Liver cancer, benign liver disease diagnostic marker and application

Abstract

The application relates to a marker containing at least one of hypoxanthine, uracil, indole-3-methanol and cis-10-pentadecenoic acid. The marker is used for liver cancer diagnosis, and the AUC of the area under the ROC curve of any two of the four metabolic markers is greater than or equal to 0.769. The AUC of the area under the ROC curve of any three of the four metabolic markers is greater than or equal to 0.824. The comprehensive performance of the combination of the four metabolic markers is obviously superior to or equivalent to that of other combinations of metabolic markers, and the AUC of the area under the ROC curve is greater than or equal to 0.919. The application further provides a liver cancer diagnosis product containing the above marker. The liver cancer diagnosis product can significantly reduce the cost of liver cancer diagnosis, improve the diagnosis accuracy and convenience, is easy to popularize in primary hospitals, and can realize large-scale early screening, early diagnosis and recurrence monitoring of high-risk groups of liver cancer.

Description

Technical Field

[0001] This invention belongs to the field of biomedical detection technology, specifically relating to a diagnostic marker for liver cancer and benign liver diseases and its application. Background Technology

[0002] The prognosis of hepatocellular carcinoma (HCC) is highly correlated with the stage of the tumor at the time of diagnosis; therefore, early diagnosis is crucial for improving patient outcomes.

[0003] According to the latest clinical research and data summary from the "Chinese Guidelines for the Diagnosis and Treatment of Primary Liver Cancer (2022 Edition)," alpha-fetoprotein (AFP) has a sensitivity of 39%-64% and a specificity of 76%-94% (with ≥20 μg / L as the cutoff value) for diagnosing early-stage liver cancer. However, it is easily affected by diseases such as hepatitis and cirrhosis, leading to a false positive rate of approximately 20%. Another biomarker, vitamin K deficiency-induced protein-II (PIVKA-II), has a sensitivity of 66%-72% and a specificity of 86%-92% (with ≥40 mAU / mL as the cutoff value) for diagnosing early-stage liver cancer. Other biomarkers, such as isoforms (AFP-L3) and abnormal prothrombin (DCP), also have limited sensitivity. Currently available diagnostic biomarkers cannot meet clinical needs. Imaging (enhanced CT / MRI) has a sensitivity of only 33%-71% for lesions <1 cm and is highly dependent on equipment and operator experience, making its widespread adoption in primary hospitals difficult. In conclusion, there is a lack of affordable and scalable early screening methods.

[0004] Therefore, exploring new strategies for liver cancer screening and treatment, scientifically identifying high-risk groups for liver cancer, and developing stratified testing programs are the most crucial steps and urgent needs for early detection, early diagnosis, and improving the overall survival rate of liver cancer. Summary of the Invention

[0005] With the widespread application of high-throughput analytical methods such as liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS), researchers can accurately identify and quantify metabolites from complex biological samples, thereby revealing metabolic reprogramming during tumorigenesis and development. In recent years, metabolomics has shown great potential in the diagnosis of liver cancer, providing a new direction for exploring biomarkers for HCC diagnosis. This study uses plasma samples from research subjects to perform metabolomics testing, screening for metabolic biomarkers for the diagnosis of early-stage liver cancer and their applications.

[0006] In a first aspect of this application, this application provides a marker comprising at least one of hypoxanthine, uracil, indole-3-methanol, and cis-10-pentadecanoic acid.

[0007] Optionally, the marker comprises uracil and cis-10-pentadecanoic acid.

[0008] Optionally, the marker comprises at least one of hypoxanthine, indole-3-methanol, uracil, and cis-10-pentadecanoic acid.

[0009] Optionally, the markers include hypoxanthine, uracil, cis-10-pentadecanoic acid, and indole-3-methanol.

[0010] In a second aspect of this application, this application provides the use of a reagent for detecting the markers described in the first aspect of this application in the preparation of products for diagnosing liver cancer.

[0011] This application also provides the use of a reagent for detecting the markers described in the first aspect of this application in the preparation of products for diagnosing benign liver diseases.

[0012] Optionally, the benign liver disease is selected from at least one of chronic hepatitis B and cirrhosis.

[0013] Optionally, the product for diagnosing liver cancer is selected from chips, reagents, test strips, and kits.

[0014] Optionally, the product for diagnosing benign liver diseases is selected from chips, reagents, test strips, and kits.

[0015] In a third aspect of this application, this application provides the use of the biomarker described in the first aspect of this application in the preparation of products for diagnosing liver cancer.

[0016] Optionally, the product for diagnosing liver cancer is selected from chips, reagents, test strips, and kits.

[0017] Furthermore, the biomarker is derived from blood plasma.

[0018] This application also provides the use of the biomarker described in the first aspect of this application in the preparation of products for diagnosing benign liver diseases.

