Abstract
BackgroundThe aim of this study was to determine the expression and function of heterogeneous nuclear ribonucleoprotein R (HNRNPR) in esophageal carcinoma (ESCA), the correlation between its expression and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computerized tomography scan (PET/CT)-related parameters. We also investigated whether 18F-FDG PET/CT can be used to predict the expression of HNRNPR in ESCA.MethodsWe analyzed patients with ESCA who underwent 18F-FDG PET/CT before surgery, and their tissues were stained with HNRNPR IHC. The associated parameters were derived using the 18F-FDG PET imaging data, and the correlation with the IHC score was evaluated. The Oncomine, TCGA, and GEO datasets were used to investigate HNRNPR expression in the pan- and esophageal cancers, as well as its relationship with N6-methyladenosine (m6A) modification and glycolysis. The R software, LinkedOmics, GeneMANIA, and StringOnline tools were used to perform GO/KEGG, GGI, and PPI analyses on the HNRNPR.ResultsHNRNPR is highly expressed in the majority of pan-cancers, including ESCA, and is associated with BMI, weight, and history of reflux in patients with ESCA. HNRNPR is somewhat accurate in predicting the clinical prognosis of ESCA. HNRNPR expression was positively correlated with SUVmax, SUVmean, and TLG in ESCA (p < 0.05). The combination of these three variables provides a strong predictive value for HNRNPR expression in ESCA. GO/KEGG analysis showed that HNRNPR played a role in the regulation of cell cycle, DNA replication, and the Fannie anemia pathway. The analysis of the TCGA and GEO data sets revealed a significant correlation between HNRNPR expression and m6A and glycolysis-related genes. GSEA analysis revealed that HNRNPR was involved in various m6A and glycolysis related-pathways.ConclusionHNRNPR overexpression correlates with 18F-FDG uptake in ESCA and may be involved in the regulation of the cell cycle, m6A modification, and cell glycolysis. 18F-FDG PET/CT-related parameters can predict the diagnostic accuracy of HNRNPR expression in ESCA.
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