WO2023082821A1 - Marqueur métabolique sérique pour le diagnostic des nodules pulmonaires bénins et malins et son utilisation - Google Patents

Marqueur métabolique sérique pour le diagnostic des nodules pulmonaires bénins et malins et son utilisation Download PDF

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WO2023082821A1
WO2023082821A1 PCT/CN2022/118713 CN2022118713W WO2023082821A1 WO 2023082821 A1 WO2023082821 A1 WO 2023082821A1 CN 2022118713 W CN2022118713 W CN 2022118713W WO 2023082821 A1 WO2023082821 A1 WO 2023082821A1
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benign
pulmonary nodules
phase
malignant
malignant pulmonary
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PCT/CN2022/118713
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娄加陶
王琳
范江
王兴
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上海市第一人民医院
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57423—Specifically defined cancers of lung
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G01N33/57488—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites involving compounds identifable in body fluids

Definitions

  • the invention relates to the technical field of detection and diagnosis, in particular to a serum metabolic marker for the diagnosis of benign and malignant pulmonary nodules, a screening method and application thereof.
  • Lung cancer is the disease with the highest morbidity and mortality in China. In the past three decades, the mortality rate of lung cancer has increased by about 5 times. The main reason is that 75% of cancer patients are diagnosed in the middle and late stages. The detection rate of early lung cancer is less than 25%, but the 5-year survival rate of early lung cancer is over 90%, but because its early features are not obvious, the best way to find lung cancer is to regularly screen for lung nodules.
  • Pulmonary nodule is an imaging term. When there is a round or oval ⁇ 3cm gray or white shadow in a place that should not be occluded on the lung image, we call it a nodule. Nodules can be granulomas or exudates formed by lung stimulation and tissue hyperplasia, or enlarged lymph nodes, abnormal blood vessels, etc. There are many reasons for the formation of nodules.
  • Malignancy is mainly lung cancer. In the early stage of lung cancer, it is a small nodule on the image, which gradually grows over time. Therefore, when a pulmonary nodule is found, especially when the diameter is >1 cm, it is necessary to distinguish whether it is benign or malignant. If it is malignant, surgical treatment should be performed as soon as possible to achieve the purpose of radical cure. If it is a benign nodule, the cause must also be clarified, such as spherical pneumonia, tuberculosis, fungi, or hemangiomas in the lungs, vascular malformations, etc., and treatment will be performed according to the cause.
  • Asymptomatic nodules with a diameter of ⁇ 7mm are mostly benign nodules, and should be observed regularly to see if there is a trend of continued enlargement over time on the images.
  • the nodules grow larger than 1 cm blood tests, bronchoscopy, lung biopsy, etc. are required to confirm the diagnosis and take appropriate treatment.
  • LDCT low-dose computed tomography
  • Metabolomics is an emerging discipline after genomics and proteomics, and is an important part of systems biology. Metabolomics has developed and rapidly penetrated into many fields, and its purpose is to study the overall metabolic differences in biological systems by monitoring the levels of small molecule metabolites in biological fluids or tissues, and to find the relative relationship between metabolites and pathophysiological changes. The occurrence of tumors is bound to be accompanied by metabolic changes, especially in the early stage, the changes of small molecule metabolites are very weak and not easy to be found (Pei-Hsuan, C., Ling, C., Kenneth, H. et al. Metabolic diversity in human non-small cell lung cancer cells. Molecular Cell. 2019, 76, 1-14.
  • NMR nuclear magnetic resonance
  • GC-MS gas chromatography-mass spectrometry
  • LC-MS liquid chromatography-mass spectrometry
  • the present invention uses serum metabolomics to search for metabolic markers to diagnose and distinguish benign and malignant pulmonary nodules, which is of great significance for early and rapid clinical diagnosis of lung cancer.
  • the present invention builds a serum-specific metabolome database for benign and malignant pulmonary nodules to conduct serum metabolomics research, obtains a large number of specific metabolites related to diseases, and verifies metabolic markers through samples from multiple medical centers to find sensitivity High, specific, stable and reliable serum metabolic markers for the diagnosis of benign and malignant pulmonary nodules.
  • the present invention adopts following technical scheme:
  • the present invention provides a metabolic marker for diagnosing or monitoring benign and malignant pulmonary nodules, the metabolic marker is at least selected from 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxyl - at least one of 6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamic acid, asparagine, formononetin, alanine, and kynuric acid.
  • the present invention provides a metabolic marker for diagnosing or monitoring benign and malignant pulmonary nodules
  • the metabolic marker is at least one selected from 4-hydroxy-L-proline, hyodeoxycholic acid, and xanthine kind. Further, the metabolic marker is at least one selected from 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, and glutamic acid. Further, the metabolic marker is at least one selected from guanine, glutamic acid, asparagine, formononetin, alanine, and kynuric acid.
  • the present invention also provides the application of the above metabolic markers for diagnosing or monitoring benign and malignant pulmonary nodules in preparing a metabolite database, reagent product or kit for diagnosing or monitoring benign and malignant pulmonary nodules.
  • the invention provides a reagent product or a detection kit, including standard products of metabolic markers for diagnosing or monitoring benign and malignant pulmonary nodules.
  • the detection kit may also include dissolution reagents, extraction reagents and internal standards.
  • the internal standard is L-phenylalanine.
  • the present invention also provides a method for screening metabolic markers for diagnosing or monitoring benign and malignant pulmonary nodules, comprising the following steps: respectively collecting samples from the benign pulmonary nodules group and the malignant pulmonary nodules group; 20% of the benign nodule group and 20% of the lung malignant nodule group, using enhanced ion scanning mass spectrometry and time-of-flight mass spectrometry combined with the metabolomics method of multiple reaction monitoring acquisition mode, and integrating the local standard database for lung benign and malignant
