Evidence map›Paper›PMID 38919088›Full record

ArticleRecent advances in food, nutrition & agriculture2025

Unravelling Phyto-Compound Therapeutics Against Colorectal Cancer: Targeting SRC proto-oncogene via Fibroblast Growth Factor Signalling Pathway -A Comprehensive Approach Integrating Omics Data Analysis, Network Pharmacology, Virtual Screening, and Molecular Dynamics.

Pankaj Kumar Tripathi, Chakresh Kumar Jain

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Article in Recent advances in food, nutrition & agriculture, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Pankaj Kumar TripathiDepartment of Biotechnology, Jaypee Institute of Information Technology, A-10, Sector-62, NOIDA, Uttar Pradesh, 201309, India.ORCID 0000-0002-9929-359X
Chakresh Kumar JainDepartment of Biotechnology, Jaypee Institute of Information Technology, A-10, Sector-62, NOIDA, Uttar Pradesh, 201309, India.ORCID 0000-0002-9226-7719

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionColorectal cancer is a complex condition influenced by genetic mutations and environmental factors. Due to its intricate nature, the diagnosis and treatment of this condition require a comprehensive approach that considers individual circumstances. The study aimed to identify genes linked with colorectal cancer and their therapeutic agents from natural bioactive compounds.

methodsThe significantly prognostic differentially expressed genes (DEGs) were screened out from NCBI Gene Expression Omnibus (GEO) datasets. A protein-protein interaction network was constructed using STRING Database, and key genes were identified using Network Analyzer and CytoNCA plugins within Cytoscape. Further analysis involved functional annotations, and biological pathways analysis, SRC mechanism to uncover the role of SRC in CRC. Additionally, we performed virtual screening and molecular docking, Physiochemical property analysis along with MD simulation study to propose suitable natural compounds for promising therapeutic targets.

resultsThe study conducted differential gene expression analysis, identifying 3621 statistically significant genes, with 1467 upregulated and 2154 downregulated. The top ten genes with the highest degree, betweenness centrality, and closeness centrality in the PPI network were selected as key genes. The SRC gene was found to have the highest degree and closeness centrality. Functional annotation and pathway analysis of key genes with a specific focus on the SRC mechanism revealed that the SRC's role in activating the RAS-RAF-MEK-ERK and Wnt/β-catenin pathways in CRC cells, promoting proliferation and invasion. Molecular modelling of SRC led to the screening of phytocompounds from tropical fruits, with Rutin exhibiting a higher docking score compared to FDAapproved anticancer drugs. MD simulations over 100 ns and the post-MD analysis i.e. RMSD, SASA, RMSF, FEL, RG, Hydrogen bond, PCA, and MMPBSA, comprehended the stable and robust interactions of a protein-ligand complex. These findings suggest Rutin's potential as a potent natural molecule for treating CRC.

conclusionThe study concludes that SRC plays a pivotal role in CRC, influencing cellular processes critical to cancer development and Rutin has been found to be a promising SRC inhibitor, suggesting a potential alternative therapeutic strategy for CRC. The consistent molecular interactions of Rutin necessitate further validation through wet lab experiments, offering hope for individuals affected by CRC.

Indexed as

Colorectal NeoplasmsPhytochemicalssrc-Family KinasesHumansMolecular Docking SimulationMolecular Dynamics SimulationNetwork PharmacologyProtein Interaction MapsProto-Oncogene MasSignal TransductionMAS1 protein, humanPhytochemicalsProto-Oncogene Massrc-Family KinasesColorectal cancerliterature miningmolecular dynamics simulation.molecular mechanismsPPI network analysistherapeutics

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.