Dataset related to article "Definition of a multi-omics signature for Esophageal Adenocarcinoma prognosis prediction "
Creators
- 1. Institute of Genetic and Biomedical Research, UoS of Milan, National Research Council, Rozzano, Milan, Italy
- 2. Laboratory of Medical Genetics, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- 3. Institute of Genetic and Biomedical Research, UoS of Milan, National Research Council, Rozzano, Milan, Italy; Laboratory of Translational Immunology and Humanitas Flow Cytometry Core, Humanitas Research Hospital, Rozzano, Milan, Italy
- 4. Upper Gastrointestinal Surgery Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
- 5. San Rafaelle Telethon Institute for Gene Therapy, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy; Vita-Salute San Raffaele University, Milan 20132, Italy
- 6. Genomic Unit, Humanitas Research Hospital, Rozzano, Milan, Italy
- 7. Human Technopole, Via Rita Levi Montalcini 1, Milan, Italy
- 8. Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico | Policlinico Maggiore
- 9. Institute of Genetic and Biomedical Research, UoS of Milan, National Research Council, Rozzano, Milan, Italy; Human Technopole, Via Rita Levi Montalcini 1, Milan, Italy
Description
This record contains raw data related to article “Definition of a multi-omics signature for Esophageal Adenocarcinoma prognosis prediction "
Abstract: Esophageal cancer is a highly lethal malignancy that accounts for 5% of all cancer deaths. The two main sub-types of the disease are esophageal squamous-cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). To date, most studies focused on analysing the transcriptional profile in ESCC only a few studies analysed EAC for transcriptional signatures that might be associated with diagnosis and/or prognosis. In this work we performed a single-cell RNA sequencing (scRNAseq) analysis of the CD45+ cells enriched from from tumor and matched non-tumor tissues obtained from 3 therapy-naïve patients to identify all the types of immune cells present in the tumor's immune infiltrate and their transcriptomic profiles, moreover we have analysed the whole transcriptome in a cohort of 23 patients from whom tissue biopsies were taken from tumor and matched non-tumor tissues. The transcriptional signatures derived from both types of analyses were then used to stratify a larger cohort of TCGA EAC patients showing a strong association with their prognosis. The transcriptional signatures here described have therefore proved capable of being able to predict the clinical outcome of patients and could be used to better define the prognosis in EAC after surgery and to direct patients towards effective therapies.
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