scholarly journals Optimizing network propagation for multi-omics data integration

2021 ◽  
Vol 17 (11) ◽  
pp. e1009161
Author(s):  
Konstantina Charmpi ◽  
Manopriya Chokkalingam ◽  
Ronja Johnen ◽  
Andreas Beyer
Keyword(s):  
Cancer Progression ◽  
Protein Function ◽  
Molecular Mechanisms ◽  
Optimal Parameter ◽  
Research Question ◽  
Real Data ◽  
Disease Genes ◽  
Omics Data ◽  
Optimal Parameters ◽  
Network Propagation

Network propagation refers to a class of algorithms that integrate information from input data across connected nodes in a given network. These algorithms have wide applications in systems biology, protein function prediction, inferring condition-specifically altered sub-networks, and prioritizing disease genes. Despite the popularity of network propagation, there is a lack of comparative analyses of different algorithms on real data and little guidance on how to select and parameterize the various algorithms. Here, we address this problem by analyzing different combinations of network normalization and propagation methods and by demonstrating schemes for the identification of optimal parameter settings on real proteome and transcriptome data. Our work highlights the risk of a ‘topology bias’ caused by the incorrect use of network normalization approaches. Capitalizing on the fact that network propagation is a regularization approach, we show that minimizing the bias-variance tradeoff can be utilized for selecting optimal parameters. The application to real multi-omics data demonstrated that optimal parameters could also be obtained by either maximizing the agreement between different omics layers (e.g. proteome and transcriptome) or by maximizing the consistency between biological replicates. Furthermore, we exemplified the utility and robustness of network propagation on multi-omics datasets for identifying ageing-associated genes in brain and liver tissues of rats and for elucidating molecular mechanisms underlying prostate cancer progression. Overall, this work compares different network propagation approaches and it presents strategies for how to use network propagation algorithms to optimally address a specific research question at hand.

scholarly journals Optimizing Network Propagation for Multi-Omics Data Integration

2021 ◽  
Author(s):  
Konstantina Charmpi ◽  
Manopriya Chokkalingam ◽  
Ronja Johnen ◽  
Andreas Beyer
Keyword(s):  
Cancer Progression ◽  
Protein Function ◽  
Molecular Mechanisms ◽  
Optimal Parameter ◽  
Research Question ◽  
Real Data ◽  
Disease Genes ◽  
Omics Data ◽  
Optimal Parameters ◽  
Network Propagation

Network propagation refers to a class of algorithms that integrate information from input data across connected nodes in a given network. These algorithms have wide applications in systems biology, protein function prediction, inferring condition-specifically altered sub-networks, and prioritizing disease genes. Despite the popularity of network propagation, there is a lack of comparative analyses of different algorithms on real data and little guidance on how to select and parameterize the various algorithms. Here, we address this problem by analyzing different combinations of network normalization and propagation methods and by demonstrating schemes for the identification of optimal parameter settings on real proteome and transcriptome data. Our work highlights the risk of a ‘topology bias’ caused by the incorrect use of network normalization approaches. Capitalizing on the fact that network propagation is a regularization approach, we show that minimizing the bias-variance tradeoff can be utilized for selecting optimal parameters. The application to real multi-omics data demonstrated that optimal parameters could also be obtained by either maximizing the agreement between different omics layers (e.g. proteome and transcriptome) or by maximizing the consistency between biological replicates. Furthermore, we exemplified the utility and robustness of network propagation on multi-omics datasets for identifying ageing-associated genes in brain and liver tissues of rats and for elucidating molecular mechanisms underlying prostate cancer progression. Overall, this work compares different network propagation approaches and it presents strategies for how to use network propagation algorithms to optimally address a specific research question at hand.

scholarly journals NPF：Network propagation for protein function prediction

2020 ◽  
Author(s):  
Bihai Zhao ◽  
Zhihong Zhang ◽  
Meiping Jiang ◽  
Sai Hu ◽  
Yingchun Luo ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Function ◽  
Cross Validation ◽  
Function Prediction ◽  
Protein Interaction Networks ◽  
Functional Similarity ◽  
Interaction Networks ◽  
Omics Data ◽  
Protein Functions ◽  
Network Propagation

Abstract Background: The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, disease treatment and new drug development. Various methods have been developed to facilitate the prediction of functions by combining protein interaction networks (PINs) with multi-omics data. However, how to make full use of multiple biological data to improve the performance of functions annotation is still a dilemma. Results We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. Comprehensive evaluation of NPF indicates that NPF archived higher performance than competing methods in terms of leave-one-out cross-validation and ten-fold cross validation. Conclusions: We demonstrated that network propagation combined with multi-omics data can not only discover more partners with similar function, but also effectively free from the constraints of the "small-world" feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information from protein correlations.