[0019] Optionally, the product for diagnosing benign liver diseases is selected from chips, reagents, test strips, and kits.

[0020] Optionally, the benign liver disease is selected from at least one of chronic hepatitis B and cirrhosis.

[0021] In a fourth aspect of this application, this application provides a product for diagnosing liver cancer or benign liver diseases, wherein the product for diagnosing liver cancer or benign liver diseases includes the markers described in the first aspect of this application.

[0022] Optionally, the benign liver disease is selected from at least one of chronic hepatitis B and cirrhosis.

[0023] Optionally, the product for diagnosing liver cancer or benign liver diseases is selected from chips, reagents, test strips, and kits.

[0024] In a fifth aspect of this application, this application provides a product for diagnosing liver cancer or benign liver diseases, the product comprising a detection reagent for the markers described in the first aspect of this application.

[0025] Optionally, the benign liver disease is selected from at least one of chronic hepatitis B and cirrhosis.

[0026] Optionally, the product for diagnosing liver cancer or benign liver diseases is selected from chips, reagents, test strips, and kits.

[0027] In a sixth aspect of this application, this application provides a system for diagnosing liver cancer, the system including a data analysis module, the data analysis module including constructing a model for diagnosing liver cancer based on the biomarkers described in the first aspect of this application.

[0028] When the biomarkers are a combination of hypoxanthine and indole-3-methanol, the model equation is:

[0029] Y=-2.16217658400085e -6 ×Indole-3-carbinol-1.74286022650877e -7 ×Hypoxanthine

[0030] +22.3029876270365;

[0031] When Y > 0.488, liver cancer can be diagnosed; when Y ≤ 0.488, it is considered normal.

[0032] When the biomarkers are a combination of hypoxanthine and uracil, the model equation is:

[0033] Y=-1.68112627617427e -6 ×Uracil-9.64780929633282e -8 ×Hypoxanthine+4.2240024300889;

[0034] When Y > 0.482, liver cancer can be diagnosed; when Y ≤ 0.482, it is considered normal.

[0035] When the biomarkers are a combination of indole-3-methanol and uracil, the model equation is:

[0036] Y=-1.80910627770631e -6×Indole-3-carbinol-1.49922136399586e -6 ×Uracil

[0037] +21.2348095158677;

[0038] When Y > 0.531, liver cancer can be diagnosed; when Y ≤ 0.531, it is considered normal.

[0039] When the biomarkers are a combination of cis-10-pentadecanoic acid and hypoxanthine, the model equation is:

[0040] Y=5.63746386619543e -7 ×FFA(15:1)-2.36693808603199e -7 ×Hypoxanthine-0.223246340529944;

[0041] When Y > 0.496, liver cancer can be diagnosed; when Y ≤ 0.496, it is considered normal.

[0042] When the biomarkers are a combination of cis-10-pentadecanoic acid and indole-3-methanol, the model equation is:

[0043] Y=5.27188709821395e -7 ×FFA(15:1)-1.5073283529384e -6 ×Indole-3-carbinol+13.6425813043705;

[0044] When Y > 0.460, liver cancer can be diagnosed; when Y ≤ 0.460, it is considered normal.

[0045] When the biomarkers are a combination of cis-10-pentadecanoic acid and uracil, the model equation is:

[0046] Y=5.34864683531189e -7 ×FFA(15:1)-1.48865775239258e -6 ×Uracil+2.01967181289607;

[0047] When Y > 0.481, liver cancer can be diagnosed; when Y ≤ 0.481, it is considered normal.

[0048] When the biomarkers are a combination of hypoxanthine, indole-3-ethanol, and uracil, the model equation is:

[0049] Y=-1.81856810120463e-6 ×Indole-3-carbinol-1.42151980371604e -6 ×Uracil-1.0318753251763e -7 ×Hypoxanthine+21.6735266805436;

[0050] When Y > 0.421, liver cancer can be diagnosed; when Y ≤ 0.421, it is considered normal.

[0051] When the biomarkers are a combination of cis-10-pentadecanoic acid, hypoxanthine, and indole-3-methanol, the model equation is:

[0052] Y=5.15567775429167e -7 ×FFA(15:1)-1.50536684913764e -6 ×Indole-3-carbinol-2.27169419199644e -7 ×Hypoxanthine+14.7615038901775;

[0053] When Y > 0.422, liver cancer can be diagnosed; when Y ≤ 0.422, it is considered normal.

[0054] When the biomarkers are a combination of cis-10-pentadecanoic acid, hypoxanthine, and uracil, the model equation is:

[0055] Y=5.31436912963206e -7 ×FFA(15:1)-1.39166527456275e -6 ×Uracil-1.5941671040418e -7 ×Hypoxanthine+2.57836461317576;

[0056] When Y > 0.503, liver cancer can be diagnosed; when Y ≤ 0.503, it is considered normal.

