BACKGROUND: Despite recent progress in screening survival-related genes, there have been few attempts to apply methods based on cancer stem cells (CSCs) for prognosis. We aimed to identify a CSC-based model to predict survival in colorectal cancer (CRC) patients. MATERIAL/ METHODS: Differentially expressed genes between CRC and normal tissues and between CD133- and CD133+ cells were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, and intersections were evaluated. Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyzes were performed. STRING was used to investigate interactions between the encoded proteins and the Kaplan-Meier method to verify mRNAs associated with survival. A prognostic model based on CSCs was established via univariate and multivariate Cox regression. Receiver operating characteristic curve analysis was conducted to test the model's sensitivity and specificity. The KS test was applied to provide evidence for relationships between expression levels of nine mRNAs in our model and pathological stage. RESULTS: In total, 155 common differentially expressed mRNAs were identified, and nine (AOC1, UCN, MTUS1, CDC20, SNCB, MAT1A, TUBB2B, GABRA4 and ALPP) were screened after regression analyses to establish a predictive model for classifying patients into high- and low-risk groups with significantly different overall survival times, especially for stage II and IV patients. CONCLUSIONS: We developed a novel model that provides additional and powerful prognostic information beyond conventional clinicopathological factors for CRC survival prediction. It also provides new insight into the molecular mechanisms underlying the transition from normal tissues to CSCs and formation of tumor tissues.
BACKGROUND: Despite recent progress in screening survival-related genes, there have been few attempts to apply methods based on cancer stem cells (CSCs) for prognosis. We aimed to identify a CSC-based model to predict survival in colorectal cancer (CRC) patients. MATERIAL/ METHODS: Differentially expressed genes between CRC and normal tissues and between CD133- and CD133+ cells were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, and intersections were evaluated. Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyzes were performed. STRING was used to investigate interactions between the encoded proteins and the Kaplan-Meier method to verify mRNAs associated with survival. A prognostic model based on CSCs was established via univariate and multivariate Cox regression. Receiver operating characteristic curve analysis was conducted to test the model's sensitivity and specificity. The KS test was applied to provide evidence for relationships between expression levels of nine mRNAs in our model and pathological stage. RESULTS: In total, 155 common differentially expressed mRNAs were identified, and nine (AOC1, UCN, MTUS1, CDC20, SNCB, MAT1A, TUBB2B, GABRA4 and ALPP) were screened after regression analyses to establish a predictive model for classifying patients into high- and low-risk groups with significantly different overall survival times, especially for stage II and IV patients. CONCLUSIONS: We developed a novel model that provides additional and powerful prognostic information beyond conventional clinicopathological factors for CRC survival prediction. It also provides new insight into the molecular mechanisms underlying the transition from normal tissues to CSCs and formation of tumor tissues.
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