@article{WeigandRonchiRizkRabinetal.2017,
  author    = {Weigand, Isabel and Ronchi, Cristina L. and Rizk-Rabin, Marthe and Dalmazi, Guido Di and Wild, Vanessa and Bathon, Kerstin and Rubin, Beatrice and Calebiro, Davide and Beuschlein, Felix and Bertherat, J{\´e}r{\^o}me and Fassnacht, Martin and Sbiera, Silviu},
  title     = {Differential expression of the protein kinase A subunits in normal adrenal glands and adrenocortical adenomas},
  series = {Scientific Reports},
  volume    = {7},
  journal   = {Scientific Reports},
  number    = {49},
  doi       = {10.1038/s41598-017-00125-8},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-157952},
  year      = {2017},
  abstract  = {Somatic mutations in protein kinase A catalytic α subunit (PRKACA) were found to be causative for 30-40\% of cortisol-producing adenomas (CPA) of the adrenal gland, rendering PKA signalling constitutively active. In its resting state, PKA is a stable and inactive heterotetramer, consisting of two catalytic and two regulatory subunits with the latter inhibiting PKA activity. The human genome encodes three different PKA catalytic subunits and four different regulatory subunits that are preferentially expressed in different organs. In normal adrenal glands all regulatory subunits are expressed, while CPA exhibit reduced protein levels of the regulatory subunit IIβ. In this study, we linked for the first time the loss of RIIβ protein levels to the PRKACA mutation status and found the down-regulation of RIIβ to arise post-transcriptionally. We further found the PKA subunit expression pattern of different tumours is also present in the zones of the normal adrenal cortex and demonstrate that the different PKA subunits have a differential expression pattern in each zone of the normal adrenal gland, indicating potential specific roles of these subunits in the regulation of different hormones secretion.},
  language  = {en}
}
@article{MarquardtLandwehrRonchietal.2021,
  author    = {Marquardt, Andr{\´e} 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},
  title     = {Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning},
  series = {Cancers},
  volume    = {13},
  journal   = {Cancers},
  number    = {18},
  issn      = {2072-6694},
  doi       = {10.3390/cancers13184671},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-246245},
  year      = {2021},
  abstract  = {Simple Summary 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. 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.},
  language  = {en}
}