A transcriptome-Based Deep Neural Network Classifier for Identifying the Site of Origin in Mucinous Cancer.

ABSTRACT:

Purpose

There is a lack of tools for identifying the site of origin in mucinous cancer. This study aimed to evaluate the performance of a transcriptome-based classifier for identifying the site of origin in mucinous cancer.

Materials and methods

Transcriptomic data of 1878 non-mucinous and 82 mucinous cancer specimens, with 7 sites of origin, namely, the uterine cervix (CESC), colon (COAD), pancreas (PAAD), stomach (STAD), uterine endometrium (UCEC), uterine carcinosarcoma (UCS), and ovary (OV), obtained from The Cancer Genome Atlas, were used as the training and validation sets, respectively. Transcriptomic data of 14 mucinous cancer specimens from a tissue archive were used as the test set. For identifying the site of origin, a set of 100 differentially expressed genes for

SUBMITTER: Ahn T 

PROVIDER: S-EPMC9669684 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature