Transcriptomic Profiling Reveals an Enhancer RNA Signature for Recurrence Prediction in Colorectal Cancer

• Published in: Genes Vol. 14; no. 1; p. 137
• Main Authors: Sahu, Divya, Lin, Chen-Ching, Goel, Ajay
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03.01.2023; MDPI
• Subjects: Biomarkers; Colon cancer; Colonic Neoplasms; Colorectal cancer; Colorectal carcinoma; Colorectal Neoplasms - diagnosis; Colorectal Neoplasms - genetics; Colorectal Neoplasms - pathology; Gene expression; Genomes; Humans; Malignancy; Medical prognosis; Neoplasm Recurrence, Local - genetics; Neoplasm Recurrence, Local - pathology; Patients; Precision Medicine; Proteins; Rectal Neoplasms; Regression analysis; Risk factors; Risk groups; RNA; Transcriptome - genetics; Transcriptomics; Tumors; recurrence prediction biomarker; enhancer RNA; transcriptome expression profiling; colorectal cancer; LASSO Cox regression
• ISSN: 2073-4425; 2073-4425
• DOI: 10.3390/genes14010137

Colorectal cancer (CRC) is one of the most fatal malignancies worldwide, and this is in part due to high rates of tumor recurrence in these patients. Currently, TNM staging remains the gold standard for predicting prognosis and recurrence in CRC patients; however, this approach is inadequate for identifying high-risk patients with the highest likelihood of disease recurrence. Recent evidence has revealed that enhancer RNAs (eRNAs) represent a higher level of cellular regulation, and their expression is frequently dysregulated in several cancers, including CRC. However, the clinical significance of eRNAs as recurrence predictor biomarkers in CRC remains unexplored, which is the primary aim of this study. We performed a systematic analysis of eRNA expression profiles in colon cancer (CC) and rectal cancer (RC) patients from the TCGA dataset. By using rigorous biomarker discovery approaches by splitting the entire dataset into a training and testing cohort, we identified a 22-eRNA panel in CC and a 19-eRNA panel in RC for predicting tumor recurrence. The Kaplan-Meier analysis showed that biomarker panels robustly stratified low and high-risk CC ( = 7.29 × 10 ) and RC ( = 6.81 × 10 ) patients with recurrence. Multivariate and LASSO Cox regression models indicated that both biomarker panels were independent predictors of recurrence and significantly superior to TNM staging in CC (HR = 11.89, = 9.54 × 10 ) and RC (HR = 3.91, = 3.52 × 10 ). Notably, the ROC curves demonstrated that both panels exhibited excellent recurrence prediction accuracy in CC (AUC = 0.833; 95% CI: 0.74-0.93) and RC (AUC = 0.834; 95% CI: 0.72-0.92) patients. Subsequently, a combination signature that included the eRNA panels and TNM staging achieved an even greater predictive accuracy in patients with CC (AUC = 0.85). Herein, we report a novel eRNA signature for predicting recurrence in patients with CRC. Further experimental validation in independent clinical cohorts, these biomarkers can potentially improve current risk stratification approaches for guiding precision oncology treatments in patients suffering from this lethal malignancy.