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Article

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

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Comprehensive Cancer Center Mainfranken, University Hospital, University of Würzburg, 97080 Würzburg, Germany
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Institute of Pathology, University of Würzburg, 97080 Würzburg, Germany
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Interdisciplinary Center for Clinical Research, University Hospital Würzburg, 97080 Würzburg, Germany
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Bavarian Cancer Research Center (BZKF), 97080 Würzburg, Germany
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Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, 97080 Würzburg, Germany
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Institute of Metabolism and Systems Research, University of Birmingham, Birmingham B15 2TT, UK
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Endocrinology Unit, Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
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Department of Endocrinology, Medizinische Klinik und Poliklinik IV, Ludwig-Maximilians-University, 80336 Munich, Germany
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Center for Computational and Theoretical Biology, University of Würzburg, 97074 Würzburg, Germany
*
Authors to whom correspondence should be addressed.
Academic Editor: Peter Igaz
Cancers 2021, 13(18), 4671; https://doi.org/10.3390/cancers13184671
Received: 6 September 2021 / Accepted: 10 September 2021 / Published: 17 September 2021
Using a visual-based clustering method on the TCGA RNA sequencing data of a large adrenocortical carcinoma (ACC) cohort, we were able to classify these tumors in two distinct clusters largely overlapping with previously identified ones. As previously shown, the identified clusters also correlated with patient survival. Applying the visual clustering method to a second dataset also including benign adrenocortical samples additionally revealed that one of the ACC clusters is more closely located to the benign samples, providing a possible explanation for the better survival of this ACC cluster. Furthermore, the subsequent use of machine learning identified new possible biomarker genes with prognostic potential for this rare disease, that are significantly differentially expressed in the different survival clusters and should be further evaluated.
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. View Full-Text
Keywords: adrenocortical carcinoma; in silico analysis; machine learning; bioinformatic clustering; biomarker prediction adrenocortical carcinoma; in silico analysis; machine learning; bioinformatic clustering; biomarker prediction
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MDPI and ACS Style

Marquardt, A.; Landwehr, L.-S.; Ronchi, C.L.; di Dalmazi, G.; Riester, A.; Kollmannsberger, P.; Altieri, B.; Fassnacht, M.; Sbiera, S. Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning. Cancers 2021, 13, 4671. https://doi.org/10.3390/cancers13184671

AMA Style

Marquardt A, Landwehr L-S, Ronchi CL, di Dalmazi G, Riester A, Kollmannsberger P, Altieri B, Fassnacht M, Sbiera S. Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning. Cancers. 2021; 13(18):4671. https://doi.org/10.3390/cancers13184671

Chicago/Turabian Style

Marquardt, André, Laura-Sophie Landwehr, Cristina L. Ronchi, Guido di Dalmazi, Anna Riester, Philip Kollmannsberger, Barbara Altieri, Martin Fassnacht, and Silviu Sbiera. 2021. "Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning" Cancers 13, no. 18: 4671. https://doi.org/10.3390/cancers13184671

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