MicroRNA expression profile predicts prognosis of pediatric adrenocortical tumors

Analysis of miR-483-3p and miR-630 expression profile in pediatric adrenocortical tumors and the effect of their modulation on adrenal tumorigenesis in vitro

Pediatric adrenocortical tumors (ACT) are rare aggressive neoplasms with heterogeneous prognosis. MicroRNA (miRNA) signatures have been associated with cancer diagnosis, treatment response, and outcomes of several types of cancer. However, the role played by miRNAs in pediatric ACT has been poorly explored. In this study, we have evaluated the expression of miR-483-3p and miR-630 in 67 pediatric ACT and 19 non-neoplastic adrenal samples, the effects of the modulations of these miRNAs, and their relationship with the TGF-β pathway in the H295R and H295A cell lines. Deregulation of both miRNAs was related to survival and disease advanced stages and hence to patients' prognosis. Moreover, modified miR-483-3p and miR-630 in vitro expression decreased cell viability and colony formation capacity, changed how some genes of the TGF-β pathway, such as TGFBR1, TGFBR2, and SMAD7, are expressed, and altered Smad3, pSmad3, Smad 2/3, N-cadherin, and Vimentin protein expression. Besides that, when inhibition of the TGF-β pathway was combined with miR-630 overexpression or miR-483-3p silencing, cell viability and colony formation capacity decreased, and protein expression in the TGF-β pathway changed. Together, the data indicate that both miRNAs participate in the TGF-β pathway and are therefore potential markers for predicting the prognosis of patients with pediatric ACT.

Identifying prognostic hub genes and key pathways in pediatric adrenocortical tumors through RNA sequencing and Co-expression analysis

Pediatric adrenocortical tumors (ACTs), rare conditions with uncertain prognoses, have high incidence in southern and southeastern Brazil. Pediatric ACTs are highly heterogeneous, so establishing prognostic markers for these tumors is challenging. We have conducted transcriptomic analysis on 14 pediatric ACT samples and compared cases with favorable and unfavorable clinical outcomes to identify prognostically significant genes. This comparison showed 1257 differentially expressed genes in favorable and unfavorable cases. Among these genes, 15 out of 60 hub genes were significantly associated with five-year event-free survival (EFS), and 10 had significant diagnostic value for predicting ACT outcomes in an independent microarray dataset of pediatric adrenocortical carcinomas (GSE76019). Overexpression of N4BP2, HSPB6, JUN, APBB1IP, STK17B, CSNK1D, and KDM3A was associated with poorer EFS, whereas lower expression of ISCU, PTPR, PRKAB2, CD48, PRF1, ITGAL, KLK15, and HIST1H3J was associated with worse outcomes. Collectively, these findings underscore the prognostic significance of these hub genes and suggest that they play a potential role in pediatric ACT progression and are useful predictors of clinical outcomes.

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.

MicroRNA-149-3p expression correlates with outcomes of adrenocortical tumor patients and affects proliferation and cell cycle progression of H295A adrenocortical cancer cell line

Pediatric adrenocortical tumor (ACT) is a rare and aggressive neoplasm, with incidence in southern and southeastern Brazil 10-15 times higher than worldwide. Although microRNAs (miRNAs) have been reported to act as tumor suppressors or oncogenes in several cancers, the role of miR-149-3p in ACT remains unknown. In this study, we evaluated the expression of miR-149-3p in 67 pediatric ACT samples and 19 non-neoplastic adrenal tissues. The overexpression of miR-149-3p was induced in H295A cell line, and cell viability, proliferation, colony formation, and cell cycle were assessed by in miR-149-3p mimic or mimic control. In silico analysis were used to predict miR-149-3p putative target genes. CDKN1A expression at the mRNA and protein levels was evaluated by qRT-PCR and western blot, respectively. Higher miR-149-3p expression was associated with unfavorable ACT outcomes. Compared to the mimic control, miR-149-3p overexpression increased cell viability and colony formation, and affected cell cycle progression. Also, we identified CDKN1A as a potential miR-149-3p target gene, with decreased expression at both the gene and protein levels in miR-149-3p mimic cells. Collectively, these findings suggest that miR-149-3p promotes H295A cell viability by downregulating CDKN1A and provide evidence that miR-149-3p may be useful as a novel therapeutic target for pediatric ACT.

Identification of potential core genes and miRNAs in pediatric ACC via bioinformatics analysis

Pediatric adrenocortical carcinomas (ACC) are rare aggressive neoplasms with heterogeneous prognosis, and often produce a most lethal malignant tumor, whereas its aetiology is still unclear. The aim of the present study was to identify the factors responsible for the development of pediatric ACC, a better understanding of the disease, and investigate new molecular biomarkers and therapeutic targets. To identify the key genes and miRNAs linked to pediatric ACC, as well as their potential molecular mechanisms, the GSEGSE75415 and GSE169253 microarray datasets were analyzed. A total of 329 differentially produced genes (DEGs) and 187 differentially produced miRNAs (DEMs) were obtained after analyzing the GSEGSE75415 and GSE169253 datasets, respectively. Next, 3,359 genes were obtained by overlapping the target mRNAs of DEMs. Following protein-protein interaction network and Gene Ontology analysis, the ten nodes with the highest degrees were screened as hub genes. Among them, the highly expressed hub genes, MAPK1 and EP300, were associated with a worse overall survival. Additionally, hsa-miR-376, hsa-miR-148, hsa-miR-139, and hsa-miR-1305 were strongly associated with poorer survival. We proposed that the hub genes (MAPK1, EP300, hsa-miR-376, hsa-miR-148, hsa-miR-139, and hsa-miR-1305) may have a definite impact on cellular proliferation and migration in adrenocortical tumors. The roles of these hub genes in adrenocortical tumors may provide novel insight to improve the diagnosis and treatment of patients with pediatric ACC.