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Additional file 1: Table S1. List of genes included in the customised panel for Next Generation Sequencing.

Supplementary files

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.

The pathogenetic mechanisms underlying the onset of adrenocortical tumors (ACTs) are still largely unknown. Recently, more attention has been paid to the role of insulin and insulin-like growth factor (IGF) system on general tumor development and progression. Increased levels of insulin, IGF-1 and IGF-2 are associated with tumor cell growth and increased risk of cancer promotion and progression in patients with type 2 diabetes. Insulin resistance and compensatory hyperinsulinemia may play a role in adrenal tumor growth through the activation of insulin and IGF-1 receptors. Interestingly, apparently non-functioning ACTs are often associated with a high prevalence of insulin resistance and metabolic syndrome. However, it is unclear if ACT develops from a primary insulin resistance and compensatory hyperinsulinemia or if insulin resistance is only secondary to the slight cortisol hypersecretion by ACT. The aim of this review is to summarize the current evidence regarding the relationship between hyperinsulinemia and adrenocortical tumors.

Non-coding RNAs (ncRNAs) are a type of genetic material that do not encode proteins but regulate the gene expression at an epigenetic level, such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs). The role played by ncRNAs in many physiological and pathological processes has gained attention during the last few decades, as they might be useful in the diagnosis, treatment and management of several human disorders, including endocrine and oncological diseases. Adrenocortical carcinoma (ACC) is a rare and aggressive endocrine cancer, still characterized by high mortality and morbidity due to both endocrine and oncological complications. Despite the rarity of this disease, recently, the role of ncRNA has been quite extensively evaluated in ACC. In order to better explore the role of the ncRNA in human ACC, this review summarizes the current knowledge on ncRNA dysregulation in ACC and its potential role in the diagnosis, treatment, and management of this tumor.

Context Pathogenesis of autonomous steroid secretion and adrenocortical tumorigenesis remains partially obscure. Objective To investigate the relationship between transcriptome profile and genetic background in a large series of adrenocortical tumors and identify new potential pathogenetic mechanisms. Design Cross-sectional study. Setting University Hospitals of the European Network for the Study of Adrenal Tumors (ENSAT). Patients We collected snap-frozen tissue from patients with adrenocortical tumors (n = 59) with known genetic background: 26 adenomas with Cushing syndrome (CS- cortisol-producing adenoma [CPA]), 17 adenomas with mild autonomous cortisol secretion (MACS-CPAs), 9 endocrine-inactive adenomas (EIAs), and 7 adrenocortical carcinomas (ACCs). Intervention Ribonucleic acid (RNA) sequencing. Main Outcome Measures Gene expression, long noncoding RNA (lncRNA) expression, and gene fusions. Correlation with genetic background defined by targeted Sanger sequencing, targeted panel- or whole-exome sequencing. Results Transcriptome analysis identified 2 major clusters for adenomas: Cluster 1 (n = 32) mainly consisting of MACS-CPAs with CTNNB1 or without identified driver mutations (46.9% of cases) and 8/9 EIAs; Cluster 2 (n = 18) that comprised CP-CPAs with or without identified driver mutation in 83.3% of cases (including all CS-CPAs with PRKACA mutation). Two CS-CPAs, 1 with CTNNB1 and 1 with GNAS mutation, clustered separately and relatively close to ACC. lncRNA analysis well differentiate adenomas from ACCs. Novel gene fusions were found, including AKAP13-PDE8A in one CS-CPA sample with no driver mutation. Conclusions MACS-CPAs and EIAs showed a similar transcriptome profile, independently of the genetic background, whereas most CS-CPAs clustered together. Still unrevealed molecular alterations in the cAMP/PKA or Wnt/beta catenin pathways might be involved in the pathogenesis of adrenocortical tumors.

Objective A common polymorphic variant of the growth hormone receptor (GHR) is because of genomic deletion of exon 3 and has been linked with increased responsiveness to exogenous GH. The impact of this polymorphism in acromegaly, a disease characterized by endogenous excess of GH and partial loss of IGF-I feedback on tumoural GH secretion, is not clear. The aim of this study was to investigate possible influences of d3GHR on the GH/IGF-I relationship and metabolic parameters in acromegaly. Design and methods Retrospective study on 76 acromegalic patients. Genotype analysis was carried out on leucocyte DNA by multiplex PCR assay. Clinical, hormonal and biochemical parameters at diagnosis were collected from patients' medical records. Results Forty-two patients (55·3%) were homozygotes for the allele encoding the full-length GHR (fl/flGHR), 27 patients were heterozygotes (fl/d3) and seven homozygotes (d3/d3) for the genomic deletion of exon 3. Heterozygotes and homozygotes for the d3 allele were considered together (d3GHR) and compared with fl/flGHR patients. d3GHR and fl/flGHR patients showed no difference in GH and IGF-I levels or in the relationship between these two parameters. Patients bearing d3GHR had a lower body mass index (BMI) than patients bearing fl/flGHR (25·8 ± 2·1 vs. 28·1 ± 4·8 kg/m2, P < 0·05). Diabetes mellitus and hypertension were equally distributed, but more d3GHR patients had a normal glucose tolerance (66·7%vs. 56·3%, P < 0·05). The presence of d3GHR allele, and not BMI or age, was a significant negative predictor of insulin levels 120 min after oral glucose load (β = -80·8, P < 0·05). Conclusions This study supports the hypothesis that the d3GHR is functionally different from the fl/fl variant mostly for the effects on body weight regulation and on glucose metabolism. © 2010 Blackwell Publishing Ltd.