scholarly journals Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Christina Y. Yu ◽  
Antonina Mitrofanova
Keyword(s):  
Regulatory Networks ◽  
Biomarker Discovery ◽  
Disease Classification ◽  
Therapeutic Targeting ◽  
Future Directions ◽  
Single Cell Sequencing ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Precision Therapeutics

Biomarker discovery is at the heart of personalized treatment planning and cancer precision therapeutics, encompassing disease classification and prognosis, prediction of treatment response, and therapeutic targeting. However, many biomarkers represent passenger rather than driver alterations, limiting their utilization as functional units for therapeutic targeting. We suggest that identification of driver biomarkers through mechanism-centric approaches, which take into account upstream and downstream regulatory mechanisms, is fundamental to the discovery of functionally meaningful markers. Here, we examine computational approaches that identify mechanism-centric biomarkers elucidated from gene co-expression networks, regulatory networks (e.g., transcriptional regulation), protein–protein interaction (PPI) networks, and molecular pathways. We discuss their objectives, advantages over gene-centric approaches, and known limitations. Future directions highlight the importance of input and model interpretability, method and data integration, and the role of recently introduced technological advantages, such as single-cell sequencing, which are central for effective biomarker discovery and time-cautious precision therapeutics.

Brief Survey of Biological Network Alignment and a Variant with Incorporation of Functional Annotations

2018 ◽  
Vol 14 (1) ◽  
pp. 4-10
Author(s):  
Fang Jing ◽  
Shao-Wu Zhang ◽  
Shihua Zhang
Keyword(s):  
Gene Ontology ◽  
Topological Structure ◽  
Metabolic Networks ◽  
Biological Network ◽  
Genomic Sequence ◽  
Network Alignment ◽  
Future Directions ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Ppi Networks

Background:Biological network alignment has been widely studied in the context of protein-protein interaction (PPI) networks, metabolic networks and others in bioinformatics. The topological structure of networks and genomic sequence are generally used by existing methods for achieving this task.Objective and Method:Here we briefly survey the methods generally used for this task and introduce a variant with incorporation of functional annotations based on similarity in Gene Ontology (GO). Making full use of GO information is beneficial to provide insights into precise biological network alignment.Results and Conclusion:We analyze the effect of incorporation of GO information to network alignment. Finally, we make a brief summary and discuss future directions about this topic.

scholarly journals The Evolving Role of Ferroptosis in Breast Cancer: Translational Implications Present and Future

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4576
Author(s):  
Hung-Yu Lin ◽  
Hui-Wen Ho ◽  
Yen-Hsiang Chang ◽  
Chun-Jui Wei ◽  
Pei-Yi Chu
Keyword(s):  
Breast Cancer ◽  
Cell Death ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Drug Resistant ◽  
Therapeutic Targeting ◽  
The Past ◽  
Regulated Cell Death ◽  
Common Malignancy

Breast cancer (BC) is the most common malignancy among women worldwide. The discovery of regulated cell death processes has enabled advances in the treatment of BC. In the past decade, ferroptosis, a new form of iron-dependent regulated cell death caused by excessive lipid peroxidation has been implicated in the development and therapeutic responses of BC. Intriguingly, the induction of ferroptosis acts to suppress conventional therapy-resistant cells, and to potentiate the effects of immunotherapy. As such, pharmacological or genetic modulation targeting ferroptosis holds great potential for the treatment of drug-resistant cancers. In this review, we present a critical analysis of the current understanding of the molecular mechanisms and regulatory networks involved in ferroptosis, the potential physiological functions of ferroptosis in tumor suppression, its potential in therapeutic targeting, and explore recent advances in the development of therapeutic strategies for BC.

scholarly journals NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis

2019 ◽  
Vol 47 (W1) ◽  
pp. W234-W241 ◽  
Author(s):  
Guangyan Zhou ◽  
Othman Soufan ◽  
Jessica Ewald ◽  
Robert E W Hancock ◽  
Niladri Basu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Visual Analytics ◽  
Regulatory Networks ◽  
Meta Analysis ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Perform Gene Expression ◽  
And Performance

