Identification of Glycolysis-Related Gene Signature and Prediction on Prognosis of Colon Cancer

被引:0
|
作者
Ran, Qingsen [1 ,2 ]
Li, Manjing [3 ]
Han, Jiayin [3 ]
Wang, Lifang [3 ]
Wang, Han [3 ,4 ]
Liu, Shaobo [5 ]
Wang, Yanping [1 ]
机构
[1] China Acad Chinese Med Sci, Inst Basic Res Clin Med, Beijing 100700, Peoples R China
[2] China Acad Chinese Med Sci, Postdoctoral Res Stn, Beijing 100700, Peoples R China
[3] China Acad Chinese Med Sci, Inst Chinese Mat Med, Beijing 100700, Peoples R China
[4] Changchun Univ Chinese Med, Affiliated Hosp, Changchun 130021, Peoples R China
[5] Guizhou Med Univ, Sch Pharmaceut Sci, Key Lab Optimal Utilizat Nat Med Resources, Guiyang 550025, Peoples R China
关键词
Colon cancer; Glycolysis; Biomarkers; Metabolic signature; Prognosis Judgment; Risk score model; EXOSOMAL GLYPICAN-1; METABOLISM; STANNIOCALCIN-1; PROGRESSION; PKM2;
D O I
10.17582/journal.pjz/20211124031155
中图分类号
Q95 [动物学];
学科分类号
071002 ;
摘要
Identification of core biomarkers for cancer prevention and treatment is crucial. Tumor glycolysis is an important metabolic phenotype and many natural compounds from medicinal plants exhibit antiglycation effects. Therefore, it is of potential significant to find biomarkers based on glycolysis-related genes in predicting the prognosis of colon cancer (CC), a common invasive gastrointestinal tumor. In this study, clinical and gene data were collected to identify glycolytic genes that significantly associated with overall survival (OS) rate of CC patients through gene set enrichment analysis (GSEA) and Cox regression models. The K -M method was used to determine the difference of OS rate with high and low risk scores. The accuracy of risk scores were determined based on receiver operating characteristic (ROC) curve. Hierarchical analysis and Cox regression were performed to assess the correlation between CC risk score and clinical symptoms. Moreover, qPCR was used to verify the expression level of prognostic genes in human colorectal cancer cell line (HCT116) and human colorectal epithelial cells (FHC cells). The results showed 202 glycolytic genes were found with statistical differences, and 4 glycolytic genes were identified, including Enolase 3 (ENO3), glypican-1 (GPC1), Nucleolar Protein 3 (NOL3) and Stanniocalcin 2 (STC2). A prognostic risk score model was established, and the OS rate of high -risk patients was found significantly reduced (p <0.001) compared to low-risk patients. The 5 -year OS ROC area under curve (AUC) of the model was 0.75. qPCR results confirmed that glycolysis-related genes ENO3, GPC1, NOL3 and STC2 were significantly upregulated in HCT116 cells compared to FHC cells. In conclusion, our study identified four glycolytic genes that significant impact prognosis of CC, and established a prognostic risk prediction model, which can provide reference for prognosis assessment and efficient treatment in CC.
引用
收藏
页码:263 / 272
页数:10
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