Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma

Bag, Arup K.; Mandloi, Sapan; Jarmalavicius, Saulius; Mondal, Susmita; Kumar, Krishna; Mandal, Chhabinath; Walden, Peter; Chakrabarti, Saikat; Mandal, Chitra

doi:10.1371/journal.pcbi.1007090

Cited by 20 publications

(17 citation statements)

References 113 publications

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“…The molecular mechanism of the pathophysiology of living organisms, effect of drugs, signaling pathways, and their connections are extensively studied [6,[21][22][23]. To address this problem in cardiovascular signaling, we looked for specific "patterns" of time dependence of 35 HPs and some of their crossrelationships in the condition of transient increase of NO bioavailability.…”

Section: Introductionmentioning

confidence: 99%

Patterns and Direct/Indirect Signaling Pathways in Cardiovascular System in the Condition of Transient Increase of NO

Misak

Kurakova

Berenyiová

et al. 2020

BioMed Research International

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Aim. To study “patterns” and connections of signaling pathways derived from the rat arterial pulse waveform (APW) under the condition of transient NO increase. Methods and Results. The right jugular vein of anesthetized Wistar rats was cannulated for administration of NO donor S-nitrosoglutathione. The left carotid artery was cannulated to detect APW. From rat APW, 35 hemodynamic parameters (HPs) and several their crossrelationships were evaluated. We introduced a new methodology to study “patterns” and connections of different signaling pathways, which are suggested from hysteresis and nonhysteresis crossrelationships of 35 rat HPs. Here, we show parallel time-dependent patterns of 35 HPs and some of their crossrelationships under the condition of transient increase of NO bioavailability by administration of S-nitrosoglutathione. Approximate nonhysteresis relationships were observed between systolic blood pressure and at least 11 HPs suggesting that these HPs, i.e., their signaling pathways, responding to NO concentration, are directly connected. Hysteresis relationships were observed between systolic blood pressure and at least 14 HPs suggesting that the signaling pathways of these HPs are indirectly connected. Totally, from the crossrelationships of 35 HPs, one can obtain 595 “patterns” and indication of direct or indirect connections between the signaling pathways. Conclusion. We described the procedure leading virtually to 595 relationships, from which “patterns” for transient NO increase and direct or indirect connections of signaling pathways can be suggested. From a clinical perspective, this approach may be used in animal models and in humans to create a data bank of patterns of crossrelationships of HPs for different cardiovascular conditions. By comparison with unknown patterns of studied APW with the data bank patterns, it would be possible to determine cardiovascular conditions of the studied subject from the recorded arterial blood pressure. Additionally, it can help to find molecular mechanism of particular (patho-) physiological conditions or drug action and may have predictive or diagnostic value.

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Section: Introductionmentioning

confidence: 99%

Patterns and Direct/Indirect Signaling Pathways in Cardiovascular System in the Condition of Transient Increase of NO

Misak

Kurakova

Berenyiová

et al. 2020

BioMed Research International

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“…Additionally, APODHIN utilizes multi-omics data and calculate cross-pathway regulatory and PPI links connecting signaling proteins or transcription factors or miRNAs to metabolic enzymes and their metabolites using network analysis and mathematical modeling. These cross-pathway links were shown to play important roles in metabolic reprogramming in cancer scenarios such as glioblastoma multiforme in a previous work [22].…”

Section: Introductionmentioning

confidence: 87%

“…We implemented an established probabilistic approach based on the Hidden Markov Model (HMM) [22,33,34] are converted to z-scores and paths having z-score 1 are considered as important crossconnecting paths. A detailed description of the mathematical models and path calculation is available in our previous publication [22]. Figure 1B shows the flow chart of 'pathway connectivity analysis' module of APODHIN.…”

Section: Pathway Connectivity Analysis and Cross-pathway Linksmentioning

confidence: 99%

Analysis of Pan-Omics Data in Human Interactome Network (APODHIN)

Biswas

Kumar

Bose

et al. 2020

Preprint

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Analysis of Pan-Omics Data in Human Interactome Network (APODHIN) is a platform for integrative analysis of transcriptomics, proteomics, genomics, and metabolomics data for identification of key molecular players and their interconnections exemplified in cancer scenario. APODHIN works on a meta-interactome networks consisting of human proteinprotein interactions, miRNA-target gene regulatory interactions, and transcription factortarget gene regulatory relationships, respectively. In its first module, APODHIN maps proteins/genes/miRNAs from different omics data in its meta-interactome network and extracts the network of biomolecules that are differentially altered in the given scenario.Using this context specific, filtered interaction network, APODHIN identifies topologically important nodes (TINs) implementing graph theory based network topology analysis and further justifies their role via pathway and disease marker mapping. These TINs could be used as prospective diagnostic and/or prognostic biomarkers and/or potential therapeutic targets.In its second module, APODHIN attempts to identify cross pathway regulatory and proteinprotein interaction (PPI) links connecting signaling proteins, transcription factors, and miRNAs to metabolic enzymes via utilization of single-omics and/or pan-omics data and implementation of mathematical modeling. Interconnections between regulatory components such as signaling proteins/TFs/miRNAs and metabolic pathways need to be elucidated more elaborately in order to understand the role of oncogene and tumor suppressors in regulation of metabolic reprogramming during cancer. APODHIN platform contains a web server component where users can upload single/multi omics data to identify TINs and cross-pathway links. Tabular, graphical and 3D network representations of the identified TINs and cross-pathway links are provided for better appreciation. Additionally, this platform also provides a database part where cancer specific, single and/or multi omics dataset centric meta-interactome networks, TINs, and crosspathway links are provided for cervical, ovarian, and breast cancers, respectively. APODHIN platform is freely available at http://www.hpppi.iicb.res.in/APODHIN/home.html.

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“…Utilization of multi-omics data via mathematical modeling-based analysis in principle can identify cross-pathway links connecting signaling proteins or transcription factors or miRNAs to metabolic enzymes and their metabolites using network analysis and mathematical modeling. These types of cross pathway links were shown to play important roles in metabolic reprogramming in cancer scenarios such as glioblastoma multiforme ( 95 ). Therefore, integrative studies aided by multi-omics analysis, mathematical modeling, and deep learning-AI-based strategies would lead to the development of a more comprehensive understanding of cancer metastasis.…”

Section: Ai and Secondary Omics Datamentioning

confidence: 99%

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Biswas

Chakrabarti

2020

Front. Oncol.

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Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of “big”-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

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Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma

Cited by 20 publications

References 113 publications

Patterns and Direct/Indirect Signaling Pathways in Cardiovascular System in the Condition of Transient Increase of NO

Patterns and Direct/Indirect Signaling Pathways in Cardiovascular System in the Condition of Transient Increase of NO

Analysis of Pan-Omics Data in Human Interactome Network (APODHIN)

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

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