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How to use UCSC XENA to comprehensively analyze the role of a gene in cancer, 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 way.

Today we will simply design a simple project based on UCSC XENA.

In the previous introduction to the use of ICGC database, we can search the entire genome through ICGC database. For example, to see which gene has the most mutations in one or several tumors. But in UCSC XENA, we need to have a certain search target. We have no way to find the genes that have changed the most. We can only know a certain gene in advance. Then the relevant query can be made. But what's better about UCSC XENA than ICGC. ICGC can only search for information related to the mutation, while in XENA, we can retrieve all the information related to the gene and its correlation with clinical features.

Identify the target gene

Since XENA can only retrieve specified genes for retrieval, we need to select a target gene in the first step of the test. In order to facilitate selection, we use GEPIA2 to find the top 10 genes with the most differences in COAD for subsequent analysis.

After such screening, we obtained a total of five genes highly expressed in COAD. These ten genes are: LCN15, NOTUM, NPSR1,ASCL2, RNF43.

After such screening, we know that these genes are related to the occurrence of COAD. However, we are not very clear about the influence of clinical parameters of COAD. So further, we want to see how useful these ten genes are in COAD.

UCSC XENA further analyzes 1. Select the appropriate dataset

In XENA. The first step we need to do is to select the target cancer species, and we can enter keywords in the interface of the dataset selection to get the relevant datasets.

two。 Select gene-related expression information

Because we have to analyze gene expression. So the first step is to place gene-related expression information. In XENA, there are two ways to extract the expression information of multiple genes:

One is to put all the genes in directly, so that the expression information of all the target genes can be obtained in one module.

The other is the selection of one gene.

In addition to better visualization, the former can also realize all the results at once in terms of posterior and clinical parameter analysis, while the latter can be used for prognostic KM analysis.

3. Prognostic analysis

When we do bioinformatics analysis, in addition to differential expression analysis, the second most likely thing to do is prognosis analysis. In XENA, we can analyze the prognosis of each single data module. If it is continuous, XENA is automatically divided into two groups for KM analysis, and if it is a classified variable, then it can be analyzed directly.

PS: we talked about that, too. When analyzing the prognosis, the gene expression should be a single module. So for the result of fusion. We can't analyze it.

Through the above analysis, we can obtain the results of prognostic analysis of five genes. After analysis, we found that there was no relationship between these five genes and prognosis.

4. Analysis of other clinicopathological parameters

If XENA can only do prognostic analysis, then many databases can be done (GEPIA, Cbio, etc.). The better thing about XENA is that it provides the results of all the relevant clinicopathological parameters in TCGA. We can do the analysis of clinical parameters that cannot be done by other databases. For example, such as TNM stages.

After a meal of data addition, we finally got the data set containing gene expression and TNM staging data in COAD.

5. Further analysis of the data

After getting the data that we finally want to analyze, we find that similar clinical data need to be processed because of the unmodified data provided by XENA. For example, M staging includes: M0, M1, M1a, M1b and Mx, which are uncertain staging data variables.

At this time, the most direct way is for us to click Download and download all the original data, then process the data ourselves, and then conduct statistical analysis.

If I am not very proficient in data processing and analysis, then I can use the filtering function of XENA to analyze it.

6. Data screening and analysis

Here we use M staging as an example to demonstrate the screening analysis of XENA. Due to the operating procedure tutorial. So I simply made a video. For specific screening principles of XENA, please refer to: https://ucsc-xena.gitbook.io/project/overview-of-features/filter-and-subgrouping

Finally, through the analysis, we get that there is a differential expression of NOTUM in M stages.

This is the answer to the question on how to use UCSC XENA to comprehensively analyze the role of a gene in cancer. 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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