• Yinzhong Sha, Abdusemer Reyimu, Wen Liu, Chuanjiang He, Aihemaitijiang Kaisaier, Pawuziye Paerhati, Li Li, Xiaoguang Zou, Aimin Xu, Xiang Cheng, and Maimaitituersun Abuduaini.
• Department of Laboratory Medicine, The First People's Hospital of Kashi, Kashi City, China.
• Medicine (Baltimore). 2024 Sep 13; 103 (37): e39639e39639.

BackgroundConstruction of a prognostic model for esophageal cancer (ESCA) based on prognostic RNA-binding proteins (RBPs) and preliminary evaluation of RBP function.MethodsRNA-seq data of ESCA was downloaded from The Cancer Genome Atlas database and mRNA was extracted to screen differentially expressed genes using R. After screening RBPs in differentially expressed genes, R packages clusterProfiler and pathview were used to analyze the RBPs for Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway. Based on the prognosis-related RBPs, COX regression was used to establish the prognostic risk model of ESCA. Risk model predictive ability was assessed using calibration analysis, receiver operating characteristic curves, Kaplan-Meier curves, decision curve analysis, and Harrell consistency index (C-index). A nomogram was established by combining the risk model with clinicopathological features.ResultsA total of 105 RBPs were screened from ESCA. A prognostic risk model consisting of 6 prognostic RBPs (ARHGEF28, BOLL, CIRBP, DKC1, SNRPB, and TRIT1) was constructed by COX regression analysis. The prognosis was worse in the high-risk group, and the receiver operating characteristic curve showed (area under the curve = 0.90) that the model better predicted patients' 5-year survival. In addition, 6 prognostic RBPs had good diagnostic power for ESCA. In addition, a total of 39 mRNAs were identified as predicted target molecules for DKC1.ConclusionARHGEF28, BOLL, CIRBP, DKC1, SNRPB, and TRIT1, as RBPs, are associated with the prognosis of ESCA, which may provide new ideas for targeted therapy of ESCA.Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.

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