WO2022247939A1 - 肝癌诊断装置 - Google Patents

肝癌诊断装置 Download PDF

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WO2022247939A1
WO2022247939A1 PCT/CN2022/095737 CN2022095737W WO2022247939A1 WO 2022247939 A1 WO2022247939 A1 WO 2022247939A1 CN 2022095737 W CN2022095737 W CN 2022095737W WO 2022247939 A1 WO2022247939 A1 WO 2022247939A1
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acid
liver cancer
diagnostic
biomarker
combination
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French (fr)
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贾伟
谢国祥
陈天璐
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深圳市绘云生物科技有限公司
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/86—Signal analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/86—Signal analysis
    • G01N30/8675—Evaluation, i.e. decoding of the signal into analytical information
    • G01N30/8679—Target compound analysis, i.e. whereby a limited number of peaks is analysed
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N2030/022—Column chromatography characterised by the kind of separation mechanism
    • G01N2030/027—Liquid chromatography

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  • the invention belongs to the field of biology, and in particular relates to a device for diagnosing liver cancer.
  • the device uses the level of biomarkers in a biological sample of a subject as a detection index; the invention also relates to a group of biomarkers that can be used for diagnosing liver cancer .
  • Hepatocellular carcinoma is a primary malignant tumor of the liver that mainly occurs in patients with chronic liver disease and cirrhosis. HCC is currently the third leading cause of cancer death in the world. Among them, the incidence of HCC is highest in Asia and Africa. The high prevalence of hepatitis B and C in this region makes chronic liver disease very prone to develop and then develop into HCC. In the past, patients with HCC typically presented with right upper quadrant pain, with weight loss and late symptoms of hepatic decompensation being diagnosed. At present, the commonly used clinical gold standard for the diagnosis of liver cancer is needle biopsy, but this method has great limitations, such as: invasive testing, sampling error, pathologist operation and image reading error, and so on.
  • liver cancer Other methods for predicting liver cancer include: measuring serum alpha-fetoprotein content (AFP) in routine screening to improve the early diagnosis rate of HCC.
  • AFP serum alpha-fetoprotein content
  • Such techniques also have obvious limitations, such as low sensitivity and poor specificity. It is expected that the threat of HCC will continue to grow in the next few years. Therefore, exploring other biomarkers as screening indicators for early liver cancer, or combining multiple biomarkers for diagnosis can improve the sensitivity and specificity of early liver cancer diagnosis and relieve the burden of patients. The puncture is painful and imperative.
  • the present invention provides a diagnostic device for liver cancer.
  • the diagnostic device uses the level of biomarkers in a biological sample of a subject as a detection index, and can be used for risk assessment, screening and diagnosis related to liver cancer and liver cirrhosis for various purposes.
  • the liver cancer diagnostic device of the present invention can adopt various product forms.
  • the present invention also provides a method for using the liver cancer diagnostic device.
  • the present invention also provides a group of biomarkers capable of predicting and diagnosing liver cancer and liver cirrhosis and applications thereof.
  • diagnostic used in the present invention is used to facilitate the purpose of expression, but it should be understood that it is not limited to the "diagnostic” behavior defined by clinical standards; , also includes all processes and behaviors that lead to valuable conclusions by evaluating the diagnostic indicators provided by the present invention, including but not limited to the following purposes and usage methods: for assessing the risk level of a subject suffering from liver cancer or cirrhosis, For example, it is used for general screening in physical examination; for regular monitoring of high-risk groups; for evaluating liver cirrhosis or liver cancer treatment drugs, or potentially used for efficacy evaluation of liver cirrhosis or liver cancer treatment drugs; for liver cirrhosis or liver cancer that may cause liver cirrhosis or liver cancer
  • the evaluation of risk substances or treatment means, etc. are listed above for the purpose of illustration, all of which are included in the scope of "diagnosis” in the present invention.
  • the diagnostic device of the present invention can be used for purposes including but not limited to early assessment of liver cancer or liver cirrhosis in subjects, general screening in physical examination, clinical diagnosis and drug evaluation, and can be used alone to draw corresponding conclusions, or can be Combined with other detection equipment or detection indicators (such as alpha-fetoprotein) for diagnosis.
  • detection equipment or detection indicators such as alpha-fetoprotein
  • the diagnostic index in the present invention includes the ratio obtained by calculating the original data, which is represented by "/" in this specification, for example, taurochenodeoxycholic acid/glycochenodeoxycholic acid means taurochenodeoxycholic acid
  • the ratio of glycochenodeoxycholic acid to glycochenodeoxycholic acid should be the ratio of the values of the two substances based on the same sample, the same detection method and the same unit.
  • HCC Hepatocellular carcinoma
  • CLD chronic liver disease
  • HBV hepatitis B virus
  • CA cholic acid
  • TCA Taurochericholic Acid
  • TCDCA taurochenodeoxycholic acid
  • GCA Glycocholic Acid
  • DCA deoxycholic acid
  • CDCA chenodeoxycholic acid
  • UDCA ursodeoxycholic acid
  • GCDCA Glycochenodeoxycholic Acid
  • the first aspect of the present invention provides a liver cancer diagnostic device.
  • the device uses the determination of the biomarker level in the subject's biological sample as a diagnostic index, and the biomarker level is selected from taurocholic acid, taurochenodeoxy Cholic acid, glycocholic acid, glycochenodeoxycholic acid, eira-linoleic acid (C18:2n6t), maltotriose, maltose and/or lactose, alpha-linolenic acid, beta-alanine, sebacic acid , 2-methylvaleric acid, valeric acid, isovaleric acid, caproic acid one or more levels; and/or the ratio of secondary bile acid to primary bile acid, and/or glycine bound primary bile acid and bovine The ratio of sulfonic acid to primary bile acid; the primary bile acid is selected from cholic acid and chenodeoxycholic acid, and the secondary bile acid includes deoxycholic acid, lithocholic
  • the level of the biomarker is selected from taurocholic acid, taurochenodeoxycholic acid, glycocholic acid, glycochenodeoxycholic acid, One or more levels of elaidic acid (C18:2n6t); as a specific example, the ratio of the secondary bile acid to the primary bile acid can be selected from deoxycholic acid/cholic acid, lithocholic acid/ Chenodeoxycholic acid, ursodeoxycholic acid/chenodeoxycholic acid; as a specific example, the ratio of glycine-binding primary bile acid and taurine-binding primary bile acid can be selected from taurochenodeoxycholic acid/glycine Chenodeoxycholic acid.
