Ghrelin as a potential molecular marker of adrenal carcinogenesis: In vivo and in vitro evidence

Role of Different Variants of Leptin Receptor in Human Adrenal Tumor Types

The aim of the study was to evaluate the diagnostic and prognostic significance of leptin receptor isoforms in adrenal tumors. In a single-center study, 96 patients (19 with adrenal cortical carcinoma and 77 with benign tumors) underwent an adrenalectomy. A total of 14 unaffected adrenal gland tissues from kidney donors were used as controls. Fasting blood samples were collected for laboratory tests, and mRNA expressions of leptin receptor isoforms were assessed by RT-qPCR. The study analyzed correlations between mRNA expressions and clinical data and measured NCI-H295R cell proliferation via a real-time cell analyzer. All adrenal lesions expressed leptin receptor isoforms. Significantly lower LepR1 expression was observed in carcinoma tissues than in adenomas and controls (p = 0.016). Expressions of LepR3&LepR6 were correlated with overall survival (p = 0.036), while LepR2&LepR4 and LepR5 expressions were inversely related to morning serum cortisol levels (p = 0.041). Leptin reduced NCI-H295R cell proliferation (p < 0.0001). The study highlights the diagnostic and prognostic significance of leptin receptor isoforms in adrenal tumors. Specifically, LepR1 may serve as a diagnostic marker for carcinomas, while LepR3&LepR6 have potential use as prognostic markers.

Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers

Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC. Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort). A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups. Results: A multi-omics signature including genes, micro RNA's and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (OS) data demonstrated that patients with differential expression levels of 8 genes and 4 micro RNA's showed a statistically significant decrease in OS. We also found an independent prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients. Conclusion: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice.

The Role of Biomarkers in Adrenocortical Carcinoma: A Review of Current Evidence and Future Perspectives

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy arising from the adrenal cortex often with unexpected biological behavior. It can occur at any age, with two peaks of incidence: in the first and between fifth and seventh decades of life. Although ACC are mostly hormonally active, precursors and metabolites, rather than end products of steroidogenesis are produced by dedifferentiated and immature malignant cells. Distinguishing the etiology of adrenal mass, between benign adenomas, which are quite frequent in general population, and malignant carcinomas with dismal prognosis is often unfeasible. Even after pathohistological analysis, diagnosis of adrenocortical carcinomas is not always straightforward and represents a great challenge for experienced and multidisciplinary expert teams. No single imaging method, hormonal work-up or immunohistochemical labelling can definitively prove the diagnosis of ACC. Over several decades' great efforts have been made in finding novel reliable and available diagnostic and prognostic factors including steroid metabolome profiling or target gene identification. Despite these achievements, the 5-year mortality rate still accounts for approximately 75% to 90%, ACC is frequently diagnosed in advanced stages and therapeutic options are unfortunately limited. Therefore, imperative is to identify new biological markers that can predict patient prognosis and provide new therapeutic options.

Differential expression of ghrelin and GHSR via the mTOR pathway during the dynamic carcinogenic process involving oral, potentially malignant disorders

The purpose was to explore the sequence changes in ghrelin and GHSR in the mTOR signaling pathway during carcinogenesis involving oral, potentially malignant disorders (OPMD). The samples were confirmed through in vivo pathologic tissue screening and diagnosis. The immunohistochemical method was used to detect the expression of the ghrelin/growth hormone secretagogue receptor (GHSR) protein. The expression of ghrelin, GHSR 1α, GHSR 1β, and mammalian target of rapamycin (mTOR) RNA were detected by real-time PCR. The expression of ghrelin, GHSR, mTOR, and phosphorylated mTOR (phosphor-mTOR) protein were detected by Western blot. The expression of ghrelin/GHSR increased gradually in the dynamic process of OPMD carcinogenesis. There was a correlation between the increase in ghrelin, GHSR, mTOR, and phospho-mTOR. The in vivo expression of ghrelin/GHSR protein was the most apparent pathologic change from normal-to-mild, moderate, and severe dysplasia, and finally to the dynamic process from normal-to-mild-to-moderate dysplasia. The in vitro cell experiments based on QPCR results also proved that GHSR 1a functional receptor of ghrelin had a peak expression in LEUK-1 cells. In conclusioin, the close relationship between ghrelin and OPMD carcinogenesis can be used as a new biological target to assess the carcinogenesis of OPMD.