  • the nodule serum metabolite database was constructed; the collected serum samples were analyzed by constructing the serum metabolite database of benign and malignant pulmonary nodules and LC-MS detection, and the original mass spectrometry data of each serum sample were obtained; using MultiQuant software, according to the mass-to-charge ratio , retention time to preprocess and correct the original mass spectrum data; calculate the peak area according to the mass spectrum peak intensity to obtain the relative content information of metabolites; conduct multivariate statistical orthogonal-partial
  • the conditions for LC-MS detection are:
  • Phase A is an aqueous solution containing 0.1% acetic acid
  • phase B is an acetonitrile solution containing 0.1% acetic acid
  • the flow rate is 0.4mL/min
  • the elution gradient program is:
  • phase A 0min
  • volume ratio of phase A to phase B 95:5;
  • the volume ratio of phase A and phase B is 10:90;
  • the volume ratio of phase A to phase B is 95:5;
  • the volume ratio of phase A to phase B is 95:5.
  • the present invention uses large-scale clinical samples to conduct serum metabolomics research to obtain 12 markers for the diagnosis of benign and malignant pulmonary nodules.
  • the AUC value of the area under the ROC curve of a single metabolic marker is greater than 0.6. Between 0.611-0.808; the performance of the combination of multiple metabolic markers is significantly better than that of a single metabolic marker, and the area under the ROC curve AUC value is 0.685-0.861.
  • the 12 serum metabolic markers of the present invention are used for detection and diagnosis, the diagnosis can be realized only by taking blood for detection, without additional collection of tissue samples, which can well replace the existing tissue biopsy and imaging diagnosis modes, and reduce trauma and radiation risk, and has the value of clinical use and promotion.
  • Fig. 1 is an S-plot diagram of the metabolite OPLS-DA provided in Example 1 of the present invention.
  • the "local standard substance database” mentioned in the present invention refers to the mass spectrometric detection of a large number of relevant metabolite molecular standards in the present invention, and the collection of mass spectrometry information of these metabolites, thereby forming a localized standard substance database.
  • This embodiment provides a screening method for serum metabolic markers, comprising the following steps:
  • peripheral venous blood was collected from 140 patients with benign pulmonary nodules (benign pulmonary nodules group) and 143 patients with malignant pulmonary nodules (malignant pulmonary nodules group) in Shanghai Chest Hospital. Serum samples. All patients with pulmonary malignant nodules had no history of other malignant tumors, other major systemic diseases, and no chronic medical history of long-term medication. The time of blood collection was in the morning on an empty stomach. All serum samples were centrifuged and stored in a -80°C refrigerator. During the study, serum samples were taken out and thawed for subsequent analysis.
  • step S1 Take out the sample collected in step S1 from the -80°C refrigerator, and thaw it on ice until there are no ice cubes in the sample (subsequent operations are required to be carried out on ice);
  • Into a numbered centrifuge tube add 300 ⁇ L pure methanol internal standard extraction solution (containing 100 ppm L-phenylalanine internal standard); vortex for 5 min, let stand for 24 h, and then centrifuge at 12000 r/min, 4 °C for 10 min ; Take 270 ⁇ L of the supernatant and concentrate for 24 hours; then add 100 ⁇ L of acetonitrile and water in a complex solution with a volume ratio of 1:1 for LC-MS/MS analysis. 20 ⁇ L of each sample was mixed to form a quality control sample (QC), which was collected every 15 samples.
  • QC quality control sample
  • Phase A is an aqueous solution containing 0.1% acetic acid
  • phase B is an acetonitrile solution containing 0.1% acetic acid.
  • the elution gradient program is: 0min, the volume ratio of phase A to B is 95:5; 11.0min, the volume ratio of phase A to B is 10:90; 12.0min, the volume ratio of phase A to B is 10 :90; 12.1min, the volume ratio of phase A to phase B is 95:5; 14.0min, the volume ratio of phase A to phase B is 95:5.
  • each ion pair is scanned in MRM mode according to the optimized declustering potential (DP) and collision energy (collision energy, CE).
  • DP declustering potential
  • CE collision energy
  • the samples were analyzed and detected according to the determined liquid chromatography conditions and mass spectrometry conditions: randomly selected 20% of the samples from the benign pulmonary nodule group and the malignant pulmonary nodule group, and used enhanced ion scanning mass spectrometry (MIM-EPI) and time-of-flight Mass spectrometry (TOF) combined with the metabolomics method of the multiple reaction monitoring acquisition mode, and the integration of the local standard database for the construction of a serum metabolite database for benign and malignant pulmonary nodules.
  • the collected serum samples were analyzed by liquid chromatography-mass spectrometry combined with metabolomics method and the constructed serum metabolite database of benign and malignant pulmonary nodules, and the original mass spectrometry data of each serum sample were obtained.
  • the metabolites of the samples were qualitatively and quantitatively analyzed by mass spectrometry. Metabolites of different molecular weights can be separated by liquid chromatography. The characteristic ions of each substance are screened out by the multiple reaction monitoring mode (MRM) of the triple quadrupole, and the signal intensity (CPS) of the characteristic ions is obtained in the detector.
  • MRM multiple reaction monitoring mode
  • CPS signal intensity
  • Use MultiQuant software to open the mass spectrum file of the sample off-machine, preprocess and correct the original mass spectrum data according to the mass-to-charge ratio and retention time, and perform the integration and calibration of the chromatographic peaks.
  • the peak area (Area) of each chromatographic peak represents the corresponding substance.
  • the repeatability of metabolite extraction and detection can be judged by overlaying and displaying the total ion chromatograms (TIC charts) of different quality control QC samples for mass spectrometry detection and analysis, that is, technical repetition.
