Gene expression network analysis of lymph node involvement in colon cancer identifies AHSA2, CDK10, and CWC22 as possible prognostic markers.

Tytuł:: Gene expression network analysis of lymph node involvement in colon cancer identifies AHSA2, CDK10, and CWC22 as possible prognostic markers.
Autorzy:: Han SW; School of Industrial Management Engineering, Korea University, Seoul, 02841, Republic of Korea.
Ahn JY; School of Industrial Management Engineering, Korea University, Seoul, 02841, Republic of Korea.
Lee S; School of Industrial Management Engineering, Korea University, Seoul, 02841, Republic of Korea.
Noh YS; School of Industrial Management Engineering, Korea University, Seoul, 02841, Republic of Korea.
Jung HC; Department of Internal Medicine, Eulji University College of Medicine 68 Hangeulbiseok-ro, Nowon-gu, Seoul, 01830, Republic of Korea.
Lee MH; School of Industrial Management Engineering, Korea University, Seoul, 02841, Republic of Korea.
Park HJ; School of Industrial Management Engineering, Korea University, Seoul, 02841, Republic of Korea.
Chun HJ; Department of Internal Medicine, Korea University College of Medicine, Seoul, 02841, Republic of Korea.
Choi SJ; Department of Internal Medicine, Korea University College of Medicine, Seoul, 02841, Republic of Korea.
Kim ES; Department of Internal Medicine, Korea University College of Medicine, Seoul, 02841, Republic of Korea. .
Lee JY; Department of Pathology, Korea University College of Medicine, Seoul, 02841, Republic of Korea. .
Źródło:: Scientific reports [Sci Rep] 2020 Apr 28; Vol. 10 (1), pp. 7170. Date of Electronic Publication: 2020 Apr 28.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
Język:: English
Imprint Name(s):: Original Publication: London : Nature Publishing Group, copyright 2011-
MeSH Terms:: Colonic Neoplasms*/metabolism
Colonic Neoplasms*/mortality
Colonic Neoplasms*/pathology
Gene Expression Regulation, Neoplastic*
Gene Regulatory Networks*
Lymph Nodes*/metabolism
Lymph Nodes*/pathology
Biomarkers, Tumor/*biosynthesis
Cyclin-Dependent Kinases/*biosynthesis
Molecular Chaperones/*biosynthesis
Neoplasm Proteins/*biosynthesis
RNA-Binding Proteins/*biosynthesis
Aged ; Aged, 80 and over ; Disease-Free Survival ; Female ; Humans ; Male ; Middle Aged ; Survival Rate
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Substance Nomenclature:: 0 (Biomarkers, Tumor)
0 (CWC22 protein, human)
0 (Molecular Chaperones)
0 (Neoplasm Proteins)
0 (RNA-Binding Proteins)
EC 2.7.11.22 (CDK10 protein, human)
EC 2.7.11.22 (Cyclin-Dependent Kinases)
Entry Date(s):: Date Created: 20200430 Date Completed: 20201125 Latest Revision: 20210428
Update Code:: 20240105
PubMed Central ID:: PMC7189385
DOI:: 10.1038/s41598-020-63806-x
PMID:: 32345988
: Czasopismo naukowe

Pełny tekst

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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