Identification of dysregulated miRNAs-genes network in ovarian cancer: An integrative approach to uncover the molecular interactions and oncomechanisms

GATA4: Regulation of expression and functions in goat granulosa cells

GATA4 plays a pivotal role in the reproductive processes of mammals. However, the research on GATA4 in goat ovary is limited. This study aimed to study the expression and function of GATA4 in goat ovary. Utilizing real-time PCR and western blot analysis, we studied the expression and regulatory mechanisms of GATA4 in goat ovary and granulosa cells (GCs). We found that GATA4 was expressed in all follicle types in the goat ovary, with significantly higher levels in GCs of larger follicles (>3 mm) compared to those in smaller follicles (<3 mm). Additionally, we demonstrated that human chorionic gonadotrophin (hCG) induced GATA4 mRNA expression via the activation of PKA, MEK, p38 MAPK, PKC, and PI3K pathways in vitro. Our study also showed that hCG suppressed the levels of miR-200b and miR-429, which in turn directly target GATA4, thereby modulating the basal and hCG-induced expression of GATA4. Functionally, we examined the effect of siRNA-mediated GATA4 knockdown on cell proliferation and hormone secretion in goat GCs. Our results revealed that knockdown of GATA4, miR-200b, and miR-429 suppressed cell proliferation. Moreover, knockdown of GATA4 decreased estradiol and progesterone production by inhibiting the promoter activities of CYP11A1, CYP19A1, HSD3B, and StAR. Collectively, our findings suggest a critical involvement of GATA4 in regulating goat GC survival and steroidogenesis.

Discovery and Validation of Novel microRNA Panel for Non-Invasive Prediction of Prostate Cancer

BACKGROUND

Early diagnosis remains a challenge for prostate cancer (PCa) due to molecular heterogeneity. The purpose of our study was to explore the diagnostic potential of microRNA (miRNA) in both tissue and serum that may aid in the precise and early clinical diagnosis of PCa.

MATERIALS AND METHODS

The miRNA expression pattern analysis was carried out in 250 subjects (discovery and validation cohort). The Discovery Cohort included the control (n = 30) and PCa (n = 35) subjects, while the Validation Cohort included the healthy control (n = 60), benign prostate hyperplasia (BPH) (n = 55), PCa (n = 50), and castration-resistant PCa (CRPC) (n = 20) patients. The expression analysis of tissue (Discovery Cohort) and serum (Validation Cohort) was carried out by quantitative polymerase chain reaction (qPCR). The diagnostic biomarker potential was evaluated using receiver operating characteristics (ROC). Bioinformatic tools were used to explore and analyze miRNA target genes.

RESULTS

MiRNA 4510 and miRNA 183 were significantly (p<0.001) upregulated and miRNA 329 was significantly (p<0.0001) downregulated in both PCa tissue and serum. ROC curve analysis showed excellent non-invasive biomarker potential of miRNA 4510 in both PCa (area under the curve (AUC) 0.984; p<0.001) and CRPC (AUC 0.944; p<0.001). The panel of serum miRNAs (miRNA 183 and miRNA 4510) designed for PCa had significant and greater AUC with both 100% sensitivity and specificity. Computational analysis shows that the maximum number of target genes are transcription factors that regulate oncogenes and tumor suppressors.

CONCLUSION

Based on ROC curve analysis, miRNAs 4510, 329, and 711 were identified as potential non-invasive diagnostic biomarkers in the early detection of PCa. Our findings imply that a panel of miRNAs 183 and 4510 has high specificity for distinguishing PCa from healthy controls and providing therapeutic targets for better and earlier PCa therapy.

Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects

Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.

Toward More Comprehensive Homologous Recombination Deficiency Assays in Ovarian Cancer, Part 1: Technical Considerations

Simple Summary

High-grade serous ovarian cancer (HGSOC) is the most frequent and lethal form of ovarian cancer and is associated with homologous recombination deficiency (HRD) in 50% of cases. This specific alteration is associated with sensitivity to PARP inhibitors (PARPis). Despite vast prognostic improvements due to PARPis, current molecular assays assessing HRD status suffer from several limitations, and there is an urgent need for a more accurate evaluation. In these companion reviews (Part 1: Technical considerations; Part 2: Medical perspectives), we develop an integrative review to provide physicians and researchers involved in HGSOC management with a holistic perspective, from translational research to clinical applications.

