Exploration of functional relations among differentially co-expressed genes identifies regulators in glioblastoma
Copyright © 2024 Elsevier Ltd. All rights reserved..
The conventional computational approaches to investigating a disease confront inherent constraints as they often need to improve in delving beyond protein functional associations and grasping their deeper contextual significance within the disease framework. Such context-specificity can be explored using clinical data by evaluating the change in interaction between the biological entities in different conditions by investigating the differential co-expression relationships. We believe that the integration and analysis of differential co-expression and the functional relationships, primarily focusing on the source nodes, will open novel insights about disease progression as the source proteins could trigger signaling cascades, mostly because they are transcription factors, cell surface receptors, or enzymes that respond instantly to a particular stimulus. A thorough contextual investigation of these nodes could lead to a helpful beginning point for identifying potential causal linkages and guiding subsequent scientific investigations to uncover mechanisms underlying observed associations. Our methodology includes functional protein-protein Interaction (PPI) data and co-expression information and filters functional linkages through a series of critical steps, culminating in the identification of a robust set of regulators. Our analysis identified eleven key regulators-AKT1, BRCA1, CAMK2G, CUL1, FGFR3, KIF3A, NUP210, PRKACB, RAB8A, RPS6KA2 and TGFB3-in glioblastoma. These regulators play a pivotal role in disease classification, cell growth control, and patient survivability and exhibit associations with immune infiltrations and disease hallmarks. This underscores the importance of assessing correlation towards causality in unraveling complex biological insights.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:109 |
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| Enthalten in: |
Computational biology and chemistry - 109(2024) vom: 01. März, Seite 108024 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Kumar, Shivam [VerfasserIn] |
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| Links: |
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| Themen: |
Cancer hallmarks |
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| Anmerkungen: |
Date Completed 18.03.2024 Date Revised 18.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.compbiolchem.2024.108024 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368253481 |
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| 520 | |a Copyright © 2024 Elsevier Ltd. All rights reserved. | ||
| 520 | |a The conventional computational approaches to investigating a disease confront inherent constraints as they often need to improve in delving beyond protein functional associations and grasping their deeper contextual significance within the disease framework. Such context-specificity can be explored using clinical data by evaluating the change in interaction between the biological entities in different conditions by investigating the differential co-expression relationships. We believe that the integration and analysis of differential co-expression and the functional relationships, primarily focusing on the source nodes, will open novel insights about disease progression as the source proteins could trigger signaling cascades, mostly because they are transcription factors, cell surface receptors, or enzymes that respond instantly to a particular stimulus. A thorough contextual investigation of these nodes could lead to a helpful beginning point for identifying potential causal linkages and guiding subsequent scientific investigations to uncover mechanisms underlying observed associations. Our methodology includes functional protein-protein Interaction (PPI) data and co-expression information and filters functional linkages through a series of critical steps, culminating in the identification of a robust set of regulators. Our analysis identified eleven key regulators-AKT1, BRCA1, CAMK2G, CUL1, FGFR3, KIF3A, NUP210, PRKACB, RAB8A, RPS6KA2 and TGFB3-in glioblastoma. These regulators play a pivotal role in disease classification, cell growth control, and patient survivability and exhibit associations with immune infiltrations and disease hallmarks. This underscores the importance of assessing correlation towards causality in unraveling complex biological insights | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Cancer hallmarks | |
| 650 | 4 | |a Differential co-expression analysis | |
| 650 | 4 | |a Glioblastoma | |
| 650 | 4 | |a Growth inhibition | |
| 650 | 4 | |a Protein-protein interaction network | |
| 650 | 4 | |a Survival analysis | |
| 650 | 7 | |a Transcription Factors |2 NLM | |
| 700 | 1 | |a Sarmah, Dipanka Tanu |e verfasserin |4 aut | |
| 700 | 1 | |a Paul, Abhijit |e verfasserin |4 aut | |
| 700 | 1 | |a Chatterjee, Samrat |e verfasserin |4 aut | |
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