Identifying new potential biomarkers in adrenocortical tumors based on mRNA expression data using machine learning

Andre Marquardt, Laura-Sophie Landwehr, Cristina Ronchi, Guido Di Dalmazi, Anna Riester, Philip Kollmannsberger, Barbara Altieri, Martin Fassnacht, Silviu Sbiera

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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.
Original languageEnglish
Article number4671
Number of pages13
Issue number18
Publication statusPublished - 17 Sept 2021


  • adrenocortical carcinoma
  • bioinformatic clustering
  • biomarker prediction
  • in silico analysis
  • machine learning


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