  • the diagnostic index of the present invention may further include alpha-fetoprotein. It is proved in the embodiment of the present invention that when the diagnostic index of the present invention is used in combination with alpha-fetoprotein, when predicting and distinguishing healthy people, chronic liver disease, liver cirrhosis and liver cancer, the It has a better diagnostic effect than alpha-fetoprotein alone.
  • the liver cancer diagnostic device of the present invention can select mammals as subjects, such as human beings; the biological samples used can be urine samples and blood samples, and when using blood samples, peripheral blood whole blood, plasma or serum can be used.
  • the serum of the peripheral blood of the subject is selected as the test sample.
  • the determination of the biomarker level is for the purpose of quantitative detection, and may include the following steps: after processing the biological sample of the subject, the combination of biomarkers in the biological sample is analyzed by chromatography-mass spectrometry coupled with metabolomics analysis method
  • the metabolomics analysis method coupled with chromatography-mass spectrometry includes a metabolomics analysis method coupled with liquid chromatography-mass spectrometry and a metabolomics analysis method coupled with gas chromatography-mass spectrometry.
  • the diagnostic device of the present invention can take a variety of product forms, for illustrative purposes, can be selected from kits, medical devices, computer systems with diagnostic modules, and detection devices with diagnostic modules.
  • the medical devices, kits, etc. as known to those skilled in the art, are defined in relation to relevant laws, regulations and policies of the local government, and have different classification methods and meanings in different countries and regions. Nouns such as medical devices and kits in the present invention are only used to illustrate the application forms of the diagnostic markers of the present invention, and are not defined under strict laws and regulations; as long as they are consistent with the purpose of the present invention, the medical devices and kits It may be a medical product registered by the relevant government department, or a product or product combination used by those skilled in the art in a temporary application manner and form.
  • the diagnostic device of the present invention includes and modules:
  • the cut-off value of the diagnostic marker can be expressed as the cut-off value of the detected diagnostic marker; the risk assessment is to compare the expression level of the biomarker in the test sample with the preset cut-off value, and the cut-off value higher than the cut-off value can be considered as detection.
  • Objects are at risk.
  • the diagnostic device of the present invention will be illustrated by taking the kit and the computer system as examples.
  • the diagnostic device of the present invention may be in the form of a kit, and the kit includes quantitative detection reagents for detecting diagnostic indicators.
  • the software can be built into a computer to run.
  • the diagnostic device of the present invention may be a computer system with a diagnostic module and a detection device with a diagnostic module, and the diagnostic module includes an information acquisition module and a liver cancer diagnosis module; wherein, the information acquisition module is at least used to obtain a diagnosis Index information; the liver cancer diagnosis module is at least used to perform the following operations: assess whether the subject suffers from liver cancer or liver cirrhosis according to the diagnosis index information acquired by the information acquisition module.
  • the second aspect of the present invention provides a combination of biomarkers for the diagnosis of liver cancer, including taurocholic acid, taurochenodeoxycholic acid, glycocholic acid, glycochenodeoxycholic acid, elineoleic acid (C18: 2n6t), maltotriose, maltose and/or lactose, ⁇ -linolenic acid, ⁇ -alanine, sebacic acid, 2-methylvaleric acid, valeric acid, isovaleric acid, caproic acid kind.
  • biomarkers or combinations thereof can optionally be used in combination with alpha-fetoprotein, which can improve the diagnostic effect.
  • the present invention also provides a quantitative detection method for the aforementioned biomarkers that can be used in the diagnosis of liver cancer.
  • the invention provides a liver cancer detection device with a specific detection index, which can predict and diagnose patients with liver cancer and liver cirrhosis through the detection of the detection index.
  • the detection index adopted by the detection device of the present invention has excellent distinguishing ability, and can be used alone, or through joint detection of multiple indexes, to improve the reliability of the detection effect; and can distinguish liver cirrhosis and liver cancer.
  • alpha-fetoprotein a commonly used clinical liver cancer detection index, it significantly improves the diagnostic effect of alpha-fetoprotein.
  • the random forest model in the embodiment is carried out by using the LiveForest software of Shenzhen Huiyun Biotechnology Co., Ltd., the software copyright registration number is 2018SR227394, and the software name is: a machine learning diagnosis system for chronic liver disease based on metabolomics V1.0.
  • Serum/plasma samples include the content of metabolites such as bile acids, fatty acids, organic acids, sugars and amino acids, as well as the detection of corresponding clinical indicators.
  • the test samples in the present invention were approved by the local ethics committee and informed consent was obtained from all subjects.
  • test tube rack At room temperature (about 25 degrees Celsius), place the test tube vertically on a test tube rack for 1.5 hours.
  • the LH750 hematology analyzer and Synchron DXC800 clinical system were used for hematology and biochemistry detection according to the manufacturer's protocol; The detection of uric acid and laminin; the detection of blood coagulation function using a coagulation function measuring instrument (STAGO Compact, Diagnostica Stago, France); the use of a real-time polymerase chain reaction system (LightCycler 480, Roche, USA) for blood HBV-DNA detection.
  • Sample preparation Take 100 ⁇ l of serum in a 1.5 mL centrifuge tube, add 150 ⁇ L of methanol (including internal standard, 50 nM deuterated-CA (cholic acid), deuterated-UDCA (ursodeoxycholic acid), deuterated-LCA (stone cholic acid)). Vortex and shake for 10 minutes, let stand for 10 minutes, and then centrifuge at 13500 rpm at 4 degrees for 20 minutes, and take the supernatant for UPLC-TQMS (ultra high performance liquid chromatography-triple quadrupole mass spectrometry) analysis.
  • methanol including internal standard, 50 nM deuterated-CA (cholic acid), deuterated-UDCA (ursodeoxycholic acid), deuterated-LCA (stone cholic acid)
  • UPLC-TQMS Waters ultra-high performance liquid chromatography system (Waters, USA), equipped with a binary solvent controller and a sample control room.
  • Chromatographic conditions UPLC BEH C18 column (100mm ⁇ 2.1mm, 1.7 ⁇ m); column temperature 45°C; mobile phase A: water (0.1% formic acid), B: acetonitrile (0.1% formic acid); flow rate 0.4mL/ min; injection volume is 5uL; gradient elution conditions: 0-1min(5%B), 1-5min(5-25%B), 5-15.5min(25-40%B), 15.5-17.5min( 40-95% B), 17.5-19 min (95% B), 19-19.5 min (95-5% B), 19.6-21 min (5% B).
