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Hello, today I will share with you an interesting article recently published on frontiers in Oncology (IF: 4.
85).
The article did not think of what to do from the beginning, but started research on the disease, and found that some results are more related to the researched disease, and then further analyze and describe it.
Not much to say, let's see how it is done.
Shengxin people provide professional and reproducible analysis of life information.
Interested scan code! Flow chart: Results: 1.
The article first screened the differential genes between disease and normal (normally used GTEx, you can learn from it if the normal sample is insufficient, but remember to go to the batch effect), showing the high and low expression The top50 of each (this picture looks better, get it).
Focus on: Analysis of differential gene enrichment, enrichment to cell cycle-related pathways: E2F targets, MYC targets, G2M checkpoint, P53 pathway.
Therefore, the author concluded that cell cycle-related genes are of great significance to the development of colon cancer.
Next, the author screened cell cycle-associated genes in the database and conducted research on them in colon cancer.
2.
Based on cell cycle-associated genes, perform NMF clustering of samples to obtain molecular subtypes, and find that the survival difference between molecular subtypes is significant.
3.
Based on NTP method and significant difference of top50 cell cycle genes, compare TCGA and independent data sets (GSE17538, GSE39582) typing verification found that the NTP method can accurately repeat typing, and the survival of independent data set typing is also different.
The study also combined clinical indicators for analysis.
4.
The ssGSEA enrichment analysis of cell cycle-related pathways (TCGA and GEO data sets) for the molecular subtypes related to the cycle of colon cancer cells found that the scores of these pathways are significantly different between the types.
5.
Display of high-frequency mutation gene differences among molecular subtypes related to the cycle of colon cancer cells (overall sample + separate) 6.
What is interesting is that the author constructed a prognostic risk model for the two subtypes based on cell cycle-related prognostic genes ( The GEO data set 7:3 is divided into internal training and internal test sets, and TCGA is the external test set), the prediction effect AUC value is basically above 0.
7, and today’s article sharing is over.
Have you learned some knowledge?
The editor believes that the desirability of this article is to choose the direction of disease research from the results of the analysis, giving the reviewers a reason to believe that the research is meaningful and valuable.
It is not an unfounded analysis, starting from the result mechanism of real analysis.
Find a meaningful molecular classification in colon cancer, and construct a dual-risk signature that can predict the prognosis of the classification.
Personal custom life letter analysis tumor typing + prognosis analysis interested scan code
85).
The article did not think of what to do from the beginning, but started research on the disease, and found that some results are more related to the researched disease, and then further analyze and describe it.
Not much to say, let's see how it is done.
Shengxin people provide professional and reproducible analysis of life information.
Interested scan code! Flow chart: Results: 1.
The article first screened the differential genes between disease and normal (normally used GTEx, you can learn from it if the normal sample is insufficient, but remember to go to the batch effect), showing the high and low expression The top50 of each (this picture looks better, get it).
Focus on: Analysis of differential gene enrichment, enrichment to cell cycle-related pathways: E2F targets, MYC targets, G2M checkpoint, P53 pathway.
Therefore, the author concluded that cell cycle-related genes are of great significance to the development of colon cancer.
Next, the author screened cell cycle-associated genes in the database and conducted research on them in colon cancer.
2.
Based on cell cycle-associated genes, perform NMF clustering of samples to obtain molecular subtypes, and find that the survival difference between molecular subtypes is significant.
3.
Based on NTP method and significant difference of top50 cell cycle genes, compare TCGA and independent data sets (GSE17538, GSE39582) typing verification found that the NTP method can accurately repeat typing, and the survival of independent data set typing is also different.
The study also combined clinical indicators for analysis.
4.
The ssGSEA enrichment analysis of cell cycle-related pathways (TCGA and GEO data sets) for the molecular subtypes related to the cycle of colon cancer cells found that the scores of these pathways are significantly different between the types.
5.
Display of high-frequency mutation gene differences among molecular subtypes related to the cycle of colon cancer cells (overall sample + separate) 6.
What is interesting is that the author constructed a prognostic risk model for the two subtypes based on cell cycle-related prognostic genes ( The GEO data set 7:3 is divided into internal training and internal test sets, and TCGA is the external test set), the prediction effect AUC value is basically above 0.
7, and today’s article sharing is over.
Have you learned some knowledge?
The editor believes that the desirability of this article is to choose the direction of disease research from the results of the analysis, giving the reviewers a reason to believe that the research is meaningful and valuable.
It is not an unfounded analysis, starting from the result mechanism of real analysis.
Find a meaningful molecular classification in colon cancer, and construct a dual-risk signature that can predict the prognosis of the classification.
Personal custom life letter analysis tumor typing + prognosis analysis interested scan code