  • the high stability of the instrument provides an important guarantee for the repeatability and reliability of the data.
  • the CV value is the coefficient of variation (Coefficient of Variation), which is the ratio of the standard deviation of the original data to the mean of the original data, which can reflect the degree of dispersion of the data.
  • the Empirical Cumulative Distribution Function (ECDF) can be used to analyze the frequency of the CV of substances less than the reference value.
  • the proportion of substances with 0.5 is higher than 85%, indicating that the experimental data is relatively stable; the proportion of substances with QC sample CV value less than 0.3 is higher than 75%, indicating that the experimental data is very stable.
  • the change of the CV value of the internal standard L-phenylalanine during the detection process was monitored, and the change of the CV value of the internal standard was less than 20%, indicating that the stability of the instrument during the detection process was good.
  • the peak area integration data were imported into SIMCA software (Version 14.1, Sweden) for multivariate statistical analysis.
  • OPLS-DA orthogonal-partial least squares discriminant
  • the metabolites with greater contribution VIP>1.0
  • the black points are the metabolites with VIP>1.0
  • the gray marked points are the metabolites with VIP ⁇ 1.0.
  • T-test test set p ⁇ 0.05 as the standard of significant difference.
  • differential metabolites with VIP>1.0 and p ⁇ 0.05 were screened out, which may be potential metabolic biomarkers for the diagnosis of benign and malignant pulmonary nodules.
  • the potential metabolic markers of benign and malignant pulmonary nodules screened by the above analysis the molecular mass and molecular formula of the markers were estimated according to their retention time, primary and secondary mass spectra, and compared with the spectral information in the metabolite spectral database , so as to qualitatively identify the metabolites. Finally, the structures of metabolic markers were verified by purchasing standard products and comparing their molecular weights, chromatographic retention times, and corresponding multilevel MS fragmentation profiles.
  • the receiver operating characteristic curve was used to analyze the diagnostic performance of metabolites for benign and malignant pulmonary nodules.
  • the results showed that 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamine Acid, asparagine, formononetin, alanine, and kynuric acid, which are 12 differential metabolites, have a strong ability to distinguish benign and malignant pulmonary nodules individually, and the areas under the ROC curve (AUC) are all greater than 0.6. It has clinical diagnostic significance.
  • a further preferred combination of metabolic markers is: 4-hydroxy-L-proline, hyodeoxycholic acid, and xanthine, to construct a diagnostic model for distinguishing benign and malignant pulmonary nodules.
  • the AUC value of these three metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.803. Under the optimal cutoff value, the sensitivity and specificity were 79.5% and 78.3%, respectively.
  • a further preferred combination of metabolic markers is: 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, Construct a model for diagnosing benign and malignant pulmonary nodules.
  • the AUC value of these 6 metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.822. Under the optimal cutoff value, the sensitivity and specificity were 80.0% and 78.8%, respectively.
  • a further preferred combination of metabolic markers is: 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, Guanine, glutamic acid, and asparagine were used to construct a model for diagnosing benign and malignant pulmonary nodules.
  • the AUC value of these 9 metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.836. Under the optimal cutoff value, the sensitivity and specificity were 81.4% and 80.2%, respectively.
  • the research objects of this example included 70 serum samples of patients with benign pulmonary nodules and 71 serum samples of malignant pulmonary nodules from 2 independent medical centers. All patients with benign and malignant pulmonary nodules had no other history of malignant tumors, other major systemic diseases, and no chronic medical history of long-term medication. The time of blood collection was in the morning on an empty stomach. All serum samples were centrifuged and stored in a -80°C refrigerator. During the study, serum samples were taken out and thawed for subsequent analysis.
  • the detection conditions and data analysis methods of this example are the same as those of Example 1, and the differential metabolites detected and analyzed are the following 12 kinds: 4-hydroxy-L-proline, hyodeoxycholic acid, xanthine, 2-hydroxy- 6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, guanine, glutamic acid, asparagine, formononetin, alanine, kynuric acid, for benign and malignant lungs Nodule diagnosis.
  • the above 12 metabolic markers have significant changes in patients with pulmonary malignant nodules, and the specific information is shown in Table 7:
  • the AUC is further improved, and the AUC value of the 12 combined diagnosis of benign and malignant pulmonary nodules reaches 0.881.
  • the sensitivity and specificity are 86.1% and 86.9 respectively %. See Table 8 and Table 9 for the AUC of single and any combination of 2-11 metabolites for diagnosis:
  • the AUC value of these three metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.815. Under the optimal cutoff value, the sensitivity and specificity were 80.2% and 78.8%, respectively.
  • metabolic markers 4-hydroxy-L-proline, xanthine, hyodeoxycholic acid, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine to construct a diagnosis Distinguish between benign and malignant pulmonary nodule models.
  • the AUC value of these 6 metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.833. Under the optimal cutoff value, the sensitivity and specificity were 81.5% and 80.6%, respectively.
  • a further preferred combination of metabolic markers is: 4-hydroxy-L-proline, xanthine, hyodeoxycholic acid, 2-hydroxy-6-aminopurine, 5,6-dihydrothymine, isobutyrylcarnitine, Guanine, glutamic acid and formononetin were used to construct a model for diagnosing benign and malignant pulmonary nodules.
  • the AUC value of these 9 metabolites combined to diagnose benign and malignant pulmonary nodules reached 0.847. Under the optimal cutoff value, the sensitivity and specificity were 82.1% and 81.8%, respectively.
  • This embodiment provides a detection kit, comprising:
  • 50% acetonitrile in water can be used as a solvent to dissolve the standards.