Abstract The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.

scholarly journals Identification of Therapeutic Targets and Prognostic Biomarkers Among Integrin Subunits in the Skin Cutaneous Melanoma Microenvironment

2021 ◽  
Vol 11 ◽  
Author(s):  
Yeltai Nurzat ◽  
Weijie Su ◽  
Peiru Min ◽  
Ke Li ◽  
Heng Xu ◽  
...  
Keyword(s):  
T Cells ◽  
Cutaneous Melanoma ◽  
Regulatory Networks ◽  
Disease Free Survival ◽  
Enrichment Analysis ◽  
Expression Levels ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Go Enrichment ◽  
Underlying Mechanisms

The roles of different integrin alpha/beta (ITGA/ITGB) subunits in skin cutaneous melanoma (SKCM) and their underlying mechanisms of action remain unclear. Oncomine, UALCAN, GEPIA, STRING, GeneMANIA, cBioPortal, TIMER, TRRUST, and Webgestalt analysis tools were used. The expression levels of ITGA3, ITGA4, ITGA6, ITGA10, ITGB1, ITGB2, ITGB3, ITGB4, and ITGB7 were significantly increased in SKCM tissues. The expression levels of ITGA1, ITGA4, ITGA5, ITGA8, ITGA9, ITGA10, ITGB1, ITGB2, ITGB3, ITGB5, ITGB6 and ITGB7 were closely associated with SKCM metastasis. The expression levels of ITGA1, ITGA4, ITGB1, ITGB2, ITGB6, and ITGB7 were closely associated with the pathological stage of SKCM. The expression levels of ITGA6 and ITGB7 were closely associated with disease-free survival time in SKCM, and the expression levels of ITGA6, ITGA10, ITGB2, ITGB3, ITGB6, ITGB7, and ITGB8 were markedly associated with overall survival in SKCM. We also found significant correlations between the expression of integrin subunits and the infiltration of six types of immune cells (B cells, CD8+ T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells). Finally, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed, and protein-protein interaction (PPI) networks were constructed. We have identified abnormally-expressed genes and gene regulatory networks associated with SKCM, improving understanding of the underlying pathogenesis of SKCM.

scholarly journals She Doesn’t Even Go Here: The Role of Inflammatory Astrocytes in CNS Disorders

2021 ◽  
Vol 15 ◽  
Author(s):  
Jacqueline Kelsey Reid ◽  
Hedwich Fardau Kuipers
Keyword(s):  
Neurodegenerative Diseases ◽  
Neurological Disorders ◽  
Therapeutic Targeting ◽  
Future Directions ◽  
Cns Disorders ◽  
Disease Modifying Therapies ◽  
Astrocyte Heterogeneity ◽  
Disease Modifying ◽  
Innovative Techniques

Astrocyte heterogeneity is a rapidly evolving field driven by innovative techniques. Inflammatory astrocytes, one of the first described subtypes of reactive astrocytes, are present in a variety of neurodegenerative diseases and may play a role in their pathogenesis. Moreover, genetic and therapeutic targeting of these astrocytes ameliorates disease in several models, providing support for advancing the development of astrocyte-specific disease modifying therapies. This review aims to explore the methods and challenges of identifying inflammatory astrocytes, the role these astrocytes play in neurological disorders, and future directions in the field of astrocyte heterogeneity.

scholarly journals Resilience and evolvability of protein-protein interaction networks

2020 ◽  
Author(s):  
Brennan Klein ◽  
Ludvig Holmér ◽  
Keith M. Smith ◽  
Mackenzie M. Johnson ◽  
Anshuman Swain ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Preferential Attachment ◽  
Network Resilience ◽  
Biological Noise ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks

AbstractProtein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype’s PPI network’s resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. One such factor is gene expression, which controls the simultaneous presence of proteins for allowed extant interactions and the possibility of novel associations. Here, we explore the influence of gene expression and network properties on a PPI network’s resilience, focusing especially on ribosomal proteins—vital molecular-complexes involved in protein synthesis, which have been extensively and reliably mapped in many species. Using publicly-available data of ribosomal PPIs for E. coli, S.cerevisae, and H. sapiens, we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms—random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network. This holds in all three species regardless of their distributions of gene expressions or their network community structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.1Author SummaryProteins in organismal cells are present at different levels of concentration and interact with other proteins to provide specific functional roles. Accumulating lists of all of these interactions, complex networks of protein interactions become apparent. This allows us to begin asking whether there are network-level mechanisms at play guiding the evolution of biological systems. Here, using this network perspective, we address two important themes in evolutionary biology (i) How are biological systems able to successfully incorporate novelty? (ii) What is the evolutionary role of biological noise in evolutionary novelty? We consider novelty to be the introduction of a new protein, represented as a new “node”, into a network. We simulate incorporation of novel proteins into Protein-Protein Interaction (PPI) networks in different ways and analyse how the resilience of the PPI network alters. We find that novel interactions guided by gene expression (indicative of concentration levels of proteins) creates a more resilient network than either uniformly random interactions or interactions guided solely by the network structure (preferential attachment). Moreover, simulated biological noise in the gene expression increases network resilience. We suggest that biological noise induces novel structure in the PPI network which has the effect of making it more resilient.

scholarly journals Identification of key candidate genes and pathways in axial Spondyloarthritis through integrated bioinformatics analysis

2020 ◽  
Author(s):  
Zhen-zhen Zhang ◽  
Jing Zeng ◽  
Hai-hong Li ◽  
Yu-cong Zou ◽  
Shuang Liang ◽  
...  
Keyword(s):  
Immunohistochemical Staining ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Axial Spondyloarthritis ◽  
Kegg Pathway ◽  
Transcriptional Regulatory Network ◽  
Rt Pcr ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Pathway Analyses

AbstractBackgroundRadiographic axial Spondyloarthritis (r-axSpA) is the prototypic form of seronegative spondyloarthritis (SpA). In the present study, we evaluated the key genes related with r-axSpA, and then elucidated the possible molecular mechanisms of r-axSpA.Material/MethodsThe gene expression GSE13782 was downloaded from the GEO database contained five proteoglycan-induced spondylitis mice and three naïve controls. The differentially expressed genes (DEGs) were identified with the Bioconductor affy package in R. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were built with the DAVID program followed by construction of a protein-protein interaction (PPI) network performed with Cytoscape. WebGestalt was performed to construct transcriptional regulatory network and microRNAs-target regulatory networks. RT-PCR and immunohistochemical staining were performed to testify the expression of hub genes, transcription factors (TFs) and microRNAs.ResultsA total of 230 DEGs were identified. PPI networks were constructed by mapping DEGs into STRING, in which 20 hub proteins were identified. KEGG pathway analyses revealed that the chemokine, NOD-like receptor, IL-17, and TNF signalling pathways were altered. GO analyses revealed that DEGs were extensively involved in the regulation of cytokine production, the immune response, the external side of the plasma membrane, and G-protein coupled chemoattractant receptor activity. The results of RT-PCR and immunohistochemical staining demonstrated that the expression of DEGs, TFs and microRNAs in our experiment were basically consistent with the predictions.ConclusionsThe results of this study offer insight into the pathomechanisms of r-axSpA and provide potential research directions.

scholarly journals Absorbance or organization into ankylosis: a microarray analysis of haemarthrosis in a sheep model of temporomandibular joint trauma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mai-Ning Jiao ◽  
Tong-Mei Zhang ◽  
Kun Yang ◽  
Zhao-Yuan Xu ◽  
Guan-Meng Zhang ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Molecular Level ◽  
Contralateral Side ◽  
Sheep Model ◽  
Tmj Ankylosis ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Or Organization ◽  
Joint Trauma

