Biomarker for early diagnosis of primary hepatocellular carcinoma as well as detection reagent and application of biomarker

A biomarker and hepatocellular carcinoma technology, applied in the field of biomarkers and detection reagents for the early diagnosis of primary hepatocellular carcinoma, can solve the problem of low diagnostic specificity of alpha-fetoprotein, delay in treatment timing, and elevated levels of alpha-fetoprotein question

Active Publication Date: 2021-06-04
THE FIRST AFFILIATED HOSPITAL OF GUANGXI MEDICAL UNIV +1
View PDF1 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, clinically, about 30% of patients with liver cancer do not have elevated levels of alpha-fetoprotein. It can be seen that the diagnostic specificity of alpha-fetoprotein is not high, and it is easy to delay the timing of treatment for patients.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Biomarker for early diagnosis of primary hepatocellular carcinoma as well as detection reagent and application of biomarker
  • Biomarker for early diagnosis of primary hepatocellular carcinoma as well as detection reagent and application of biomarker
  • Biomarker for early diagnosis of primary hepatocellular carcinoma as well as detection reagent and application of biomarker

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0062] Screening of intestinal biomarkers for early liver cancer

[0063] 1. DNA extraction of intestinal bacteria

[0064] Stool samples from 141 patients with early-stage liver cancer and 194 stool samples from healthy people were collected from Guangxi to screen for early-stage liver cancer intestinal biomarkers. The specific steps are as follows:

[0065] 1: Aliquot the above stool samples and freeze them at -80°C;

[0066] 2: Extract the DNA of fecal bacteria from liver cancer patients and healthy people mentioned in S1;

[0067] 3: Perform 16S rRNA amplification and library construction on fecal bacterial DNA, the specific steps are as follows:

[0068] S1. Nucleic acid extraction (Surbiopure Fecal Nucleic Acid Extraction Kit (magnetic bead method), Guangzhou Surbiopure Biotechnology Co., Ltd.).

[0069] S2. Take 0.25g of feces sample (set a blank control and MOCK control at the same time) and add it to the Dry beads Tube, add 900μl S1-Lysis Enhancer, vortex thoroughl...

Embodiment 2

[0091] The ability of a predictive model using biomarkers to distinguish early-stage liver cancer patients from healthy controls

[0092] It is expected to establish a random forest model that can specifically identify early liver cancer and healthy population samples through the 15 intestinal biomarkers found above. The feces of 113 cases of early liver cancer and 155 cases of healthy controls were collected, and the same processing method as in Example 1 was adopted, and the grid search method was used to adjust the model training parameters and ten times of cross-validation were performed on the 15 intestinal organisms of the 268 cases of feces. Marker training random forest model, specifically setting random forest model parameter gradient n_estimators=[100,500,1000]; max_depth=[1,2,3,7,9]; max_features=["log2","sqrt"]. Use the python software sklearn.model_selection.GridSearchCV module to search for the best parameters, a total of 3*5*2=30 parameter combinations, each par...

Embodiment 3

[0106] Evaluating the diagnostic ability of biomarkers to establish a predictive model for early-stage liver cancer patients and healthy controls

[0107]The isolation set data (including 28 early-stage liver cancer patients and 39 healthy people) separated from model training was used to verify the accuracy of the trained random forest model for early diagnosis of liver cancer. Using the same processing method as in Example 1, it is to be verified that the screened biomarkers and the trained random forest model can be used for early diagnosis of liver cancer.

[0108] Results: The results of the confusion matrix are shown in Table 5. The regression result of the test has a precision of 0.97, a recall rate of 1.0, and a specificity of 0.98. It can be seen that the biomarker, random forest training model and detection method of the present invention can be used for early diagnosis of liver cancer.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a biomarker for early diagnosis of primary hepatocellular carcinoma as well as a detection reagent and application of biomarker, and belongs to the technical field of liver cancer diagnosis. According to the invention, bacterial DNA is extracted from an excrement sample of a detected object by a 16S rRNA sequencing method; then, Illumina Miseq sequencing is carried out; intestinal biomarkers for early liver cancer and healthy control are identified; furthermore, a random forest model for specifically identifying early liver cancer and healthy population samples is established through the biomarkers; a fitting condition of a learning curve evaluation model is drawn; the effect of the model is evaluated through a receiver operating characteristic curve; the area under the curve of the prediction model is 0.95; the precision of the test result is 0.97; the recall rate is 1.0; and the specificity is 0.98. Therefore, the biomarker, the built random forest training model and the detection method in the invention have a good market prospect in early diagnosis of liver cancer.

Description

technical field [0001] The invention belongs to the technical field of liver cancer diagnosis, and in particular relates to a biomarker for early diagnosis of primary hepatocellular carcinoma, a detection reagent and application thereof. Background technique [0002] Liver cancer is the sixth most common malignancy in the world. There are many causes of liver cancer, the most common being persistent infection of hepatitis B virus and hepatitis C virus; there is also liver cirrhosis caused by alcohol and drug factors, which can gradually transform into liver cancer; in addition, liver cancer is also related to genetic and mental factors. [0003] The clinical picture of liver cancer (especially primary liver cancer) is very atypical, and its symptoms are generally not obvious, especially in the early stage of the disease, about 70% of patients have no obvious symptoms when the tumor size is less than 5cm. Once the symptoms appear, it means that the tumor is already large, an...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): C12Q1/6886C12Q1/689C12N15/11C12Q1/10C12Q1/14G16H50/20G16B40/00C12R1/19C12R1/46C12R1/01
CPCC12Q1/6886C12Q1/689G16H50/20G16B40/00
Inventor 彭涛陈晓东王向坤庞世福洪志帆
Owner THE FIRST AFFILIATED HOSPITAL OF GUANGXI MEDICAL UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap