Computation Approach to Predict Key Differential Expressed Gene and Therapeutic Strategies in Lung Cancer: Exploration of Phytochemicals Inhibitors

Lung cancer is a leading cause of cancer-related mortality globally, with delayed diagnosis and complex tumor biology contributing to poor survival rates. The discovery of reliable differentially expressed genes (DEGs) and novel therapeutic agents is critical for improving early detection and treatment outcomes. This study aimed to identify differentially expressed genes (DEGs) as potential DEGs and to evaluate the inhibitory potential of selected phytochemicals against key targets through integrated in silico analysis. Gene expression profiles from five GEO datasets comprising 232 lung tissue samples were analyzed using GEO2R to identify DEGs (adjusted p < 0.05, |log2FC| > 1). Cytoscape plugins (CytoHubba, MCODE) and GO/KEGG enrichment tools were employed to identify and annotate key DEG genes. Survival analysis using TCGA data and GEPIA revealed prognostically significant genes. SHP-1 (PTPN6), an immune-modulating phosphatase, was selected for molecular docking with a curated library of 76 anticancer phytochemicals. The top candidates were filtered using ADMET and toxicity screening, and the most promising compounds were subjected to molecular docking and 200 ns molecular dynamics (MD) simulations using AutoDock Vina and GROMACS. A total of 12 key DEGs were identified both from up and downregulate, with PTPN6 emerging as the only upregulated gene significantly associated with improved survival (HR = 0.49, p = 2.8e-06). Downregulated genes such as CCNB1, BUB1B, and AURKB were linked to poor prognosis. GO and pathway enrichment revealed dysregulation of mitotic control, chromatin remodeling, and immune response pathways. Of the 76 phytochemicals analyzed, 12 passed ADMET filters, with Quercetin, Luteolin, and Rhamnetin demonstrating the strongest binding to SHP-1 (binding affinities: -7.9 to -7.8 kcal/mol). MD simulations confirmed the structural stability of Quercetin and Luteolin complexes over 200 ns, supported by RMSD, RMSF, SASA, and radius of gyration metrics. This study identifies SHP-1 (PTPN6) as a promising DEG and therapeutic target in lung cancer, emphasizing its role in immune regulation and survival prediction. The phytochemicals Quercetin and Luteolin exhibited stable binding and favorable drug-like properties, suggesting their potential as SHP-1 inhibitors. These findings align with prior reports on phytochemical immunomodulation and underscore the therapeutic promise of targeting SHP-1. While results are encouraging, in vitro and in vivo validation is essential to confirm these compounds' efficacy and safety. The integrative in silico pipeline presented here provides a foundation for future DEG-driven therapeutic strategies in lung cancer.

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Publication Details

Journal
African Journal of Biomedical Research
Published
2025-01-01
DOI
https://doi.org/10.53555/ajbr.v28i3s.7920
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Computation Approach to Predict Key Differential Expressed Gene and Therapeutic Strategies in Lung Cancer: Exploration of Phytochemicals Inhibitors

Afnan Alkathiri
African Journal of Biomedical Research
Computational Drug Discovery Methods
article

Computation Approach to Predict Key Differential Expressed Gene and Therapeutic Strategies in Lung Cancer: Exploration of Phytochemicals Inhibitors

Afnan Alkathiri
article en

Abstract

Lung cancer is a leading cause of cancer-related mortality globally, with delayed diagnosis and complex tumor biology contributing to poor survival rates. The discovery of reliable differentially expressed genes (DEGs) and novel therapeutic agents is critical for improving early detection and treatment outcomes. This study aimed to identify differentially expressed genes (DEGs) as potential DEGs and to evaluate the inhibitory potential of selected phytochemicals against key targets through integrated in silico analysis. Gene expression profiles from five GEO datasets comprising 232 lung tissue samples were analyzed using GEO2R to identify DEGs (adjusted p < 0.05, |log2FC| > 1). Cytoscape plugins (CytoHubba, MCODE) and GO/KEGG enrichment tools were employed to identify and annotate key DEG genes. Survival analysis using TCGA data and GEPIA revealed prognostically significant genes. SHP-1 (PTPN6), an immune-modulating phosphatase, was selected for molecular docking with a curated library of 76 anticancer phytochemicals. The top candidates were filtered using ADMET and toxicity screening, and the most promising compounds were subjected to molecular docking and 200 ns molecular dynamics (MD) simulations using AutoDock Vina and GROMACS. A total of 12 key DEGs were identified both from up and downregulate, with PTPN6 emerging as the only upregulated gene significantly associated with improved survival (HR = 0.49, p = 2.8e-06). Downregulated genes such as CCNB1, BUB1B, and AURKB were linked to poor prognosis. GO and pathway enrichment revealed dysregulation of mitotic control, chromatin remodeling, and immune response pathways. Of the 76 phytochemicals analyzed, 12 passed ADMET filters, with Quercetin, Luteolin, and Rhamnetin demonstrating the strongest binding to SHP-1 (binding affinities: -7.9 to -7.8 kcal/mol). MD simulations confirmed the structural stability of Quercetin and Luteolin complexes over 200 ns, supported by RMSD, RMSF, SASA, and radius of gyration metrics. This study identifies SHP-1 (PTPN6) as a promising DEG and therapeutic target in lung cancer, emphasizing its role in immune regulation and survival prediction. The phytochemicals Quercetin and Luteolin exhibited stable binding and favorable drug-like properties, suggesting their potential as SHP-1 inhibitors. These findings align with prior reports on phytochemical immunomodulation and underscore the therapeutic promise of targeting SHP-1. While results are encouraging, in vitro and in vivo validation is essential to confirm these compounds' efficacy and safety. The integrative in silico pipeline presented here provides a foundation for future DEG-driven therapeutic strategies in lung cancer.

African Journal of Biomedical Research
Albaha University
Openalex Percentile: Top 83%
Computational Drug Discovery Methods
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