenylalaninePhe1.44294.935 x 10 -08 0.8960-0.158444OrnithineOrn1.47904.935 x 10 -08 0.7640-0.388345PC(o-16:1(9Z) / 16:1(9Z))PC-O(32:2)1.06005.335 x 10 -080.3319-1.591346SM(d18:1 / 24:0)SM(42:1)1.13558.121 x 10 -08 0.8318-0.265847LysoPC(20:4(5Z,8Z,11Z,14Z))LPC(20:4)1.05041.599 x 10 -07 0.7775-0.363148PC(16:0 / 22:6(4Z,7Z,10Z,13Z,16Z,19Z))PC(38:6)1.19952.233 x 10 -07 0.8450-0.243049L-ThreonineThr1.46714.148 x 10 -07 0.8697-0.201550PC(16:0 / 18:3(6Z,9Z,12Z))PC(34:3)1.18694.262 x 10 -07 0.8280-0.272451sphingomyelin 34:2SM(34:2)1.21674.621 x 10 -07 0.8604-0.216952SM(d18:1 / 16:0)SM(34:1)1.19044.636 x 10 -07 0.8628-0.212953PC(o-16:0 / 20:4(8Z,11Z,14Z,17Z))PC-O(36:4)1.19086.449 x 10 -07 0.8356-0.259154SM(d17:1 / 24:1(15Z))SM(41:2)1.33406.883 x 10 -07 0.8526-0.230155PC(o-16:0 / 22:6(4Z,7Z,10Z,13Z,16Z,19Z))PC-O(38:6)1.17091.029 x 10 -06 0.8321-0.265256sphingomyelin 33:1SM(33:1)1.01351.587 x 10 -06 0.8330-0.263657PC(14:0 / 22:6(4Z,7Z,10Z,13Z,16Z,19Z))PC(36:6)1.11601.628 x 10 -06 0.8226-0.281858PC(16:0 / 18:1(11Z))PC(34:1)1.22201.675 x 10 -060.8642-0.210559ButyrylcarnitineAC(4:0)1.16023.002 x 10 -06 0.5561-0.846560PC(16:0 / 16:0)PC(32:0)1.27173.002 x 10 -06 0.8720-0.197561L-GlutamineGln1.28874.250 x 10 -06 0.9180-0.123462SM(d18:1 / 14:0)SM(32:1)1.20524.790 x 10 -06 0.8518-0.231363CE(18:3(6Z,9Z,12Z))CE(18:3)1.12306.359 x 10 -06 0.7926-0.335464TG(16:1(9Z) / 14:0 / 16:1(9Z))TG(46:2)1.00539.455 x 10 -05 0.5784-0.789865phosphatidylcholine O-37:6PC-O(37:6)1.13161.335 x 10 -05 0.2907-1.782666sphingomyelin 39:1SM(39:1)1.26111.496 x 10 -05 0.8488-0.236567PC(P-18:0 / 20:4(5Z,8Z,11Z,14Z))PC-O(38:5)1.23374.106 x 10 -05 0.8691-0.202568SM(d18:1 / 22:0)SM(40:1)1.03034.658 x 10 -05 1.32560.406769PC(15:0 / 22:6(4Z,7Z,10Z,13Z,16Z,19Z))PC(37:6)1.13645.550 x 10 -05 0.8868-0.173370phosphatidylcholine O-40:7PC-O(40:7)1.17877.963 x 10 -05 0.8681-0.204171PC(16:0 / 20:4(5Z,8Z,11Z,14Z))PC(36:4)1.13458.623 x 10 -05 0.8952-0.159772L-CarnitineAC(0:0)1.27559.692 x 10-05 0.9000-0.152073phosphatidylcholine 43:2PC(43:2)1.19759.779 x 10 -05 3.32971.735474L-Aspartic acidAsp1.08262.291 x 10 -04 1.56160.643075PC(o-18:1(9Z) / 16:0)PC-O(34:1)1.13132.428 x 10 -04 0.8810-0.182976PC(15:0 / 22:4(7Z,10Z,13Z,16Z))PC(37:4)1.03643.609 x 10 -04 0.9026-0.147977SM(d18:1 / 24:1(15Z))SM(42:2)1.13106.306 x 10 -04 0.8966-0.157578phosphatidylcholine 43:6PC(43:6)1.06236.306 x 10 -04 0.8317-0.265979CreatinineCreatinine1.08439.730 x 10 -04 0.9982-0.002680DG(20:2(11Z,14Z) / 24:1(15Z) / 0:0)DG(44:3)1.09109.805 x 10 -04 0.8539-0.227881SM(d18:1 / 20:0)SM(38:1)1.27671.138 x 10 -03 0.8926-0.163982PC(o-18:0 / 22:6(4Z,7Z,10Z,13Z,16Z,19Z))PC-O(40:6)1.08571.341 x 10 -03 0.8891-0.169683PC(18:0 / 22:6(4Z,7Z,10Z,13Z,16Z,19Z))PC(40:6)1.23621.431 x 10 -03 0.8837-0.178384CE(16:1(9Z))CE(16:1)1.13073.516 x 10 -03 0.8592-0.218985phosphatidylcholine O-40:8PC-O(40:8)1.33445.207 x 10 -030.9013-0.150086PC(16:0 / 22:4(7Z,10Z,13Z,16Z))PC(38:4)1.06415.824 x 10 -03 0.9065-0.141687TG(15:0 / 15:0 / 20:3n6)TG(50:3)1.02946.253 x 10 -03 0.8419-0.248288PC(14:0 / 20:4(5Z,8Z,11Z,14Z))PC(34:4)1.04027.802 x 10 -03 0.8824-0.180489PC(20:4(8Z,11Z,14Z,17Z) / 22:6(4Z,7Z,10Z,13Z,16Z,19Z))PC(42:10)1.04881.008 x 10 -02 0.8979-0.155490PC(20:1(11Z) / 22:6(4Z,7Z,10Z,13Z,16Z,19Z))PC(42:7)1.27881.188 x 10 -02 0.9204-0.119891phosphatidylcholine 37:3PC(37:3)1.18981.363 x 10 -02 1.08830.122092D-GlucoseD-Glucose1.03201.592 x 10 -02 1.07400.103093SM(d18:0 / 20:2(11Z,14Z))SM(38:2)1.24311.592 x 10 -02 0.9205-0.119594PC(15:0 / 20:4(5Z,8Z,11Z,14Z))PC(35:4)1.14882.092 x 10 -02 0.9041-0.145495sphingomyelin 35:1SM(35:1)1.06493.261 x 10 -02 0.9213-0.118396Asymmetric dimethylarginineADMA1.12263.295 x 10 -02 0.9517-0.071497PC(18:4(6Z,9Z,12Z,15Z) / 22:5(4Z,7Z,10Z,13Z,16Z))PC(40:9)1.15973.329 x 10 -020.9144-0.129198TG(16:0 / 16:1(9Z) / 18:1(9Z))TG(50:2)1.00073.452 x 10 -02 0.8698-0.201399SM(d18:1 / 18:0)SM(36:1)1.34343.580 x 10 -02 0.9306-0.1038