  • the electrospray ion source adopts negative ion scanning mode (ESI-), the specific conditions are as follows: capillary voltage 1.2kV, cone voltage 55V, extraction cone voltage 4V, ion source temperature 150°C, desolvation temperature 550°C, reaction The airflow to the cone hole is 50L/h, the desolvation gas is 650L/h, the resolution of the low-mass area is 4.7, and the resolution of the high-mass area is 15, and the multiple reaction detection mode collects data.
  • ESI- negative ion scanning mode
  • Sample preparation Take 30 ⁇ L of serum, add 500 ⁇ L of isopropanol/n-hexane/2M phosphoric acid (40:10:1) and 10 ⁇ L of isotope-labeled C19:0-d37 internal standard solution (5 ⁇ g/mL), vortex 2min, stand at room temperature for 20min. Add 400 microliters of n-hexane, 300 microliters of water, vortex for 2 minutes, centrifuge at 12,000 rpm for 5 minutes, and take 400 microliters of supernatant; Lift. The supernatants were combined and dried under vacuum at room temperature. Add 80 microliters of methanol to the dried centrifuge tube for reconstitution and analysis.
  • UPLC-TQMS Waters ultra-high performance liquid chromatography system (Waters, USA), equipped with a binary solvent controller and a sample control room.
  • Chromatographic conditions UPLC BEH C18 column (100mm ⁇ 2.1mm, 1.7 ⁇ m); column temperature 40°C; mobile phase A is water, mobile phase B is acetonitrile/isopropanol (volume ratio 8:2); flow rate is 0.4mL/min; injection volume is 5uL; gradient elution conditions: 0-2min: 70%B, 2-5min: 70%-75%B, 5-10min: 75%-80%B, 10-13min: 80%-90%B, 13-16min: 90%-100%B, 16-21min: 100%B, 21-22.5min: 100%-70%B, 22.5-24min: 70%B. The total analysis time is 24min.
  • the electrospray ion source adopts negative ion scanning mode (ESI-), the specific conditions are as follows: capillary voltage 2.5kV, cone voltage 55V, extraction cone voltage 4V, ion source temperature 120°C, desolvation temperature 450°C, reaction Cone gas flow 50L/h, desolvation gas 650L/h, low-mass area resolution 4.7, high-mass area resolution 15, detector voltage 2390V, scan time 0.35s, scan time interval 0.02s, mass-to-charge ratio range: m/z 50-1000.
  • the lock mass is 554.2615.
  • Sample preparation Take 40 ⁇ L of serum, add 500 ⁇ L of methanol-acetonitrile mixed solvent (1:9, v:v), vortex for 2 minutes; place the centrifuge tube at -20°C for 10 minutes to promote protein precipitation, and centrifuge at 12,000 rpm at 4°C for 15 minutes. Take 20 ⁇ L of the supernatant and dry it under vacuum at room temperature. Add 100 ⁇ L methanol-water mixed solvent (1:1, v:v, containing 1 ⁇ g/mL dichlorophenylalanine as internal standard) to the dried centrifuge tube for reconstitution and analysis.
  • UPLC-TQMS Waters ultra-high performance liquid chromatography system (Waters, USA), equipped with a binary solvent controller and a sample control room.
  • Chromatographic conditions UPLC BEH C18 column (100mm ⁇ 2.1mm, 1.7 ⁇ m); column temperature 40°C; mobile phase A: water (0.1% formic acid), B: acetonitrile (0.1% formic acid); flow rate 0.4mL/ min; injection volume is 5uL; gradient elution conditions: 0-0.5min (1% B), 0.5-9min (1-20% B), 9-11min (20-75% B), 11-16min (75 -99%B), 16-16.5min (99%B).
  • the electrospray ion source adopts negative ion scanning mode (ESI-), the specific conditions are as follows: capillary voltage 3.0, cone voltage 55V, extraction cone voltage 4V, ion source temperature 150°C, desolvation temperature 450°C, reverse The cone gas flow is 50L/h, the desolvation gas is 800L/h, the resolution of the low-mass area is 4.7, and the resolution of the high-mass area is 15, and the multiple reaction detection mode collects data.
  • ESI- negative ion scanning mode
  • Serum triglycerides were detected by enzymatic colorimetry.
  • the pathological evaluation was independently evaluated by three pathologists from Shanghai Medical College of Fudan University in a blinded manner, and the consistency of the results was verified by the Kappa test. When the assessment results failed the Kappa test, the samples were reanalyzed to reach a consensus result.
  • 422 healthy people, 433 CLD patients and 900 HCC patients are randomly divided into training set and test set according to the ratio of 70% and 30%.
  • the training set in order to distinguish between healthy people and HCC patients, CLD patients and HCC patients, we use one-way Wilcoxon rank sum test and LASSO to select and identify candidate biomarkers, and use random forest model to evaluate candidate variables and build models, Finally, the above models are verified on the test set and independent validation set.
  • biomarkers including taurocholic acid, taurochenodeoxycholic acid, glycocholic acid, glycinechenodeoxycholic acid, elineoleic acid (C18:2n6t), maltotriose , maltose and/or lactose, ⁇ -linolenic acid, ⁇ -alanine, sebacic acid, 2-methylvaleric acid, valeric acid, isovaleric acid, caproic acid have predictive and Diagnostic capability; and the ratio of secondary bile acids to primary bile acids, and/or the ratio of glycine-bound primary bile acids/taurine-bound primary bile acids also has predictive and diagnostic capabilities; said primary bile acids are selected from the group consisting of cholic acid, Chenodeoxycholic acid, the secondary bile acid includes deoxycholic acid, lithocholic acid, and ursodeoxycholic acid.
  • a standard curve is drawn with the concentration of the standard solution of the diagnostic marker to be tested and the area ratio of the corresponding diagnostic marker to be tested and the same stable isotope internal standard as the diagnostic marker to be tested, and the isotope internal standard is used for quantitative determination.
  • the quality control of the sample detection process is carried out by adding an isotope internal standard to the sample.
  • the detection method refers to Example 1.
