Esophageal cancer is one of the major life-threatening diseases in the world. RNA methylation is the most common post-transcriptional modification and a wide-ranging regulatory system controlling gene expression. Numerous studies have revealed that dysregulation of RNA methylation is critical for cancer development and progression. However, the diverse role of RNA methylation and its regulators in esophageal cancer remains to be elucidated and summarized. In this review, we focus on the regulation of major RNA methylation, including m 6A, m 5C, and m 7G, as well as the expression patterns and clinical implications of its regulators in esophageal cancer. We systematically summarize how these RNA modifications affect the “life cycle” of target RNAs, including mRNA, microRNA, long non-coding RNA, and tRNA. The downstream signaling pathways associated with RNA methylation during the development and treatment of esophageal cancer are also discussed in detail. Further studies on how these modifications function together in the microenvironment of esophageal cancer will draw a clearer picture of the clinical application of novel and specific therapeutic strategies.

The diverse role of RNA methylation in esophageal cancer

Esophageal cancer (EC) is one of the fatal malignant neoplasms worldwide. Neoadjuvant therapy (NAT) combined with surgery has become the standard treatment for locally advanced EC. However, the treatment efficacy for patients with EC who received NAT varies from patient to patient. Currently, the evaluation of efficacy after NAT for EC lacks accurate and uniform criteria. Radiomics is a multi-parameter quantitative approach for developing medical imaging in the era of precision medicine and has provided a novel view of medical images. As a non-invasive image analysis method, radiomics is an inevitable trend in NAT efficacy prediction and prognosis classification of EC by analyzing the high-throughput imaging features of lesions extracted from medical images. In this literature review, we discuss the definition and workflow of radiomics, the advances in efficacy prediction after NAT, and the current application of radiomics for predicting efficacy after NAT.

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The application of radiomics in esophageal cancer: Predicting the response after neoadjuvant therapy

Background Esophageal cancer (ESCA) is the sixth most common malignancy in the world, and its incidence is rapidly increasing. Recently, several microRNAs (miRNAs) and messenger RNA (mRNA) targets were evaluated as potential biomarkers and regulators of epigenetic mechanisms involved in early diagnosis. In addition, computed tomography (CT) radiomic studies on ESCA improved the early stage identification and the prediction of response to treatment. Radiogenomics provides clinically useful prognostic predictions by linking molecular characteristics such as gene mutations and gene expression patterns of malignant tumors with medical images and could provide more opportunities in the management of patients with ESCA. Aim To explore the combination of CT radiomic features and molecular targets associated with clinical outcomes for characterization of ESCA patients. Methods Of 15 patients with diagnosed ESCA were included in this study and their CT imaging and transcriptomic data were extracted from The Cancer Imaging Archive and gene expression data from The Cancer Genome Atlas, respectively. Cancer stage, history of significant alcohol consumption and body mass index (BMI) were considered as clinical outcomes. Radiomic analysis was performed on CT images acquired after injection of contrast medium. In total, 1302 radiomics features were extracted from three-dimensional regions of interest by using PyRadiomics. Feature selection was performed using a correlation filter based on Spearman's correlation (ρ) and Wilcoxon-rank sum test respect to clinical outcomes. Radiogenomic analysis involved ρ analysis between radiomic features associated with clinical outcomes and transcriptomic signatures consisting of eight N6-methyladenosine RNA methylation regulators and five up-regulated miRNA. The significance level was set at P Results Of 25, five and 29 radiomic features survived after feature selection, considering stage, alcohol history and BMI as clinical outcomes, respectively. Radiogenomic analysis with stage as clinical outcome revealed that six of the eight mRNA regulators and two of the five up-regulated miRNA were significantly correlated with ten and three of the 25 selected radiomic features, respectively (-0.61 Conclusion Our study revealed interesting relationships between the expression of eight N6-methyladenosine RNA regulators, as well as five up-regulated miRNAs, and CT radiomic features associated with clinical outcomes of ESCA patients.

Lorenzo Mannelli

Papers

Impact of radiogenomics in esophageal cancer on clinical outcomes: A pilot study.