Saudi Journal of Gastroenterology
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ORIGINAL ARTICLE
Year : 2019  |  Volume : 25  |  Issue : 5  |  Page : 293-301

COX-2/C-MET/KRAS status-based prognostic nomogram for colorectal cancer: A multicenter cohort study


1 Department of Oncology, Cancer Center, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
2 Department of Gastrointestinal Surgery, Cancer Center, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
3 Department of Gastroenterology, Sun Yat-Sen Memorial Hospital, Guangzhou, China

Correspondence Address:
Dr. Jianhua Liu
Department of Oncology, Cancer Center, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 123 Huifu Road West, Guangzhou 510180
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/sjg.SJG_502_18

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Background/Aim: To construct quantitative prognostic models for colorectal cancer (CRC) based on COX-2/C-MET/KRAS expression status in clinical practice. Patients and Methods: Clinical factors and COX-2/C-MET/KRAS expression status of 578 eligible patients from two Chinese hospitals were included. The patients were randomly allocated into training and validation datasets. We created several models using Cox proportional hazard models: SignatureC contained clinical factors, SignatureG contained COX-2/C-MET/KRAS expression status, and SignatureCG contained both. After comparing their accuracy, nomograms for progression-free survival (PFS) and overall survival (OS) were built for the best signatures, with their concordance index and calibration tested. Further, patients were subgrouped by the median of the best signatures, and survival differences between the subgroups were compared. Results: For PFS, among the three signatures, SignaturePFS-CG had the best area under the curve (AUC), with the 1-, 2- and 3-year AUCs being 0.70, 0.73 and 0.89 in the training dataset, respectively and 0.67, 0.73 and 0.87 in the validation dataset, respectively. For OS, the AUCs of SignatureOS-CG for 1-, 2- and 3-years were 0.63, 0.71 and 0.81 in the training dataset, respectively and 0.68, 0.71 and 0.76 in validation dataset, respectively. The nomograms based on SignaturePFS-CG and SignatureOS-CG had good calibrations. Subsequent stratification analysis demonstrated that the subgroups were significantly different for both PFS (training:P < 0.001; validation:P< 0.001) and OS (training:P < 0.001; validation:P < 0.001). Conclusions: Combining clinical factors and COX-2/C-MET/KRAS expression status, our models provided accurate prognostic information in CRC. They can be used to aid treatment decisions in clinical practice.


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