  • routine HCC patients and 296 routine healthy people in the training set 270 routine HCC patients and 126 routine healthy people in the testing set, utilize the biomarker that obtains in embodiment 1, the biomarker combination random forest model trained in training set, can Outputting the probability of HCC for the above subjects, and finding the best cut-off value through the Youden optimal point in the ROC analysis, the overall ability of the model to distinguish HCC patients from healthy people can be evaluated. Test the test set, and the results are shown in Table 1 and Table 2.
  • HCC patients and 296 healthy people in the training set There are 630 HCC patients and 296 healthy people in the training set, and 270 HCC patients and 126 healthy people in the test set.
  • biomarker combination random forest model trained in the training set the possibility of the above-mentioned subjects suffering from HCC can be output, and The overall ability of the model to distinguish HCC patients from healthy individuals was assessed by finding the optimal cutoff value through the Youden optimal point in the ROC analysis. The results are shown in Figure 1 and Table 5.
  • the area under the ROC curve and the 95% confidence interval in the training set were 1.000 (95% CI 0.999-1.000), the best cut-off value was 0.078, and the sensitivity and specificity percentages at the best cut-off value were 99.7% and 100% respectively;
  • the area under the centralized ROC curve and the 95% confidence interval were 1.000 (95% CI 0.999-1.000), the best cut-off value was 0.078, and the sensitivity and specificity percentages at the best cut-off value were 99.2% and 100%, respectively.
  • the area under the ROC curve and the 95% confidence interval in the training set were 0.912 (95% CI 0.874-0.946), the best cut-off value was -0.502, and the sensitivity and specificity percentages at the best cut-off value were 83.6% and 90.6% respectively;
  • the area under the centralized ROC curve and the 95% confidence interval were 0.918, the best cut-off value was -0.502, and the sensitivity and specificity percentages at the best cut-off value were 81.8% and 80.4%, respectively.
  • Embodiment 7 A method for diagnosing liver cancer, comprising performing quantitative detection of the combination of biomarkers in the biological sample by using chromatography-mass spectrometry coupled with metabolomics analysis method after the biological sample of the subject is processed according to the above-mentioned embodiment;
  • the combination of biomarkers includes the combination of biomarkers proved to be effective as in the above examples;
  • the analysis method of metabolomics by chromatography-mass spectrometry includes the analysis method of metabolomics by liquid chromatography-mass spectrometry and gas phase as described in the above examples. Chromatography-mass spectrometry coupled with metabolomics analysis method.

