Published online Sep 28, 2021. doi: 10.3748/wjg.v27.i36.6110
Peer-review started: March 2, 2021
First decision: June 3, 2021
Revised: June 16, 2021
Accepted: July 30, 2021
Article in press: July 30, 2021
Published online: September 28, 2021
Processing time: 204 Days and 18.5 Hours
Esophageal cancer (ESCA) is the sixth most common malignancy in the world, and its incidence is rapidly increasing. 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.
Recently, several microRNAs (miRNAs) and messenger (RNA) targets were evaluated as potential biomarkers and regulators of epigenetic mechanisms involved in ESCA. In addition, the use of computed tomography (CT) radiomics is rapidly increasing in the field of ESCA and plays an important role in different ESCA management steps. Moreover, there are no previous radiogenomic studies on ESCA investigating the association with clinical staging and potential risk factors. This has motivated us to investigate on the relationship between the expression levels of eight N6-methyladenosine (m6A) RNA methylation regulators, as well as the five up-regulated miRNAs, and radiomic features extracted from CT images that were significantly associated to clinical outcomes.
To explore the combination of CT radiomic features and molecular targets associated with clinical outcomes for characterization of ESCA patients.
Fifteen 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. Radiogenomic analysis involved Spearman’s correlation (ρ) analysis between radiomic features associated with clinical outcomes and transcriptomic signatures consisting in eight m6A RNA methylation regulators and five up-regulated miRNA.
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 10 and three of the 25 selected radiomic features, respectively. Assuming alcohol history as clinical outcome, no correlation was found between the five selected radiomic features and mRNA regulators, while a significant correlation was found between one radiomic feature and three up-regulated miRNA. Radiogenomic analysis with BMI as clinical outcome revealed that four mRNA regulators and one up-regulated miRNA were significantly correlated with 10 and two radiomic features, respectively.
Our study revealed interesting relationships between the expression of eight m6a RNA regulators, as well as five up-regulated miRNAs, and CT radiomic features associated with clinical outcomes of ESCA patients.
This preliminary study revealed interesting associations between m6a RNA regulators, as well as miRNAs, and CT radiomic features associated with clinical outcomes of ESCA patients. Further investigations on different ESCA omics data are required. Moreover, multimodal data combined with artificial intelligence techniques characteristics are desirable.