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Construction method of hepatocellular carcinoma patient postoperative recurrence prediction model

A technology for hepatocellular carcinoma and prediction model, applied in the fields of molecular biology and medical diagnosis, which can solve the problems of poor prognosis, prone to early recurrence, and controversial early recurrence of patients.

Inactive Publication Date: 2022-04-05
PEOPLES HOSPITAL PEKING UNIV
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Problems solved by technology

In 2021, Cadoux et al. found that ULBP1 and other ligands MICA, MICB, and ULBP2 were highly expressed in liver cancer, and their expression was positively correlated with tumor grade and low levels of differentiation. High levels of MICA, MICB, ULBP1, and ULBP2 Expressed patients have a poor prognosis and are prone to early relapse
This suggests that the relationship between the expression of NKG2D ligands in HCC and early recurrence in patients is still controversial

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  • Construction method of hepatocellular carcinoma patient postoperative recurrence prediction model
  • Construction method of hepatocellular carcinoma patient postoperative recurrence prediction model
  • Construction method of hepatocellular carcinoma patient postoperative recurrence prediction model

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Embodiment 1

[0029] 1. Experimental materials and methods

[0030] 1.1 Data Collection

[0031] As of February 21, 2021, a total of 374 HCC transcriptome sequencing data and corresponding clinical data downloaded from the TCGA database (TCGA-LIHC) were used as the training cohort (https: / / portal.gdc.cancer.gov / ) . The OEP000321 dataset downloaded from NODE contains transcriptome sequencing data of 159 HCC patients and a validation cohort of corresponding clinical data (https: / / www.biosino.org / node). In addition, some liver cancer samples from the Affiliated Hospital of Guilin Medical College (Guilin cohort) were randomly collected from January 1, 2006 to December 31, 2016. These samples were used for QRT-PCR, tissue microarray construction and immunohistochemistry. Each participant signed a written informed consent. This study was approved by the Ethics Committees of the Affiliated Hospital of Guilin Medical College and Peking University People's Hospital, in compliance with the Declar...

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Abstract

The invention provides a construction method of a hepatocellular carcinoma patient postoperative recurrence prediction model. The invention systematically studies the expression and distribution of eight NKG2D ligands (MICA, MICB and ULBP1-6) in HCC, and finds that MICA, MICB and ULBP4 are highly expressed in HCC tissues, and ULBP3 possibly plays an important role in HCC progress. The result shows that the NKG2D-CAR-NK cell with high affinity can be constructed in the HCC by targeting the MICB. The RFS prediction model based on MICA, ULBP3 and ULBP5 can predict recurrence of liver cancer patients after liver resection, and possibly has potential clinical value.

Description

technical field [0001] The invention relates to the technical fields of molecular biology and medical diagnosis, in particular to a method for constructing a postoperative recurrence prediction model for patients with hepatocellular carcinoma. Background technique [0002] Hepatocellular carcinoma is one of the most common malignant tumors in the world, and its case and mortality rates are still high. Despite rapid advances in the treatment of HCC in recent years, however, there are still few prognostic diagnostic methods for this lethal disease. Currently, surgical resection has become the safest treatment for HCC due to its low intraoperative mortality. However, the high recurrence rate remains a major cause of postoperative mortality, leading to a poor overall prognosis for HCC patients. Therefore, finding new markers for predicting tumor recurrence can guide us to risk stratify patients, adopt individualized monitoring and carry out targeted intervention in clinical pr...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): C12Q1/6886C12N15/11G16H50/20G16H50/30G16B25/00G06Q10/04
Inventor 陈冬波陈红松
Owner PEOPLES HOSPITAL PEKING UNIV
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