ArticleScientific reports2020
Gene expression network analysis of lymph node involvement in colon cancer identifies AHSA2, CDK10, and CWC22 as possible prognostic markers.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 14 citations in OpenAlex.
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Authors and funding
11 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colon cancer has been well studied using a variety of molecular techniques, including whole genome sequencing. However, genetic markers that could be used to predict lymph node (LN) involvement, which is the most important prognostic factor for colon cancer, have not been identified. In the present study, we compared LN(+) and LN(-) colon cancer patients using differential gene expression and network analysis. Colon cancer gene expression data were obtained from the Cancer Genome Atlas and divided into two groups, LN(+) and LN(-). Gene expression networks were constructed using LASSO (Least Absolute Shrinkage and Selection Operator) regression. We identified hub genes, such as APBB1, AHSA2, ZNF767, and JAK2, that were highly differentially expressed. Survival analysis using selected hub genes, such as AHSA2, CDK10, and CWC22, showed that their expression levels were significantly associated with the survival rate of colon cancer patients, which indicates their possible use as prognostic markers. In addition, protein-protein interaction network, GO enrichment, and KEGG pathway analysis were performed with selected hub genes from each group to investigate the regulatory relationships between hub genes and LN involvement in colon cancer; these analyses revealed differences between the LN(-) and LN(+) groups. Our network analysis may help narrow down the search for novel candidate genes for the treatment of colon cancer, in addition to improving our understanding of the biological processes underlying LN involvement. All R implementation codes are available at journal website as Supplementary Materials.
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