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CN115128257B - Metabolic markers for predicting the risk of liver cancer and their application - Google Patents

Metabolic markers for predicting the risk of liver cancer and their application Download PDF

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CN115128257B
CN115128257B CN202210788440.2A CN202210788440A CN115128257B CN 115128257 B CN115128257 B CN 115128257B CN 202210788440 A CN202210788440 A CN 202210788440A CN 115128257 B CN115128257 B CN 115128257B
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liver cancer
acid
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沈洪兵
胡志斌
马红霞
杭栋
靳光付
戴俊程
宋词
杨晓林
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Nanjing Medical University
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Abstract

The invention discloses a metabolism marker for predicting liver cancer incidence risk and application thereof, and development and utilization of the metabolism marker provide technical support for liver cancer incidence risk prediction and early diagnosis and treatment. The invention utilizes a non-targeted metabonomics detection method to screen and verify 44 plasma metabolites related to the liver cancer incidence risk, and determines a group of 18 plasma metabolites, which can obviously improve the risk prediction level of liver cancer.

Description

Metabolism marker for predicting liver cancer incidence risk and application thereof
Technical Field
The invention belongs to the technical field of biomedicine, and relates to a metabolic marker for liver cancer risk prediction and application thereof.
Background
Primary liver cancer is the sixth most common cancer worldwide and is also the third leading cause of cancer death. The incidence rate of liver cancer in China exceeds that of other countries in the world, and the new and dead liver cancer cases account for over 50% of the world every year, and the incidence rate and the death rate of liver cancer in China are respectively second and fourth in cancers, so that the life and the health of residents are seriously threatened. The liver cancer is hidden, the disease progress is rapid, about 70% -80% of patients already belong to middle and late stages when they are diagnosed, the chance of surgery or other local treatment is lost, the recurrence rate is high, and the survival rate in 5 years is only 14%. Therefore, the method improves the prediction level of the liver cancer incidence risk, identifies the high-risk group for early intervention and diagnosis and treatment, and has important public health significance for reducing the liver cancer incidence rate and the death rate.
Up to now, screening or auxiliary diagnosis techniques for liver cancer mainly include serum alpha fetoprotein detection and imaging techniques. However, although alpha fetoprotein is the most widely used serological index in clinical application, it is found that the sensitivity of early liver cancer is lower than 40%, and 30% -40% of liver cancer patients have no significant elevation of alpha fetoprotein. Therefore, alpha fetoprotein has limited sensitivity and specificity in screening liver cancer. Imaging techniques include computed tomography, ultrasound, and magnetic resonance imaging, which are relatively expensive, limited by the skill level of the operator, and have low sensitivity to early liver cancer. In addition, the method is mainly used for screening or auxiliary diagnosis, the potential risk of liver cancer onset cannot be effectively estimated, and a new technical method is urgently needed for improving the early prevention level of liver cancer.
Metabonomics is a new histology technology emerging after genomics, transcriptomics and proteomics, and can quantitatively analyze thousands of intermediate products and final products participating in biochemical reactions in organisms, and has wide application in the fields of etiology, diagnosis, biological function research, drug research and the like. Compared with other methods for studying the metabonomics, ① has the advantages that metabolic response of an organism to physiological and pathological condition changes is measured from the whole angle, particularly, the metabonomics is located at the downstream of life network regulation, detection of metabolites is closer to reflecting change of biological phenotype, tiny changes of ② gene and protein expression on functional level can be amplified through the metabolites, nonfunctional changes of the ② gene and protein expression cannot be reflected on metabolic level, therefore, detection of the metabolites is easier to find key events for changing the biological phenotype, and a sample measured by ③ can be biological fluid (such as blood, urine and the like), so that the sample is easier to obtain, less damage to human body and easy to popularize and apply. Therefore, the metabonomics technology is helpful for finding early events of tumors, and identifying biomarkers with crowd application value so as to improve the risk prediction and intervention capability of the tumors.