Abstract

High-grade serous ovarian cancer (HGSOC), the most frequent and lethal form of ovarian cancer, exhibits homologous recombination deficiency (HRD) in 50% of cases. In addition to mutations in BRCA1 and BRCA2, which are the best known thus far, defects can also be caused by diverse alterations to homologous recombination-related genes or epigenetic patterns. HRD leads to genomic instability (genomic scars) and is associated with PARP inhibitor (PARPi) sensitivity. HRD is currently assessed through BRCA1/2 analysis, which produces a genomic instability score (GIS). However, despite substantial clinical achievements, FDA-approved companion diagnostics (CDx) based on GISs have important limitations. Indeed, despite the use of GIS in clinical practice, the relevance of such assays remains controversial. Although international guidelines include companion diagnostics as part of HGSOC frontline management, they also underscore the need for more powerful and alternative approaches for assessing patient eligibility to PARP inhibitors. In these companion reviews, we review and present evidence to date regarding HRD definitions, achievements and limitations in HGSOC. Part 1 is dedicated to technical considerations and proposed perspectives that could lead to a more comprehensive and dynamic assessment of HR, while Part 2 provides a more integrated approach for clinicians.

Circular RNA hsa_circ_0044234 as distinct molecular signature of triple negative breast cancer: a potential regulator of GATA3

BACKGROUND

Circular RNAs (circRNAs) have been implicated in the initiation and development of breast cancer as functional non-coding RNAs (ncRNA). The roles of circRNAs as the competing endogenous RNAs (ceRNAs) to sponge microRNAs (miRNAs) have also been indicated. However, the functions of circRNAs in breast cancer have not been totally elucidated. This study aimed to explore the clinical implications and possible roles of circ_0044234 in carcinogenesis of the most problematic BC subtype, triple negative breast cancer (TNBC), which are in desperate need of biomarkers and targeted therapies.

METHODS

The importance of circ_0044234 as one of the most dysregulated circRNAs in TNBC was discovered through microarray expression profile analysis. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to confirm the downregulation of circ_0044234 in triple negative tumors and cell lines versus non-triple negative ones. The bioinformatics prediction revealed that circ_0044234 could act as an upstream sponge in the miR-135b/GATA3 axis, two of the most dysregulated transcripts in TNBC.

RESULTS

Our experimental investigation of circ_0044234 expressions in various BC subtypes as well as cell lines reveals that TNBC expresses circ_0044234 at a substantially lower level than non-TNBC. The ROC curve analysis indicates that it could be applied as a discriminative biomarker to identify TNBC from other BC subtypes. Moreover, circ_0044234 expression could be an independent prognostic biomarker in BC. Interestingly, a substantial inverse expression correlation was detected between circ_0044234 and miR-135b-5p as well as between miR-135b-5p and GATA3 in breast tumors.

CONCLUSIONS

The possible clinical usefulness of circ_0044234 as a promising distinct biomarker and upcoming therapeutic target for TNBC have been indicated in this research. Our comprehensive approach revealed the potential circ_0044234/miR135b-5p/GATA3 ceRNA axis in TNBC.

Identification of dysregulated miRNAs-genes network in ovarian cancer: An integrative approach to uncover the molecular interactions and oncomechanisms

Background

Ovarian (OV) cancer is considered as one of the most deadly malignancies in women, since it is unfortunately diagnosed in advanced stages. Nowadays, the importance of bioinformatics tools and their frequent usage in tracking dysregulated cancer‐related genes and pathways have been highlighted in researches.

Aim

The aim of this study is to investigate dysregulated miRNAs‐genes network and its function in OV tumors based on the integration of microarray data through a system biology approach.

Methods

Two microarray data (GSE119056 and GSE4122) were analyzed to explore the differentially expressed miRNAs (DEmiRs) and genes among OV tumors and normal tissues. Then, through the help of TargetScan, miRmap, and miRTarBase databases, the dysregulated miRNA‐gene network in OV tumors was constructed by Cytoscape. In the next step, co‐expression and protein‐protein interaction networks were made using GEPIA and STRING databases. Moreover, the functional analysis of the hub genes was done by DAVID, KEGG, and Enrichr databases. Eventually, the regulatory network of TF‐miRNA‐gene was constructed.

Results

The potential dysregulated miRNAs‐genes network in OV tumors has been constructed, including 109 differentially expressed genes (DEGs), 25 DEmiRs, and 213 interactions. Two down‐regulated microRNAs, miR‐660‐3p and hsa‐miR‐4510, have the most interactions with up‐expressed oncogenic DEGs. CDK1, PLK1, CCNB1, CCNA2, and EZH2 are involved in protein module, which show significant overexpression in OV tumors according to The Cancer Genome Atlas (TCGA) data. EZH2 shows amplification in OV tumors with remarkable percentage. The transcription factors TFAP2C and GATA4 have the pivotal regulatory functions in oncotranscriptomic profile of OV tumors.

Conclusion

In current study, we have collected and integrated different data to uncover the complex molecular interactions and oncomechanisms in OV tumors. The DEmiRs‐DEGs and TF‐miRNA‐gene networks reveal the potential interactions that could be a significant piece of the OV onco‐puzzle.