TY - JOUR
T1 - Gene expression network analysis of lymph node involvement in colon cancer identifies AHSA2, CDK10, and CWC22 as possible prognostic markers
AU - Han, Sung Won
AU - Ahn, Ji Young
AU - Lee, Soobin
AU - Noh, Young Seon
AU - Jung, Hee Chan
AU - Lee, Min Hyung
AU - Park, Hae Jun
AU - Chun, Hoon Jai
AU - Choi, Seong Ji
AU - Kim, Eun Sun
AU - Lee, Ji Yun
N1 - Funding Information:
This article contains a portion of the MS thesis compiled by Jiyoung Ahn, which followed the policy and guidelines of Korea University. This work was supported by Grant of Korea University Medical Center (O1801011), Korea University Grant (K1809631, K1912841), National Research Foundation of Korea (NRF-2019R1F1A1060250, NRF-2017R1C1B5076677, NRF-2017R1A2B4003233, NRF-2019R1A2C1083909) and by the Ministry of Trade Industry & Energy(MOTIE, Korea), Ministry of Science & ICT (MSIT, Korea), and Ministry of Health & Welfare (MOHW, Korea) under Technology Development Program for AI-Bio-Robot-Medicine Convergence (20001533). This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI19C0201) and by the Technology Innovation Program (20006045, 20003767) funded By the Ministry of Trade, Industry & Energy(MOTIE, Korea).
Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85083962625&partnerID=8YFLogxK
U2 - 10.1038/s41598-020-63806-x
DO - 10.1038/s41598-020-63806-x
M3 - Article
C2 - 32345988
AN - SCOPUS:85083962625
VL - 10
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 7170
ER -