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Abstract

本发明提供了一种肝癌诊断装置，所述装置以测定受试体生物样本中的生物标志物水平作为诊断指标，所述生物标志物水平选自牛磺胆酸、牛磺鹅去氧胆酸、甘氨胆酸、甘氨鹅去氧胆酸、反亚油酸(C18:2n6t)、麦芽三糖、麦芽糖和/或乳糖、α-亚麻酸、β-丙氨酸、葵二酸、2-甲基戊酸、戊酸、异戊酸、己酸中之一种或多种的水平；和/或次级胆汁酸和初级胆汁酸的比值、和/或甘氨酸结合初级胆汁酸/牛磺酸结合初级胆汁酸的比值；所述初级胆汁酸选自胆酸、鹅去氧胆酸，所述次级胆汁酸包括去氧胆酸、石胆酸、熊去氧胆酸。本发明诊断装置可用于肝癌和肝硬化的早期诊断，为患者争取时间，提高临床治疗效果。

Description

肝癌诊断装置
本申请要求申请日为2021/5/28的中国专利申请202110593051X的优先权。本申请引用上述中国专利申请的全文。
技术领域
本发明属于生物领域，具体涉及一种用于肝癌诊断的装置，所述装置以受试体生物样本中的生物标志物水平作为检测指标；本发明还涉及一组可用于肝癌诊断的生物标志物。
背景技术
肝细胞癌(HCC)是肝脏原发性恶性肿瘤，主要发生于慢性肝病和肝硬化患者。HCC目前是全球第三大癌症死亡原因，其中，亚洲和非洲的HCC发病率最高，乙型肝炎和丙型肝炎在该区域内的高流行性使得慢性肝病极易发生并继而发展成为HCC。过去，HCC患者一般呈现右上象限疼痛，体重减轻和肝脏失代偿的晚期症状才被确诊。目前，临床上常用的肝癌诊断金标准为穿刺活检，而此种手段有很大的局限性，如：侵入性检验，抽样误差，病理医师操作和读片误差，等等。其他预测肝癌的方法有：在常规筛查中测量血清甲胎蛋白含量(AFP)以提高HCC的早期诊断率。然而，此类技术也有明显的局限性，如敏感性低，特异性差。预计未来几年HCC的威胁将持续增长，因此，探索其他生物学标记物作为早期肝癌的筛选指标，或联合多种生物学标记物进行诊断以提高早起肝癌诊断的敏感性和特异性并减轻患者的穿刺痛苦，势在必行。
发明内容
本发明提供一种肝癌诊断装置，所述诊断装置以受试体生物样本中生物 标志物水平作为检测指标，可用于多种目的与肝癌和肝硬化有关的风险评估、筛查和诊断。本发明肝癌诊断装置可采用多种产品形式。本发明还提供所述肝癌诊断装置的使用方法。本发明还提供一组对肝癌和肝硬化具有预测和诊断能力的生物标志物及其应用。
以下对本发明所用名词和缩写进行说明：
本发明所用名词“诊断”以利于表述目的而使用，但应理解并不限于按照临床标准所定义的“诊断”行为；本发明所述“诊断”，除包括临床标准所定义的“诊断”行为，还包括通过评估本发明所提供的诊断指标从而带来有价值结论的所有过程和行为，包括但不限于以下目的和使用方式：用于评估受试体患有肝癌或肝硬化的风险等级，例如应用于体检中一般筛查；用于高危群体的定期监测；用于评估肝硬化或肝癌治疗药物、或潜在地用于肝硬化或肝癌治疗药物的疗效评价；用于可能导致肝硬化或肝癌风险的物质或治疗手段的评价等，以上以示例目的列举，均包括在本发明所述“诊断”的范围。
相应地，本发明所述诊断装置，其使用目的可以包括但不限于受试体肝癌或肝硬化早期评估、体检中一般筛查、临床诊断和药物评价，可以单独使用得出相应结论，也可与其他检测设备或检测指标(如甲胎蛋白)相结合进行诊断。有实施例表明，本发明诊断装置与甲胎蛋白联合使用时，可提高临床诊断的精确度和可靠性。
本发明中诊断指标包括经过对原始数据进行计算得到的比值，在本说明书中以“/”表示，例如，牛磺鹅去氧胆酸/甘氨鹅去氧胆酸表示牛磺鹅去氧胆酸和甘氨鹅去氧胆酸的比值，按照本领域一般技术人员的理解，其应是两种物质基于相同样本、相同检测方法和相同单位的值的比值。
除非本说明书其他部分另有定义，在本说明书中使用的所有技术和科学术语均具有与本发明所属技术领域的一般技术人员通常理解一样的含义。如本说明书及所附权利要求中使用的，单数形式“一”及“该”包括多于一个的所 提及对象，除非内容明确指示不同于此的含义。例如，提及“组分”时包括一个或一个以上的组分的组合等。
除非本说明书其他部分另有定义，本发明中所采用的缩写具有以下含义：
HCC：肝细胞癌
CLD：慢性肝病
HBV：乙型肝炎病毒
CA：胆酸
TCA：牛磺鹅胆酸
TCDCA：牛磺鹅去氧胆酸
GCA：甘氨胆酸
DCA：去氧胆酸
LCA：石胆酸
CDCA：鹅去氧胆酸
UDCA：熊去氧胆酸
GCDCA：甘氨鹅去氧胆酸
本发明通过以下技术方案实现：
本发明第一方面提供一种肝癌诊断装置，所述装置以测定受试体生物样本中的生物标志物水平作为诊断指标，所述生物标志物水平选自牛磺胆酸、牛磺鹅去氧胆酸、甘氨胆酸、甘氨鹅去氧胆酸、反亚油酸(C18:2n6t)、麦芽三糖、麦芽糖和/或乳糖、α-亚麻酸、β-丙氨酸、葵二酸、2-甲基戊酸、戊酸、异戊酸、己酸中之一种或多种的水平；和/或次级胆汁酸和初级胆汁酸的比值、和/或甘氨酸结合初级胆汁酸和牛磺酸结合初级胆汁酸的比值；所述初级胆汁酸选自胆酸、鹅去氧胆酸，所述次级胆汁酸包括去氧胆酸、石胆酸、熊去氧胆酸。
以上诊断指标可以单独或组合使用，例如：作为具体实施例，所述生物标志物水平选自牛磺胆酸、牛磺鹅去氧胆酸、甘氨胆酸、甘氨鹅去氧胆酸、 反亚油酸(C18:2n6t)之一种或多种的水平；作为具体实施例，所述次级胆汁酸和初级胆汁酸的比值可以选自去氧胆酸/胆酸，石胆酸/鹅去氧胆酸，熊去氧胆酸/鹅去氧胆酸；作为具体实施例，甘氨酸结合初级胆汁酸和牛磺酸结合初级胆汁酸的比值可以选自牛磺鹅去氧胆酸/甘氨鹅去氧胆酸。
本发明的诊断指标可进一步包括甲胎蛋白，在本发明实施例中证明，当本发明诊断指标与甲胎蛋白联合使用时，在预测和区分健康人、慢性肝病、肝硬化和肝癌时，取得了比单独使用甲胎蛋白具有更好的诊断效果。
本发明肝癌诊断装置可选择哺乳动物作为受试体，例如人类；所采用的生物样本可以是尿液样本和血液样本，当采用血液样本时，可采用外周血的全血、血浆或血清。在本发明中选择受试体的外周血的血清作为检测样本。
所述生物标记物水平的测定以定量检测为目的，可包括以下步骤：对受试者的生物样本进行处理后，以色谱质谱联用代谢组学分析方法对生物样本中的生物标志物组合进行定量检测，所述色谱质谱联用代谢组学分析方法包括液相色谱质谱联用代谢组学分析方法和气相色谱质谱联用代谢组学分析方法。
