A nine-gene signature to improve prognosis prediction of colon carcinoma

This study aims to establish a gene model that can robustly and effectively predict the prognosis of colon carcinoma patients via bioinformatics. Data along with clinical information in GSE39582 Series Matrix were firstly downloaded from Gene Expression Omnibus (GEO) database. Next, differentially e...

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Bibliographic Details
Published in:Cell cycle (Georgetown, Tex.) Tex.), 2021-05, Vol.20 (10), p.1021-1032
Main Authors: Zhao, Jinlai, Wang, Yigang, Gao, Jianchao, Wang, Yang, Zhong, Xuan, Wu, Xiaotang, Li, Hua
Format: Article
Language:English
Subjects:
Aged
Algorithms
Colon carcinoma
Colonic Neoplasms - genetics
Colonic Neoplasms - pathology
Databases, Genetic
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
gene signature
Humans
Male
Middle Aged
Multivariate Analysis
Prognosis
prognosis prediction
Proportional Hazards Models
Research Paper
Risk Assessment
Survival Analysis
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Summary:This study aims to establish a gene model that can robustly and effectively predict the prognosis of colon carcinoma patients via bioinformatics. Data along with clinical information in GSE39582 Series Matrix were firstly downloaded from Gene Expression Omnibus (GEO) database. Next, differentially expressed genes (DEGs) were obtained through "edgeR" analysis. Finally, a risk predication model was established through a series of regression analyses, and then prognostic performance of the model was comprehensively evaluated though Kaplan-Meier and receiver operating characteristic (ROC) analysis. Gene set enrichment analysis (GSEA) was further performed. Totally, 846 DEGs were obtained by analyzing the gene expression data in GSE39582 dataset. A 9-gene signature-based risk predication model was established via regression analyses, and the model-based risk score was formulated as: Riskscore = (−0.1214) * TNFRSF11A + (−0.2617) * TMEM97 + (−0.1041) * LGR5 + 0.0973 * KLK10 + 0.1655 * HOXB8 + 0.227 * FKBP10 + (−0.1312) * CXCL13 + (−0.1316) * CXCL10 + 0.2593 * CD36. Kaplan-Meier curve showed that colon carcinoma patients in the high-risk group had a lower survival rate. GSEA showed that high-risk group and low-risk group displayed significant difference in biological pathways including ECM RECEPTOR INTERACTION. Besides, correlation analysis between the riskscore of the model and clinical features of patients revealed that the model could effectively predict the prognosis of patients in different ages (age>65, age
ISSN:1538-4101
1551-4005
DOI:10.1080/15384101.2021.1919827