The traditional metabonomics research of liver cancer has small sample size, the detected metabolite quantity is small, and external verification is lacking, and especially the predictive value of the metabolic markers for future morbidity risk is not evaluated in a prospective queue. Therefore, it is necessary to develop a queue-based liver cancer metabonomics study, and the system screens metabolic biomarkers with application value, which has important significance for realizing liver cancer prevention gateway forward and accurate prevention and reducing incidence and death rate of liver cancer in China.
Disclosure of Invention
In order to overcome the defects, the invention provides a plasma metabolite for predicting liver cancer risk and application thereof. The method can be applied to detection of novel biomarkers related to liver cancer pathogenesis.
A first object of the present invention is to provide a marker combination associated with liver cancer, which is a combination of one or more of the following 44 compounds:
copper quinolinate
3- (4-Hydroxyphenyl) lactic acid
Cystathionine (cystathionine)
Glycocholic acid salt
Citrulline
Phenylalanine (Phe)
Vitamin A
Tyrosine
Sarcosine
Glycine chenodeoxycholic acid
Taurochenon deoxycholate
Ribitol
1, 2-Dipalmitoyl-glycerophosphorylcholine (16:0/16:0)
1-Myristoyl-2-palmitoyl-glycerophosphorylcholine (14:0/16:0)
Dehydroepiandrosterone sulfate
N-acetylglycine
Androsterone sulfate
N-acetyl tyrosine
Epiandrosterone sulfate
N1-methyl-2-pyridone-5-carboxamide
1-Arachidonyl-glycerophosphorylcholine (20:4/0:0)
1-Arachidonyl-glycerophosphoryl ethanolamine (20:4/0:0)
5 Alpha-androstane-3 beta, 17 beta-diol bisulphate
Taurocholate sulfate
Androstenediol disulfate
5 Alpha-pregna-3 beta, 20 alpha-diol monosulfate
Androstenediol (3 alpha, 17 alpha) monosulfate
17-Androstenediol sulfate (1)
17-Androstenediol sulfate (2)
16 Alpha-hydroxy dehydroepiandrosterone 3-sulfate
Androsterone glucuronide
Arginine (Arg)
Glycine ursodeoxycholic acid
Citraconate salt
Glycine chenodeoxycholic acid 3-sulfate
1- (1-Alkenyl-palmitoyl) -2-palmitoyl-glycerophosphorylcholine (P-16:1/16:1)
1- (1-Alkenyl-palmitoyl) -2-palmitoyl-glycerophosphorylcholine (P-16:0/16:0)
Glycine chenodeoxycholic acid glucuronide
Ceramides (d18:2/24:1, d18:1/24:2)
11 Beta-hydroxy androsterone glucuronide
Gan Anxiong deoxycholate sulphate
2, 3-Dihydroxy-5-methylsulfanyl-4-pentenoate
Lactone sulfuric acid
Tetrahydrocortisol glucuronide.
Further, the marker combination is a combination of one or more of the following 33 compounds:
copper quinolinate
Cystathionine (cystathionine)
Citrulline
Sarcosine
Ribitol
1, 2-Dipalmitoyl-glycerophosphorylcholine (16:0/16:0)
1-Myristoyl-2-palmitoyl-glycerophosphorylcholine (14:0/16:0)
N-acetylglycine
Androsterone sulfate
N-acetyl tyrosine
Epiandrosterone sulfate
N1-methyl-2-pyridone-5-carboxamide
1-Arachidonyl-glycerophosphoryl ethanolamine (20:4/0:0)
5 Alpha-androstane-3 beta, 17 beta-diol bisulphate
Taurocholate sulfate
Androstenediol disulfate
5 Alpha-pregna-3 beta, 20 alpha-diol monosulfate
Androstenediol (3 alpha, 17 alpha) monosulfate
17-Androstenediol sulfate (1)
17-Androstenediol sulfate (2)
16 Alpha-hydroxy dehydroepiandrosterone 3-sulfate
Androsterone glucuronide
Arginine (Arg)
Citraconate salt
Glycine chenodeoxycholic acid 3-sulfate
1- (1-Alkenyl-palmitoyl) -2-palmitoyl-glycerophosphorylcholine (P-16:1/16:1)
Glycine chenodeoxycholic acid glucuronide
Ceramides (d18:2/24:1, d18:1/24:2)
11 Beta-hydroxy androsterone glucuronide
Gan Anxiong deoxycholate sulphate
2, 3-Dihydroxy-5-methylsulfanyl-4-pentenoate
Lactone sulfuric acid
Tetrahydrocortisol glucuronide.