本发明所述诊断装置可采用多种产品形式，作为示例目的，可选自试剂盒、医疗器械、具有诊断模块的计算机系统和具有诊断模块的检测装置。所述医疗器械、试剂盒等，如本领域一般技术人员所知，其定义与当地政府相关法律法规和政策的规定有关，其在不同国家地区有不同分类方法和含义。本发明所述医疗器械、试剂盒等名词，仅为说明本发明诊断标志物的应用形式，并非严格的法律法规定义之下的含义；只要与本发明目的相一致，所述医疗器械、试剂盒可以为经相关政府部门注册的医疗产品，也可以是本领域一般技术人员以临时应用的方式和形式进行使用的产品或产品组合。
作为具体实施例，本发明所述诊断装置包括以及模块：
(1)用于接收检测对象的检测样品的模块；
(2)检测诊断标志物表达水平的数据的模块；
(3)基于向数据库输入作为诊断指标的生物标志物的表达水平来产生风险评分的模块，所述数据库包含与检测样本和检测方法相关的对照表达谱；所述对照表达谱根据检测样本和检测方法而事先得出，可表达为检测的诊断标志物的截断值；进行风险评估是将测试样本中生物标志物的表达水平与预先设定的截断值相比较，高于截断值可认为是检测对象具有相应风险。
以下以试剂盒和计算机系统为例对本发明诊断装置进行举例目的说明。
(一)试剂盒
作为具体实施例，本发明诊断装置可采用试剂盒形式，所述试剂盒包含检测诊断指标的定量检测试剂。作为示例目的，例如实施例中所述的定量检测试剂，以及进一步还可包括内标物、生物样本提取试剂，以及进一步包括可用于统计和评估检测结果的软件。所述软件可内置于计算机内运行。
(二)计算机系统
作为具体实施例，本发明诊断装置可以是具有诊断模块的计算机系统和具有诊断模块的检测装置，所述诊断模块包括信息获取模块和肝癌诊断模块；其中，所述信息获取模块至少用于获取诊断指标信息；所述肝癌诊断模块至少用于执行以下操作：根据所述信息获取模块获取的诊断指标信息，评估受试者体是否患有肝癌或肝硬化。
本发明第二方面提供用于肝癌诊断的生物标志物组合，包括牛磺胆酸、牛磺鹅去氧胆酸、甘氨胆酸、甘氨鹅去氧胆酸、反亚油酸(C18:2n6t)、麦芽三糖、麦芽糖和/或乳糖、α-亚麻酸、β-丙氨酸、葵二酸、2-甲基戊酸、戊酸、异戊酸、己酸中之一种或多种。这些生物标志物或其组合可以任选地与甲胎蛋白联合使用，可以提高诊断效果。
本发明还提供了上述可用于肝癌诊断的生物标志物的定量检测方法。
本发明有益的技术效果
本发明提供了具有特定检测指标的肝癌检测装置，通过对其检测指标的检测可以预测和诊断肝癌和肝硬化患者。本发明检测装置所采用的检测指标 具有优良的区分能力，可以单独使用，或通过多个指标联合检测，提高检测效果的可靠性；并且可区分肝硬化和肝癌。当与临床常用肝癌检测指标甲胎蛋白联合使用时，显著提高了甲胎蛋白的诊断效果。
为使本发明的目的、技术方案及效果更加清楚、明确，以下结合附图并通过实施例对本发明进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本发明，并不用于限定本发明。
附图说明
附图1为实施例5诊断图。
附图2为实施例6诊断图。
具体实施方式
下面将结合本发明具体实施例及附图，对本发明的技术方案进行详细地描述。显然，本部分所描述的具体实施例仅仅是实现本发明技术方案的一部分实施例，而不应理解为全部的实施方式。应当理解，本部分所描述的具体实施例仅用以解释本发明，并不用于限定本发明。基于本部分实施例，本领域一般技术人员可基于其启示，在没有进行创造性劳动前提下所能够获得的所有其他实施方式，都应属于本发明保护的范围。
实施例中的随机森林模型通过使用深圳市绘云生物科技有限公司LiveForest软件来进行，该软件著作权登记号2018SR227394，软件名称：一个基于代谢组学的慢性肝病机器学习诊断系统V1.0.。
实施例1生物标志物研究
本实施例共入组1755名受试者。在训练集和测试集中，使用超高效液相色谱技术检测了422例健康人，433例经肝穿刺活检确诊的慢性肝病(CLD)患者和900例经肝组织病理确诊的HCC患者空腹(12小时)血清/血浆标本中包括胆汁酸、脂肪酸、有机酸、糖类和氨基酸等代谢物的含量，以及相 应临床指标的检测。本发明中的试验样本得到本地伦理委员会的批准并获得所有受试者的知情同意书。
(一)血清样本的采集和制备
采集空腹静脉血5mL置塑料离心管中。
血清制备：
1)缓慢颠转血清制备管5次。
2)在室温下(大约25摄氏度)，将试管垂直置于试管架上1.5小时。
3)将试管在2500rpm转速下离心10分钟(4摄氏度)。
4)将上清(约2.5毫升)用移液器分装到塑料离心管(eppendorf，1.5ml离心管)中，每只冻存管0.5ml血清。
5)在离心管上标好样品号码。
6)迅速放入-80摄氏度冰箱。
(二)血清临床指标检测
使用LH750血液学分析仪和Synchron DXC800临床系统(Beckman Coulter，美国)根据制造商的试验方案进行血液学和生物化学检测；使用化学发光免疫分析仪(LUMO,Shinova Systems，上海，中国)进行血液透明质酸和层粘连蛋白的检测；使用凝血功能测量仪(STAGO Compact,Diagnostica Stago，法国)进行凝血功能的检测；使用实时聚合酶链式反应系统(LightCycler 480,Roche,美国)进行血液HBV-DNA检测。
(三)血清样本的胆酸检测
样本制备：取100μl血清于1.5mL的离心管中，加入150μL甲醇(含内标,50nM氘代-CA(胆酸),氘代-UDCA(熊去氧胆酸),氘代-LCA(石胆酸))。漩涡震荡混匀10分钟，静置10分钟，然后4度13500转离心20分钟，取上清液进行UPLC-TQMS(超高效液相色谱-三重四级杆质谱)分析。
分析仪器测试：UPLC-TQMS：采用沃特斯超高效液相色谱系统(沃特斯公司，美国)，配备二元溶剂控制器和样本控制室。采用沃特斯XEVO三 重四级杆质谱仪(沃特斯公司，美国)，配备双电喷雾离子源。
色谱条件：采用UPLC BEH C18色谱柱(100mm×2.1mm,1.7μm)；柱温45℃；流动相A：水(0.1％甲酸)，B：乙腈(0.1％甲酸，)；流速为0.4mL/min；进样量为5uL；梯度洗脱条件：0-1min(5％B),1-5min(5-25％B),5-15.5min(25-40％B),15.5-17.5min(40-95％B),17.5-19min(95％B),19-19.5min(95-5％B),19.6-21min(5％B)。
质谱条件：电喷雾离子源采用负离子扫描模式(ESI-)，具体条件如下：毛细管电压1.2kV,锥孔电压55V,萃取锥孔电压4V,离子源温度150℃,脱溶剂气温度550℃,反向锥孔气流50L/h,脱溶剂气650L/h,低质量区分辨率4.7,高质量区分辨率15,多反应检测模式采集数据。
(四)血清样本游离脂肪酸的检测
