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

Abstract

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 < 0.05.

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 < ρ < -0.60 and 0.53 < ρ < 0.69, P < 0.05). 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 miRNAs (ρ = -0.56, ρ = -0.64 and ρ = 0.61, P < 0.05). 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 (-0.67 < ρ < -0.54 and 0.53 < ρ < 0.71, P < 0.05).

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.

Keywords: Computed tomography; Esophageal cancer; MicroRNAs; N6-methyladenosine; Radiogenomics; Radiomics.

Figure 1

Flowchart reporting the organization of data and analyses in the study. BMI: Body mass index; CT: Computed tomography; TCGA: The Cancer Genome Atlas; TCIA: The Cancer Imaging Archive.

Figure 2

Workflow of radiogenomic analysis implemented in the study. On the first row the radiomic analysis steps. On the second row the radiogenomic analysis for each clinical outcome. BMI: Body mass index; miRNA: MicroRNA; mRNA: Messenger RNA.

Figure 3

Radiogenomic analysis using stage as clinical outcome. Heatmap depicting the correlation matrix between transcriptomic features and radiomic features significantly associated with stage. GLCM: Gray level co-occurrence matrix; GLDM: Gray level dependence matrix; GLRLM: Gray level run length matrix; GLSZM: Gray level size zone matrix; H: High-pass filter; L: Low-pass filter; NGTDM: Neighboring gray tone difference matrix.

Figure 4

Radiogenomic analysis using alcohol history as clinical outcome. Heatmap depicting the correlation matrix between transcriptomic features and radiomic features significantly associated with alcohol history. H: High-pass filter; L: Low-pass filter; miRNA: MicroRNA.

Figure 5

Radiogenomic analysis using body mass index as clinical outcome. Heatmap depicting the correlation matrix between transcriptomic features and radiomic features significantly associated with body mass index. GLCM: Gray level co-occurrence matrix; GLDM: Gray level dependence matrix; GLRLM: Gray level run length matrix; GLSZM: Gray level size zone matrix; H: High-pass filter; L: Low-pass filter; miRNA: MicroRNA; mRNA: Messenger RNA; NGTDM: Neighboring gray tone difference matrix.