Identification of potential diagnostic targets and therapeutic strategies for anoikis-related biomarkers in lung squamous cell carcinoma using machine learning and computational virtual screening
ObjectiveLung squamous cell carcinoma (LUSC) is a common subtype of non-small cell lung cancer (NSCLC) characterized by high invasiveness, high metastatic potential, and drug resistance, resulting in poor patient prognosis. Anoikis, a specific form of apoptosis triggered by cell detachment from the...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2025.1500968/full |
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| author | Xin Zhang Jing Zou Jinghua Ning Yanhong Zhao Run Qu Yuzhe Zhang Yuzhe Zhang Yuzhe Zhang |
| author_facet | Xin Zhang Jing Zou Jinghua Ning Yanhong Zhao Run Qu Yuzhe Zhang Yuzhe Zhang Yuzhe Zhang |
| author_sort | Xin Zhang |
| collection | DOAJ |
| description | ObjectiveLung squamous cell carcinoma (LUSC) is a common subtype of non-small cell lung cancer (NSCLC) characterized by high invasiveness, high metastatic potential, and drug resistance, resulting in poor patient prognosis. Anoikis, a specific form of apoptosis triggered by cell detachment from the extracellular matrix (ECM), plays a crucial role in tumor metastasis. Resistance to anoikis is a key mechanism by which cancer cells acquire metastatic potential. Although several studies have identified biomarkers related to LUSC, the role of anoikis-related genes (ARGs) remains largely unexplored.MethodsAnoikis-related genes were obtained from the Harmonizome and GeneCards databases, and 222 differentially expressed genes (DEGs) in LUSC were identified via differential expression analysis. Univariate Cox regression analysis identified 74 ARGs significantly associated with survival, and a prognostic model comprising 8 ARGs was developed using LASSO and multivariate Cox regression analyses. The model was internally validated using receiver operating characteristic (ROC) curves and Kaplan-Meier (K-M) survival curves. Differences in immune cell infiltration and gene expression between high- and low-risk groups were analyzed. Virtual drug screening and molecular dynamics simulations were performed to evaluate the therapeutic potential of CSNK2A1, a key gene in the model. Finally, in vitro experiments were conducted to validate the therapeutic effects of the identified drug on LUSC.ResultsThe 8-gene prognostic model demonstrated excellent predictive performance and stability. Significant differences in immune cell infiltration and immune microenvironment characteristics were observed between the high- and low-risk groups, suggesting the critical role of ARGs in shaping the immune landscape of LUSC. Virtual drug screening identified Dihydroergotamine as having the highest binding affinity for CSNK2A1. Molecular dynamics simulations confirmed that the CSNK2A1-Dihydroergotamine complex exhibited strong binding stability. Further in vitro experiments demonstrated that Dihydroergotamine significantly inhibited LUSC cell viability, migration, and invasion, and downregulated CSNK2A1 expression.ConclusionThis study is the first to construct an anoikis-related prognostic model for LUSC, highlighting its role in the tumor immune microenvironment and providing insights into personalized therapy. Dihydroergotamine exhibited significant anti-LUSC activity and holds promise as a potential therapeutic agent. CSNK2A1 emerged as a robust candidate for early diagnosis and a therapeutic target in LUSC. |
| format | Article |
| id | doaj-art-8ce4efab7c4b4e68a2d72121b174ad08 |
| institution | DOAJ |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Pharmacology |
| spelling | doaj-art-8ce4efab7c4b4e68a2d72121b174ad082025-08-20T03:11:37ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-02-011610.3389/fphar.2025.15009681500968Identification of potential diagnostic targets and therapeutic strategies for anoikis-related biomarkers in lung squamous cell carcinoma using machine learning and computational virtual screeningXin Zhang0Jing Zou1Jinghua Ning2Yanhong Zhao3Run Qu4Yuzhe Zhang5Yuzhe Zhang6Yuzhe Zhang7College of Basic Medical sciences, Dali University, Dali, ChinaDepartment of Respiratory Medicine, First Affiliated Hospital of Dali University, Dali, ChinaCollege of Basic Medical sciences, Dali University, Dali, ChinaCollege of Basic Medical sciences, Dali University, Dali, ChinaCollege of Basic Medical sciences, Dali University, Dali, ChinaCollege of Basic Medical sciences, Dali University, Dali, ChinaKey Laboratory of Insect Biomedicine, Dali, Yunnan, ChinaKey Laboratory of Anti-Pathogen Medicinal Plants Screening, Dali, Yunnan, ChinaObjectiveLung squamous cell carcinoma (LUSC) is a common subtype of non-small cell lung cancer (NSCLC) characterized by high invasiveness, high metastatic potential, and drug resistance, resulting in poor patient prognosis. Anoikis, a specific form of apoptosis triggered by cell detachment from the extracellular matrix (ECM), plays a crucial role in tumor metastasis. Resistance to anoikis is a key mechanism by which cancer cells acquire metastatic potential. Although several studies have identified biomarkers related to LUSC, the role of anoikis-related genes (ARGs) remains largely unexplored.MethodsAnoikis-related genes were obtained from the Harmonizome and GeneCards databases, and 222 differentially expressed genes (DEGs) in LUSC were identified via differential expression analysis. Univariate Cox regression analysis identified 74 ARGs significantly associated with survival, and a prognostic model comprising 8 ARGs was developed using LASSO and multivariate Cox regression analyses. The model was internally validated using receiver operating characteristic (ROC) curves and Kaplan-Meier (K-M) survival curves. Differences in immune cell infiltration and gene expression between high- and low-risk groups were analyzed. Virtual drug screening and molecular dynamics simulations were performed to evaluate the therapeutic potential of CSNK2A1, a key gene in the model. Finally, in vitro experiments were conducted to validate the therapeutic effects of the identified drug on LUSC.ResultsThe 8-gene prognostic model demonstrated excellent predictive performance and stability. Significant differences in immune cell infiltration and immune microenvironment characteristics were observed between the high- and low-risk groups, suggesting the critical role of ARGs in shaping the immune landscape of LUSC. Virtual drug screening identified Dihydroergotamine as having the highest binding affinity for CSNK2A1. Molecular dynamics simulations confirmed that the CSNK2A1-Dihydroergotamine complex exhibited strong binding stability. Further in vitro experiments demonstrated that Dihydroergotamine significantly inhibited LUSC cell viability, migration, and invasion, and downregulated CSNK2A1 expression.ConclusionThis study is the first to construct an anoikis-related prognostic model for LUSC, highlighting its role in the tumor immune microenvironment and providing insights into personalized therapy. Dihydroergotamine exhibited significant anti-LUSC activity and holds promise as a potential therapeutic agent. CSNK2A1 emerged as a robust candidate for early diagnosis and a therapeutic target in LUSC.https://www.frontiersin.org/articles/10.3389/fphar.2025.1500968/fulllung squamous cell carcinomaanoikisCSNK2A1virtual screeningmachine learning |
| spellingShingle | Xin Zhang Jing Zou Jinghua Ning Yanhong Zhao Run Qu Yuzhe Zhang Yuzhe Zhang Yuzhe Zhang Identification of potential diagnostic targets and therapeutic strategies for anoikis-related biomarkers in lung squamous cell carcinoma using machine learning and computational virtual screening Frontiers in Pharmacology lung squamous cell carcinoma anoikis CSNK2A1 virtual screening machine learning |
| title | Identification of potential diagnostic targets and therapeutic strategies for anoikis-related biomarkers in lung squamous cell carcinoma using machine learning and computational virtual screening |
| title_full | Identification of potential diagnostic targets and therapeutic strategies for anoikis-related biomarkers in lung squamous cell carcinoma using machine learning and computational virtual screening |
| title_fullStr | Identification of potential diagnostic targets and therapeutic strategies for anoikis-related biomarkers in lung squamous cell carcinoma using machine learning and computational virtual screening |
| title_full_unstemmed | Identification of potential diagnostic targets and therapeutic strategies for anoikis-related biomarkers in lung squamous cell carcinoma using machine learning and computational virtual screening |
| title_short | Identification of potential diagnostic targets and therapeutic strategies for anoikis-related biomarkers in lung squamous cell carcinoma using machine learning and computational virtual screening |
| title_sort | identification of potential diagnostic targets and therapeutic strategies for anoikis related biomarkers in lung squamous cell carcinoma using machine learning and computational virtual screening |
| topic | lung squamous cell carcinoma anoikis CSNK2A1 virtual screening machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2025.1500968/full |
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