Radiogenomics predicts the expression of microRNA-1246 in the serum of esophageal cancer patients

Abstract

Radiogenomics is a new field that provides clinically useful prognostic predictions by linking molecular characteristics such as the genetic aberrations of malignant tumors with medical images. the abnormal expression of serum microRNA-1246 (miR-1246) has been reported as a prognostic factor of esophageal squamous cell carcinoma (ESCC). To evaluate the power of the miR-1246 level predicted with radiogenomics techniques as a predictor of the prognosis of ESCC patients. The real miR-1246 expression (miR-1246 real) was measured in 92 ESCC patients. Forty-five image features (IFs) were extracted from tumor regions on contrast-enhanced computed tomography. A prediction model for miR-1246 real was constructed using linear regression with selected features identified in a correlation analysis of miR-1246 real and each IF. A threshold to divide the patients into two groups was defined according to a receiver operating characteristic analysis for miR-1246 real. Survival analyses were performed between two groups. Six IFs were correlated with miR-1246 real and were included in the prediction model. The survival curves of high and low groups of miR-1246 real and miR-1246 pred showed significant differences (p = 0.001 and 0.016). Both miR-1246 real and miR-1246 pred were independent predictors of overall survival (p = 0.030 and 0.035). miR-1246 pred produced by radiogenomics had similar power to miR-1246 real for predicting the prognosis of eScc.

Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer

“… 37 Taguchi et al had reported KRAS mutations in colorectal cancer predicted from CT images using AI technology. 38 A previous report had identified blood microRNAs associated with prognosis in oesophageal squamous cell carcinoma and predicted microRNA level and prognosis from IFs based on CT. 39 Although there was only one report on pancreatic cancer, Attiyeh et al reported that in 35 cases, only 28 IFs were significant for SMAD4, out of 255 IFs extracted from CT. Using a two-dimensional scale, difference in the distribution of SMAD4 between normal and abnormal cases could be demonstrated, and the status of SMAD4 (normal / abnormal) could be identified by CT; the proportion of stroma could also be predicted from IFs.…”

Circulating miR-1246 Targeting UBE2C, TNNI3, TRAIP, UCHL1 Genes and Key Pathways as a Potential Biomarker for Lung Adenocarcinoma: Integrated Biological Network Analysis

“…MiR-1246 plays an imperative role in different cancers through their targets. For example, the expression of miR-1246 was significantly correlated with chemoresistance and cancer stem-cell-like characteristics and could identify a worse prognosis in cancer patients (pancreatic) by targeting cyclin G2 protein-coding genes such as CCNG2 [ 40 ]. Moreover, Li et al [ 41 ] reported that miR-1246 enhances the proliferation and invasion of breast cancer cells by repressing the level of its CCNG2 target gene.…”

Development and Validation of a 18F-FDG PET-Based Radiomic Model for Evaluating Hypermetabolic Mediastinal–Hilar Lymph Nodes in Non-Small-Cell Lung Cancer

“…GLRLM_GLNU, GLRLM_RLNU, and NGLDM_ Coarseness are the higher-order radiomic features. Several studies have demonstrated that the GLRLM and NGLDM texture analysis can assess the heterogeneity of the lesion (27,28). Wu et al (29) reported that the GLRLM textural feature could be useful in differentiating the invasiveness of lung adenocarcinoma.…”

“…GLRLM_GLNU, GLRLM_RLNU, and NGLDM_ Coarseness are the higher-order radiomic features. Several studies have demonstrated that the GLRLM and NGLDM texture analysis can assess the heterogeneity of the lesion ( 27 , 28 ). Wu et al.…”