J. Yang1, W-C. Qiu1, X-P. Wang1 and Z. Jia1,2*

1Department of General Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China;
2Department of Clinical Nutrition, Putuo People’s Hospital, Tongji University, Shanghai, China

Abstract

Objective: This study aimed to develop an amino acid metabolism-related gene signature for recurrence prediction in colon adenocarcinoma (COAD).
Methods: We downloaded RNA sequencing profiles of COAD from The Cancer Genome Atlas (TCGA) as a training set and GSE39582 from the Gene Expression Omnibus database as a validation set. Differentially expressed RNAs (DERs) were screened from recurrence tumor samples by defined thresholds. The amino acid metabolism-related gene signature was identified by LASSO cox regression analysis. Independent prognostic clinical factors were also assessed by using survival analysis. A risk score (RS) signature was established and validated in two independent datasets.
Results: We obtained 498 differentially expressed mRNAs and 71 differentially expressed lncRNAs based on data mining. Compared with amino acid metabolism genes of Gene Set Enrichment Analysis database, we screened 197 overlapped DERs. A twelve genes-based RS signature was established. This model exhibited a high accuracy for recurrence prediction with an area under the ROC curve (AUC) of 0.924 and 0.843 in TCGA and GSE39582, respectively. In addition, pathological stage and RS model status were identified as independent clinical factors associated with recurrence. The combined model integrating these two factors reached a higher AUC value of 0.940 in the TCGA dataset and 0.876 in the validation set.
Conclusion: We established a high accuracy prognostic model for recurrence prediction. Our findings suggested that the combined model can identify high-risk recurrence COAD patients and might be a reliable tool for decision-making in a clinic.

Keywords:

amino acid metabolism, COAD, prognosis, recurrence

Publication type

Journal Article

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