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.
Full Text PDF:/Users/philip/Zotero/storage/EVWSUJXW/Marquardt et al. - 2021 - Identifying New Potential Biomarkers in Adrenocort.pdf:application/pdf;Snapshot:/Users/philip/Zotero/storage/DS2S3TAT/4671.html:text/html
%0 Journal Article
%1 marquardt_identifying_2021
%A Marquardt, André
%A Landwehr, Laura-Sophie
%A Ronchi, Cristina L.
%A di Dalmazi, Guido
%A Riester, Anna
%A Kollmannsberger, Philip
%A Altieri, Barbara
%A Fassnacht, Martin
%A Sbiera, Silviu
%D 2021
%J Cancers
%K cctb computationalimageanalysis imported philipkollmannsberger
%N 18
%P 4671
%R 10.3390/cancers13184671
%T Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning
%U https://www.mdpi.com/2072-6694/13/18/4671
%V 13
%X 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.
@article{marquardt_identifying_2021,
abstract = {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.},
added-at = {2021-10-06T16:08:41.000+0200},
author = {Marquardt, André and Landwehr, Laura-Sophie and Ronchi, Cristina L. and di Dalmazi, Guido and Riester, Anna and Kollmannsberger, Philip and Altieri, Barbara and Fassnacht, Martin and Sbiera, Silviu},
biburl = {https://www.bibsonomy.org/bibtex/2dd3ab97cd9146b32d893bcfcf666b03d/philipk},
copyright = {http://creativecommons.org/licenses/by/3.0/},
doi = {10.3390/cancers13184671},
file = {Full Text PDF:/Users/philip/Zotero/storage/EVWSUJXW/Marquardt et al. - 2021 - Identifying New Potential Biomarkers in Adrenocort.pdf:application/pdf;Snapshot:/Users/philip/Zotero/storage/DS2S3TAT/4671.html:text/html},
interhash = {420d7e986420c78e39c443c9662ed7e0},
intrahash = {dd3ab97cd9146b32d893bcfcf666b03d},
journal = {Cancers},
keywords = {cctb computationalimageanalysis imported philipkollmannsberger},
language = {en},
month = jan,
note = {Number: 18Publisher: Multidisciplinary Digital Publishing Institute},
number = 18,
pages = 4671,
timestamp = {2021-10-06T16:08:41.000+0200},
title = {Identifying {New} {Potential} {Biomarkers} in {Adrenocortical} {Tumors} {Based} on {mRNA} {Expression} {Data} {Using} {Machine} {Learning}},
url = {https://www.mdpi.com/2072-6694/13/18/4671},
urldate = {2021-10-06},
volume = 13,
year = 2021
}