Lung cancer early diagnostic marker based on metabonomics and artificial intelligence technology and application thereof

A technology of diagnostic markers and metabolic markers, applied in the field of early diagnostic markers for lung cancer, can solve the problems of high detection sensitivity, multiple data features, and huge data volume, and achieve high sensitivity, strong universality, and simple and fast methods Effect

Active Publication Date: 2019-06-14
北京博远精准医疗科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, a bottleneck in the application of metabolomics technology to discover biomarkers lies in its high detection sensitivity, many data features, and a large amount of data. The traditional principal component analysis method will ignore many factors that have a certain impact on the distinction between the two types of samples in order to reduce the number of features. feature

Method used

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  • Lung cancer early diagnostic marker based on metabonomics and artificial intelligence technology and application thereof
  • Lung cancer early diagnostic marker based on metabonomics and artificial intelligence technology and application thereof
  • Lung cancer early diagnostic marker based on metabonomics and artificial intelligence technology and application thereof

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0063] Example 1: Screening of markers for early diagnosis of lung cancer

[0064] 1. Research object

[0065] A total of 171 plasma samples from patients with early lung cancer and 140 plasma samples from normal healthy controls were included in this study. Among them, the diagnostic standard of early lung cancer is a single lung cancer with a diameter of less than 3 cm confirmed by imaging examination and tissue biopsy. The basic information of these research subjects can be seen in Table 1.

[0066] Table 1. Baseline and pathological characteristics of non-targeted metabolomics studies for early diagnosis of lung cancer

[0067]

[0068] 2. Plasma non-targeted metabolomics analysis using liquid chromatography-mass spectrometry

[0069] All plasma samples were centrifuged and stored in a -80°C refrigerator. Plasma samples were taken out during the research, and after sample pretreatment, metabolomics analysis was performed using high-performance liquid chromatography-...

Embodiment 2

[0101] Example 2: Construction of an early diagnosis model of lung cancer using 9 plasma metabolic markers

[0102] 1. Research object

[0103] A total of 449 plasma samples from early lung cancer patients and 243 healthy controls with normal physical examination were included in this study. The 350 lung cancer patients and 203 healthy controls used in the training set were from the same source as the feature screening samples (311 cases), and the 99 lung cancer patients and 40 healthy controls used in the test set came from two independent third-party hospitals. Among them, the diagnostic standard of lung cancer is the existence of single or multiple lung cancers with a diameter of less than 3 cm confirmed by imaging examination and tissue biopsy. The basic information of these research objects is shown in Table 3 and Table 4.

[0104] Table 3. Baseline and pathological characteristics of the subjects in the training set in the targeted metabolomics study of early diagnosis...

Embodiment 3

[0136] Example 3: Construction of an early diagnosis model of lung cancer using 8 plasma metabolic markers

[0137] The research objects and detection and analysis methods of this embodiment are the same as those of Example 2, except that 8 plasma metabolic markers (including lysophosphatidylcholine LPC 16:0, lysophosphatidylcholine 16:0, lysophosphatidylcholine Alkaline LPC 18:0, Lysophosphatidylcholine LPC 20:4, Phosphatidylcholine PC 16:0-18:1, Phosphatidylcholine PC 16:0-18:2, Phosphatidylcholine PC 18:0 -18:1, phosphatidylcholine PC 18:0-18:2, phosphatidylcholine PC 16:0-22:6) two-dimensional matrix data for machine learning and modeling, the sensitivity of the obtained model (sensitivity ), specificity, accuracy and AUC values ​​are shown in Table 6. It can be seen that the constructed diagnostic model has high sensitivity, specificity, accuracy and area under the ROC curve AUC value for early lung cancer.

[0138] Table 6. Classification performance of the early lung c...

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Abstract

The invention discloses a lung cancer early diagnostic marker based on metabonomics and a screening method thereof. The diagnostic marker comprises any one or combination of various markers in 25 plasma metabolism markers. The invention also provides a method for establishing a diagnostic model through utilization of the lung cancer early diagnostic marker and application of the lung cancer earlydiagnostic marker in a diagnostic kit. According to the lung cancer early diagnostic marker, the methods and the application, through utilization of a high performance liquid chromatography-mass spectrometry, non-target metabonomics analysis is carried out on plasma of patients; through utilization of an artificial intelligence data analysis technology, different metabolite between lung cancer patients and normal people is discovered; and further through target metabonomics analysis and machine learning modeling, diagnostic capability of specificity different metabolite, namely, the lung cancer early diagnostic marker in lung cancer early diagnosis is verified.

Description

technical field [0001] The invention belongs to the field of clinical examination and diagnosis, and specifically relates to early diagnostic markers for lung cancer based on metabolomics and artificial intelligence analysis technology, a screening method for the diagnostic markers, a method for constructing a diagnostic model using the diagnostic markers, and the diagnostic method. Application of markers in early diagnosis of lung cancer. Background technique [0002] Lung cancer is one of the most threatening malignant tumors to human health and life. According to the national cancer statistics released by the National Cancer Center of China in February 2018, lung cancer is the malignant tumor with the highest incidence and mortality in my country. Common pathogenic factors of lung cancer include smoking, occupational and environmental exposure, ionizing radiation, previous chronic lung infection, air pollution, and heredity. The early symptoms of lung cancer are not obv...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/574G01N30/02G16H50/20G06K9/62
Inventor 尹玉新王光熙周骏拓
Owner 北京博远精准医疗科技有限公司
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