Metabolomics-based diagnostic markers for pancreatic cancer and its screening methods and applications

A diagnostic marker and pancreatic cancer technology, applied in the field of clinical examination and diagnosis, can solve the problems of high detection sensitivity, many data features, and huge data volume, and achieve high sensitivity, simple construction method, and good specificity

Active Publication Date: 2022-05-13
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

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  • Metabolomics-based diagnostic markers for pancreatic cancer and its screening methods and applications
  • Metabolomics-based diagnostic markers for pancreatic cancer and its screening methods and applications
  • Metabolomics-based diagnostic markers for pancreatic cancer and its screening methods and applications

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0062] Example 1: Screening of pancreatic cancer diagnostic markers

[0063] 1. Research object

[0064] A total of 333 plasma samples from patients with pancreatic cancer and 262 plasma samples from normal healthy controls were included in this study from 4 independent medical centers. The diagnostic criteria for pancreatic cancer were pancreatic ductal adenocarcinoma confirmed by postoperative pathology.

[0065] 2. Plasma non-targeted metabolomics analysis using liquid chromatography-mass spectrometry

[0066] 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-mass spectrometry to obtain the original metabolic fingerprint of the sample including chromatographic and mass spectrometric information. The specific operation is as follows:

[0067] 2.1 Instruments and reagents

[0068] Experime...

Embodiment 2

[0097] Example 2: Construction of a pancreatic cancer diagnostic model using 19 plasma metabolic markers

[0098] 1. Research object

[0099] This study included 333 plasma samples from pancreatic cancer patients and 262 healthy controls with normal physical examination from 4 independent medical centers, which were from the same source as the characteristic screening samples (595 cases). Among them, 495 cases of pancreatic cancer patients and healthy controls were used for the training set, and 100 cases were used for the test set. Among them, the diagnostic standard of pancreatic cancer is the presence of single or multiple pancreatic cancers with a diameter of less than 3 cm confirmed by imaging examination and tissue biopsy.

[0100] 2. Plasma Targeted Metabolomics Analysis Using Liquid Chromatography-Mass Spectrometry

[0101] All plasma samples were centrifuged and stored in a -80°C refrigerator. Plasma samples were taken out during the research, and after sample pret...

Embodiment 3

[0128] Example 3: Construction of a pancreatic cancer diagnostic model using 17 plasma metabolic markers

[0129] The research objects and detection and analysis methods of this embodiment are the same as those of Example 2, except that 17 plasma metabolic markers (including lysophosphatidylcholine LPC 14:0, lysophosphatidylcholine 14:0, lysophosphatidylcholine Alkaline LPC 16:0, Lysophosphatidylcholine LPC 18:1, Lysophosphatidylcholine LPC 20:4, Phosphatidylcholine PC 16:0-16:0, Phosphatidylcholine PC 16:0-18: 1. Phosphatidylcholine PC 18:0-18:2, Phosphatidylcholine PC 18:0-20:3, Phosphatidylcholine PC16:0-22:5, Phosphatidylcholine PC 18:0-22 :5, Phosphatidylcholine PC O-16:0-18:2, Lysophosphatidylethanolamine LPE22:4, Phosphatidylethanolamine PE 16:0-18:2, Sphingomyelin SM d18:1 / 18:0, Sphingomyelin Phospholipids SM d18:2 / 24:1, sphingomyelin SM d18:2 / 24:2, diglycerides DG 18:1-18:1) for machine learning and modeling, the sensitivity of the obtained model (sensitivity), spec...

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Abstract

The invention discloses a metabolomics-based diagnostic marker for pancreatic cancer and a screening method thereof. The diagnostic marker comprises any one or a combination of 31 plasma metabolic markers. The present invention also provides a method for constructing a diagnostic model using the pancreatic cancer diagnostic marker and its application in a diagnostic kit. The present invention conducts non-target metabolomics analysis on patient plasma through high-performance liquid chromatography-mass spectrometry technology, discovers differential metabolites between pancreatic cancer patients and normal people through artificial intelligence data analysis technology, and further analyzes target metabolomics and Machine learning modeling verifies the diagnostic ability of the specific differential metabolites, namely pancreatic cancer diagnostic markers, in the diagnosis of pancreatic cancer.

Description

technical field [0001] The invention belongs to the field of clinical examination and diagnosis, and specifically relates to a diagnostic marker for pancreatic cancer based on metabolomics and machine learning analysis technology, a screening method for the diagnostic marker, a method for constructing a diagnostic model using the diagnostic marker, and the diagnostic method. Application of markers in the diagnosis of pancreatic cancer. Background technique [0002] Pancreatic cancer (pancreatic cancer) is a malignant disease of the digestive tract with a high degree of malignancy and is difficult to diagnose and treat. Its incidence has been rising rapidly in recent years. According to the national cancer statistics released by the National Cancer Center of China in January 2019, pancreatic cancer ranks tenth in the incidence of malignant tumors in my country, and ranks seventh in the mortality rate. The current status of diagnosis and treatment is not optimistic. Long-term...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N30/86
CPCG01N30/8686
Inventor 尹玉新王光熙庞瑞芳
Owner 北京博远精准医疗科技有限公司
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