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pmid:32337262

Construction of a CXC Chemokine-Based Prediction Model for the Prognosis of Colon Cancer.

Kaisheng Liu · Minshan Lai · Shaoxiang Wang · Kai Zheng · Shouxia Xie · Xiao Wang

BioMed research international · 2020 doi:10.1155/2020/6107865

Paper

complete

Experiment

partial

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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title
Construction of a CXC Chemokine-Based Prediction Model for the Prognosis of Colon Cancer.
doi
10.1155/2020/6107865
pmid
32337262
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[0]
Kaisheng Liu
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Minshan Lai
[2]
Shaoxiang Wang
[3]
Kai Zheng
[4]
Shouxia Xie
[5]
Xiao Wang
journal
BioMed research international
year
2020
abstract
Colon cancer is the third most common cancer, with a high incidence and mortality. Construction of a specific and sensitive prediction model for prognosis is urgently needed. In this study, profiles of patients with colon cancer with clinical and gene expression data were downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA). CXC chemokines in patients with colon cancer were investigated by differential expression gene analysis, overall survival analysis, receiver operating characteristic analysis, gene set enrichment analysis (GSEA), and weighted gene coexpression network analysis. CXCL1, CXCL2, CXCL3, and CXCL11 were upregulated in patients with colon cancer and significantly correlated with prognosis. The area under curve (AUC) of the multigene forecast model of CXCL1, CXCL11, CXCL2, and CXCL3 was 0.705 in the GSE41258 dataset and 0.624 in TCGA. The prediction model was constructed using the risk score of the multigene model and three clinicopathological risk factors and exhibited 92.6% and 91.8% accuracy in predicting 3-year and 5-year overall survival of patients with colon cancer, respectively. In addition, by GSEA, expression of CXCL1, CXCL11, CXCL2, and CXCL3 was correlated with several signaling pathways, including NOD-like receptor, oxidative phosphorylation, mTORC1, interferon-gamma response, and IL6/JAK/STAT3 pathways. Patients with colon cancer will benefit from this prediction model for prognosis, and this will pave the way to improve the survival rate and optimize treatment for colon cancer.
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