Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets

Ramos, Pablo Ivan Pereira; Arge, Luis Willian Pacheco; Lima, Nicholas Costa Barroso; Fukutani, Kiyoshi F.; Queiroz, Artur T. L.

doi:10.3389/fgene.2019.01120

Cited by 14 publications

(10 citation statements)

References 133 publications

(138 reference statements)

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“…In order to identify regulatory elements, we further filtered the regulatory network based on genes falling into more than one GO term of interest. These elements show a high amount of interconnectedness, further supporting a regulatory role in the phenotype presentation of FGR [ 28 ] ( Figure 4 C).…”

Section: Resultsmentioning

confidence: 86%

“…The remaining genes still show high interconnectedness, including sharing interactions with SREBP-2, which has been indicated as a target of synergistically working miRNAs ( Figure 4 ). These elements are thus indicated as regulatory elements for the exhibited phenotype and deserve further study as biomarkers and therapeutic targets for FGR [ 28 ].…”

Section: Figurementioning

confidence: 99%

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Overexpression of microRNAs miR-25-3p, miR-185-5p and miR-132-3p in Late Onset Fetal Growth Restriction, Validation of Results and Study of the Biochemical Pathways Involved

Loscalzo

Scheel

Cabellos

et al. 2021

IJMS

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In a prospective study, 48 fetuses were evaluated with Doppler ultrasound after 34 weeks and classified, according to the cerebroplacental ratio (CPR) and estimated fetal weight (EFW), into fetuses with normal growth and fetuses with late-onset fetal growth restriction (LO-FGR). Overexpression of miRNAs from neonatal cord blood belonging to LO-FGR fetuses, was validated by real-time PCR. In addition, functional characterization of overexpressed miRNAs was performed by analyzing overrepresented pathways, gene ontologies, and prioritization of synergistically working miRNAs. Three miRNAs: miR-25-3p, miR-185-5p and miR-132-3p, were significantly overexpressed in cord blood of LO-FGR fetuses. Pathway and gene ontology analysis revealed over-representation of certain molecular pathways associated with cardiac development and neuron death. In addition, prioritization of synergistically working miRNAs highlighted the importance of miR-185-5p and miR-25-3p in cholesterol efflux and starvation responses associated with LO-FGR phenotypes. Evaluation of miR-25-3p; miR-132-3p and miR-185-5p might serve as molecular biomarkers for the diagnosis and management of LO-FGR; improving the understanding of its influence on adult disease.

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Section: Resultsmentioning

confidence: 86%

Section: Figurementioning

confidence: 99%

Overexpression of microRNAs miR-25-3p, miR-185-5p and miR-132-3p in Late Onset Fetal Growth Restriction, Validation of Results and Study of the Biochemical Pathways Involved

Loscalzo

Scheel

Cabellos

et al. 2021

IJMS

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“…A confidence score ≥ 0.7 was set for conducting the PPI network. Cytoscape (version 3.7.1) is an open-source bioinformatics software platform that is used for visualizing the PPI network and for further analyses [ 20 , 21 ]. The Molecular Complex Detection (MCODE) plugin in Cytoscape was used to identify significant modules based on the PPI network topology.…”

Section: Methodsmentioning

confidence: 99%

Aurora kinase A (AURKA) promotes the progression and imatinib resistance of advanced gastrointestinal stromal tumors

Cheng

Wang

et al. 2021

Cancer Cell Int

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Background Gastrointestinal stromal tumor (GIST) is a common tumor that originates from the alimentary system mesenchyme. Compared to typical gastrointestinal carcinomas, GISTs exhibit unique malignant behaviors. Bioinformatic tools and subsequent experiments were applied to investigate novel targets involved in GIST progression and imatinib resistance. Methods Differences in gene expression profiles between advanced and nonadvanced GISTs were comprehensively analyzed based on the Gene Expression Omnibus (GEO) dataset GSE136755. A protein–protein interaction (PPI) network was constructed to identify the potential target gene. Gene set enrichment analysis (GSEA) was used to elucidate relevant biological events related to the target gene based on the GSE47911 dataset. Subsequently, immunohistochemistry and Kaplan–Meier analysis were performed to validate the prognostic value of the target gene in GISTs. Overexpression of the target gene was conducted to analyze its function in the proliferation, apoptosis, and imatinib resistance of GIST/T1 cells. Results In the current study, a total of 606 differentially expressed genes (DEGs) were screened based on the GSE136755 dataset, and the upregulated DEGs in advanced GISTs were mainly involved in cell division through functional annotations. The intersecting hub gene, Aurora kinase A (AURKA), was identified by degree and bottleneck algorithms. GSEA revealed that AURKA was involved in cell cycle-related biological processes. Analysis of the Oncomine and GEPIA databases revealed a pattern of elevated AURKA expression in most human malignances. Clinical assays demonstrated that AURKA could be an independent prognostic factor for GISTs. Additionally, overexpression of AURKA was experimentally demonstrated to promote cell proliferation, inhibit cell apoptosis, and enhance imatinib resistance in GIST/T1 cells. Conclusions These findings indicated that overexpression of AURKA promoted GIST progression and enhanced imatinib resistance, implying that AURKA is a potential therapeutic target for GISTs.

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“…Since its launch in 2003, more than 200 apps have been developed for complex network analysis and visualization. For further information on network-based analysis, refer to Ramos et al ( 2019 ).…”

Section: Data Integration and Network Analysismentioning

confidence: 99%

Perspectives in systems nephrology

et al. 2021

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Chronic kidney diseases (CKD) are a major health problem affecting approximately 10% of the world’s population and posing increasing challenges to the healthcare system. While CKD encompasses a broad spectrum of pathological processes and diverse etiologies, the classification of kidney disease is currently based on clinical findings or histopathological categorizations. This descriptive classification is agnostic towards the underlying disease mechanisms and has limited progress towards the ability to predict disease prognosis and treatment responses. To gain better insight into the complex and heterogeneous disease pathophysiology of CKD, a systems biology approach can be transformative. Rather than examining one factor or pathway at a time, as in the reductionist approach, with this strategy a broad spectrum of information is integrated, including comprehensive multi-omics data, clinical phenotypic information, and clinicopathological parameters. In recent years, rapid advances in mathematical, statistical, computational, and artificial intelligence methods enable the mapping of diverse big data sets. This holistic approach aims to identify the molecular basis of CKD subtypes as well as individual determinants of disease manifestation in a given patient. The emerging mechanism-based patient stratification and disease classification will lead to improved prognostic and predictive diagnostics and the discovery of novel molecular disease-specific therapies.

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Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets

Cited by 14 publications

References 133 publications

Overexpression of microRNAs miR-25-3p, miR-185-5p and miR-132-3p in Late Onset Fetal Growth Restriction, Validation of Results and Study of the Biochemical Pathways Involved

Overexpression of microRNAs miR-25-3p, miR-185-5p and miR-132-3p in Late Onset Fetal Growth Restriction, Validation of Results and Study of the Biochemical Pathways Involved

Aurora kinase A (AURKA) promotes the progression and imatinib resistance of advanced gastrointestinal stromal tumors

Perspectives in systems nephrology

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