scholarly journals Biological Networks for Cancer Candidate Biomarkers Discovery

2016 ◽  
Vol 15s3 ◽  
pp. CIN.S39458 ◽  
Author(s):  
Wenying Yan ◽  
Wenjin Xue ◽  
Jiajia Chen ◽  
Guang Hu
Keyword(s):  
Cancer Diagnosis ◽  
Biological Networks ◽  
Molecular Mechanisms ◽  
Outcome Prediction ◽  
Biomarker Discovery ◽  
Cancer Biomarkers ◽  
System Level ◽  
Omics Data ◽  
Model Case ◽  
Level Data

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.

Advancing Biopharmaceutical Process Development by System-Level Data Analysis and Integration of Omics Data

2011 ◽  
pp. 133-163 ◽  
Author(s):  
Jochen Schaub ◽  
Christoph Clemens ◽  
Hitto Kaufmann ◽  
Torsten W. Schulz
Keyword(s):  
Data Analysis ◽  
Process Development ◽  
System Level ◽  
Omics Data ◽  
Level Data

Extracellular Vesicles in Tumor Diagnosis: A Mini-Review

2020 ◽  
Vol 20 ◽  
Author(s):  
Si Yu ◽  
Menglin Huang ◽  
Jingyu Wang ◽  
Yongchang Zheng ◽  
Haifeng Xu
Keyword(s):  
Clinical Diagnosis ◽  
Sensitivity And Specificity ◽  
Cancer Diagnosis ◽  
Cancer Biomarkers ◽  
Related Substances ◽  
Physiological Processes ◽  
Cancer Pathogenesis ◽  
Limited Application ◽  
Low Sensitivity ◽  
Shed Light

: Widely exploration of noninvasive tumor/cancer biomarkers has shed light on clinical diagnosis. However, many under-investigated biomarkers showed limited application potency due to low sensitivity and specificity, while extracellular vehicles (EVs) were gradually recognized as promising candidates. EVs are small vesicles transporting bioactive cargos between cells in multiple physiological processes and also in tumor/cancer pathogenesis. This review aimed to offer recent studies of EVs on structure, classification, physiological functions, as well as changes in tumor initiation and progression. Furthermore, we focused on advances of EVs and/or EV-related substances in cancer diagnosis, and summarized ongoing studies of promising candidates for future investigations.

scholarly journals Glycosylation of Cancer Extracellular Vesicles: Capture Strategies, Functional Roles and Potential Clinical Applications

Cells ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 109
Author(s):  
Álvaro M. Martins ◽  
Cátia C. Ramos ◽  
Daniela Freitas ◽  
Celso A. Reis
Keyword(s):  
Extracellular Vesicles ◽  
Biomarker Discovery ◽  
De Novo ◽  
Recipient Cell ◽  
Cell Communication ◽  
Cancer Biomarkers ◽  
Functional Roles ◽  
Molecular Information ◽  
Glycosylation Pathway ◽  
Organ Tropism

Glycans are major constituents of extracellular vesicles (EVs). Alterations in the glycosylation pathway are a common feature of cancer cells, which gives rise to de novo or increased synthesis of particular glycans. Therefore, glycans and glycoproteins have been widely used in the clinic as both stratification and prognosis cancer biomarkers. Interestingly, several of the known tumor-associated glycans have already been identified in cancer EVs, highlighting EV glycosylation as a potential source of circulating cancer biomarkers. These particles are crucial vehicles of cell–cell communication, being able to transfer molecular information and to modulate the recipient cell behavior. The presence of particular glycoconjugates has been described to be important for EV protein sorting, uptake and organ-tropism. Furthermore, specific EV glycans or glycoproteins have been described to be able to distinguish tumor EVs from benign EVs. In this review, the application of EV glycosylation in the development of novel EV detection and capture methodologies is discussed. In addition, we highlight the potential of EV glycosylation in the clinical setting for both cancer biomarker discovery and EV therapeutic delivery strategies.

