text: user6504: I have a list of genes (n = 120) that are involved in breast cancer. And I have a list of differentially expressed genes (n = 70) that are present in the breast cancer gene list. My question is: Are differentially expressed genes significantly more likely to be breast cancer related vs non-differentially expressed genes?
  which statistical test will be suitable to address this question and how I can visualize its results in R.
  
  user15931: This question can be addressed by gene set enrichment analysis. You should use Gene Ontology and KEGG Pathways to find out which genes from your gene set are enriched in GO terms and KEGG pathway terms. You can use clusterProfiler package in R for this anlysis and for visualization also. Here is the link to the guide:
  https://yulab-smu.top/biomedical-knowledge-mining-book/enrichment-overview.html
  
original_text: 6504: <p>I have a list of genes (n = 120) that are involved in breast cancer. And I have a list of differentially expressed genes (n = 70) that are present in the breast cancer gene list. My question is: Are differentially expressed genes significantly more likely to be breast cancer related vs non-differentially expressed genes?
  which statistical test will be suitable to address this question and how I can visualize its results in <code>R</code>.</p>
   15931: <p>This question can be addressed by gene set enrichment analysis. You should use Gene Ontology and KEGG Pathways to find out which genes from your gene set are enriched in GO terms and KEGG pathway terms. You can use <a href="https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html" rel="nofollow noreferrer">clusterProfiler</a> package in R for this anlysis and for visualization also. Here is the link to the guide:
  <a href="https://yulab-smu.top/biomedical-knowledge-mining-book/enrichment-overview.html" rel="nofollow noreferrer">https://yulab-smu.top/biomedical-knowledge-mining-book/enrichment-overview.html</a></p>
  