I have compared the expression pattern of sequential stages of a certain type of cancer.
I detected 150 genes with down-regulation in transition from stage 1 to stage 2. However all genes were also detected as up-regulated in moving from stage 2 to stage 3.
The result of functional annotation of these genes showed that the majority of these genes are involved in immune system.
Now my question is how can I interpret this converse pattern of expression?
The result of functional annotation of these genes showed that the
majority of these genes are involved in immune system.
Just be careful about that. The immune system is 'always' statistically significantly enriched through gene enrichment. In part, this is due to the fact that the immune system is well studied / annotated and also due to the fact that so many genes have some involvement in immune cell cascades. While saying this, one cannot diminish the importance that the immune system plays in cancer evolution.
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If you wanted to explore the patterns of expression, then you should consider doing some form of network analysis. For example, taking a set of genes in Stage I samples, one can determine 'communities' (modules) of genes that are statistically significantly in high correlation with each other. Taking the same set of genes, you can then determine the modules in Stage II and, finally, Stage III samples. In each case, different genes will be grouped together, which may help to delineate the different pathways involved.
As an aside, one can also do pathway analysis / gene enrichment on each of the modules identified.
Thanks Kevin,
You mean that I have to construct for example co-expression network for each individual sample and then focus on the co-expressed modules of each stage and compare them with modules of different stages?
Then I can looking for these 150 genes in co-expressed modules?
Yes, you can construct a separate co-expression network for each cancer Stage (I, II, and III), and then compare the module structures across the 3 stages. Other tools include WGCNA, of course.
Thanks Kevin, You mean that I have to construct for example co-expression network for each individual sample and then focus on the co-expressed modules of each stage and compare them with modules of different stages? Then I can looking for these 150 genes in co-expressed modules?
Yes, you can construct a separate co-expression network for each cancer Stage (I, II, and III), and then compare the module structures across the 3 stages. Other tools include WGCNA, of course.
I've already conducted WGCNA and ARACNE. I will try to run them. Thank u so much for your helps Kevin. I hope everything goes well with you!
Always great here. Trust that all is well with you.