TY - JOUR A1 - Fuss, Carmina Teresa A1 - Other, Katharina A1 - Heinze, Britta A1 - Landwehr, Laura-Sophie A1 - Wiegering, Armin A1 - Kalogirou, Charis A1 - Hahner, Stefanie A1 - Fassnacht, Martin T1 - Expression of the chemokine receptor CCR7 in the normal adrenal gland and adrenal tumors and its correlation with clinical outcome in adrenocortical carcinoma JF - Cancers N2 - Background: The chemokine receptor CCR7 is crucial for an intact immune function, but its expression is also associated with clinical outcome in several malignancies. No data exist on the expression of CCR7 in adrenocortical tumors. Methods: CCR7 expression was investigated by qRT-PCR and immunohistochemistry in 4 normal adrenal glands, 59 adrenocortical adenomas, and 181 adrenocortical carcinoma (ACC) samples. Results: CCR7 is highly expressed in the outer adrenocortical zones and medulla. Aldosterone-producing adenomas showed lower CCR7 protein levels (H-score 1.3 ± 1.0) compared to non-functioning (2.4 ± 0.5) and cortisol-producing adenomas (2.3 ± 0.6), whereas protein expression was variable in ACC (1.8 ± 0.8). In ACC, CCR7 protein expression was significantly higher in lymph node metastases (2.5 ± 0.5) compared to primary tumors (1.8±0.8) or distant metastases (2.0 ± 0.4; p < 0.01). mRNA levels of CCR7 were not significantly different between ACCs, normal adrenals, and adrenocortical adenomas. In contrast to other tumor entities, neither CCR7 protein nor mRNA expression significantly impacted patients' survival. Conclusion: We show that CCR7 is expressed on mRNA and protein level across normal adrenals, benign adrenocortical tumors, as well as ACCs. Given that CCR7 did not influence survival in ACC, it is probably not involved in tumor progression, but it could play a role in adrenocortical homeostasis. KW - CCR7 KW - chemokine receptor KW - adrenocortical carcinoma KW - adrenal tumors Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-250112 SN - 2072-6694 VL - 13 IS - 22 ER - TY - JOUR A1 - Marquardt, André A1 - Landwehr, Laura-Sophie A1 - Ronchi, Cristina L. A1 - di Dalmazi, Guido A1 - Riester, Anna A1 - Kollmannsberger, Philip A1 - Altieri, Barbara A1 - Fassnacht, Martin A1 - Sbiera, Silviu T1 - Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning JF - Cancers N2 - 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. KW - adrenocortical carcinoma KW - in silico analysis KW - machine learning KW - bioinformatic clustering KW - biomarker prediction Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-246245 SN - 2072-6694 VL - 13 IS - 18 ER -