[0057] When the biomarkers are a combination of cis-10-pentadecanoic acid, indole-3-methanol, and uracil, the model equation is:

[0058] Y=4.97540126420793e -7 ×FFA(15:1)-1.19528673111266e -6 ×Indole-3-carbinol-1.33956395825998e -6×Uracil+13.6000913024439;

[0059] When Y > 0.426, liver cancer can be diagnosed; when Y ≤ 0.426, it is considered normal.

[0060] When the biomarkers are a combination of inosine, indole-3-methanol, uracil, and cis-10-pentadecanoic acid, the model equation is:

[0061] Y=4.90290867501596e -7 ×FFA(15:1)-1.24310492425758e -6 ×Indole-3-carbinol-1.2423719399564e -6 ×Uracil-1.65609551176373e -7 ×Hypoxanthine+14.6844994567476;

[0062] When Y > 0.463, liver cancer can be diagnosed; when Y ≤ 0.463, it is considered normal.

[0063] Hypoxanthine, Uracil, FFA (15:1), and Indole-3-carbinol represent the detection values ​​of hypoxanthine, uracil, cis-10-pentadecanoic acid, and indole-3-carbinol, respectively.

[0064] In a seventh aspect, this application provides a method for screening the markers described in the first aspect of this application, the screening method comprising the following steps:

[0065] 1) Samples were collected from the healthy group and the liver cancer patient group respectively;

[0066] 2) LC-MS was used to detect samples from healthy individuals and liver cancer patients, and candidate differentially expressed metabolites were obtained through discriminant analysis;

[0067] 3) Receiver operating characteristic (ROC) curve analysis was performed on the differential metabolites and their combinations to identify biomarkers for the diagnosis of liver cancer.

[0068] Preferably, the chromatographic column used for LC-MS detection is a Waters ACQUITY UPLC HSS T3.

[0069] Preferably, the mobile phase for LC-MS detection is phase A, which is an aqueous solution containing 0.04% acetic acid, and phase B, which is an acetonitrile solution containing 0.04% acetic acid.

[0070] Preferably, the flow rate for LC-MS detection is 0.4 mL / min.

[0071] Preferably, the gradient elution conditions for LC-MS detection are as follows:

[0072] At 0 min, the volume ratio of phase A to phase B was 95:5;

[0073] At 11.0 min, the volume ratio of phase A to phase B was 10:90;

[0074] At 12.0 min, the volume ratio of phase A to phase B was 10:90;

[0075] At 12.1 min, the volume ratio of phase A to phase B was 95:5;

[0076] At 14.0 min, the volume ratio of phase A to phase B was 95:5.

[0077] In summary, the present invention has at least one of the following beneficial technical effects:

[0078] 1. This invention provides a biomarker comprising at least one of hypoxanthine, uracil, and indole-3-carbinol, and cis-10-pentadecanoic acid. This application also provides the application of the above biomarker in the preparation of a diagnostic kit for liver cancer. Any pairwise combination of the four metabolic biomarkers, in the validation set, exhibits an AUC ≥ 0.769, sensitivity ≥ 0.652, and specificity ≥ 0.870. Any combination of any three of the four metabolic biomarkers, in the validation set, exhibits an AUC ≥ 0.824, sensitivity ≥ 0.739, and specificity ≥ 0.870. Furthermore, the overall performance of a combination of the four metabolic biomarkers is significantly better than or equivalent to combinations of other metabolic biomarkers, exhibiting an AUC ≥ 0.919, sensitivity ≥ 0.826, and specificity ≥ 0.957 in the validation set. It is significantly superior to existing serological indicators such as AFP and PIVKA-II; at the same time, this biomarker can be used to distinguish between healthy people and benign liver diseases, with an area under the ROC curve (AUC) ≥ 0.833, sensitivity ≥ 0.870, and specificity ≥ 0.667.

[0079] 2. The present invention provides the use of the above-mentioned markers or reagents for detecting the above-mentioned markers in the preparation of products for diagnosing liver cancer or benign liver diseases.

[0080] 3. This invention provides a product for diagnosing liver cancer or benign liver diseases, comprising the above-mentioned biomarkers or reagents for detecting the above-mentioned biomarkers. This kit can significantly reduce the cost of liver cancer diagnosis while improving diagnostic accuracy and convenience, and has clinical application and promotion value. Attached Figure Description

[0081] Figure 1The figure shows the OPLS-DA diagram of the metabolites according to Example 1 of this invention (in the figure, NC is the normal group, BLD is the benign liver disease group, HCC is the liver cancer group, and QC is the quality control sample).

