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Liver cancer prognosis prediction model and application thereof

A liver cancer prognosis and model technology, applied in the field of biomedicine, can solve the problems of high recurrence rate, failure to clearly clarify the mechanism of liver cancer recurrence, and poor efficacy of liver cancer

Pending Publication Date: 2021-09-28
BEIJING MEDINTELL BIOMED CO LTD
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Problems solved by technology

[0004] After decades of hard work, liver cancer research has made great progress. The last century was marked by "early treatment of small liver cancer" and "two-stage resection of liver cancer after shrinkage", which contributed 10% to the improvement of the survival rate of liver cancer surgery. However, due to the rapid progression of liver cancer and the extremely high recurrence rate, the overall curative effect of liver cancer is still poor, and the overall 5-year survival rate of the liver cancer population is still only about 5%.
In recent years, although some progress has been made in the basic and clinical research of liver cancer, the mechanism of liver cancer recurrence has not been clearly elucidated, and effective intervention measures have not been found

Method used

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  • Liver cancer prognosis prediction model and application thereof
  • Liver cancer prognosis prediction model and application thereof
  • Liver cancer prognosis prediction model and application thereof

Examples

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Embodiment 1

[0061] Example 1 Screening of Liver Cancer Prognosis-Related Genes

[0062] 1. Data download

[0063] Search public gene expression data and complete clinical annotations in the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). For datasets in TCGA, RNA-sequencing data of gene expression (FPKM values) and clinical information were downloaded from UCSC Xena (https: / / gdc.xenahubs.net). FPKM values ​​were then converted to transcripts per kilobase million (TPM) values. Download the gene expression data of GSE76427 from the GEO database (http: / / www.ncbi.nlm.nih.gov / geo / ), and use the annotation file to annotate it, and take the average value of multiple probes corresponding to the same gene as its expression level, and then obtain the gene expression matrix file. Among them, the TCGA dataset is used as the discovery queue, and the GEO dataset is used as the verification queue. After removing samples with incomplete clinical information, the number of samples in...

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Abstract

The invention relates to a liver cancer prognosis prediction model and application thereof. The invention also comprises application of the reagent for detecting the expression quantity of the prognosis prediction marker in preparation of a product for evaluating prognosis survival. The built prognosis prediction model and the prognosis prediction product containing the prognosis prediction model provide effective guidance suggestions for clinicians to make treatment decisions on liver cancer patients and reduce invalid treatment, so that the treatment cost and uncomfortable experience of the patients are reduced.

Description

technical field [0001] The invention belongs to the field of biomedicine, and in particular relates to a liver cancer prognosis prediction model and its application. Background technique [0002] Prognosis refers to the empirical prediction of disease progression. Prognosis involves three main areas, what will happen, the likelihood of an adverse outcome, and when. The purpose of researching and rating prognosis is to facilitate the understanding of the degree of harm of diseases to human beings, to explore the factors affecting prognosis, and to study specific measures to improve prognosis. Prognosis analysis is a clinical research that is very practical and instructive. [0003] The liver is one of the most important organs to maintain the stability of the internal environment and the health of the body. Fatty liver, hepatitis, cirrhosis and liver cancer are the four most common serious diseases in the liver, and they are also one of the important reasons that endanger h...

Claims

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Application Information

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IPC IPC(8): G16H50/20G16H50/30
CPCG16H50/20G16H50/30
Inventor 杨承刚宋宏涛董东
Owner BEIJING MEDINTELL BIOMED CO LTD
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