Disorders of the adrenal cortex: Genetic and molecular aspects

Chimeric Genes Causing 11β-Hydroxylase Deficiency: Implications in Clinical and Molecular Diagnosis

Deficiency of 11β-hydroxylase (11β-OHD) is the second most common cause of congenital adrenal hyperplasia (CAH), accounting for 0.2-8% of all cases. The disease is transmitted as an autosomal recessive trait and the underlying genetic causes of 11β-OHD are primarily small pathogenic variants affecting the CYP11B1 gene coding the 11β-hydroxylase enzyme. However, special events complicate the molecular diagnosis of 11β-OHD such as an unequal crossing over between the CYP11B2 (coding aldosterone synthase enzyme) and CYP11B1 genes. The resulting allele contains a hybrid gene, with a CYP11B2 5'-end and a CYP11B1 3'-end, where the CYP11B1 gene is under the control of the CYP11B2 promoter and thus not responding to the adrenocorticotropin (ACTH) but to angiotensin II and K+. This leads a reduction of cortisol production in 11β-OHD. In particular, CYP11B2/CYP11B1 chimeric genes can be distinguished into two groups depending on the breakpoint site: chimeras with breakpoint after the exon 5 of CYP11B2 preserve the aldosterone synthase activity, the others with breakpoint before exon 5 lose this function. In the last case, a more severe phenotype is expected. The aim of this review was to explore the setting of CYP11B2/CYP11B1 chimeras in 11β-OHD, performing a careful review of clinical literature cases.

Prevalence of Steroid-Induced Hyperglycemia in King Abdulaziz Specialist Hospital, Taif City, Saudi Arabia

Background Hyperglycemia is a common side effect of high-dose steroid therapy in hospitalized patients. Objectives To assess the prevalence of hyperglycemia among hospitalized patients receiving steroid therapy. Methods A retrospective study was conducted among 245 patients. The inclusion criteria were patients undergoing steroid therapy and admitted to a single tertiary care hospital due to medical complications or exacerbation of the diseases they were suffering from. Data encompassing patient demographics, admission, discharge dates, comorbidities, medication histories, laboratory results (including blood glucose levels), and documented corticosteroid administrations were meticulously gathered from electronic health records (EHRs). A logistic regression model analysis was done to predict the risk factors of poor glycemic control among hospitalized patients. Results The prevalence of hyperglycemia among the patients who were on steroid therapy was 34.2%. About 70.7% of the patients who required insulin at the time of admission required >17 units, and the insulin requirement was significantly higher among patients who received dexamethasone compared to other steroids (p<0.05). Older age (>65 years) was found to be independently associated with poor glycemic control (p<0.05). Conclusion The study revealed that almost one-third of patients on steroid therapy had hyperglycemia. Monitoring of patients for hyperglycemia after beginning high-dose steroid therapy should be done.

Identification of Key Genes and Related Drugs of Adrenocortical Carcinoma by Integrated Bioinformatics Analysis

Adrenocortical carcinoma (ACC) is a malignant carcinoma with an extremely poor prognosis, and its pathogenesis remains to be understood to date, necessitating further investigation. This study aims to discover biomarkers and potential therapeutic agents for ACC through bioinformatics, enhancing clinical diagnosis and treatment strategies. Differentially expressed genes (DEGs) between ACC and normal adrenal cortex were screened out from the GSE19750 and GSE90713 datasets available in the GEO database. An online Venn diagram tool was utilized to identify the common DEGs between the two datasets. The identified DEGs were subjected to functional assessment, pathway enrichment, and identification of hub genes by performing the protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The differences in the expressions of hub genes between ACC and normal adrenal cortex were validated at the GEPIA2 website, and the association of these genes with the overall patient survival was also assessed. Finally, on the QuartataWeb website, drugs related to the identified hub genes were determined. A total of 114 DEGs, 10 hub genes, and 69 known drugs that could interact with these genes were identified. The GO and KEGG analyses revealed a close association of the identified DEGs with cellular signal transduction. The 10 hub genes identified were overexpressed in ACC, in addition to being significantly associated with adverse prognosis in ACC. Three genes and the associated known drugs were identified as potential targets for ACC treatment.