scholarly journals NPF：Network propagation for protein function prediction

2020 ◽  
Author(s):  
bihai zhao ◽  
Zhihong Zhang ◽  
Meiping Jiang ◽  
Sai Hu ◽  
Yingchun Luo ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Function ◽  
Function Prediction ◽  
Biological Data ◽  
Protein Interaction Networks ◽  
Functional Similarity ◽  
Interaction Networks ◽  
Omics Data ◽  
Protein Functions ◽  
Network Propagation

Abstract Background: The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, disease treatment and new drug development. Various methods have been developed to facilitate the prediction of functions by combining protein interaction networks (PINs) with multi-omics data. However, how to make full use of multiple biological data to improve the performance of functions annotation is still a dilemma.Results: We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. Comprehensive evaluation of NPF indicates that NPF archived higher performance than competing methods in terms of leave-one-out cross-validation and ten-fold cross validation.Conclusions: We demonstrated that network propagation combined with multi-omics data can not only discover more partners with similar function, but also effectively free from the constraints of the "small-world" feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information from protein correlations.

scholarly journals NPF: Network propagation for protein function prediction

2020 ◽  
Author(s):  
bihai zhao ◽  
Zhihong Zhang ◽  
Meiping Jiang ◽  
Sai Hu ◽  
Yingchun Luo ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Function ◽  
Network Architecture ◽  
Functional Module ◽  
Function Prediction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Omics Data ◽  
Protein Functions ◽  
Network Propagation

Abstract Background: The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the prediction of these functions by combining protein interaction networks (PINs) with multi-omics data. However, it is still challenging to make full use of multiple biological to improve the performance of functions annotation.Results: We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. According to the comprehensive evaluation of NPF, it delivered a better performance than other competing methods in terms of leave-one-out cross-validation and ten-fold cross validation.Conclusions: We demonstrated that network propagation, together with multi-omics data, can both discover more partners with similar function, and is unconstricted by the “small-world” feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional information of similarity from protein correlations.

scholarly journals ActiveDriverDB: human disease mutations and genome variation in post-translational modification sites of proteins

2017 ◽  
Author(s):  
Michal Krassowski ◽  
Marta Paczkowska ◽  
Kim Cullion ◽  
Tina Huang ◽  
Irakli Dzneladze ◽  
...  
Keyword(s):  
Protein Function ◽  
Molecular Mechanisms ◽  
Application Programming Interface ◽  
The Cancer Genome Atlas ◽  
Disease Genes ◽  
Sequence Motifs ◽  
Inherited Disease ◽  
Post Translational Modification ◽  
Single Nucleotide Variants ◽  
Disease Mutations

AbstractInterpretation of genetic variation is required for understanding genotype-phenotype associations, mechanisms of inherited disease, and drivers of cancer. Millions of single nucleotide variants (SNVs) in human genomes are known and thousands are associated with disease. An estimated 20% of disease-associated missense SNVs are located in protein sites of post-translational modifications (PTMs), chemical modifications of amino acids that extend protein function. ActiveDriverDB is a comprehensive human proteo-genomics database that annotates disease mutations and population variants using PTMs. We integrated >385,000 published PTM sites with ∼3.8 million missense SNVs from The Cancer Genome Atlas (TCGA), the ClinVar database of disease genes, and inter-individual variation from human genome sequencing projects. The database includes interaction networks of proteins, upstream enzymes such as kinases, and drugs targeting these enzymes. We also predicted network-rewiring impact of mutations by analyzing gains and losses of kinase-bound sequence motifs. ActiveDriverDB provides detailed visualization, filtering, browsing and searching options for studying PTM-associated SNVs. Users can upload mutation datasets interactively and use our application programming interface for pipelines. Integrative analysis of SNVs and PTMs helps decipher molecular mechanisms of phenotypes and disease, as exemplified by case studies of disease genes TP53, BRCA2 and VHL. The open-source database is available at https://www.ActiveDriverDB.org.

Novel Findings of Anti-cancer Property of Propofol

2018 ◽  
Vol 18 (2) ◽  
pp. 156-165 ◽  
Author(s):  
Jiaqiang Wang ◽  
Chien-shan Cheng ◽  
Yan Lu ◽  
Xiaowei Ding ◽  
Minmin Zhu ◽  
...  
Keyword(s):  
General Anesthesia ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Intravenous Anesthetic ◽  
The Past ◽  
Anti Cancer ◽  
Underlying Mechanisms ◽  
Recent Attention

Background: Propofol, a widely used intravenous anesthetic agent, is traditionally applied for sedation and general anesthesia. Explanation: Recent attention has been drawn to explore the effect and mechanisms of propofol against cancer progression in vitro and in vivo. Specifically, the proliferation-inhibiting and apoptosis-inducing properties of propofol in cancer have been studied. However, the underlying mechanisms remain unclear. Conclusion: This review focused on the findings within the past ten years and aimed to provide a general overview of propofol's malignance-modulating properties and the potential molecular mechanisms.