[0082] Figure 2 The diagram shows the validation parameters of the OPLS-DA model provided in Example 1;

[0083] Figure 3 ROC curve (HCC_VS_NC) in the validation set for the model of diagnosing liver cancer using the combination of metabolic markers (hypoxanthine + indole-3-methanol + uracil + cis-10-pentadecanoic acid) provided in Example 1.

[0084] Figure 4 Box plots showing the expression of the metabolic marker combination (hypoxanthine + indole-3-methanol + uracil + cis-10-pentadecanoic acid) provided in Example 1 in liver cancer patients and healthy individuals.

[0085] Figure 5 ROC curve (BLD_VS_NC) of the model for diagnosing benign liver disease using the metabolic marker combination (hypoxanthine + indole-3-methanol + uracil + cis-10-pentadecanoic acid) provided in Example 1 in the validation set. Detailed Implementation

[0086] The present invention will now be described in further detail with reference to specific embodiments, so that those skilled in the art can more clearly understand the present invention.

[0087] The following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. All other embodiments obtained by those skilled in the art based on the specific embodiments of the invention without inventive effort are within the protection scope of the invention.

[0088] In the embodiments of the present invention, unless otherwise specified, all raw material components are commercially available products well known to those skilled in the art; in the embodiments of the present invention, unless specifically specified, the technical means used are conventional means well known to those skilled in the art.

[0089] Key experimental reagents are listed in Table 1 below:

[0090] Table 1 Experimental Reagents

[0091]

[0092] Key instrument information is shown in Table 2 below:

[0093] Table 2 Experimental Instrument Information

[0094]

[0095] Example 1

[0096] This embodiment provides a method for screening plasma metabolic markers for diagnosing liver cancer, including the following steps:

[0097] S1. Sample collection

[0098] With patient consent, this study collected peripheral venous blood samples from 70 patients with liver cancer, 55 patients with benign liver diseases (chronic hepatitis B and cirrhosis), and 70 healthy individuals at the Clinical Medical Research Center. All samples were from individuals with no history of other malignant tumors, other major systemic diseases, or a history of long-term medication use for chronic diseases. Age, weight, height, and other indicators were matched among the groups.

[0099] Blood samples were collected in the early morning on an empty stomach. All plasma samples were centrifuged and stored at -80°C. Samples were thawed before each study for subsequent analysis.

[0100] S2. Broadly Targeted Plasma Metabolomics Analysis

[0101] (1) Sample pretreatment

[0102] Remove the sample collected in step S1 from the -80℃ freezer and thaw it on ice until no ice remains (all subsequent operations must be performed on ice). After thawing, vortex for 10 seconds to mix, and add 50µL of the sample to the corresponding numbered centrifuge tube. Add 300µL of pure methanol internal standard extraction buffer (containing 100ppm of L-phenylalanine internal standard). Vortex for 5 minutes, let stand for 24 hours, and then centrifuge at 12000 rpm and 4℃ for 10 minutes. Collect 270µL of the supernatant and concentrate for 24 hours. Add 100µL of the reconstitution solution (composed of acetonitrile and water in a 1:1 volume ratio) for LC-MS / MS analysis. Take 20µL of the extraction buffer from each sample and mix it to form a quality control sample (QC). During the instrument testing process, place one QC sample every 10 samples to monitor the stability of the instrument and the test results.

[0103] (2) Detection of metabolites in samples

[0104] The liquid chromatography conditions were determined as follows: Column: Waters ACQUITY UPLC HSS T3 C18 1.8µm, 2.1mm×100mm; Column temperature: 40℃; Injection volume: 2µL.

[0105] Mobile phases: Phase A was an aqueous solution containing 0.04% acetic acid, and Phase B was an acetonitrile solution containing 0.04% acetic acid. The elution gradient program was as follows: 0 min, volume ratio of Phase A to Phase B 95:5; 11.0 min, volume ratio of Phase A to Phase B 10:90; 12.0 min, volume ratio of Phase A to Phase B 10:90; 12.1 min, volume ratio of Phase A to Phase B 95:5; 14.0 min, volume ratio of Phase A to Phase B 95:5. Flow rate: 0.4 mL / min.

[0106] The mass spectrometry conditions were determined as follows: electrospray ionization (ESI) temperature 500℃, mass spectrometry voltage 5500V (positive) or -4500V (negative), ion source gas I (GS I) 55psi, gas II (GS II) 60psi, curtain gas (CUR) 25psi, and collision-activated dissociation (CAD) parameter set to high.

[0107] In the triple quadrupole (Qtrap), each ion pair is detected by MRM mode scanning based on optimized declustering potential (DP) and collision energy (CE).

