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Arai, Hiroyuki, Cao, Shu, Battaglin, Francesca, Wang, Jingyuan, Kawanishi, Natsuko, Tokunaga, Ryuma, Loupakis, Fotios, Stintzing, Sebastian, Soni, Shivani, Zhang, Wu, Mancao, Christoph, Salhia, Bodour, Mumenthaler, Shannon M., Cremolini, Chiara, Heinemann, Volker, Falcone, Alfredo, Millstein, Joshua and Lenz, Heinz-Josef (2021): RNA-Binding Protein Polymorphisms as Novel Biomarkers to Predict Outcomes of Metastatic Colorectal Cancer: A Meta-analysis from TRIBE, FIRE-3, and MAVERICC. In: Molecular Cancer Therapeutics, Vol. 20, No. 6: pp. 1153-1160

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RNA-binding proteins (RBPs) regulate many posttranscriptional cellular activities. Accumulating evidence suggests associations between RBPs with colonic tumorigenesis and chemosensitivity. We investigated the prognostic and predictive values of SNPs of genes encoding RBPs in metastatic colorectal cancer (mCRC), using clinical and genomic data from three randomized clinical trials of standard first-line chemotherapy for mCRC (TRIBE, FIRE-3, and MAVERICC). Genomic DNA extracted from blood samples was genotyped using an OncoArray. We tested 30 candidate SNPs of 10 major RBP-related genes with additive models. Prognostic values were estimated by meta-analysis approach. Treatment-by-SNP interactions were tested to estimate predictive values for targeted drugs and cytotoxic backbone chemotherapies. This study included 884 patients. The meta-analysis revealed prognostic values of LIN28B rs314277 [HR, 1.26;95% confidence interval (CI), 1.06-1.49, P = 0.005, FDR-adjusted P = 0.072 for overall survival (OS)] and LIN28B rs314276 (HR, 1.25;95% CI, 1.08-1.44, P = 0.002, FDR-adjusted P = 0.062 for OS). Although some SNPs showed potentially predictive values, these associations were not confirmed after FDR adjustment. In conclusion, the results of this study are warranting additional studies to provide the evidence that RBP-related SNPs may be associated with the prognosis of patients with mCRC treated with standard first-line chemotherapies. In addition, further studies are warranted to study the predictive value.

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