Further, the marker combination is a combination of one or more of the following 18 compounds:
copper quinolinate
3- (4-Hydroxyphenyl) lactic acid
Cystathionine (cystathionine)
Glycocholic acid salt
Citrulline
Sarcosine
1, 2-Dipalmitoyl-glycerophosphorylcholine (16:0/16:0)
Androsterone sulfate
1-Arachidonyl-glycerophosphorylcholine (20:4/0:0)
5 Alpha-pregna-3 beta, 20 alpha-diol monosulfate
17-Androstenediol sulfate (1)
17-Androstenediol sulfate (2)
16 Alpha-hydroxy dehydroepiandrosterone 3-sulfate
Arginine (Arg)
Citraconate salt
Glycine chenodeoxycholic acid 3-sulfate
Glycine chenodeoxycholic acid glucuronide
Ceramides (d18:2/24:1, d18:1/24:2).
Further, the marker combination is a combination of one or more of the following 15 compounds:
copper quinolinate
Cystathionine (cystathionine)
Citrulline
Sarcosine
1, 2-Dipalmitoyl-glycerophosphorylcholine (16:0/16:0)
Androsterone sulfate
5 Alpha-pregna-3 beta, 20 alpha-diol monosulfate
17-Androstenediol sulfate (1)
17-Androstenediol sulfate (2)
16 Alpha-hydroxy dehydroepiandrosterone 3-sulfate
Arginine (Arg)
Citraconate salt
Glycine chenodeoxycholic acid 3-sulfate
Glycine chenodeoxycholic acid glucuronide
Ceramides (d18:2/24:1, d18:1/24:2).
A second object of the present invention is to provide the use of a product for detecting a marker combination as described above for the preparation of a liver cancer diagnosis and/or risk prediction product.
Further, the marker combination is derived from plasma.
A third object of the present invention is to provide the use of the aforementioned marker combination for screening a drug for treating and/or alleviating liver cancer.
Further, the marker combination is derived from plasma.
The fourth object of the invention is to provide the application of the marker combination in preparing a detection kit for diagnosing and/or predicting liver cancer.
Further, the marker combination is derived from plasma.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention adopts a strict screening, verifying and evaluating system, and develops nest type case control research of liver cancer based on 2 prospective queues in China, 44 metabolites are independently screened and verified to be related with the incidence risk of the liver cancer, wherein the association of 33 metabolites is first reported in the world;
(2) The invention determines a group of 18 plasma metabolites, which are used for predicting the occurrence risk of liver cancer, shows good sensitivity and specificity, and provides a new technical support for identifying high-risk groups of liver cancer and early diagnosis and treatment;
(3) The invention shows that the specific plasma metabolite can be used as a novel micro-invasive biomarker to improve the disease risk prediction level, and the successful development of the biomarker provides a method and a strategic reference for the development of other disease biomarkers.
Drawings
Figure 1 results of a subject work profile analysis for 44 metabolites. A is a result of a screening set of a Nantong queue, B is a result of a verification set of a Changzhou queue, and a logistic regression calculation C index in the verification set is 0.79 (95% CI: 0.70-0.88).
Figure 2 results of a subject work profile analysis for 18 metabolites. A is a result of a screening set of a Nantong queue, B is a result of a verification set of a Changzhou queue, and a logistic regression calculation C index in the verification set is 0.86 (95% CI: 0.80-0.93).
Detailed Description
Experiment design:
(1) A unified standard queue specimen library and database are established, standard blood samples are collected by Standard Operation Procedure (SOP), and complete demographic data and clinical data are collected by the system.
(2) Metabolome detection, including the confirmed liver cancer cases in 2 prospective queues and the healthy controls matched with the ages and sexes of the liver cancer cases, screening and verifying the metabolic markers related to liver cancer incidence by using a non-target metabolomics technology.
