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A prognostic early warning system for esophageal squamous cell carcinoma and its application

A technology of esophageal squamous cell carcinoma and early warning system, applied in the field of bioinformatics, can solve limitations and other problems, and achieve the effect of accurate prognosis and staging

Active Publication Date: 2020-11-10
THE FIRST AFFILIATED HOSPITAL OF HENAN UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, at present, many researchers only consider the influence of genes or clinical factors on the occurrence and development of esophageal cancer, and the prediction models established in this way have limitations.

Method used

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  • A prognostic early warning system for esophageal squamous cell carcinoma and its application
  • A prognostic early warning system for esophageal squamous cell carcinoma and its application
  • A prognostic early warning system for esophageal squamous cell carcinoma and its application

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0057] Example 1 Mining of gene markers in esophageal squamous cell carcinoma

[0058] 1. Experimental method

[0059] 1.1 EdgeR algorithm to find differential genes

[0060] First, annotate the GSE53625 raw data (chip data) to obtain the gene ID and gene expression matrix (complete gene expression value), then use the edgeR package of R software to screen differential genes, and finally use the surv_cutpoint() function of the survminer package Determine the cutoff value of the gene ( figure 2 and image 3 ), the expression value higher than the cutoff value is high expression, and the expression value lower than the cutoff value is low expression. The specific screening steps for differential genes are as follows:

[0061] (1) Build a DGEList object

[0062] According to the gene expression matrix and sample grouping information, construct the DGEList object, the specific command is:

[0063] dgelist<-DGEList(counts=targets, group=group)

[0064] (2) Filter low-expres...

Embodiment 2

[0101] Example 2 Single factor analysis of clinicopathological characteristics

[0102] 1. Experimental method

[0103] We initially identified clinical features previously shown to be associated with survival and considered these as candidate features: age, sex, smoking, alcohol consumption, tumor invasion, tumor grade, T stage, N stage, TNM stage, arrhythmia, pneumonia, Anastomotic leakage, adjuvant therapy. For each factor, Cox single factor analysis was used to analyze the training samples and internal test samples, and the p-values ​​were all less than 0.05 were selected. Due to the certain repeatability of the N stage and the TNM stage, the N stage was eliminated. In this way, only age and TNM stage are left as the clinicopathological characteristic factors.

[0104] 2. Result analysis

[0105] 2.1 Clinicopathological features of patients

[0106] The characteristics of the patients in the training sample and the internal test sample are shown in Table 4.

[0107] 2...

Embodiment 3

[0112] Example 3 Construction and Verification of Nomogram

[0113] 1. Experimental method

[0114] 1.1 Cox multivariate analysis

[0115] Example 1 The transcriptome sequencing data of 179 ESCC patients of GSE53625 were processed by edgeR and rbsure algorithm, and then the differential genes were calculated and then subjected to dimensionality reduction processing of associated survival data, Cox univariate and multivariate analysis, and the prognosis was obtained Directly related gene sets; poor prognosis clinicopathological characteristics obtained by Cox univariate analysis of clinical characteristic factors in Example 2.

[0116] In this example, on the basis of Example 1 and Example 2, the above factors were added to the established Nomogram model, and Cox multivariate analysis was performed on the gene set and clinicopathological characteristics, and further verification showed that age (p=0.031), age (p=0.031), TNM stage (p=0.004), PDZK1IP1 expression value (p=0.001)...

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Abstract

The invention belongs to the field of biological information, and particularly relates to a prognosis early warning system for esophageal squamous cell carcinoma, and applications thereof. The prognosis early warning system for esophageal squamous cell carcinoma comprises a data input module, a model calculation module and a result output module. According to the system, the age, the TNM staging,the TM9SF1 gene expression value and the PDZK1IP1 gene expression value of a patient are used as prediction factors. Compared with the TNM staging system, the system of the invention is precise and visual in prognosis staging of esophageal squamous cell carcinoma patients, and can conveniently and visually judge the survival rates of the patients in one year, three years and four years according to the scores of various risk factors.

Description

technical field [0001] The invention belongs to the field of biological information, and in particular relates to a prognosis early warning system for esophageal squamous cell carcinoma and its application. Background technique [0002] Esophageal squamous cell carcinoma (ESCC) is the eighth most common cancer worldwide, and ESCC is characterized by high aggressiveness and poor prognosis. Despite comprehensive treatments such as surgery, radiotherapy, and chemotherapy, the 5-year survival rate of patients is still lower than 22%. Significant geographic variation suggested that environmental and genetic factors played an important role in the development of esophageal squamous cell carcinoma. Known risk factors for esophageal squamous cell carcinoma include smoking and alcohol consumption, while fruit and vegetable intake has a high likelihood of protection against esophageal squamous cell carcinoma. Currently, the TNM staging system is used to predict the prognosis of ESCC...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16H50/30G16B25/00
CPCG16B25/00G16H50/30
Inventor 高社干刘轲王艺璇许锋波齐义军
Owner THE FIRST AFFILIATED HOSPITAL OF HENAN UNIV OF SCI & TECH
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