Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma

Uterine corpus endometrial carcinoma (UCEC) is the second most common type of gynecological tumor. Several research studies have recently shown the potential of different ncRNAs as biomarkers for prognostics and diagnosis in different types of cancers, including UCEC. Thus, we hypothesized that long...

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Bibliographic Details
Main Authors: Ana Carolina Mello, Martiela Freitas, Laura Coutinho, Tiago Falcon, Ursula Matte
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:BioMed Research International
Online Access:http://dx.doi.org/10.1155/2020/3968279
Description
Summary:Uterine corpus endometrial carcinoma (UCEC) is the second most common type of gynecological tumor. Several research studies have recently shown the potential of different ncRNAs as biomarkers for prognostics and diagnosis in different types of cancers, including UCEC. Thus, we hypothesized that long noncoding RNAs (lncRNAs) could serve as efficient factors to discriminate solid primary (TP) and normal adjacent (NT) tissues in UCEC with high accuracy. We performed an in silico differential expression analysis comparing TP and NT from a set of samples downloaded from the Cancer Genome Atlas (TCGA) database, targeting highly differentially expressed lncRNAs that could potentially serve as gene expression markers. All analyses were performed in R software. The receiver operator characteristics (ROC) analyses and both supervised and unsupervised machine learning indicated a set of 14 lncRNAs that may serve as biomarkers for UCEC. Functions and putative pathways were assessed through a coexpression network and target enrichment analysis.
ISSN:2314-6133
2314-6141