(3) And (3) identifying the metabolites with independent prediction values by further adopting methods such as machine learning and the like for the screened positive associated metabolites, and evaluating the combined prediction efficacy of the metabolites.
The inventors have conducted a nest-like case control study using 2 prospective chinese crowd cohorts, detected 612 named metabolites in baseline plasma by non-targeted metabonomics technology, and found that 44 of these metabolites were significantly correlated with the onset of liver cancer, including 12 androgens/progestins, 8 bile acids, 10 amino acids, 6 phospholipids and 8 other metabolites. The machine learning technology is adopted to further identify 18 metabolic markers with predictive value, so that technical support is provided for risk assessment of liver cancer, and data support is provided for finding novel small molecular drugs with potential intervention value.
Example 1 sample collection and sample data arrangement
1. Selection of study samples:
163 new liver cancer patients from two prospective queues in Nantong and Changzhou City of Jiangsu province were matched with healthy controls by age.+ -.2 years, same sex and region 1:1.
The study was conducted with 326 standard-meeting samples, 216 in southern city and 110 in Changzhou city.
2. Extraction of plasma samples:
Each study subject adopts a vacuum anticoagulation (EDTA) blood collection tube to collect 5ml of fasting venous blood, plasma is immediately separated according to a standard method and frozen and stored at-80 ℃ for standby, 100 mu L of plasma sample is removed to an EP tube, 300 mu L of extracting solution (methanol and isotope-labeled internal standard mixture) is added, vortex mixing is carried out for 30s, ultrasound is carried out for 10min (ice water bath), standing is carried out at-40 ℃ for 1h, the sample is centrifuged at 4 ℃ and 12000rpm for 15min, and the supernatant is taken and loaded into a sample bottle for machine detection.
Example 2 plasma metabolome detection
3. Metabonomics detection:
and detecting a sample by adopting a technical platform combining ultra-high performance liquid chromatography (Waters acquisition) and quadrupole-orbitrap high-resolution mass spectrum (Thermo SCIENTIFIC Q Exactive).
(1) A C18 column (UPLC BEH C18-2.1X100 mM,1.7 μm) from Waters was used, eluting with 80% mobile phase A (95:5:0.1vol/vol/vol 10mM ammonium acetate/methanol/formic acid) for 1 min, 80% mobile phase B (99.9:0.1vol/vol methanol/formic acid) for 2 min, 100% mobile phase B for 7 min, and the mass spectrometry was performed using electrospray ionization anion mode for full scan analysis in the range of 200-1000m/z with a resolution of 70000 and a data acquisition rate of 3hz, and other parameters were sheath gas flow rate 50, in-source CID 5ev, scavenging 5, spray voltage 3kv, capillary temperature 300℃and s-lens radio frequency voltage 50v, heater temperature 300 ℃.
(2) HILIC chromatographic column (UPLC BEH Amide 2.1X150 mM,1.7 μm) from Waters was used, eluting with 5% mobile phase A (10 mM ammonium formate and 0.1% formic acid in water) for 0.5 min, 40% mobile phase B (acetonitrile containing 0.1% formic acid) for 10 min, full scan analysis in the range of 70-800m/z with resolution 70000 and data acquisition rate of 3hz, other parameters of sheath gas flow rate 40, scavenging 2, spray voltage 3.5kV, capillary temperature 350℃s-lens radio frequency voltage 40, heater temperature 300 ℃.
4. And (3) data processing:
processing such as peak identification, peak extraction, peak alignment and integration is performed, material annotation is performed to determine 612 named metabolites in baseline plasma, outliers are filtered based on relative standard deviation (RELATIVE STANDARD displacement), missing data are filled in by one half of the minimum value, and normalization is performed by using an internal standard (INTERNAL STANDARD).