[0108] Samples were analyzed under defined liquid chromatography and mass spectrometry conditions: extracts from 20% of samples each from liver cancer patients, benign liver disease patients, and healthy individuals were randomly selected. A liver cancer plasma metabolite database was constructed using enhanced ion scanning mass spectrometry (MIM-EPI) and time-of-flight mass spectrometry (TOF) combined with multiple reaction monitoring (MRM), and integrated with a local standard database. The collected plasma samples were analyzed using a metabolomics method coupled with liquid chromatography-mass spectrometry and the constructed plasma metabolite databases for liver cancer, benign liver disease, and healthy individuals, yielding raw mass spectrometry data for each plasma sample.

[0109] (3) Preprocessing and integration of peak area in the spectrum

[0110] Based on a database of plasma-specific metabolites from liver cancer, benign liver diseases, and healthy individuals, mass spectrometry was used for qualitative and quantitative analysis of metabolites in the samples. Liquid chromatography (LC) can separate metabolites of different molecular weights. A triple quadrupole multiple reaction monitoring (MRM) mode was used to screen for characteristic ions of each substance, and the signal intensity (CPS) of the characteristic ions was obtained in the detector. The sample mass spectrometry file was opened using MultiQuant software. The raw mass spectrometry data was preprocessed and corrected according to the mass-to-charge ratio and retention time. Peak integration and correction were performed. The peak area (Area) of each chromatographic peak represents the relative content of the corresponding substance. Peaks with an S / N > 5 and a retention time shift not exceeding 0.2 min were retained. The relative content information of metabolites was obtained by calculating the peak area based on the mass spectrometry peak intensity. Finally, all integrated peak area data were exported and saved for further statistical analysis.

[0111] (4) Experimental quality control

[0112] By overlaying and analyzing the total ion chromatograms of mass spectrometry analysis of different QC samples, the repeatability of metabolite extraction and detection, i.e., technical repeatability, can be determined. The high stability of the instrument provides crucial assurance for data repeatability and reliability. The coefficient of variation (CV) is the ratio of the standard deviation to the mean of the original data, reflecting the degree of data dispersion. Using the empirical cumulative distribution function (ECDF), the frequency of CV values ​​for substances with values ​​less than the reference value can be analyzed. A higher proportion of substances with lower CV values ​​in the QC samples indicates more stable experimental data: a proportion of substances with CV values ​​less than 0.5 exceeding 85% indicates relatively stable experimental data; a proportion of substances with CV values ​​less than 0.3 exceeding 75% indicates very stable experimental data. Simultaneously monitoring the change in the CV value of the L-phenylalanine internal standard during detection, a change of less than 20% in the internal standard CV value indicates good instrument stability during detection.

[0113] (5) Data processing and analysis

[0114] All peak area integral data from the sample tests were imported into SIMCA software (Version 14.1, Sweden) for multivariate statistical analysis. Figure 1Orthogonal partial least squares-discriminant analysis (OPLS-DA) preliminarily revealed the degree of separation between the healthy group, the benign liver disease group, and the hepatocellular carcinoma group. The 95% confidence interval ellipses of the three groups were clearly separated, indicating significant differences in metabolites among the three groups. Samples within the same group clustered tightly together, indicating good intra-group reproducibility. The tighter the clustering in the QC group, the more stable the instrument and data were during the testing process. In the figure, NC represents the normal group, BLD represents the benign liver disease group, HCC represents the hepatocellular carcinoma group, and QC represents the quality control group. Figure 2 Validation parameter plots for the OPLS-DA model, R 2 Y is 0.959, Q 2 The value was 0.918, and the p-value was less than 0.005, further demonstrating that the model is reliable.

[0115] In the healthy control group, benign liver disease group, and liver cancer patient group, 302 metabolites with consistently elevated or decreased expression levels were selected as candidate metabolic biomarkers. Plasma samples from the healthy control group and liver cancer patient group were used as datasets. The data were split into training and validation sets in a 6:4 ratio. In the training set, LASSO (L1 regularized) regression was used for feature dimensionality reduction. This algorithm automatically selected the most relevant metabolite combinations by penalizing redundant variables, and finally constructed a combination of liver cancer diagnostic biomarkers with both high accuracy and good interpretability.

[0116] The plasma metabolic biomarkers for diagnosing liver cancer identified in the above analysis were used to infer their molecular weight and formula based on their retention time and primary and secondary mass spectrometry results. These biomarkers were then compared with spectral information in a metabolite spectral database for qualitative identification. Finally, the structures of the metabolic biomarkers were verified by purchasing standards and comparing their molecular weight, chromatographic retention time, and corresponding multi-stage MS fragmentation spectra.

[0117] In this embodiment, four differentially expressed metabolic biomarkers screened using the LASSO regression-forward stepwise method can be used for the diagnosis of liver cancer: hypoxanthine (CAS: 68-94-0), uracil (CAS: 66-22-8), cis-10-pentadecanoic acid (FFA(15:1)) (CAS: 84743-29-3), and indole-3-carbinol (CAS: 700-06-1). Detailed information on these metabolic biomarkers is shown in Table 3 below.

[0118] Table 3. Four plasma metabolic markers used for diagnosing liver cancer.

[0119]

[0120] Plasma samples from 70 hepatocellular carcinoma (HCC) patients and 70 healthy controls were used as the dataset. The dataset was randomly split into a training set and a validation set in a 6:4 ratio (training set: 42 healthy individuals, 42 HCC patients; validation set: 28 healthy individuals, 28 HCC patients). In the training set, four plasma metabolic biomarkers—hypoxanthine, uracil, cis-10-pentadecanoic acid, and indole-3-carbinol—were identified as diagnostic markers for HCC using LASSO regression stepwise forward screening. Based on the selected four metabolic biomarker combinations, a model for diagnosing HCC was established using LASSO regression in the training set. The receiver operating characteristic (ROC) curve method was used to evaluate the AUC, sensitivity, and specificity of the LASSO regression model for diagnosing HCC in the training set, with results shown in Table 4.

[0121] Table 4. Comparison of the areas under the ROC curves for models constructed with different combinations of markers in the training set.

[0122]

[0123] The equations for the model constructed based on parameter combinations are: Where Y is the predicted value, i represents the i-th biomarker, m represents the number of metabolic biomarkers, and X... i K represents the detection value of the i-th metabolic biomarker. i Let represent the coefficient of the i-th metabolic marker, and b is a constant.

[0124] When the metabolic markers are a combination of hypoxanthine and indole-3-methanol, the complete model equation is:

[0125] Y=-2.16217658400085e -6 ×Indole-3-carbinol-1.74286022650877e -7 ×Hypoxanthine

[0126] +22.3029876270365;

[0127] The optimal cutoff value is 0.488, meaning that when Y>0.488, liver cancer can be diagnosed, and when Y≤0.488, it is considered normal.

[0128] When the metabolic markers are a combination of hypoxanthine and uracil, the complete model equation is:

[0129] Y=-1.68112627617427e -6 ×Uracil-9.64780929633282e -8×Hypoxanthine+4.2240024300889;

[0130] The optimal cutoff value is 0.482, meaning that when Y>0.482, liver cancer can be diagnosed, and when Y≤0.482, it is considered normal.

[0131] When the metabolic markers are a combination of indole-3-methanol and uracil, the complete model equation is:

[0132] Y=-1.80910627770631e -6 ×Indole-3-carbinol-1.49922136399586e -6 ×Uracil

[0133] +21.2348095158677;

[0134] The optimal cutoff value is 0.531, meaning that when Y>0.531, liver cancer can be diagnosed, and when Y≤0.531, it is considered normal.

[0135] When the metabolic markers are a combination of cis-10-pentadecanoic acid and hypoxanthine, the complete model equation is:

[0136] Y=5.63746386619543e -7 ×FFA(15:1)-2.36693808603199e -7 ×Hypoxanthine-0.223246340529944;

[0137] The optimal cutoff value is 0.496, meaning that when Y>0.496, liver cancer can be diagnosed, and when Y≤0.496, it is considered normal.

[0138] When the metabolic markers are a combination of cis-10-pentadecanoic acid and indole-3-methanol, the complete model equation is:

[0139] Y=5.27188709821395e -7 ×FFA(15:1)-1.5073283529384e -6 ×Indole-3-carbinol+13.6425813043705;

[0140] The optimal cutoff value is 0.460, meaning that when Y>0.460, liver cancer can be diagnosed, and when Y≤0.460, it is considered normal.

[0141] When the metabolic markers are a combination of cis-10-pentadecanoic acid and uracil, the complete model equation is:

[0142] Y=5.34864683531189e -7 ×FFA(15:1)-1.48865775239258e -6 ×Uracil+2.01967181289607;

[0143] The optimal cutoff value is 0.481, meaning that when Y>0.481, liver cancer can be diagnosed, and when Y≤0.481, it is considered normal.

[0144] When the metabolic markers are a combination of hypoxanthine, indole-3-methanol, and uracil, the complete model equation is:

[0145] Y=-1.81856810120463e -6 ×Indole-3-carbinol-1.42151980371604e -6 ×Uracil-1.0318753251763e -7 ×Hypoxanthine+21.6735266805436;

[0146] The optimal cutoff value is 0.421, meaning that when Y > 0.421, liver cancer can be diagnosed, and when Y ≤ 0.421, it is considered normal.