Molecular tools for diagnosing diseases of the adrenal cortex

PURPOSE OF REVIEW

The adrenal glands produce some of the most essential for life hormones, including cortisol and other steroids, and catecholamines. The former is produced from the adrenal cortex, whereas the latter is from the medulla. The two parts are anatomically and functionally distinct and it would be impossible in the context of one short article to cover all molecular updates on both the cortex and the medulla. Thus, in this review, we focus on the molecular tools available for diagnosing adrenocortical diseases, such as adrenal insufficiency, Cushing and Conn syndromes, and their potential for advancing medical care and clinical outcome.

RECENT FINDINGS

The advent of next generation sequencing opened doors for finding genetic diseases and signaling pathways involved in adrenocortical diseases. In addition, the combination of molecular data and clinicopathologic assessment might be the best approach for an early and precise diagnosis contributing to therapeutic decisions and improvement of patient outcomes.

SUMMARY

Diagnosing adrenocortical diseases can be challenging; however, the progress of molecular tools for adrenocortical disease diagnosis has greatly contributed to early detection and to meliorate patient outcomes.

Comprehensive pan-cancer analysis and the regulatory mechanism of AURKA, a gene associated with prognosis of ferroptosis of adrenal cortical carcinoma in the tumor micro-environment

Background: The only curative option for patients with locally or locally advanced adrenocortical carcinoma is primary tumor curative sexual resection (ACC). However, overall survival remains low, with most deaths occurring within the first 2 years following surgery. The 5-year survival rate after surgery is less than 30%. As a result, more accurate prognosis-related predictive biomarkers must be investigated urgently to detect patients' disease status after surgery. Methods: Data from FerrDb were obtained to identify ferroptosis-related genes, and ACC gene expression profiles were collected from the GEO database to find differentially expressed ACC ferroptosis-related genes using differential expression analysis. The DEFGs were subjected to Gene Ontology gene enrichment analysis and KEGG signaling pathway enrichment analysis. PPI network building and predictive analysis were used to filter core genes. The expression of critical genes in ACC pathological stage and pan-cancer was then investigated. In recent years, immune-related factors, DNA repair genes, and methyltransferase genes have been employed in diagnosing and prognosis of different malignancies. Cancer cells are mutated due to DNA repair genes, and highly expressed DNA repair genes promote cancer. Dysregulation of methyltransferase genes and Immune-related factors, which are shown to be significantly expressed in numerous malignancies, also plays a crucial role in cancer. As a result, we investigated the relationship of AURKA with immunological checkpoints, DNA repair genes, and methyltransferases in pan-cancer. Result: The DEGs found in the GEO database were crossed with ferroptosis-related genes, yielding 42 differentially expressed ferroptosis-related genes. Six of these 42 genes, particularly AURKA, are linked to the prognosis of ACC. AURKA expression was significantly correlated with poor prognosis in patients with multiple cancers, and there was a significant positive correlation with Th2 cells. Furthermore, AURKA expression was positively associated with tumor immune infiltration in Lung adenocarcinoma (LUAD), Liver hepatocellular carcinoma (LIHC), Sarcoma (SARC), Esophageal carcinoma (ESCA), and Stomach adenocarcinoma (STAD), but negatively correlated with the immune score, matrix score, and calculated score in these tumors. Further investigation into the relationship between AURKA expression and immune examination gene expression revealed that AURKA could control the tumor-resistant pattern in most tumors by regulating the expression level of specific immune examination genes. Conclusion: AURKA may be an independent prognostic marker for predicting ACC patient prognosis. AURKA may play an essential role in the tumor microenvironment and tumor immunity, according to a pan-cancer analysis, and it has the potential to be a predictive biomarker for multiple cancers.