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Gene markers, evaluation methods and applications for stratified evaluation of tumor prognosis

A gene marker and prognosis technology, applied in the field of medical diagnosis, can solve the problems of neglected research and unsatisfactory patient stratification effect.

Active Publication Date: 2022-04-05
CANCER INST & HOSPITAL CHINESE ACADEMY OF MEDICAL SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] At present, the research on the effect of RNA methylation on tumors is concentrated in the field of mRNA methylation, while the related research on ribosomal RNA methylation is ignored, and the existing m6A-related prognostic stratification model has no effect on patient stratification. ideal

Method used

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  • Gene markers, evaluation methods and applications for stratified evaluation of tumor prognosis
  • Gene markers, evaluation methods and applications for stratified evaluation of tumor prognosis
  • Gene markers, evaluation methods and applications for stratified evaluation of tumor prognosis

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0026] Example 1 Constructing a risk scoring model based on METTL5 in the TCGA lung adenocarcinoma cohort based on gene expression profile data

[0027] To develop a METTL5-based prognostic risk model, the Spathial algorithm was first applied to implement evolutionary analysis to find genes associated with different expression levels of METTL5. Spathial uses the main path algorithm to find the most important genes in the change process. The samples were divided into two groups according to the upper and lower quantiles of METTL5 expression, and the starting point and end point of the path were set as the centroids of the two groups. During path analysis, the number of waypoints was set to 50. The significance of each gene was sorted by the corrected P value, and the top 100 genes were screened out, as shown in Gene List 1.

[0028] Table 1

[0029]

[0030]

[0031] Use random survival forests via randomForestSRC to further reduce the number of features in the table a...

Embodiment 2

[0032] Example 2 Validate the prognostic risk model based on an independent external data set

[0033] The microarray data set comes from Gene Expression Omnibus (GEO), and the details of the data set are shown in Table 2.

[0034] Table 2

[0035]

[0036] Firstly, the probe tags are gene-annotated according to the platform file provided by GEO, different probes corresponding to the same gene are merged according to the mean value, and then the samples are normalized. Substituting the genes included in the model into the formula to calculate the risk score for each sample, grouping according to the median risk score of each cohort, and performing survival analysis and 2-year, 3-year and 5-year AUC value calculations for the subgroups to evaluate the prognosis of the model efficacy.

[0037] Kaplan-Meier survival curves showed that the overall survival of the high-risk group in the three cohorts was significantly lower than that of the low-risk group (P<0.05). In additio...

Embodiment 3

[0038]Example 3 Verification that the constructed risk score is an independent risk factor for the prognosis of patients with lung adenocarcinoma

[0039] To further demonstrate that the constructed risk score is an independent risk factor, relevant information was extracted from a cohort with clinical information, specifically including age at diagnosis, gender, tumor pathological stage, and smoking history. Multivariate Cox regression analyzes were performed on these datasets to further illustrate the prognostic significance of the maps. The results showed that in the three datasets, the constructed risk score was an independent prognostic factor (P<0.05), and its overall survival hazard ratios in TCGA lung adenocarcinoma, GSE13213 and GSE31210 datasets were 2.106, 2.514 and 2.514 respectively. 3.666.

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Abstract

The invention discloses a gene marker, an assessment method and an application for stratified assessment of tumor prognosis. The gene marker includes METTL5, RAC1, RCCD1, C11orf24, and SLC7A5. The evaluation method includes: substituting the expression level of the gene marker into the model formula to calculate the risk score of each sample; and grouping the samples according to the threshold value of the model and performing survival analysis between groups. The present invention provides a prognostic stratification evaluation of lung adenocarcinoma based on the METTL5 gene. The prognostic stratification is independent of tumor pathological stages, so it can be used for lung adenocarcinoma patients at various stages, and different risk stratifications can reduce the heterogeneity of lung adenocarcinoma It provides an important guiding significance for the precision medicine treatment of patients.

Description

technical field [0001] The present invention relates to the field of medical diagnosis, in particular to a gene marker, evaluation method and application for stratified evaluation of tumor prognosis. Background technique [0002] Lung cancer is the cancer with the highest morbidity and mortality in the world, causing more than 700,000 deaths every year. More than 40% of all lung cancer patients are lung adenocarcinoma. Especially in Asian populations, the proportion of adenocarcinoma among non-smokers has further increased. The prognosis of patients with different stages of tumor is different, no matter which stage of tumor, there is greater heterogeneity in response to treatment and prognosis. Therefore, identifying patients with a poor prognosis is of great help in guiding treatment. At the same time, for patients with advanced lung adenocarcinoma, despite the rapid development of chemotherapy, targeted therapy, and immunotherapy in recent years, the 5-year survival rat...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): C12Q1/6886G16B5/00G16B20/00
CPCC12Q1/6886G16B5/00G16B20/00C12Q2600/118C12Q2600/158
Inventor 赫捷高亦博孙思进
Owner CANCER INST & HOSPITAL CHINESE ACADEMY OF MEDICAL SCI
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