Comprehensive RNA dataset of tissue and plasma from patients with esophageal cancer or precursor lesions

• Published in: Scientific data Vol. 9; no. 1; p. 86
• Main Authors: Schoofs, Kathleen, Philippron, Annouck, Avila Cobos, Francisco, Koster, Jan, Lefever, Steve, Anckaert, Jasper, De Looze, Danny, Vandesompele, Jo, Pattyn, Piet, De Preter, Katleen
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 14.03.2022; Nature Publishing Group UK; Nature Portfolio
• Subjects: Adenocarcinoma; Adenocarcinoma - blood; Adenocarcinoma - genetics; Barrett Esophagus - blood; Barrett Esophagus - genetics; Barrett's esophagus; Biomarkers; Biopsy; Biotypes; Cancer; Data Descriptor; Datasets; Disease Progression; Esophageal cancer; Esophageal Neoplasms - blood; Esophageal Neoplasms - genetics; Esophagus; Etiology; Fusion protein; Humans; MicroRNAs; MicroRNAs - genetics; miRNA; mRNA; Non-coding RNA; Plasma - metabolism
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-022-01176-x

In the past decades, the incidence of esophageal adenocarcinoma has increased dramatically in Western populations. Better understanding of disease etiology along with the identification of novel prognostic and predictive biomarkers are urgently needed to improve the dismal survival probabilities. Here, we performed comprehensive RNA (coding and non-coding) profiling in various samples from 17 patients diagnosed with esophageal adenocarcinoma, high-grade dysplastic or non-dysplastic Barrett's esophagus. Per patient, a blood plasma sample, and a healthy and disease esophageal tissue sample were included. In total, this comprehensive dataset consists of 102 sequenced libraries from 51 samples. Based on this data, 119 expression profiles are available for three biotypes, including miRNA (51), mRNA (51) and circRNA (17). This unique resource allows for discovery of novel biomarkers and disease mechanisms, comparison of tissue and liquid biopsy profiles, integration of coding and non-coding RNA patterns, and can serve as a validation dataset in other RNA landscaping studies. Moreover, structural RNA differences can be identified in this dataset, including protein coding mutations, fusion genes, and circular RNAs.