Junfeng Hong 1 , Xiangwu Lin 2 , Xinyu Hu 2 , Xiaolong Wu 2 , Wenzheng Fang 2 Show Affiliations »
Abstract
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. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
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. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.
Entities: Chemical
Keywords:
CLDN23; CRC; CXCL13.; FABP4; MMP10; Prognosis; predictive; signature
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Year: 2021
PMID： 33045967 DOI： 10.2174/1566523220666201012151803
Source DB: PubMed Journal: Curr Gene Ther ISSN： 1566-5232 Impact factor: 4.391