scholarly journals TEAD4 modulated LncRNA MNX1-AS1 contributes to gastric cancer progression partly through suppressing BTG2 and activating BCL2

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
You Shuai ◽  
Zhonghua Ma ◽  
Weitao Liu ◽  
Tao Yu ◽  
Changsheng Yan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Mechanistic Model ◽  
Ectopic Expression ◽  
Luciferase Reporter ◽  
Migration And Invasion ◽  
Tissue Samples ◽  
Reporter Assays

Abstract Background Gastric cancer (GC) is the third leading cause of cancer-related mortality globally. Long noncoding RNAs (lncRNAs) are dysregulated in obvious malignancies including GC and exploring the regulatory mechanisms underlying their expression is an attractive research area. However, these molecular mechanisms require further clarification, especially upstream mechanisms. Methods LncRNA MNX1-AS1 expression in GC tissue samples was investigated via microarray analysis and further determined in a cohort of GC tissues via quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays. Cell proliferation and flow cytometry assays were performed to confirm the roles of MNX1-AS1 in GC proliferation, cell cycle regulation, and apoptosis. The influence of MNX1-AS1 on GC cell migration and invasion was explored with Transwell assays. A xenograft tumour model was established to verify the effects of MNX1-AS1 on in vivo tumourigenesis. The TEAD4-involved upstream regulatory mechanism of MNX1-AS1 was explored through ChIP and luciferase reporter assays. The mechanistic model of MNX1-AS1 in regulating gene expression was further detected by subcellular fractionation, FISH, RIP, ChIP and luciferase reporter assays. Results It was found that MNX1-AS1 displayed obvious upregulation in GC tissue samples and cell lines, and ectopic expression of MNX1-AS1 predicted poor clinical outcomes for patients with GC. Overexpressed MNX1-AS1 expression promoted proliferation, migration and invasion of GC cells markedly, whereas decreased MNX1-AS1 expression elicited the opposite effects. Consistent with the in vitro results, MNX1-AS1 depletion effectively inhibited the growth of xenograft tumour in vivo. Mechanistically, TEAD4 directly bound the promoter region of MNX1-AS1 and stimulated the transcription of MNX1-AS1. Furthermore, MNX1-AS1 can sponge miR-6785-5p to upregulate the expression of BCL2 in GC cells. Meanwhile, MNX1-AS1 suppressed the transcription of BTG2 by recruiting polycomb repressive complex 2 to BTG2 promoter regions. Conclusions Our findings demonstrate that MNX1-AS1 may be able to serve as a prognostic indicator in GC patients and that TEAD4-activatd MNX1-AS1 can promote GC progression through EZH2/BTG2 and miR-6785-5p/BCL2 axes, implicating it as a novel and potent target for the treatment of GC.

Galectin-3: A factotum in carcinogenesis bestowing an archery for prevention

Tumor Biology ◽  
2021 ◽  
Vol 43 (1) ◽  
pp. 77-96
Author(s):  
T. Jeethy Ram ◽  
Asha Lekshmi ◽  
Thara Somanathan ◽  
K. Sujathan
Keyword(s):  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Cancer Metastasis ◽  
Epithelial Mesenchymal Transition ◽  
Therapy Resistance ◽  
Cellular Level ◽  
Clinical Samples ◽  
Innovative Strategy ◽  
Mesenchymal Transition ◽  
Galectin 3

Cancer metastasis and therapy resistance are the foremost hurdles in oncology at the moment. This review aims to pinpoint the functional aspects of a unique multifaceted glycosylated molecule in both intracellular and extracellular compartments of a cell namely galectin-3 along with its metastatic potential in different types of cancer. All materials reviewed here were collected through the search engines PubMed, Scopus, and Google scholar. Among the 15 galectins identified, the chimeric gal-3 plays an indispensable role in the differentiation, transformation, and multi-step process of tumor metastasis. It has been implicated in the molecular mechanisms that allow the cancer cells to survive in the intravascular milieu and promote tumor cell extravasation, ultimately leading to metastasis. Gal-3 has also been found to have a pivotal role in immune surveillance and pro-angiogenesis and several studies have pointed out the importance of gal-3 in establishing a resistant phenotype, particularly through the epithelial-mesenchymal transition process. Additionally, some recent findings suggest the use of gal-3 inhibitors in overcoming therapeutic resistance. All these reports suggest that the deregulation of these specific lectins at the cellular level could inhibit cancer progression and metastasis. A more systematic study of glycosylation in clinical samples along with the development of selective gal-3 antagonists inhibiting the activity of these molecules at the cellular level offers an innovative strategy for primary cancer prevention.

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