5. Statistical analysis:
Performing orthogonal-partial least squares discriminant analysis (variable importance projection, VIP) and paired t test (P value), avoiding false positive results through multiple correction (FDR), finding that 44 metabolites have significant differences between cases and control groups, satisfying VIP >1 and P FDR <0.05 (table 1);
Metabolic markers were further found in which 18 metabolites had independent predictive value using lasso regression (Lasso regression), predictive models were built using machine learning, subject work characteristics (Receiver operating characteristic, ROC) analysis found that the predictive effect of the models was excellent, and the area under the ROC curve (AUC) calculated by logistic regression in the validation cohort was 0.86 (95% ci: 0.80-0.93), with sensitivity and specificity of 81.8% and 74.5%, respectively.
As can be seen from the ROC curves in FIG. 1, the AUCs of the 44 metabolic markers in the two queues are respectively 0.90 and 0.79, and the ROC curves of the 18 metabolic markers in the two queues in FIG. 2 have a certain accuracy in the liver cancer diagnosis process, and the area under the ROC curves of the 18 metabolic markers in the two queues in FIG. 2 is larger than 0.85, so that the ROC curves have higher accuracy and clinical diagnosis significance.
Example 3 data analysis
The 44 significantly different metabolites are shown in table 1 in the liver cancer case compared with the control.
TABLE 1
The above examples are not intended to limit the present invention, but are merely illustrative of the present invention. The experimental methods used in the above examples, unless otherwise specified, and the experimental methods in which the specific conditions are not specified in the examples are conventional conditions and conventional methods, and the raw reagent materials in the above examples are all commercially available, and if otherwise specified, are all commercially available.

Claims (8)

1.一种标志物组合，其特征在于，所述标志物组合为以下18种化合物的组合：1. A marker combination, characterized in that the marker combination is a combination of the following 18 compounds: 羟基喹啉铜Copper Hydroxyquinoline 3-(4-羟基苯基)乳酸3-(4-Hydroxyphenyl)lactic acid 胱硫醚Cystathionine 甘胆酸盐Glycocholate 瓜氨酸Citrulline 肌氨酸Creatine 1,2-二棕榈酰基-甘油磷脂酰胆碱(16:0/16:0)1,2-Dipalmitoyl-glycerophosphatidylcholine (16:0/16:0) 硫酸雄酮Androsterone Sulfate 1-花生四烯酰-甘油磷脂酰胆碱(20:4/0:0)1-Arachidonic acid-glycerophosphatidylcholine (20:4/0:0) 5α-孕甾-3β,20α-二醇单硫酸盐5α-Pregnane-3β,20α-diol monosulfate 17-硫酸雄烯二醇(1)17-Androstenediol sulfate (1) 17-硫酸雄烯二醇(2)17-Androstenediol sulfate (2) 16α-羟基脱氢异雄酮3-硫酸盐16α-Hydroxydehydroisoandrosterone 3-sulfate 精氨酸Arginine 柠康酸盐Citrate 甘氨鹅脱氧胆酸3-硫酸盐Glycochenodeoxycholic acid 3-sulfate 甘氨鹅脱氧胆酸葡糖苷酸Glycochenodeoxycholic acid glucuronide 神经酰胺(d18:2/24:1,d18:1/24:2)。Ceramide (d18:2/24:1, d18:1/24:2). 2.