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Abstract

La présente invention concerne un marqueur métabolique permettant de diagnostiquer des nodules pulmonaires bénins et malins et son utilisation. Le marqueur métabolique est un ou une combinaison de plus d'un élément choisi parmi la 4-hydroxy-L-proline, l'acide hyodésoxycholique, la xanthine, la 2-hydroxy-6-aminopurine, la 5,6-dihydrothymine, l'isobutyryl carnitine, la guanine, l'acide glutamique, l'asparagine, la formononétine, l'alanine et l'acide kynurique. Les 12 marqueurs métaboliques selon la présente invention permettent de diagnostiquer et de distinguer avec précision les nodules pulmonaires bénins et malins, présentent une sensibilité et une spécificité élevées, peuvent remplacer entièrement les modes de diagnostic par biopsie tissulaire et par imagerie existants, réduisent les risques de traumatisme et de radiation, et présentent une valeur en termes d'utilisation clinique et de popularisation.
PCT/CN2022/118713 2021-11-09 2022-09-14 Marqueur métabolique sérique pour le diagnostic des nodules pulmonaires bénins et malins et son utilisation WO2023082821A1 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116338210A (zh) * 2023-05-22 2023-06-27 天津云检医学检验所有限公司 用于诊断原发性中枢神经系统淋巴瘤的生物标志物及检测试剂盒

