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Gastric cancer metabolism gene prognosis prediction method and device

A prediction method and prediction device technology, which is applied in character and pattern recognition, instruments, calculations, etc., can solve the problems of large number of genes and lack of further exploration of the metabolic characteristics of gastric cancer, and achieve strong robustness and stable prediction performance , the effect of good prognosis

Pending Publication Date: 2022-05-13
FUDAN UNIV SHANGHAI CANCER CENT
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Problems solved by technology

[0005] The program calculates the number of samples of genes with SNV in the peritoneal transfer and non-peritoneal transfer groups, and obtains the proportion of the positive rate of each gene in the two groups, and uses the hypothesis test to statistically screen out the significant difference in the positive rate between the two groups ( That is, p<0.05) genes, 22 genes for predicting peritoneal metastasis of gastric cancer were obtained, but the number of genes selected by this scheme is still too large, and the metabolic characteristics of gastric cancer have not been explored in depth

Method used

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  • Gastric cancer metabolism gene prognosis prediction method and device
  • Gastric cancer metabolism gene prognosis prediction method and device
  • Gastric cancer metabolism gene prognosis prediction method and device

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Embodiment 1

[0042] Such as figure 1 As shown, this embodiment provides a method for predicting the prognosis of gastric cancer metabolic genes, comprising the following steps:

[0043] S1: Obtain the sample to be tested, and detect the RNA expression levels of multiple genes, including DYNC1I1 gene, GPER1 gene, MFAP2 gene, ARRB1 gene, C3 gene and GLI1 gene;

[0044] S2: Calculate the gastric cancer prognosis risk score according to the detected expression levels of multiple genes.

[0045] Most preferably, the calculation expression of the gastric cancer prognosis risk score is:

[0046] RiskScore 6 =0.38585*exp DYNC1I1 +0.10411*exp GPER1 +0.04476*exp MFAP2 -0.70386*exp ARRB1 +0.09187*exp C3 +0.21797*exp GLI1

[0047] In the formula, RiskScore 6 is the prognostic risk score for gastric cancer, exp DYNC1I1 is the expression level result of DYNC1I1 gene based on the natural constant e, exp GPER1 is the expression level result of GPER1 gene based on the natural constant e, exp M...

Embodiment approach

[0048] As an optional implementation, the method further includes: loading the detected expression levels of multiple genes into a pre-established and trained classifier, calculating the gastric cancer prognostic risk score, and calculating the gastric cancer prognostic risk score greater than the preset The samples to be tested at the risk threshold are divided into a high-risk group, otherwise they are divided into a low-risk group.

[0049] As a preferred embodiment, the method also includes:

[0050] In this example, calculate the RiskScore 6 After that, the RiskScore 6 Perform Z-score transformation into Risk Score (data standardization), and divide samples with Risk Score greater than zero into the high-risk group, and samples less than zero into the low-risk group.

[0051] This embodiment also provides a gastric cancer metabolic gene prognosis prediction device, including:

[0052] The data acquisition module is configured to: acquire the expression levels of multip...

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Abstract

The invention relates to a gastric cancer metabolism gene prognosis prediction method and device, and the method comprises the following steps: obtaining genome RNA of a sample to be detected, and detecting expression levels of a plurality of genes, the plurality of genes including a DYNC1I1 gene, a GPER1 gene, an MFAP2 gene, an ARRB1 gene, a C3 gene and a GLI1 gene; and calculating a gastric cancer prognosis risk score according to the detected expression levels of the plurality of genes. Compared with the prior art, the clinical prognosis state of the gastric cancer patient is predicted through metabolism-related genes DYNC1I1, GPER1, MFAP2, ARRB1, C3 and GLI1 for the first time, and the kit has the advantages of being excellent in efficiency, small in number of detected genes, high in robustness 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 of gastric cancer metabolic genes. Background technique [0002] Gastric cancer is one of the most common malignant tumors of the digestive system. In recent decades, due to the adoption of effective preventive measures and early diagnosis strategies, the incidence of gastric cancer has gradually decreased in some regions. However, inoperable gastric cancer cases diagnosed at a late stage still have a poor prognosis. According to GLOBOCAN 2018 data, gastric cancer ranks third in global cancer mortality, after lung cancer and colorectal cancer. Therefore, for more individualized management, there is still an urgent need to accurately predict the clinical outcome of gastric cancer patients. [0003] Reprogrammed metabolic patterns have long been considered a hallmark of cancer. Compared with normal cells, tumor cells can a...

Claims

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Application Information

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IPC IPC(8): G16B40/00G16B25/10G06K9/62
CPCG16B40/00G16B25/10G06F18/231G06F18/24
Inventor 王鑫许蜜蝶盛伟琪常瑾嘉谭聪
Owner FUDAN UNIV SHANGHAI CANCER CENT
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