样本制备：取血清30μL，加入异丙醇/正己烷/2M磷酸(40:10:1)500微升及同位素标记的C19:0-d37内标溶液(5μg/mL)10微升，涡旋2min，室温静置20min。加入正己烷400微升，水300微升，涡旋2min，12000rpm离心5min，取上清400微升；剩余液中加入正己烷400微升，涡旋2min，12000rpm离心5min，取上清400微升。合并上清，室温下真空干燥。在干燥后的离心管中加入80微升甲醇复溶后分析。
分析仪器测试：UPLC-TQMS：采用沃特斯超高效液相色谱系统(沃特斯公司，美国)，配备二元溶剂控制器和样本控制室。采用沃特斯XEVO三重四级杆质谱仪(沃特斯公司，美国)，配备双电喷雾离子源。
色谱条件：采用UPLC BEH C18色谱柱(100mm×2.1mm,1.7μm)；柱温40℃；流动相A为水，流动相B为乙腈/异丙醇(体积比为8:2)；流速为0.4mL/min；进样量为5uL；梯度洗脱条件：0-2min:70％B,2-5min:70％-75％B,5-10min:75％-80％B,10-13min:80％-90％B,13-16min:90％-100％B,16-21min:100％B,21-22.5min:100％-70％B，22.5-24min:70％B。总分析时间为24min。
质谱条件：电喷雾离子源采用负离子扫描模式(ESI-)，具体条件如下：毛细管电压2.5kV,锥孔电压55V,萃取锥孔电压4V,离子源温度120℃,脱溶剂气温度450℃,反向锥孔气流50L/h,脱溶剂气650L/h,低质量区分辨率4.7,高质量区分辨率15,检测器电压2390V,扫描时间0.35s,扫描时间间隔0.02s,质荷比范围:m/z 50-1000。锁定质量数为554.2615。
(五)血清样本的氨基酸检测
样本制备：取血清40μL，加入500μL甲醇乙腈混合溶剂(1:9，v:v)，涡旋振荡2min；置离心管于-20℃放置10min以促进蛋白沉淀，12000rpm4℃离心15min。取上清20μL，室温下真空干燥。在干燥后的离心管中加入100μL甲醇水混合溶剂(1:1，v:v，含1μg/mL二氯苯丙氨酸作为内标)复溶后分析。
分析仪器测试：UPLC-TQMS：采用沃特斯超高效液相色谱系统(沃特斯公司，美国)，配备二元溶剂控制器和样本控制室。采用沃特斯XEVO三重四级杆质谱仪(沃特斯公司，美国)，配备双电喷雾离子源。
色谱条件：采用UPLC BEH C18色谱柱(100mm×2.1mm,1.7μm)；柱温40℃；流动相A：水(0.1％甲酸)，B：乙腈(0.1％甲酸，)；流速为0.4mL/min；进样量为5uL；梯度洗脱条件：0-0.5min(1％B),0.5-9min(1-20％B),9-11min(20-75％B),11-16min(75-99％B),16-16.5min(99％B)。
质谱条件：电喷雾离子源采用负离子扫描模式(ESI-)，具体条件如下：毛细管电压3.0,锥孔电压55V,萃取锥孔电压4V,离子源温度150℃,脱溶剂气温度450℃,反向锥孔气流50L/h,脱溶剂气800L/h,低质量区分辨率4.7,高质量区分辨率15,多反应检测模式采集数据。
(六)血清样本甘油三酯的检测：
血清甘油三酯的检测采用酶比色法测定。
(七)肝脏活检
所有患者都实施了超声引导下肝脏穿刺活检。采用“7点”基线取材法，在肿瘤的12点、3点、6点和9点位置上于癌与癌旁肝组织交界处取材按1：1取材；在肿瘤内部至少取材1块；对距肿瘤边缘≤1cm(近癌旁)和＞1cm(远癌旁)范围内的肝组织分别取材1块，用10％福尔马林固定12～24小时，石蜡包埋，组织切片用苏木精-伊红染色和Masson染色。病理评估由复旦大学上海医学院三位病理专家分别盲法独立评估，结果用Kappa检验验证一致性。当评估结果未通过Kappa检验时，重新分析样本以达成一致的结果。
(八)生物信息学方法
本发明将422例健康人，433例CLD患者和900例HCC患者按70％和30％的比例随机分为训练集和测试集。在训练集中，为了区分健康人和HCC患者、CLD患者和HCC患者，我们使用单因素Wilcoxon秩和检验和LASSO来挑选和鉴定候选的生物标记物，并用随机森林模型来评价候选变量并建立模型，后在测试集和独立验证集中分别验证以上模型。
通过以上研究，发现一组生物标志物包括牛磺胆酸、牛磺鹅去氧胆酸、甘氨胆酸、甘氨鹅去氧胆酸、反亚油酸(C18:2n6t)、麦芽三糖、麦芽糖和/或乳糖、α-亚麻酸、β-丙氨酸、葵二酸、2-甲基戊酸、戊酸、异戊酸、己酸中之一种或多种的水平具有预测和诊断能力；以及次级胆汁酸和初级胆汁酸的比值、和/或甘氨酸结合初级胆汁酸/牛磺酸结合初级胆汁酸的比值也具有预测和诊断能力；所述初级胆汁酸选自胆酸、鹅去氧胆酸，所述次级胆汁酸包括去氧胆酸、石胆酸、熊去氧胆酸。
实施例2诊断标志物浓度的定量检测
以待测诊断标志物的标准品溶液的浓度与对应的待测诊断标志物以及与待测诊断标志物一样的稳定同位素内标面积比绘制标准曲线，采用同位素内标定量测定。同时通过样品添加同位素内标对样品检测过程进行质量控制。
检测方法参照实施例1。
实施例3区分健康对照和HCC
训练集中630例HCC患者和296例健康人，测试集中270例HCC患者和126例健康人，利用在实施例1中得到的生物标志物，在训练集中训练的生物标记物组合随机森林模型，可输出上述受试者患HCC的可能性，并通过ROC分析中的约登最优点找到最佳截断值，可评估该模型对于区分HCC患者和健康人的总能力。对测试集进行测试，结果见表1和表2。
表1 ROC曲线下面积和置信区间
Figure PCTCN2022095737-appb-000001
Figure PCTCN2022095737-appb-000002
Figure PCTCN2022095737-appb-000003
表2
Figure PCTCN2022095737-appb-000004
Figure PCTCN2022095737-appb-000005
实施例4区分肝硬化和HCC
训练集中630例HCC患者和303例肝硬化患者，测试集中270例HCC 患者和130例肝硬化患者，利用在实施例1获得的生物标志中，训练集中训练的生物标记物组合随机森林模型，可输出上述受试者患HCC的可能性，并通过ROC分析中的约登最优点找到最佳截断值，可评估该模型对于区分HCC患者和健康人的总能力。对测试集进行测试，结果见表3和表4。
表3
Figure PCTCN2022095737-appb-000006
Figure PCTCN2022095737-appb-000007
表4
Figure PCTCN2022095737-appb-000008
Figure PCTCN2022095737-appb-000009
