Pancreatic cancer prognosis risk prediction method and device established based on metabolic genes

A technology for risk prediction and pancreatic cancer, applied in the fields of instrumentation, hybridization, bioinformatics, etc., can solve problems that are not yet known, and achieve stable prediction performance, good prognostic effect, and strong robustness.

Pending Publication Date: 2022-04-19
FUDAN UNIV SHANGHAI CANCER CENT
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

However, whether the gene expression profiles or their characteristics of energy metabolism-related genes are correlated with prognosis, and whether pancreatic cancer can be molecularly subtyped based on these gene expression profiles remains unknown.

Method used

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  • Pancreatic cancer prognosis risk prediction method and device established based on metabolic genes
  • Pancreatic cancer prognosis risk prediction method and device established based on metabolic genes
  • Pancreatic cancer prognosis risk prediction method and device established based on metabolic genes

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Embodiment 1

[0038] Such asfigure 1 As shown, this embodiment provides a method for predicting the prognosis risk of pancreatic cancer based on metabolic genes, including the following steps:

[0039] S1: Acquire GJB5 gene expression level data, MET gene expression level data, TMEM139 gene expression level data and AFF3 gene expression level data;

[0040] S2: Calculate the prognostic risk score of pancreatic cancer patients according to the data of GJB5 gene expression level, MET gene expression level data, TMEM139 gene expression level data and AFF3 gene expression level data.

[0041] Specifically, the calculation expression of the prognostic risk score for patients with pancreatic cancer is:

[0042] RiskScore 4 =-0.1513*exp AFF3 +0.0156*exp GJB5 +0.0045*exp MET +0.0164*exp TMEM139

[0043] In the formula, RiskScore 4 For the prognostic risk score of pancreatic cancer patients, exp AFF3 is the result of AFF3 gene expression level based on the natural constant e, exp GJB5 is th...

Embodiment approach

[0048] As a preferred embodiment, the method also includes:

[0049] S3: Obtain age data, gender data, T staging data, N staging data, M staging data, TNM staging data and pathological grading data;

[0050] S4: Combine age data, gender data, T stage data, N stage data, M stage data, TNM stage data and pathological grade data with the calculated prognostic risk score of pancreatic cancer patients to comprehensively calculate the final prognosis risk of pancreatic cancer patients score.

[0051] Further, as an optional implementation, according to age data, gender data, T stage data, N stage data, M stage data, TNM stage data, pathological grading data and the calculated prognostic risk score of pancreatic cancer patients, A nomogram was constructed to obtain the final prognostic risk score for patients with pancreatic cancer.

[0052] This embodiment also provides a pancreatic cancer prognosis risk prediction device based on metabolic genes, including:

[0053] The data acq...

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Abstract

The invention relates to a pancreatic cancer prognosis risk prediction method and device established based on metabolic genes, and the method comprises the following steps: obtaining GJB5 gene expression level data, MET gene expression level data, TMEM139 gene expression level data and AFF3 gene expression level data, and calculating a prognosis risk score of a pancreatic cancer patient. Compared with the prior art, the four-gene model provided by the invention has relatively strong robustness, can exert stable prediction efficiency in data sets of different platforms, and has the advantages of excellent efficiency, small number of detected genes and the like.

Description

technical field [0001] The invention relates to the technical field of gene detection, in particular to a method and device for predicting the prognosis risk of pancreatic cancer based on metabolic genes. Background technique [0002] Pancreatic cancer is one of the deadliest malignancies, and according to GLOBOCAN 2018, it has caused 459,000 deaths and 432,000 deaths worldwide. Current understanding of the complex genetic and epigenetic alterations and their correlation with the microenvironment has not resulted in a qualitative leap in patient survival. Substantial efforts are also needed to further explore the pathogenesis and progression of the disease and to identify biomarkers for early detection and risk assessment that could translate into multiple treatment options. [0003] Metabolic reprogramming of cells plays an integral role in tumorigenesis, both as a direct and indirect consequence of oncogenic alterations. Metabolic reprogramming enables tumor cells to gen...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/30G16B25/00
CPCG16H50/30G16B25/00
Inventor 王鑫许蜜蝶盛伟琪谭聪孙慧王旭黄丹王磊倪淑娟翁微微张萌
Owner FUDAN UNIV SHANGHAI CANCER CENT
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