A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer

Wu, Isabella; Wang, Xin

doi:10.1038/s41598-023-35165-w

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…However, due to the small sample sizes with high-dimensional features, training a large-scale generalizable ML model with multi-omics data alone can be challenging. Moreover, ML has also been successfully used to predict disease biomarkers using PPI network topological data (Wu and Wang (2023)). A study by Yu.…”

Section: Discussionmentioning

confidence: 99%

Predicting “pain genes”: multi-modal data integration using probabilistic classifiers and interaction networks

Zhao,

Bennett,

Baskozos

et al. 2024

Preprint

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Accurate identification of pain-related genes remains challenging due to the complex nature of pain pathophysiology and the subjective nature of pain reporting in humans, or inferring pain states in animals on the basis of behaviour. Here, we use a machine learning approach to identify possible pain genes. Labelling was based on a gold-standard list of genes with validated involvement across pain conditions, and was trained on a selection of -omics (eg. transcriptomics, proteomics, etc.), protein-protein interaction (PPI) network features, and biological function readouts for each gene. Multiple classifiers were trained, and the top-performing model was selected to predict a pain score per gene. The top ranked genes were then validated against pain-related human SNPs to validate against human genetics studies. Functional analysis revealed JAK2/STAT3 signal, ErbB, and Rap1 signalling pathways as promising targets for further exploration, while network topological features contribute significantly to the identification of pain genes. As such, a PPI network based on top-ranked genes was constructed to reveal previously uncharacterised pain-related genes including CHRFAM7A and UNC79. These analyses can be further explored using the linked open-source database at https://livedataoxford.shinyapps.io/drg-directory, which is accompanied by a freely accessible code template and user guide for wider adoption across disciplines. Together, the novel insights into pain pathogenesis can indicate promising directions for future experimental research.

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Section: Discussionmentioning

confidence: 99%

Predicting “pain genes”: multi-modal data integration using probabilistic classifiers and interaction networks

Zhao,

Bennett,

Baskozos

et al. 2024

Preprint

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Predicting ‘pain genes’: multi-modal data integration using probabilistic classifiers and interaction networks

Zhao,

Bennett,

Baskozos

et al. 2024

Bioinformatics Advances

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Accurate identification of pain-related genes remains challenging due to the complex nature of pain pathophysiology and the subjective nature of pain reporting in humans, or inferring pain states in animals on the basis of behaviour. Here, we use a machine learning approach to identify possible “pain genes”. Labelling was based on a gold-standard list of genes with validated involvement across pain conditions, and was trained on a selection of -omics, protein-protein interaction network features, and biological function readouts for each gene. Multiple classifiers were trained, and the top-performing model was selected to predict a “pain score” per gene. The top ranked genes were validated against pain-related human SNPs to validate against human genetics studies. Functional analysis revealed JAK2/STAT3 signal, ErbB, and Rap1 signalling pathways as promising targets for further exploration, while network topological features contribute significantly to the identification of “pain” genes. As such, a network based on top-ranked genes was constructed to reveal previously uncharacterised pain-related genes including CHRFAM7A and UNC79. These analyses can be further explored using the linked open-source database at https://livedataoxford.shinyapps.io/drg-directory/, which is accompanied by a freely accessible code template and user guide for wider adoption across disciplines. Together, the novel insights into pain pathogenesis can indicate promising directions for future experimental research.

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Evaluation of Antifibrotic Mechanisms of 3′5-Dimaleamylbenzoic Acid on Idiopathic Pulmonary Fibrosis: A Network Pharmacology and Molecular Docking Analysis

González-García,

Santos-Álvarez,

Velázquez-Enríquez

et al. 2024

DDC

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Background: Idiopathic pulmonary fibrosis (IPF) is a chronic, disabling disorder of unknown etiology, poor prognosis, and limited therapeutic options. Previously, 3′5-dimaleamylbenzoic acid (3′5-DMBA) was shown to exert resolving effects in IPF, offering a promising alternative for treating this disease; however, the molecular mechanisms associated with this effect have not been explored. Objetive: We evaluated the potential antifibrotic mechanisms of 3′5-DMBA by network pharmacology (NP) and molecular docking (MD). Methods: 3′5-DMBA-associated targets were identified by screening in SwissTargetPrediction. IPF-associated targets were identified using lung tissue meta-analysis and public databases. Common targets were identified, and a protein–protein interaction (PPI) network was constructed; we ranked the proteins in the PPI network by topological analysis. MD validated the binding of 3′5-DMBA to the main therapeutic targets. Results: A total of 57 common targets were identified between 3′5-DMBA and IPF; caspase 8, 9, 3, and 7; myeloid leukemia-induced cell differentiation protein Mcl-1; and poly [ADP-ribose] polymerase 1 are primary targets regulating PPI networks. Functional analysis revealed that the common targets are involved in the pathological features of tissue fibrosis and primarily in the apoptotic process. MD revealed favorable interaction energies among the three main targets regulating PPI networks. Conclusions: NP results suggest that the antifibrotic effect of 3′5-DMBA is due to its regulation of the pathological features of IPF, mainly by modulating signaling pathways leading to apoptosis, suggesting its therapeutic potential to treat this disease.

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A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer

Cited by 3 publications

References 43 publications

Predicting “pain genes”: multi-modal data integration using probabilistic classifiers and interaction networks

Predicting “pain genes”: multi-modal data integration using probabilistic classifiers and interaction networks

Predicting ‘pain genes’: multi-modal data integration using probabilistic classifiers and interaction networks

Evaluation of Antifibrotic Mechanisms of 3′5-Dimaleamylbenzoic Acid on Idiopathic Pulmonary Fibrosis: A Network Pharmacology and Molecular Docking Analysis

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