CCNB2 and AURKA overexpression may cause atypical mitosis in Japanese cortisol-producing adrenocortical carcinoma with TP53 somatic variant

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.

Identification of key genes and pathways in adrenocortical carcinoma: evidence from bioinformatic analysis

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with poor prognosis. The disease originates from the cortex of adrenal gland and lacks effective treatment. Efforts have been made to elucidate the pathogenesis of ACC, but the molecular mechanisms remain elusive. To identify key genes and pathways in ACC, the expression profiles of GSE12368, GSE90713 and GSE143383 were downloaded from the Gene Expression Omnibus (GEO) database. After screening differentially expressed genes (DEGs) in each microarray dataset on the basis of cut-off, we identified 206 DEGs, consisting of 72 up-regulated and 134 down-regulated genes in three datasets. Function enrichment analyses of DEGs were performed by DAVID online database and the results revealed that the DEGs were mainly enriched in cell cycle, cell cycle process, mitotic cell cycle, response to oxygen-containing compound, progesterone-mediated oocyte maturation, p53 signaling pathway. The STRING database was used to construct the protein-protein interaction (PPI) network, and modules analysis was performed using Cytoscape. Finally, we filtered out eight hub genes, including CDK1, CCNA2, CCNB1, TOP2A, MAD2L1, BIRC5, BUB1 and AURKA. Biological process analysis showed that these hub genes were significantly enriched in nuclear division, mitosis, M phase of mitotic cell cycle and cell cycle process. Violin plot, Kaplan-Meier curve and stage plot of these hub genes confirmed the reliability of the results. In conclusion, the results in this study provided reliable key genes and pathways for ACC, which will be useful for ACC mechanisms, diagnosis and candidate targeted treatment.

CCNB2 as a potential biomarker of bladder cancer via the high throughput technology

Bladder cancer and oral squamous cell carcinoma (OSCC) seriously affect people's health. However, the relationship between bladder cancer and OSCC remains unclear. Got GSE138206, GSE146483, GSE184616, and bladder cancer datasets GSE65635, GSE100926 from Gene Expression Omnibus database. Weighted gene co-expression network analysis was used to identify the significant module. Functional enrichment analysis was performed via the Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes. Furthermore, the Gene Set Enrichment Analysis was also used to complete the enrichment analysis. Comparative Toxicogenomics Database found most relevant diseases to core genes. TargetScan is used to forecast analysis of microRNA and target genes. In Gene Ontology analysis, differentially expressed genes were mostly concentrated in cell differentiation, extrallular region, structural molecule activity, and actin binding. In Kyoto Encyclopedia of Genes and Genomes analysis, the differentially expressed genes were mainly enriched in PI3K-Akt signaling pathway, pathway in cancer, and extracellular matrix-receptor interaction. Seven hub genes (cyclin B2 [CCNB2], TK1, CDC20, PCNA, CKS1B, CDCA5, MCM4) were obtained. Hub genes (CCNB2, CDC20) are highly expressed in OSCC and bladder cancer samples. CCNB2 was one common oncogene of bladder cancer and OSCC.

Ferroptosis-based molecular prognostic model for adrenocortical carcinoma based on least absolute shrinkage and selection operator regression

Background

This study aimed to find ferroptosis‐related genes linked to clinical outcomes of adrenocortical carcinoma (ACC) and assess the prognostic value of the model.

Methods

We downloaded the mRNA sequencing data and patient clinical data of 78 ACC patients from the TCGA data portal. Candidate ferroptosis‐related genes were screened by univariate regression analysis, machine‐learning least absolute shrinkage, and selection operator (LASSO). A ferroptosis‐related gene‐based prognostic model was constructed. The effectiveness of the prediction model was accessed by KM and ROC analysis. External validation was done using the GSE19750 cohort. A nomogram was generated. The prognostic accuracy was measured and compared with conventional staging systems (TNM stage). Functional analysis was conducted to identify biological characterization of survival‐associated ferroptosis‐related genes.

Results

Seventy genes were identified as survival‐associated ferroptosis‐related genes. The prognostic model was constructed with 17 ferroptosis‐related genes including STMN1, RRM2, HELLS, FANCD2, AURKA, GABARAPL2, SLC7A11, KRAS, ACSL4, MAPK3, HMGB1, CXCL2, ATG7, DDIT4, NOX1, PLIN4, and STEAP3. A RiskScore was calculated for each patient. KM curve indicated good prognostic performance. The AUC of the ROC curve for predicting 1‐, 3‐, and 5‐ year(s) survival time was 0.975, 0.913, and 0.915 respectively. The nomogram prognostic evaluation model showed better predictive ability than conventional staging systems.

Conclusion

We constructed a prognosis model of ACC based on ferroptosis‐related genes with better predictive value than the conventional staging system. These efforts provided candidate targets for revealing the molecular basis of ACC, as well as novel targets for drug development.

Prognostic Significance of CCNB2 Expression in Triple-Negative Breast Cancer

Purpose

The aim of this study was to explore potential gene therapy targets for triple-negative breast cancer (TNBC).