[0101]

[0102] 2-3. Analysis of differential metabolites in HC and RCC

[0103] Among the 99 metabolites above, 35 pathways were identified through RCC-related metabolic pathway analysis. In addition, as shown in Figure 3, the results of quantitative analysis showed that L-glutamic acid was higher in RCC patients than in the control group (HC), whereas LysoPC(16:0), L-tryptophan, LysoPC(18:2), Decanoylcarnitine, LysoPC(18:0), and LysoPC(18:1) were lower.

[0104]

[0105] 2-4. Discovery of Seven Potential Diagnostic Biomarkers for RCC

[0106] For RCC diagnostic screening, plasma-based metabolic profiles were analyzed using receiver operating characteristic (ROC) analysis between the control (HC) and RCC patient groups. Potential RCC diagnostic biomarkers were selected based on an area under the curve (AUC) value >0.8 and a p-value <0.05, and seven metabolites were included: LysoPC(16:0), L-tryptophan, LysoPC(18:2), Decanoylcarnitine, LysoPC(18:0), L-glutamic acid, and LysoPC(18:1), as shown in Figure 4 .

[0107] We analyzed the case where all seven metabolites were combined as diagnostic markers for RCC. As shown in Figure 5, the ROC analysis results confirmed that the AUC value improved to 0.981, the accuracy value was 0.9516, the sensitivity value was 0.9655, and the specificity value was 0.9378. Therefore, it can be seen that the seven metabolites LysoPC(16:0), L-Tryptophan, LysoPC(18:2), Decanoylcarnitine, LysoPC(18:0), L-Glutamic acid, and LysoPC(18:1) can be used as biomarkers for the diagnosis of RCC.