根据权利要求1所述的与肝癌相关的标志物组合，其特征在于，所述标志物组合除权利要求1中所述18种化合物的组合外，还包括以下化合物中的一种或多种：2. The combination of markers associated with liver cancer according to claim 1, characterized in that, in addition to the combination of 18 compounds described in claim 1, the combination of markers further comprises one or more of the following compounds: 苯丙氨酸Phenylalanine 维生素AVitamin A 酪氨酸Tyrosine 甘氨鹅脱氧胆酸Glycochenodeoxycholic acid 牛磺鹅脱氧胆酸盐Taurochenodeoxycholate 核糖醇Ribitol 1-肉豆蔻酰-2-棕榈酰-甘油磷脂酰胆碱(14:0/16:0)1-Myristoyl-2-palmitoyl-glycerophosphatidylcholine (14:0/16:0) 硫酸脱氢表雄酮Dehydroepiandrosterone Sulfate N-乙酰甘氨酸N-acetyl glycine N-乙酰酪氨酸N-Acetyl Tyrosine 表雄甾酮硫酸盐Epiandrosterone Sulfate N1-甲基-2-吡啶酮-5-甲酰胺N1-Methyl-2-pyridone-5-carboxamide 1-花生四烯酰-甘油磷酰乙醇胺(20:4/0:0)1-Arachidonic acid-glycerophosphoethanolamine (20:4/0:0) 牛磺胆烯酸硫酸盐Taurocholine Sulfate 雄烯二醇二硫酸盐Androstenediol disulfate 5α-孕甾-3β,20α-二醇单硫酸盐5α-Pregnane-3β,20α-diol monosulfate 雄烯二醇(3α,17α)单硫酸盐Androstenediol (3α,17α) monosulfate 雄酮葡萄糖苷酸Androsterone glucuronide 甘氨酸熊脱氧胆酸Glycine ursodeoxycholic acid 1-(1-烯基-棕榈酰)-2-棕榈油酰-甘油磷脂酰胆碱(P-16:1/16:1)1-(1-Alkenyl-palmitoyl)-2-palmitoleoyl-glycerophosphatidylcholine (P-16:1/16:1) 1-(1-烯基-棕榈酰)-2-棕榈酰-甘油磷脂酰胆碱(P-16:0/16:0)1-(1-Alkenyl-palmitoyl)-2-palmitoyl-glycerophosphatidylcholine (P-16:0/16:0) 11β-羟基雄甾酮葡糖苷酸11β-Hydroxyandrosterone glucuronide 甘氨熊脱氧胆酸硫酸盐Glycoursodeoxycholic acid sulfate 2,3-二羟基-5-甲硫基-4-戊烯酸酯2,3-Dihydroxy-5-methylthio-4-pentenoate 内酯硫酸Lactone sulfuric acid 四氢皮质醇葡糖苷酸。Tetrahydrocortisol glucuronide. 3.检测权利要求1或2所述的标志物组合的产品在制备肝癌诊断和/或风险预测制品中的应用。3. Use of a product for detecting the marker combination according to claim 1 or 2 in the preparation of liver cancer diagnosis and/or risk prediction products. 4.根据权利要求3所述的应用，其特征在于，所述标志物组合来源于血浆。4. The use according to claim 3, characterized in that the marker combination is derived from plasma. 5.权利要求1或2所述的标志物组合在筛选治疗和/或缓解肝癌的药物中的应用。5. Use of the marker combination according to claim 1 or 2 in screening drugs for treating and/or alleviating liver cancer. 6.根据权利要求5所述的应用，其特征在于，所述标志物组合来源于血浆。6. The use according to claim 5, characterized in that the marker combination is derived from plasma. 7.权利要求1或2所述的标志物组合在制备用于肝癌诊断和/或风险预测的检测试剂盒中的应用。7. Use of the marker combination according to claim 1 or 2 in the preparation of a detection kit for liver cancer diagnosis and/or risk prediction. 8.根据权利要求7所述的应用，其特征在于，所述标志物组合来源于血浆。8. The use according to claim 7, characterized in that the marker combination is derived from plasma.
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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101995441A (en) * 2010-02-10 2011-03-30 中国科学院大连化学物理研究所 Kit for testing 3-sulfate glycochenodeoxycholic acids and glycochenodeoxycholic acids in blood

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KR102379017B1 (en) * 2020-08-18 2022-03-28 서울대학교 산학협력단 Composition for diagnosing liver cancer comprising glycine labeled isotope 13C or 2H as an active ingredient
WO2022115574A2 (en) * 2020-11-25 2022-06-02 Venn Biosciences Corporation Biomarkers for diagnosing non-alcoholic steatohepatitis (nash) or hepatocellular carcinoma (hcc)
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Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101995441A (en) * 2010-02-10 2011-03-30 中国科学院大连化学物理研究所 Kit for testing 3-sulfate glycochenodeoxycholic acids and glycochenodeoxycholic acids in blood

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
Untargeted plasma metabolomics for risk prediction of hepatocellular carcinoma: A prospective study in two Chinese cohorts;Dong Hang et al;《 INTERNATIONAL JOURNAL OF CANCER》;20220722;第151卷(第12期);2144-2154 *

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