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113960312A (zh) * 2021-11-09 2022-01-21 上海市第一人民医院 用于肺部良恶性结节诊断的血清代谢标志物及其应用
CN114778656B (zh) * 2022-03-29 2023-02-14 浙江苏可安药业有限公司 用于检测耐药性肺结核的血清代谢标志物及其试剂盒
CN114689754B (zh) * 2022-03-31 2023-09-12 广东省结核病控制中心 一种与肺结核相关的血清代谢标志物及其应用
CN115201375A (zh) * 2022-07-15 2022-10-18 佛山市南海区第四人民医院(佛山市南海区西樵人民医院) 一种用于鉴别、诊断和/或筛查潜伏感染肺结核的血清代谢标志物及其应用

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101806805A (zh) * 2010-03-16 2010-08-18 华中师范大学 一组用于肺癌患者的血清代谢生物标志物
CN105021804A (zh) * 2014-04-30 2015-11-04 湖州市中心医院 肺癌代谢标志物在肺癌诊断和治疗中的用途
CN108414660A (zh) * 2018-03-08 2018-08-17 中国药科大学 一组与肺癌早期诊断相关的血浆代谢小分子标志物及其应用
CN112782403A (zh) * 2019-11-06 2021-05-11 中国科学院大连化学物理研究所 一种组合物及应用和诊断试剂盒
CN113267586A (zh) * 2021-04-30 2021-08-17 上海交通大学医学院 嘌呤代谢标志物在制备肺癌分子靶向药物获得性耐药筛查和诊断试剂中的应用
CN113960312A (zh) * 2021-11-09 2022-01-21 上海市第一人民医院 用于肺部良恶性结节诊断的血清代谢标志物及其应用

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101806805A (zh) * 2010-03-16 2010-08-18 华中师范大学 一组用于肺癌患者的血清代谢生物标志物
CN105021804A (zh) * 2014-04-30 2015-11-04 湖州市中心医院 肺癌代谢标志物在肺癌诊断和治疗中的用途
CN108414660A (zh) * 2018-03-08 2018-08-17 中国药科大学 一组与肺癌早期诊断相关的血浆代谢小分子标志物及其应用
CN112782403A (zh) * 2019-11-06 2021-05-11 中国科学院大连化学物理研究所 一种组合物及应用和诊断试剂盒
CN113267586A (zh) * 2021-04-30 2021-08-17 上海交通大学医学院 嘌呤代谢标志物在制备肺癌分子靶向药物获得性耐药筛查和诊断试剂中的应用
CN113960312A (zh) * 2021-11-09 2022-01-21 上海市第一人民医院 用于肺部良恶性结节诊断的血清代谢标志物及其应用

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
HUANG HUAI, SHI LI-YING, WEI LI-LIANG, HAN YU-SHUAI, YI WEN-JING, PAN ZHI-WEN, JIANG TING-TING, CHEN JING, TU HUI-HUI, LI ZHI-BIN,: "Plasma metabolites Xanthine, 4-Pyridoxate, and d-glutamic acid as novel potential biomarkers for pulmonary tuberculosis", CLINICA CHIMICA ACTA, ELSEVIER BV, AMSTERDAM, NL, vol. 498, 1 November 2019 (2019-11-01), AMSTERDAM, NL , pages 135 - 142, XP093066427, ISSN: 0009-8981, DOI: 10.1016/j.cca.2019.08.017 *
REN YAN; ZHAO JUANJUAN; SHI YANAN; CHEN CAIYUN; CHEN XIANGMING; LV CHANGJUN: "Simple determination of L-hydroxyproline in idiopathic pulmonary fibrosis lung tissues of rats using non-extractive high-performance liquid chromatography coupled with fluorescence detection after pre-column derivatization with novel synthetic 9-acetylimidazol-carbazole", JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS, ELSEVIER B.V., AMSTERDAM, NL, vol. 142, 23 April 2017 (2017-04-23), AMSTERDAM, NL , pages 1 - 6, XP085046980, ISSN: 0731-7085, DOI: 10.1016/j.jpba.2017.04.033 *

Cited By (2)

* Cited by examiner, † Cited by third party
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CN116338210A (zh) * 2023-05-22 2023-06-27 天津云检医学检验所有限公司 用于诊断原发性中枢神经系统淋巴瘤的生物标志物及检测试剂盒
CN116338210B (zh) * 2023-05-22 2023-08-11 天津云检医学检验所有限公司 用于诊断原发性中枢神经系统淋巴瘤的生物标志物及检测试剂盒

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