Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning

Adrenocortical carcinoma (ACC) is a rare disease, associated with poor survival. Several “multiple-omics” studies characterizing ACC on a molecular level identified two different clusters correlating with patient survival (C1A and C1B). We here used the publicly available transcriptome data from the...

Full description

Saved in:
Bibliographic Details
Published in:Cancers 2021-09, Vol.13 (18), p.4671
Main Authors: Marquardt, André, Landwehr, Laura-Sophie, Ronchi, Cristina L., di Dalmazi, Guido, Riester, Anna, Kollmannsberger, Philip, Altieri, Barbara, Fassnacht, Martin, Sbiera, Silviu
Format: Article
Language:English
Subjects:
Adrenal glands
Benign
Biomarkers
Cell cycle
Consortia
Datasets
DNA methylation
Epigenetics
Gene expression
Genomes
Growth factors
Learning algorithms
Machine learning
Medical prognosis
Mutation
Patients
Probability
Rare diseases
Steroids
Survival
Transcriptomes
Tumorigenesis
Tumors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Adrenocortical carcinoma (ACC) is a rare disease, associated with poor survival. Several “multiple-omics” studies characterizing ACC on a molecular level identified two different clusters correlating with patient survival (C1A and C1B). We here used the publicly available transcriptome data from the TCGA-ACC dataset (n = 79), applying machine learning (ML) methods to classify the ACC based on expression pattern in an unbiased manner. UMAP (uniform manifold approximation and projection)-based clustering resulted in two distinct groups, ACC-UMAP1 and ACC-UMAP2, that largely overlap with clusters C1B and C1A, respectively. However, subsequent use of random-forest-based learning revealed a set of new possible marker genes showing significant differential expression in the described clusters (e.g., SOAT1, EIF2A1). For validation purposes, we used a secondary dataset based on a previous study from our group, consisting of 4 normal adrenal glands and 52 benign and 7 malignant tumor samples. The results largely confirmed those obtained for the TCGA-ACC cohort. In addition, the ENSAT dataset showed a correlation between benign adrenocortical tumors and the good prognosis ACC cluster ACC-UMAP1/C1B. In conclusion, the use of ML approaches re-identified and redefined known prognostic ACC subgroups. On the other hand, the subsequent use of random-forest-based learning identified new possible prognostic marker genes for ACC.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers13184671