[0108] In addition, as shown in Fig. 6, when all of the above seven metabolites were combined, the AUC value between HC and stage 1 (T stage 1) was 0.977, and the AUC value between HC and stages 2 and 3 (T stage 2&3) was 0.957. Therefore, it was confirmed that the combination of seven metabolites, LysoPC(16:0), L-Tryptophan, LysoPC(18:2), Decanoylcarnitine, LysoPC(18:0), L-Glutamic acid, and LysoPC(18:1), was superior to the combination of five or six metabolites in terms of the ability to distinguish cancer stages. In particular, it was confirmed that the combination of the seven metabolites was superior to the ability to distinguish T stage 1 (T stage 2&3).

[0109] Finally, the above seven metabolites were confirmed to be powerful RCC diagnostic markers, particularly specialized for the early detection of RCC.

[0110]

[0111] 2-5. CPT family gene expression analysis

[0112] As shown in Figure 7, the results of the CPT family gene expression analysis confirmed through the GEO database that the mRNA expression of CPT1A, CPT1B, CPT2, CACT, and CrAT were all decreased in RCC compared to the control group, and ELISA confirmed that the concentration of CPT1 was decreased in RCC compared to the control group. In particular, CPT1 was identified as a factor that regulates decanoylcarnitine, and it was confirmed that CPT1 can also be used as a biomarker for RCC diagnosis.

[0113]

[0114] 2-6. Machine Learning for RCC Diagnosis Prediction

[0115] After comparing the models, the optimal LightGBM was selected, and as a result of evaluating the model, as shown in Fig. 8, the AUC value was 0.9850, the accuracy value was 0.9386, the sensitivity value was 0.9259, and the specificity value was 0.9649, confirming that it could effectively distinguish between RCC patients and the control group (HC).

Claims

1. (a) at least one metabolite selected from the group consisting of L-Glutamic acid, decanoylcarnitine, L-Tryptophan and lysophosphatidylcholine; or (b) A biomarker composition for diagnosing renal cell carcinoma, comprising CPT1 (carnitine palmitoyltransferase I).

2. A biomarker composition for diagnosing renal cell carcinoma, wherein in paragraph 1, the lysophosphatidylcholine is at least one selected from the group consisting of LysoPC (16:0), LysoPC (18:0), LysoPC (18:1), and LysoPC (18:2).

3. A biomarker composition for diagnosing renal cell carcinoma, wherein in the first paragraph, the L-glutamic acid exhibits an AUC value of 0.833, the decanoylcarnitine exhibits an AUC value of 0.842, and the L-tryptophan exhibits an AUC value of 0.

848.

4. A biomarker composition for diagnosing renal cell carcinoma, wherein in the second paragraph, LysoPC(16:0) exhibits an AUC value of 0.853, LysoPC(18:0) exhibits an AUC value of 0.833, LysoPC(18:1) exhibits an AUC value of 0.811, and LysoPC(18:2) exhibits an AUC value of 0.

841.

5. In the first paragraph, the composition is a biomarker composition for diagnosing renal cell carcinoma, which diagnoses renal cancer at an early stage.

6. In the fifth paragraph, the composition is a biomarker composition for diagnosing renal cell carcinoma, which distinguishes stage 1 renal cell carcinoma.

7. A kit for diagnosing renal cell carcinoma, comprising the composition of clause 1.

8. From biological samples isolated from the test subject (a) at least one metabolite selected from the group consisting of L-Glutamic acid, decanoylcarnitine, L-Tryptophan and lysophosphatidylcholine; or (b) A method for providing information for diagnosing renal cell carcinoma, comprising the step of measuring the expression level of CPT1 (carnitine palmitoyltransferase I).

9. A method for providing information for diagnosing renal cell carcinoma, wherein in paragraph 8, the lysophosphatidylcholine is at least one selected from the group consisting of LysoPC (16:0), LysoPC (18:0), LysoPC (18:1), and LysoPC (18:2).

10. A method for providing information for diagnosing renal cell carcinoma, wherein the method for providing information in paragraph 8 further includes a step of comparing the measurement result of the step with a normal control sample.

11. In paragraph 8, the information providing method further includes a step of determining the subject as a renal cancer patient if, in the measurement results of the step, the expression level of L-glutamic acid increases compared to a normal control sample, or the expression level of decanoylcarnitine, L-tryptophan, lysophosphatidylcholine or CPT1 decreases compared to a normal control sample. A method for providing information for diagnosing renal cell carcinoma.

12. A method for providing information for diagnosing renal cell carcinoma, wherein the biological sample is blood or plasma in paragraph 8.

13. A method for providing information for diagnosing renal cell carcinoma, wherein the method for providing information in paragraph 8 is for diagnosing renal cell carcinoma at an early stage.

14. A method for providing information for diagnosing renal cell carcinoma, wherein the method for providing information in paragraph 13 distinguishes stage 1 renal cell carcinoma.

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