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What is the gene survival analysis in TCGA database? in order to solve this problem, this article introduces the corresponding analysis and answer in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible method.

Recently, I saw that the survival analysis of METTL3 gene for bladder cancer was mentioned in the literature. I looked closely and found that it was not the expression data in TCGA, but the result of IHC, as follows:

F. Kaplan-Meier survival curves of overall survival in 180 bladder cancer patients based on METTL3 expression analyzed by IHC staining. The log-rank test was used to compare differences between two groups (P = 0.0128)

Image-20200414003447516

The result is quite significant, so I wonder why the author wants to look far away. Obviously, there is a lot of public data in the tcga database, so it is easy to test whether the genes they are interested in have survival effects.

So I helped the author look at the survival analysis of the METTL3 gene of bladder cancer at http://gepia2.cancer-pku.cn/#survival. The same UCSC xena browser can also do it, and those who are interested can learn: GEPIA2 detailed explanation (China Intelligent-Oncology Database) and, of course, self-programming exploration.

As follows, here comes the interesting part:

What's even more interesting about image-20200414003502774 is that you can also use the km-plot web tool to view the survival analysis of the METTL3 gene in bladder cancer, although I don't believe in the results of the km-plot web tool. We have introduced survival analysis many times: brainstorming-can survival analysis be grouped according to expression? can survival analysis be divided into groups at will? the time point of survival analysis is to find the best gene expression grouping threshold for survival analysis. There is still a difference between the apply family function and the for cycle (batch survival analysis map bug). It is reasonable to speculate whether the web tool for survival analysis in TCGA database is strong or not, because the author finds out the genes he is interested in. That is, METTL3 does not have a statistically significant survival significance in bladder cancer in the TCGA database, so try to buy TMA and take a look at the IHC results of the survival analysis of the METTL3 gene for bladder cancer. The answer to the question about the gene survival analysis of TCGA database is shared here. I hope the above content can be of some help to you. If you still have a lot of doubts to be solved, you can follow the industry information channel for more related knowledge.

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