Proteome analysis of adrenal cortical tumors

Construction of a robust prognostic model for adult adrenocortical carcinoma: Results from bioinformatics and real-world data

This study aims to construct a robust prognostic model for adult adrenocortical carcinoma (ACC) by large‐scale multiomics analysis and real‐world data. The RPPA data, gene expression profiles and clinical information of adult ACC patients were obtained from The Cancer Proteome Atlas (TCPA), Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Integrated prognosis‐related proteins (IPRPs) model was constructed. Immunohistochemistry was used to validate the prognostic value of the IPRPs model in Fudan University Shanghai Cancer Center (FUSCC) cohort. 76 ACC cases from TCGA and 22 ACC cases from GSE10927 in NCBI’s GEO database with full data for clinical information and gene expression were utilized to validate the effectiveness of the IPRPs model. Higher FASN (P = .039), FIBRONECTIN (P < .001), TFRC (P < .001), TSC1 (P < .001) expression indicated significantly worse overall survival for adult ACC patients. Risk assessment suggested significantly a strong predictive capacity of IPRPs model for poor overall survival (P < .05). IPRPs model showed a little stronger ability for predicting prognosis than Ki‐67 protein in FUSCC cohort (P = .003, HR = 3.947; P = .005, HR = 3.787). In external validation of IPRPs model using gene expression data, IPRPs model showed strong ability for predicting prognosis in TCGA cohort (P = .005, HR = 3.061) and it exhibited best ability for predicting prognosis in GSE10927 cohort (P = .0898, HR = 2.318). This research constructed IPRPs model for predicting adult ACC patients’ prognosis using proteomic data, gene expression data and real‐world data and this prognostic model showed stronger predictive value than other biomarkers (Ki‐67, Beta‐catenin, etc) in multi‐cohorts.

Genomics of benign adrenocortical tumors

Benign adrenocortical adenomas and hyperplasia are relatively common and include a spectrum of distinct entities, which diagnosis depends on the macroscopic aspect and the secretion profile. Recent advances in genomics have proposed high-throughput molecular characterization of adrenal tumors, thereby improving our knowledge on the pathophysiology and tumorigenesis of these tumors. Genomic (exome and chromosome alteration profiles), epigenomic (micro-RNAs expression and methylation profiles) and transcriptomic (gene expression profiles) studies highlighted the major roles of intracellular calcium signaling in aldosterone-producing adenomas (APA), of protein kinase A (PKA)/cAMP pathway in cortisol-producing tumors, and of Wnt/beta-catenin pathway in non-secreting tumors. Exome sequencing revealed new major drivers in all tumor types, including KCNJ5, ATP1A1, ATP2B3, CACNA1D and CACNA1H mutations in APA, PRKACA mutations in cortisol-producing adenomas (CPA) and ARMC5 mutations in primary macronodular adrenocortical hyperplasia (PMAH). The clinical impact of these findings is just starting to evolve. The identification of genetic syndromes, such as germline ARMC5 mutations in PMAH, has allowed genetic counseling. Key molecular alterations could serve as a basis for the development of targeted medical treatments for benign adrenal tumors. The recent developments in genomics, including single-cell technologies, and in proteomics and metabolomics will probably offer new perspectives for characterizing benign adrenal tumorigenesis.

ITRAQ-based proteomic analysis reveals possible target-related proteins in human adrenocortical adenomas

Background

Adrenocortical adenomas (ACAs) can lead to the autonomous secretion of aldosterone responsible for primary aldosteronism (PA), which is the most common form of secondary arterial hypertension. However, the authentic fundamental mechanisms underlying ACAs remain unclear.

Objective

Isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomics and bioinformatics analyses from etiological studies of ACAs were performed to screen the differentially expressed proteins (DEPs) and investigate the relevant mechanisms of their occurrence and development. Results could help determine therapeutic targets of clinical significance.

Methods

In the present study, iTRAQ-based proteomics was applied to analyze ACA tissue samples from normal adrenal cortex tissues adjacent to the tumor. Using proteins extracted from a panel of four pairs of ACA samples, we identified some upregulated proteins and other downregulated proteins in all four pairs of ACA samples compared with adjacent normal tissue. Subsequently, we predicted protein–protein interaction networks of three DEPs to determine the authentic functional factors in ACA.

Results

A total of 753 DEPs were identified, including 347 upregulated and 406 downregulated proteins. The expression of three upregulated proteins (E2F3, KRT6A, and ALDH1A2) was validated by Western blot in 24 ACA samples. Our data suggested that some DEPs might be important hallmarks during the development of ACA.

Conclusions

This study is the first proteomic research to investigate alterations in protein levels and affected pathways in ACA using the iTRAQ technique. Thus, this study not only provides a comprehensive dataset on overall protein changes but also sheds light on its potential molecular mechanism in human ACAs.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-6030-5) contains supplementary material, which is available to authorized users.