实施例5生物标记物组合牛磺胆酸、牛磺鹅去氧胆酸、甘氨胆酸、反亚油酸(C18:2n6t)区分健康组和HCC组
训练集中例630HCC患者和296例健康人，测试集中例270HCC患者和126例健康人，利用在训练集中训练的上述生物标记物组合随机森林模型，可输出上述受试者患HCC的可能性，并通过ROC分析中的约登最优点找到最佳截断值，可评估该模型对于区分HCC患者和健康人的总能力。结果见附图1和表5。训练集中ROC曲线下面积和95％置信区间为1.000(95％CI0.999-1.000)，最佳截断值为0.078，最佳截断值处的灵敏度和特异性百分比 分别为99.7％和100％；测试集中ROC曲线下面积和95％置信区间为1.000(95％CI 0.999-1.000)，最佳截断值为0.078，最佳截断值处的灵敏度和特异性百分比分别为99.2％和100％。如所述对象测定标志物水平，将这些测定值带入随机森林模型后，如果个体得分阈值大于0.078，表明此个体具有较高的患HCC风险；如果个体得分阈值小于0.078，表明此个体具有较低的患HCC风险。
表5生物标记物组合的诊断能力
模型方法 分组 数据集 ROC曲线下面积 特异度 灵敏度 截断值
随机森林 Control-HCC 训练集 0.997(0.992-1) 99.15％ 98.55％ 0.08
FIB-4指数 Control-HCC 训练集 0.839(0.811-0.866) 82.10％ 69.41％ 0.48
APRI指数 Control-HCC 训练集 0.975(0.965-0.984) 91.98％ 93.24％ 0.36
AST/ALT比值 Control-HCC 训练集 0.738(0.702-0.774) 63.27％ 73.31％ 0.52
随机森林模型 Control-HCC 验证集 1(0.999-1) 99.25％ 97.24％ 0.08
FIB-4指数 Control-HCC 验证集 0.895(0.855-0.93) 85.83％ 77.46％ 0.48
APRI指数 Control-HCC 验证集 0.988(0.975-0.996) 93.70％ 95.77％ 0.36
AST/ALT比值 Control-HCC 验证集 0.501(0.43-0.569) 55.12％ 47.18％ 0.52
实施例6生物标记物组合牛磺胆酸、牛磺鹅去氧胆酸、甘氨胆酸、反亚油酸(C18:2n6t)区分CLD组和HCC组
训练集中例630HCC患者和303例CLD患者，测试集中例303HCC患者和130例CLD患者，利用在训练集中训练的生物标记物组合随机森林模型，可输出上述受试者患HCC的可能性，并通过ROC分析中的约登最优点找到最佳截断值，可评估该模型对于区分HCC患者和CLD患者的总能力。结果见附图2和表6。训练集中ROC曲线下面积和95％置信区间为0.912(95％CI 0.874-0.946)，最佳截断值为-0.502，最佳截断值处的灵敏度和特异性百分比分别为83.6％和90.6％；测试集中ROC曲线下面积和95％置信区间为0.918，最佳截断值为-0.502，最佳截断值处的灵敏度和特异性百分比分别 为81.8％和80.4％。如所述对象测定标志物水平，将这些测定值带入随机森林模型后，如果个体得分阈值大于-0.502，表明此个体具有较高的患HCC风险；如果个体得分阈值小于-0.502，表明此个体具有较低的患HCC风险。
表6生物标记物组合的诊断能力
Figure PCTCN2022095737-appb-000010
实施例7一种肝癌诊断方法，包括对受试者的生物样本按照上述实施例进行处理后，以色谱质谱联用代谢组学分析方法对生物样本中的生物标志物组合进行定量检测；所述生物标志物组合包括如上述实施例中证明有效的生物标志物组合；所述色谱质谱联用代谢组学分析方法包括如上述实施例中所述液相色谱质谱联用代谢组学分析方法和气相色谱质谱联用代谢组学分析方法。

Claims (11)

  1. 一种肝癌诊断装置，其特征在于，所述装置以测定受试体生物样本中的生物标志物水平作为诊断指标，所述生物标志物水平选自牛磺胆酸、牛磺鹅去氧胆酸、甘氨胆酸、甘氨鹅去氧胆酸、反亚油酸(C18:2n6t)、麦芽三糖、麦芽糖和/或乳糖、α-亚麻酸、β-丙氨酸、葵二酸、2-甲基戊酸、戊酸、异戊酸、己酸中之一种或多种的水平；和/或次级胆汁酸和初级胆汁酸的比值、和/或甘氨酸结合初级胆汁酸/牛磺酸结合初级胆汁酸的比值；所述初级胆汁酸选自胆酸、鹅去氧胆酸，所述次级胆汁酸包括去氧胆酸、石胆酸、熊去氧胆酸。
  2. 如权利要求1所述的肝癌诊断装置，其特征在于，所述生物标志物水平的测定包括以下步骤：对受试者的生物样本进行处理后，以色谱质谱联用代谢组学分析方法对生物样本中的生物标志物组合进行定量检测，所述色谱质谱联用代谢组学分析方法包括液相色谱质谱联用代谢组学分析方法和气相色谱质谱联用代谢组学分析方法。
  3. 如权利要求1所述的肝癌诊断装置，其特征在于，所述诊断装置选自试剂盒、医疗器械、具有诊断模块的计算机系统和具有诊断模块的检测装置。
  4. 如权利要求3所述的肝癌诊断装置，其特征在于，所述诊断模块包括信息获取模块和肝癌诊断模块；其中，所述信息获取模块至少用于获取诊断指标信息；所述肝癌诊断模块至少用于执行以下操作：根据所述信息获取模块获取的诊断指标信息，评估受试者体是否患有肝癌或肝硬化。
  5. 如权利要求3所述的肝癌诊断装置，其特征在于，所述试剂盒包括检测诊断指标的定量检测试剂。
  6. 如权利要求1所述的肝癌诊断装置，其特征在于，所述诊断指标包括甲胎蛋白。
  7. 用于肝癌诊断的生物标志物组合，其特征在于，包括牛磺胆酸、牛磺 鹅去氧胆酸、甘氨胆酸、甘氨鹅去氧胆酸、反亚油酸(C18:2n6t)、麦芽三糖、麦芽糖和/或乳糖、α-亚麻酸、β-丙氨酸、葵二酸、2-甲基戊酸、戊酸、异戊酸、己酸中之一种或多种。
  8. 如权利要求7所述的生物标志物组合，其特征在于，所述生物标志物组合进一步包括甲胎蛋白。
  9. 如权利要求7或8所述的生物标志物组合用于制备诊断肝癌或肝硬化的诊断产品的应用。
  10. 如权利要求7或8所述的生物标志物组合在评价肝癌或肝硬化治疗药物中的应用。
  11. 一种肝癌诊断方法，包括对受试者的生物样本进行处理后，以色谱质谱联用代谢组学分析方法对生物样本中的生物标志物组合进行定量检测；所述生物标志物组合包括如权利要求7或8所述的生物标志物组合；所述色谱质谱联用代谢组学分析方法包括液相色谱质谱联用代谢组学分析方法和气相色谱质谱联用代谢组学分析方法。
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