[0147] When the metabolic markers are a combination of cis-10-pentadecanoic acid, hypoxanthine, and indole-3-methanol, the complete model equation is:

[0148] Y=5.15567775429167e -7 ×FFA(15:1)-1.50536684913764e -6 ×Indole-3-carbinol-2.27169419199644e -7 ×Hypoxanthine+14.7615038901775;

[0149] The optimal cutoff value is 0.422, meaning that when Y > 0.422, liver cancer can be diagnosed, and when Y ≤ 0.422, it is considered normal.

[0150] When the metabolic markers are a combination of cis-10-pentadecanoic acid, hypoxanthine, and uracil, the complete model equation is:

[0151] Y=5.31436912963206e -7 ×FFA(15:1)-1.39166527456275e -6 ×Uracil-1.5941671040418e-7 ×Hypoxanthine+2.57836461317576;

[0152] The optimal cutoff value is 0.503, meaning that when Y>0.503, liver cancer can be diagnosed, and when Y≤0.503, it is considered normal.

[0153] When the metabolic markers are a combination of cis-10-pentadecanoic acid, indole-3-methanol, and uracil, the complete model equation is:

[0154] Y=4.97540126420793e -7 ×FFA(15:1)-1.19528673111266e -6 ×Indole-3-carbinol-1.33956395825998e -6 ×Uracil+13.6000913024439;

[0155] The optimal cutoff value is 0.426, meaning that when Y>0.426, liver cancer can be diagnosed, and when Y≤0.426, it is considered normal.

[0156] When the metabolic markers are a combination of hypoxanthine, indole-3-methanol, uracil, and cis-10-pentadecanoic acid, the coefficients of the four metabolic markers are shown in Table 5.

[0157] Table 5. Coefficients of the four metabolic biomarkers in the model.

[0158]

[0159] The complete model equations are as follows:

[0160] Y=4.90290867501596e -7 ×FFA(15:1)-1.24310492425758e -6 ×Indole-3-carbinol-1.2423719399564e -6 ×Uracil-1.65609551176373e -7 ×Hypoxanthine+14.6844994567476;

[0161] The optimal cutoff value is 0.463, meaning that when Y>0.463, liver cancer can be diagnosed, and when Y≤0.463, it is considered normal.

[0162] In the validation set, the receiver operating characteristic (ROC) curve method was used to evaluate the effectiveness of LASSO regression in establishing a model for diagnosing liver cancer and identifying metabolic biomarkers. Figure 3ROC curves (HCC_VS_NC) of the metabolic biomarker combination (hypoxanthine + indole-3-methanol + uracil + cis-10-pentadecanenoic acid) in the validation set were analyzed. The area under the ROC curve (AUC) in the validation cohort was 0.919 (95% CI: 0.834-1.000), with sensitivity and specificity of 0.826 and 0.957, respectively, indicating excellent diagnostic performance of the model. Box plots of the expression of the four metabolic biomarkers (hypoxanthine + indole-3-methanol + uracil + cis-10-pentadecanenoic acid) in the hepatocellular carcinoma group and healthy individuals are shown below. Figure 4 As shown, by Figure 4 It was found that the expression of the four metabolic markers (hypoxanthine, indole-3-methanol, uracil, and cis-10-pentadecanenoic acid) differed significantly between the hepatocellular carcinoma (HCC) group and the healthy control group. Specifically, hypoxanthine, indole-3-methanol, and uracil were significantly decreased in the HCC group, while cis-10-pentadecanenoic acid was significantly increased. The AUC, sensitivity, and specificity of the LASSO regression model for diagnosing HCC were evaluated on the validation set using receiver operating characteristic (ROC) curve analysis for two, three, or four of the four metabolic markers. The results are shown in Table 6.

[0163] Table 6. Comparison of the areas under the ROC curves for models constructed with different combinations of biomarkers in the validation set.

[0164]

[0165] As shown in Table 6, the AUC, sensitivity, and specificity of liver cancer diagnostic models constructed using two, three, or four of the four metabolic markers (hypoxanthine, indole-3-methanol, uracil, and cis-10-pentadecanoic acid) are very close to those in the training set. This indicates that liver cancer diagnostic models constructed using two, three, or four of the four metabolic markers have good accuracy.

[0166] In addition, plasma samples from 70 healthy controls and 55 patients with benign liver disease were used as the dataset. The dataset was randomly split into training and validation sets in a 6:4 ratio (training set: 42 healthy individuals and 33 patients with benign liver disease; validation set: 28 healthy individuals and 22 patients with benign liver disease). Furthermore, in the training set, a model for diagnosing benign liver disease was constructed using LASSO regression with the combination of these four metabolic markers (hypoxanthine + indole-3-methanol + uracil + cis-10-pentadecanenoic acid).

