A Novel Prognostic Score Based on ZG16 for Predicting CRC Survival

• Main Authors: Wang W, Sun JF, Wang XZ, Ying HQ, You XH, Sun F
• Format: Article
• Language: English
• Published: Dove Medical Press 2020-12-01
• Series: Pharmacogenomics and Personalized Medicine
• Subjects: colorectal cancer; bioinformatics; zg16; prognostic score; biomarker
• Online Access: https://www.dovepress.com/a-novel-prognostic-score-based-on-zg16-for-predicting-crc-survival-peer-reviewed-article-PGPM

Wei Wang,1,2,* Jian-Fang Sun,3,* Xiao-Zhong Wang,1 Hou-Qun Ying,4 Xia-Hong You,1 Fan Sun1 1Jiangxi Province Key Laboratory of Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, People’s Republic of China; 2Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi, People’s Republic of China; 3Neonatology Department, Dongguan Eighth People’s Hospital, Dongguan Children’s Hospital, Dongguan 523000, People’s Republic of China; 4Department of Nuclear Medicine, Jiangxi Province Key Laboratory of Laboratory Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, People’s Republic of China*These authors contributed equally to this workCorrespondence: Fan SunJiangxi Province Key Laboratory of Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital of Nanchang University, No. 1 Min De Road, Nanchang 330006, People’s Republic of ChinaTel/Fax [phone]Email [e-mail]: Colorectal cancer (CRC) is one of the lethal malignant tumors worldwide. However, the underlying mechanism of CRC and its biomarkers remain unclear. The aim of this study was to identify the key genes associated with CRC and to further explore their prognostic significance.Methods: Four expression profile datasets (GSE41657, GSE74602, GSE113513, and GSE40967) downloaded from Gene Expression Omnibus (GEO) and one RNAseq dataset of CRC from The Cancer Genome Atlas (TCGA) database were included in our study. The Cox model was utilized for univariate or multivariate survival analysis. GEPIA and HAP database were adopted for verification of DEGs (ZG16). The decision curve analysis (DCA) and time-dependent ROC were chosen for evaluating the prognostic effectiveness of biomarkers.Results: In total, 88 differentially expressed genes (DEGs) were identified, and the GO and KEGG enrichment analyses of DEGs were processed. After, the protein–protein interaction (PPI) network was constructed and 15 hub genes including ZG16 were identified. The differential expression of ZG16 between tumor and normal colorectal tissues were further verified in GEPIA and HAP database. Subsequent survival indicated that expression of ZG16 is negatively correlated with overall survival of OS and is an independent prognostic factor for CRC patients. Furthermore, the construction of a prognostic score containing ZG16, TNM stage and age exhibited superior effectiveness for predicting long-term survival of CRC patients. Additionally, our results were verified using the GSE40967 dataset, which indicated an improved performance of combined risk score based on ZG16 for predicting OS of CRC patients.Conclusion: ZG16 is a potential parameter for predicting prognosis in CRC. Furthermore, a combination of ZG16, TNM stage, and age allows improved prognosis of CRC.Keywords: colorectal cancer, bioinformatics, ZG16, prognostic score, biomarker