Current Gene Therapy · 2020 · 23 citations · 0 references
MedicineBioinformaticsOncologyGene Expression AnalysisMolecular OncologySurvival AnalysisCancer BiomarkersCancer GenomicsColorectal CancerDifferential Gene Expression AnalysisExpression Signature DiscoveryMolecular Tumor ProfilingCancer Prognostic ModelingColorectal Cancer PrognosisCox RegressionPredictive PerformancePrognostic Gene SignaturesPrognostic PredictionPrognostic Prediction ModelsPrognostic Signature Development
BACKGROUND: Colorectal cancer (CRC) is a kind of tumor with high incidence and its treatment situation is still very difficult despite the constant renewal and development of treatment methods. OBJECTIVE: To assist the prognosis, monitoring and survival of CRC patients with a model. METHODS: In this study, we established a new prognostic model for CRC. Four groups of CRC data were accessed from the GEO database, and then differential analysis (logFoldChange>1, adjust- P<0.05) was carried out by using the limma package along with the RobustRankAggreg package used to identify the overlapping differentially expressed genes (DEGs). Univariate and multivariate Cox regression analyses were performed on the DEGs to screen the genes related to the patient's prognosis, and a five-gene prognostic prediction model (including RPX, CXCL13, MMP10, FABP4 and CLDN23) was constructed. Then, we further plotted ROC curves to evaluate the predictive performance of the five-gene prognostic signature in the TCGA data sets (the AUC values of 1, 3, 5-year survival were 0.68, 0.632, 0.675, respectively) and an external independent data set GSE2962 (the AUC values of 1, 3, 5-year survival were 0.689, 0.702, 0.631, respectively). RESULTS: The results showed that the model could effectively predict the prognosis of CRC patients, which provides a robust predictive model for the prognosis of CRC patients. CONCLUSION: The model could effectively predict the prognosis of CRC patients, which provides a robust predictive model for the prognosis of CRC patients.