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A system for predicting the prognosis of locally advanced gastric cancer

A technology for gastric cancer and prognosis, applied in the direction of measuring devices, microbiological determination/testing, instruments, etc., can solve the problems of insufficient information used in treatment plans, inability to apply clinically, and genetic characteristics are unlikely to be reproduced

Active Publication Date: 2017-11-24
NOVOMICS CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, most of the genetic traits that have been developed are unlikely to be reproduced, and insufficient information is available in selecting treatment options to be clinically applicable

Method used

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  • A system for predicting the prognosis of locally advanced gastric cancer
  • A system for predicting the prognosis of locally advanced gastric cancer
  • A system for predicting the prognosis of locally advanced gastric cancer

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0133] [Example 1] Investigation of the gene expression profile of NO patients with gastric cancer

[0134] Scatter filtering was performed sequentially by varying the filtering criteria, generating 15 clusters consisting of unique two main clusters. After dispersive filtering, most genes have 701-5612 probes, which are diverse, and the p-values ​​based on dispersive filtering criteria in the log rank test have various values, among which the maximum value is 0.291 (M2_1: by pair with Genes with at least one probe showing a 2-fold or more increase or decrease compared to the median were selected and had a cluster of 5612 probes after scatter filtering), while the minimum value was 0.0181 (M3_3: pairs with at least 3 genes showing a 3-fold or more increase or decrease in probes when compared to the median were selected and had clusters of 706 probes after scatter filtering). Of the 15 clusters, 11 of them generated 2 major categories in unsupervised hierarchical cluster analys...

Embodiment 2

[0140] [Example 2] The biological characteristics of two main clusters

[0141] To define the main gene identity of the two classifications showing this difference in prognostic outcome, t-tests were performed on 2 samples. 2886 significantly different probes (p<0.001 ) were generated by comparison between the 2 taxa showing two major clusters of M2_5 after unsupervised cluster analysis.

[0142] image 3 A shows a heat map of conditional cluster analysis using probes that were statistically significant (p<0.001) when compared between 2 classifications of M2_5 and showed 2-fold or more differences. The expression of many genes related to immune response (IFNG, GZMA, GZMB, CD8A, STAT1, JAK2, HLADPA1) was significantly increased in the good response group.

[0143] When GSEA analysis was performed on the above two classifications in the Biocarta pathway database, the most significantly improved pathways were Antigen Processing and presentation (MHC pathway) with statistical si...

Embodiment 3

[0148] [Example 3] Formation of prognosis prediction model

[0149] In order to form a prognostic prediction model, 3 different prognostic prediction algorithms can be used, namely the mixed covariate prediction method (CCP), linear discriminant analysis (LDA), and nearest centroid method (NC). To make predictions for taxonomic groups, significantly different genes were used for the two taxa at a significance level of 0.001, and calibrated prediction ratios were calculated by using leave-one-out cross-validation.

[0150] The difference in prognosis between the two predicted groups in the training set (YUSH dataset) relative to the M3_3 classification group was statistically significant (log rank test, CCP: p=0.00933, LDA: p=0.0137 and NC: p = 0.00217), and the calibrated prediction ratios for the M3_3 classification group ranged from 85% to 92% (CCP: 86%, LDA: 85% and NC: 92%) (Fig. 7A-C).

[0151] The MDACC dataset was used to examine the taxonomic groups. The prediction r...

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Abstract

The present invention relates to a novel prognosis prediction system that can predict the prognosis of locally advanced gastric cancer. More specifically, the present invention can predict the clinical outcome after gastric cancer resection by comparative analysis of gene set expression.

Description

technical field [0001] The present invention relates to a novel prognostic system capable of predicting the prognosis of locally advanced gastric cancer through comparative analysis of gene expression. Background technique [0002] Gastric adeno-carcinoma was the second leading cause of 700,349 deaths in 2000 and the fourth most commonly diagnosed cancer in the world. Gastric adenocarcinoma is considered as a single heterogeneous disease with several epidemiological and histopathological features. Treatment of gastric cancer is mainly based on clinical parameters such as TNM (Tumor, Node, Metastasis) staging, which is used to decide whether it can be treated with surgery alone or with surgery and chemotherapy. Unlike breast cancer and colorectal cancer, gastric cancer can vary significantly from stage I to stage IV according to the TNM staging system. That is, the five-year survival rate of stage I is 90% or more, while that of stage IV is less than 20%, showing a huge dif...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): C12Q1/68G16B25/10
CPCC12Q1/6886C12Q2600/158G16B25/00C12Q2600/118G16B25/10C12Q1/6837G01N33/5308G01N33/57446C12Q2537/165
Inventor 许镛敏卢圣勳郑载镐徐振锡朴恩成
Owner NOVOMICS CO LTD
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