Abstract Background Traumatic haemarthrosis was hypothesized to be the etiology of temporomandibular (TMJ) ankylosis. Here, taking haematoma absorbance as a control, we aimed to reveal the molecular mechanisms involved in haematoma organizing into ankylosis using transcriptome microarray profiles. Material/methods Disk removal was performed to building haematoma absorbance (HA) in one side of TMJ, while removal of disk and articular fibrous layers was performed to induced TMJ ankylosis through haematoma organization (HO) in the contralateral side in a sheep model. Haematoma tissues harvested at days 1, 4 and 7 postoperatively were examined by histology, and analyzed by Affymetrix OviGene-1_0-ST microarrays. The DAVID were recruited to perform the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis for the different expression genes (DEGs). The DEGs were also typed into protein–protein interaction (PPI) networks to get the interaction data. Six significant genes screened from PPI analysis, were confirmed by real-time PCR. Results We found 268, 223 and 17 DEGs at least twofold at days 1, 4 and 7, respectively. At day 1, genes promoting collagen ossification (POSTN, BGN, LUM, SPARC), cell proliferation (TGF-β), and osteogenic differentiation of mesenchymal stem cells (BMP-2) were up-regulated in the HO side. At day 4, several genes involved in angiogenesis (KDR, FIT1, TEK) shower higher expression in the HO side. While HA was characterized by a continuous immune and inflammatory reaction. Conclusions Our results provide a comprehensive understanding of the role of haematoma in the onset and progress of TMJ ankylosis. The study will contribute to explaining why few injured TMJs ankylose and most do not from the molecular level.

scholarly journals An Overview of the Statistical Methods Used for Inferring Gene Regulatory Networks and Protein-Protein Interaction Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Amina Noor ◽  
Erchin Serpedin ◽  
Mohamed Nounou ◽  
Hazem Nounou ◽  
Nady Mohamed ◽  
...  
Keyword(s):  
Statistical Methods ◽  
Gene Regulatory Networks ◽  
Protein Interaction ◽  
Regulatory Networks ◽  
Space Representation ◽  
Graphical Modeling ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Modeling Techniques ◽  
Gene Regulatory

The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. This work reviews some of the most important statistical methods used for modeling of gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks. The paper focuses on the recent advances in the statistical graphical modeling techniques, state-space representation models, and information theoretic methods that were proposed for inferring the topology of GRNs. It appears that the problem of inferring the structure of PPI networks is quite different from that of GRNs. Clustering and probabilistic graphical modeling techniques are of prime importance in the statistical inference of PPI networks, and some of the recent approaches using these techniques are also reviewed in this paper. Performance evaluation criteria for the approaches used for modeling GRNs and PPI networks are also discussed.

scholarly journals The Role and Therapeutic Targeting of JAK/STAT Signaling in Glioblastoma

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 437
Author(s):  
Alexander Ou ◽  
Martina Ott ◽  
Dexing Fang ◽  
Amy B. Heimberger
Keyword(s):  
Immune Escape ◽  
Janus Kinase ◽  
Molecular Heterogeneity ◽  
Signal Transducer ◽  
Therapeutic Targeting ◽  
Future Directions ◽  
Stem Cell Maintenance ◽  
Current State ◽  
Stat Signaling

Glioblastoma remains one of the deadliest and treatment-refractory human malignancies in large part due to its diffusely infiltrative nature, molecular heterogeneity, and capacity for immune escape. The Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway contributes substantively to a wide variety of protumorigenic functions, including proliferation, anti-apoptosis, angiogenesis, stem cell maintenance, and immune suppression. We review the current state of knowledge regarding the biological role of JAK/STAT signaling in glioblastoma, therapeutic strategies, and future directions for the field.

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