scholarly journals Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jingru Zhou ◽  
Yingping Zhuang ◽  
Jianye Xia
Keyword(s):  
Aspergillus Niger ◽  
Large Scale ◽  
Measurement Techniques ◽  
Metabolic Model ◽  
System Level ◽  
Metabolic Phenotype ◽  
Omics Data ◽  
Prediction Ability ◽  
Phenotype Prediction ◽  
Genome Scale

Abstract Background Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level. Results Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale $$k_{{cat}}$$ k cat values, predicting the differential expression of enzymes under different growth conditions. Conclusions This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.

scholarly journals The Winding Road of Cardiac Regeneration—Stem Cell Omics in the Spotlight

Cells ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 255 ◽  
Author(s):  
Miruna Mihaela Micheu ◽  
Alina Ioana Scarlatescu ◽  
Alexandru Scafa-Udriste ◽  
Maria Dorobantu
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Cell Fate ◽  
Cell Biology ◽  
Molecular Mechanisms ◽  
Unmet Need ◽  
Cardiac Regeneration ◽  
Cell Types ◽  
Great Promise ◽  
Omics Data

Despite significant progress in treating ischemic cardiac disease and succeeding heart failure, there is still an unmet need to develop effective therapeutic strategies given the persistent high-mortality rate. Advances in stem cell biology hold great promise for regenerative medicine, particularly for cardiac regeneration. Various cell types have been used both in preclinical and clinical studies to repair the injured heart, either directly or indirectly. Transplanted cells may act in an autocrine and/or paracrine manner to improve the myocyte survival and migration of remote and/or resident stem cells to the site of injury. Still, the molecular mechanisms regulating cardiac protection and repair are poorly understood. Stem cell fate is directed by multifaceted interactions between genetic, epigenetic, transcriptional, and post-transcriptional mechanisms. Decoding stem cells’ “panomic” data would provide a comprehensive picture of the underlying mechanisms, resulting in patient-tailored therapy. This review offers a critical analysis of omics data in relation to stem cell survival and differentiation. Additionally, the emerging role of stem cell-derived exosomes as “cell-free” therapy is debated. Last but not least, we discuss the challenges to retrieve and analyze the huge amount of publicly available omics data.

scholarly journals Nucleases as molecular targets for cancer diagnosis

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Alien Balian ◽  
Frank J. Hernandez
Keyword(s):  
Enzymatic Activity ◽  
Cancer Diagnosis ◽  
Essential Feature ◽  
Treatment Options ◽  
Cancer Biomarkers ◽  
Diagnostic Biomarkers ◽  
Early Cancer Diagnosis ◽  
Novel Biomarkers ◽  
Made In

AbstractEarly cancer diagnosis is a crucial element to improved treatment options and survival. Great research efforts have been made in the search for better performing cancer diagnostic biomarkers. However, the quest continues as novel biomarkers with high accuracy for an early diagnosis remain an unmet clinical need. Nucleases, which are enzymes capable of cleaving nucleic acids, have been long considered as potential cancer biomarkers. The implications of nucleases are key for biological functions, their presence in different cellular counterparts and catalytic activity led the enthusiasm towards investigating the role of nucleases as promising cancer biomarkers. However, the most essential feature of these proteins, which is their enzymatic activity, has not been fully exploited. This review discusses nucleases interrogated as cancer biomarkers, providing a glimpse of their physiological roles. Moreover, it highlights the potential of harnessing the enzymatic activity of cancer-associated nucleases as a novel diagnostic biomarker using nucleic acid probes as substrates.

scholarly journals Enriching human interactome with functional mutations to detect high-impact network modules underlying complex diseases

2019 ◽  
Author(s):  
Hongzhu Cui ◽  
Suhas Srinivasan ◽  
Dmitry Korkin
Keyword(s):  
Biological Networks ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Genetic Diseases ◽  
Detection Methods ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Human Interactome ◽  
Module Detection ◽  
Disease Module

AbstractProgress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, DIMSUM, which enables the integration of genome-wide association studies (GWAS), functional effects of mutations, and protein-protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for disease module analysis, facilitating discovery of new disease markers.