Patients and Methods

Three gene expression profiles (GSE64790, GSE62931, and GSE38959) from the Gene Expression Omnibus (GEO) database were analyzed. The GEO2R analysis tool was used to screen for differentially expressed genes (DEGs) between TNBC and normal tissues, followed by Gene Ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the DEGs. The protein–protein interaction network of DEGs was visualized using Metascape to identify the core genes. Subsequently, transcriptional data for the core genes in patients with breast cancer were investigated in the ONCOMINE database. Kaplan–Meier survival analysis was used to evaluate the prognostic value of core gene expression levels in patients with TNBC. Finally, the clinicopathological and long-term follow-up data of 39 patients with TNBC were retrospectively analyzed at the First Affiliated Hospital of the Bengbu Medical College between January 2014 and July 2020. Immunohistochemistry was used to evaluate the expression and subcellular localization of CCNB2 in TNBC tissues.

Results

A total of 66 DEGs were identified between TNBC and normal tissues, including 33 upregulated and 33 downregulated genes in TNBC. Furthermore, a potential protein complex was identified for five core genes. The high expression of these core genes, especially the overexpression of CCNB2, was correlated with a poor prognosis of patients with TNBC. The CCNB2 protein was expressed in the cytoplasm, and its expression was significantly higher in TNBC tissues than that in the adjacent nontumor tissues. Overall survival of patients was significantly correlated with the expression of CCNB2 (p < 0.05).

Conclusion

CCNB2 may play a crucial role in the development of TNBC and has the potential to be used as a prognostic biomarker for TNBC.

Adrenocortical Carcinoma in Childhood: A Systematic Review

Adrenocortical tumors are rare in children. This systematic review summarizes the published evidence on pediatric adrenocortical carcinoma (ACC) to provide a basis for a better understanding of the disease, investigate new molecular biomarkers and therapeutic targets, and define which patients may benefit from a more aggressive therapeutic approach. We included 137 studies with 3680 ACC patients (~65% female) in our analysis. We found no randomized controlled trials, so this review mainly reflects retrospective data. Due to a specific mutation in the TP53 gene in ~80% of Brazilian patients, that cohort was analyzed separately from series from other countries. Hormone analysis was described in 2569 of the 2874 patients (89%). Most patients were diagnosed with localized disease, whereas 23% had metastasis at primary diagnosis. Only 72% of the patients achieved complete resection. In 334 children (23%), recurrent disease was reported: 81%-local recurrence, 19% (n = 65)-distant metastases at relapse. Patients < 4 years old had a different distribution of tumor stages and hormone activity and better overall survival (p < 0.001). Although therapeutic approaches are typically multimodal, no consensus is available on effective standard treatments for advanced ACC. Thus, knowledge regarding pediatric ACC is still scarce and international prospective studies are needed to implement standardized clinical stratifications and risk-adapted therapeutic strategies.

The landscape of gene co-expression modules correlating with prognostic genetic abnormalities in AML

Background

The heterogenous cytogenetic and molecular variations were harbored by AML patients, some of which are related with AML pathogenesis and clinical outcomes. We aimed to uncover the intrinsic expression profiles correlating with prognostic genetic abnormalities by WGCNA.

Methods

We downloaded the clinical and expression dataset from BeatAML, TCGA and GEO database. Using R (version 4.0.2) and ‘WGCNA’ package, the co-expression modules correlating with the ELN2017 prognostic markers were identified (R² ≥ 0.4, p < 0.01). ORA detected the enriched pathways for the key co-expression modules. The patients in TCGA cohort were randomly assigned into the training set (50%) and testing set (50%). The LASSO penalized regression analysis was employed to build the prediction model, fitting OS to the expression level of hub genes by ‘glmnet’ package. Then the testing and 2 independent validation sets (GSE12417 and GSE37642) were used to validate the diagnostic utility and accuracy of the model.

Results

A total of 37 gene co-expression modules and 973 hub genes were identified for the BeatAML cohort. We found that 3 modules were significantly correlated with genetic markers (the ‘lightyellow’ module for NPM1 mutation, the ‘saddlebrown’ module for RUNX1 mutation, the ‘lightgreen’ module for TP53 mutation). ORA revealed that the ‘lightyellow’ module was mainly enriched in DNA-binding transcription factor activity and activation of HOX genes. The ‘saddlebrown’ module was enriched in immune response process. And the ‘lightgreen’ module was predominantly enriched in mitosis cell cycle process. The LASSO- regression analysis identified 6 genes (NFKB2, NEK9, HOXA7, APRC5L, FAM30A and LOC105371592) with non-zero coefficients. The risk score generated from the 6-gene model, was associated with ELN2017 risk stratification, relapsed disease, and prior MDS history. The 5-year AUC for the model was 0.822 and 0.824 in the training and testing sets, respectively. Moreover, the diagnostic utility of the model was robust when it was employed in 2 validation sets (5-year AUC 0.743–0.79).

Conclusions

We established the co-expression network signature correlated with the ELN2017 recommended prognostic genetic abnormalities in AML. The 6-gene prediction model for AML survival was developed and validated by multiple datasets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02914-2.

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.