[0167] The complete model equations are as follows:

[0168] Y=4.45005426551646e -9×FFA(15:1)+3.03272941176135e -8 ×Hypoxanthine-5.49616272993213e -8 ×(Indole-3-carbinol)-1.60246087294475e -6 ×Uracil+3.85486932591772;

[0169] The optimal cutoff value is 0.438, meaning that when Y>0.438, it can be diagnosed as benign liver disease, and when Y≤0.438, it can be diagnosed as normal.

[0170] The AUC value of the model for diagnosing benign liver diseases using LASSO regression was evaluated in the training set using the subject ROC curve method. The area under the ROC curve (AUC) in the training cohort was 0.748 (95% CI: 0.643-0.852), and the sensitivity and specificity were 0.660 and 0.757, respectively.

[0171] Validation of the diagnostic model: In the validation set, the ROC curve method was used to evaluate the effectiveness of the LASSO regression model for diagnosing benign liver diseases and identifying metabolic biomarkers. The results are as follows: Figure 5 As shown. By Figure 5 It can be seen that the area under the ROC curve (AUC) in the validation cohort is 0.833 (95% CI: 0.709-0.958), and the sensitivity and specificity are 0.870 and 0.667, respectively.

[0172] Therefore, the diagnostic model for benign liver diseases constructed using four metabolic markers (hypoxanthine + indole-3-methanol + uracil + cis-10-pentadecanenoic acid) showed very close AUC, sensitivity, and specificity in the validation set to its diagnostic performance in the training set. This indicates that the diagnostic model for benign liver diseases constructed using these four metabolic markers has good accuracy.

[0173] It should be noted that the above embodiments are only for further elaboration and explanation of the technical solution of the present invention, and are not intended to further limit the technical solution of the present invention. The method of the present invention is only a preferred embodiment and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. The use of a reagent for detecting biomarkers in the preparation of products for diagnosing liver cancer in patients with liver cancer and healthy individuals, characterized in that, The markers comprise at least one of hypoxanthine, uracil, indole-3-methanol, and cis-10-pentadecanoic acid.

2. The application according to claim 1, characterized in that, The markers include uracil and cis-10-pentadecanoic acid.

3. The application according to claim 1, characterized in that, The markers include at least one of hypoxanthine, indole-3-methanol, uracil, and cis-10-pentadecanoic acid.

4. The application according to claim 1, characterized in that, The markers include hypoxanthine, uracil, cis-10-pentadecanoic acid, and indole-3-methanol.

5. The use of a reagent for detecting biomarkers in the preparation of products for distinguishing between healthy individuals and individuals with benign liver disease, characterized in that, The markers include hypoxanthine, uracil, cis-10-pentadecanoic acid, and indole-3-methanol.

6. The use of a biomarker in the preparation of products for diagnosing liver cancer in patients with liver cancer and healthy individuals, characterized in that, The markers comprise at least one of hypoxanthine, uracil, indole-3-methanol, and cis-10-pentadecanoic acid.

7. The application according to claim 6, characterized in that, The markers include uracil and cis-10-pentadecanoic acid.

8. The application according to claim 6, characterized in that, The markers include at least one of hypoxanthine, indole-3-methanol, uracil, and cis-10-pentadecanoic acid.

9. The application according to claim 6, characterized in that, The markers include hypoxanthine, uracil, cis-10-pentadecanoic acid, and indole-3-methanol.

10. The application of a biomarker in the preparation of products that distinguish between healthy individuals and individuals with benign liver disease, characterized in that, The markers include hypoxanthine, uracil, cis-10-pentadecanoic acid, and indole-3-methanol.

11. The application according to claim 10, characterized in that, The benign liver disease is selected from at least one of chronic hepatitis B and cirrhosis.

12. A product for diagnosing liver cancer in patients with liver cancer and healthy individuals, characterized in that, It includes at least one of hypoxanthine, uracil, indole-3-methanol, and cis-10-pentadecanoic acid.

13. A product for distinguishing between healthy individuals and individuals with benign liver disease, characterized in that, These include hypoxanthine, uracil, cis-10-pentadecanoic acid, and indole-3-methanol.

14. A product for diagnosing liver cancer in patients with liver cancer and healthy individuals, characterized in that, The reagent includes at least one of hypoxanthine, uracil, indole-3-methanol, and cis-10-pentadecanoic acid.

15. A product for distinguishing between healthy individuals and individuals with benign liver disease, characterized in that, Reagents for detecting hypoxanthine, uracil, cis-10-pentadecanoic acid, and indole-3-methanol.

16. A system for diagnosing liver cancer in patients with liver cancer and healthy individuals, the system comprising a data analysis module, the data analysis module comprising a model for diagnosing liver cancer in patients with liver cancer and healthy individuals based on at least one of hypoxanthine, uracil, indole-3-carbinol and cis-10-pentadecanoic acid.

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