First I am not sure why, but it seems EMT can be used to infer the status of cancer progression.

What kind of databases are there for the EMT process in the context of cancer?

I saw this book which mentions ArrayExpress, but I need some help to understand what kind of data it stores.

Are there similar/better resources? How can I use EMT in the context of cancer progression study?

  1. Are there other process like EMT that are being used to study cancer progression?
  2. Are there EMT like events in leukemia progression?
  • $\begingroup$ Please note that the fields of Bioinformatics and Cancer Genomics move very quickly with new technologies. A source from 2010 would be regarded by many as out of date. $\endgroup$ – Tom Kelly Nov 26 '17 at 1:05
  • $\begingroup$ @TomKelly so you don't recommend on ArrayExpress? $\endgroup$ – 0x90 Nov 26 '17 at 2:16
  • $\begingroup$ Not unless you need to access the data from a particular study. Large scale genomics projects have far higher sample sizes and used newer technologies. What exactly are you trying to do? It does not seem like you are familiar with this field. $\endgroup$ – Tom Kelly Nov 26 '17 at 2:20

EMT describe cells' change in their state from being epithelial to the mesenchymal class. So, if a cancer cell line is gaining properties that allow it to move, it might reach the metastasis phase, were a cell can move to any point of the body and start a new tumor there. As in the wikipedia page you linked:

EMT has also been shown to occur in wound healing, in organ fibrosis and in the initiation of metastasis in cancer progression.

I am not aware of any kind of database for the EMT process, the most relevant resource might be the gene ontology, which has a term for it, which lists 185 (human) genes as related to this biological process.

ArrayExpress is a database of transcriptomics, it :

stores data from high-throughput functional genomics experiments

You can evaluate the role of EMT in the data stored in the database.

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  • $\begingroup$ What do you mean by evaluate the role of EMT in the data stored in the DB? $\endgroup$ – 0x90 Nov 21 '17 at 17:55
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    $\begingroup$ @0x90 Use one of the gene set enrichment techniques to the data, from doing a PCA with those genes to perform GSEA (like in the broad institute), or look for different expressed pathways like in SIPA or ... $\endgroup$ – llrs Nov 21 '17 at 17:57
  • $\begingroup$ thanks, if you have a good reference for similar work it would be much appreciated. $\endgroup$ – 0x90 Nov 21 '17 at 18:00
  • $\begingroup$ Similar work to what? To use analyze a pathway in a dataset? $\endgroup$ – llrs Nov 21 '17 at 18:01
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    $\begingroup$ This is a hard topic, the pathways are loosely described and there isn't any canonical pathway database, but GSVA is a good software tool that let you convert your matrix of genes x samples to pathways x sample, so you can use it to evaluate if a pathway is more expressed than another $\endgroup$ – llrs Nov 21 '17 at 18:07

The epithelial-mesenchymal is a process by which cells lose some of the structures specific to their cell type. This is normal during embryonic development or wound healing for the formation of new cells. However, it is very uncommon in healthy adults and is thereby associated with the cancers in which it occurs. EMT allows cancer cells to become more like stem cells and could facilitate metastasis or immortal cell growth, key processes for cancer progression.

If you can detect the molecular components involved in EMT, you may be able to make some inferences about the progression of the patient’s disease. This process is specific to cancers in epithelial tissues (breast, stomach, and skin cancers for example). It is just one of vastly many pathways by which cancers can grow and develop. Many of these are specific to different cancers types by tissue or molecular subtype. Cancer research is therefore very challenging and requires a deep understanding of the particular system you are focusing on.

Gene expression data sets are a great resource for this. You can compare many pathways to clinical properties from previous studies. Microarray datasets such as ArrayExpress and Gene Expression Omnibus (GEO) were very popular for this but are now largely out of date. Many databases now support higher quality RNASeq datasets with larger sample sizes and fewer batch effects. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) have among the largest Cancer databases. These are a tool for researchers to investigate any genes or pathways. They will not specifically address EMT, unless you focus on the genes involved in a relevant cancer type.

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  • $\begingroup$ What do you mean by They will not specifically address EMT, unless you focus on the genes involved in a relevant cancer type. when referring to TCGA and ICGC. How can I find EMT related stuff there? $\endgroup$ – 0x90 Nov 26 '17 at 5:20
  • $\begingroup$ Exactly the same way as you would with ArrayExpress data. None of them are designed for this purpose: they all simply store expression data. It is only with 3rd party databases or knowledge of the biological context that you could extract this information from expression datasets of all genes. $\endgroup$ – Tom Kelly Nov 26 '17 at 8:58
  • $\begingroup$ Pathway analysis is a broad topic, far beyond the scope of an SE question. There are entire courses dedicated to it. Please be more precise in your questions if you wish to get meaningful answers. An example of how the data would be used would be valuable. $\endgroup$ – Tom Kelly Nov 26 '17 at 11:08

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