scholarly journals A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ramin Hasibi ◽  
Tom Michoel
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Gene Expression Data ◽  
Biological Networks ◽  
Molecular Interaction ◽  
Interaction Networks ◽  
Omics Data ◽  
Expression Data ◽  
Molecular Interaction Networks ◽  
Graph Neural Networks

Abstract Background Molecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, proteins, or metabolites across individuals or experimental conditions. Integrating the complementary viewpoints of biological networks and omics data is an important task in bioinformatics, but existing methods treat networks as discrete structures, which are intrinsically difficult to integrate with continuous node features or activity measures. Graph neural networks map graph nodes into a low-dimensional vector space representation, and can be trained to preserve both the local graph structure and the similarity between node features. Results We studied the representation of transcriptional, protein–protein and genetic interaction networks in E. coli and mouse using graph neural networks. We found that such representations explain a large proportion of variation in gene expression data, and that using gene expression data as node features improves the reconstruction of the graph from the embedding. We further proposed a new end-to-end Graph Feature Auto-Encoder framework for the prediction of node features utilizing the structure of the gene networks, which is trained on the feature prediction task, and showed that it performs better at predicting unobserved node features than regular MultiLayer Perceptrons. When applied to the problem of imputing missing data in single-cell RNAseq data, the Graph Feature Auto-Encoder utilizing our new graph convolution layer called FeatGraphConv outperformed a state-of-the-art imputation method that does not use protein interaction information, showing the benefit of integrating biological networks and omics data with our proposed approach. Conclusion Our proposed Graph Feature Auto-Encoder framework is a powerful approach for integrating and exploiting the close relation between molecular interaction networks and functional genomics data.

scholarly journals Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e053674
Author(s):  
Enrico Glaab ◽  
Armin Rauschenberger ◽  
Rita Banzi ◽  
Chiara Gerardi ◽  
Paula Garcia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scoping Review ◽  
Biomarker Discovery ◽  
Biomedical Literature ◽  
Molecular Signature ◽  
Biological Knowledge ◽  
Measurement Technology ◽  
Omics Data ◽  
Patient Stratification ◽  
Complex Disorders

ObjectiveTo review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects.DesignScoping review.MethodsWe searched PubMed, EMBASE and Web of Science to obtain a comprehensive list of articles from the biomedical literature published between January 2000 and July 2021, describing clinically validated biomarker signatures for patient stratification, derived using statistical learning approaches. All documents were screened to retain only peer-reviewed research articles, review articles or opinion articles, covering supervised and unsupervised machine learning applications for omics-based patient stratification. Two reviewers independently confirmed the eligibility. Disagreements were solved by consensus. We focused the final analysis on omics-based biomarkers which achieved the highest level of validation, that is, clinical approval of the developed molecular signature as a laboratory developed test or FDA approved tests.ResultsOverall, 352 articles fulfilled the eligibility criteria. The analysis of validated biomarker signatures identified multiple common methodological and practical features that may explain the successful test development and guide future biomarker projects. These include study design choices to ensure sufficient statistical power for model building and external testing, suitable combinations of non-targeted and targeted measurement technologies, the integration of prior biological knowledge, strict filtering and inclusion/exclusion criteria, and the adequacy of statistical and machine learning methods for discovery and validation.ConclusionsWhile most clinically validated biomarker models derived from omics data have been developed for personalised oncology, first applications for non-cancer diseases show the potential of multivariate omics biomarker design for other complex disorders. Distinctive characteristics of prior success stories, such as early filtering and robust discovery approaches, continuous improvements in assay design and experimental measurement technology, and rigorous multicohort validation approaches, enable the derivation of specific recommendations for future studies.

System-level data format exploration for dynamically allocated data structures

2000 ◽  
Author(s):  
Peeter Ellervee ◽  
Miguel Miranda ◽  
Francky Catthoor ◽  
Ahmed Hemani
Keyword(s):  
Data Structures ◽  
System Level ◽  
Data Format ◽  
Level Data
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