Machine learning modeling and analysis of prognostic hub genes in cervical adenocarcinoma: a multi target therapy for enhancement in immunosurveillance
Abstract Endocervical adenocarcinoma (ECA) the fatal and intrusive subtype of cervical carcinoma is on rise from the last decade. Its improper detection leads to worst clinical outcomes that urges the discovery of novel biomarkers. Therefore, we proposed insilico and invitro based approches to ident...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-02834-3 |
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| author | Madiha Jabeen Abbasi Rashid Abbasi ShuPeng Wu Md Belal Bin Heyat Ding Xianfeng Huijie Jia Aiwen Zheng |
| author_facet | Madiha Jabeen Abbasi Rashid Abbasi ShuPeng Wu Md Belal Bin Heyat Ding Xianfeng Huijie Jia Aiwen Zheng |
| author_sort | Madiha Jabeen Abbasi |
| collection | DOAJ |
| description | Abstract Endocervical adenocarcinoma (ECA) the fatal and intrusive subtype of cervical carcinoma is on rise from the last decade. Its improper detection leads to worst clinical outcomes that urges the discovery of novel biomarkers. Therefore, we proposed insilico and invitro based approches to identify key genes that could be used as potential targeted therapies. RNA-seq and gene expression data was operated via R-programming that identified 11,592 differential expressed genes which are mainly enriched in metabolic pathways, chemical carcinogenesis-receptor activation, amoebias, MAPK and PI3K-AKT signaling pathway. Clustering modules and hub genes were retrieved to design network of immune cells with varying expression using multiple statistical algorithms. The Drugs targeting hub genes were determined from Drug gene interaction database which was further categorized for docking and dynamics based simulations. Results indicate high binding affinity of Imatinib compound into active pockets of BIRC5 which is confirmed by cell viability lab experiment. Current study demonstrates novel biomarkers and therapeutic drugs for in depth understanding of endocervical carcinogensis. |
| format | Article |
| id | doaj-art-a42f1e8fe2064c3fa13e236ca7a8b303 |
| institution | DOAJ |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-a42f1e8fe2064c3fa13e236ca7a8b3032025-08-20T03:05:05ZengSpringerDiscover Oncology2730-60112025-07-0116111910.1007/s12672-025-02834-3Machine learning modeling and analysis of prognostic hub genes in cervical adenocarcinoma: a multi target therapy for enhancement in immunosurveillanceMadiha Jabeen Abbasi0Rashid Abbasi1ShuPeng Wu2Md Belal Bin Heyat3Ding Xianfeng4Huijie Jia5Aiwen Zheng6School of Life Science and Medicine, Zhejiang Sci-Tech UniversityCollege of Computer Science and Artificial Intelligent, Wenzhou UniversitySchool of Life Science and Medicine, Zhejiang Sci-Tech UniversityCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake UniversitySchool of Life Science and Medicine, Zhejiang Sci-Tech UniversityOakham SchoolDepartment of Gynecologic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesAbstract Endocervical adenocarcinoma (ECA) the fatal and intrusive subtype of cervical carcinoma is on rise from the last decade. Its improper detection leads to worst clinical outcomes that urges the discovery of novel biomarkers. Therefore, we proposed insilico and invitro based approches to identify key genes that could be used as potential targeted therapies. RNA-seq and gene expression data was operated via R-programming that identified 11,592 differential expressed genes which are mainly enriched in metabolic pathways, chemical carcinogenesis-receptor activation, amoebias, MAPK and PI3K-AKT signaling pathway. Clustering modules and hub genes were retrieved to design network of immune cells with varying expression using multiple statistical algorithms. The Drugs targeting hub genes were determined from Drug gene interaction database which was further categorized for docking and dynamics based simulations. Results indicate high binding affinity of Imatinib compound into active pockets of BIRC5 which is confirmed by cell viability lab experiment. Current study demonstrates novel biomarkers and therapeutic drugs for in depth understanding of endocervical carcinogensis.https://doi.org/10.1007/s12672-025-02834-3Endocervical adenocarcinomaMolecular docking and dynamics simulationHub genesMTT AssayFunctional enrichment analysisTumor immunosurveillance |
| spellingShingle | Madiha Jabeen Abbasi Rashid Abbasi ShuPeng Wu Md Belal Bin Heyat Ding Xianfeng Huijie Jia Aiwen Zheng Machine learning modeling and analysis of prognostic hub genes in cervical adenocarcinoma: a multi target therapy for enhancement in immunosurveillance Discover Oncology Endocervical adenocarcinoma Molecular docking and dynamics simulation Hub genes MTT Assay Functional enrichment analysis Tumor immunosurveillance |
| title | Machine learning modeling and analysis of prognostic hub genes in cervical adenocarcinoma: a multi target therapy for enhancement in immunosurveillance |
| title_full | Machine learning modeling and analysis of prognostic hub genes in cervical adenocarcinoma: a multi target therapy for enhancement in immunosurveillance |
| title_fullStr | Machine learning modeling and analysis of prognostic hub genes in cervical adenocarcinoma: a multi target therapy for enhancement in immunosurveillance |
| title_full_unstemmed | Machine learning modeling and analysis of prognostic hub genes in cervical adenocarcinoma: a multi target therapy for enhancement in immunosurveillance |
| title_short | Machine learning modeling and analysis of prognostic hub genes in cervical adenocarcinoma: a multi target therapy for enhancement in immunosurveillance |
| title_sort | machine learning modeling and analysis of prognostic hub genes in cervical adenocarcinoma a multi target therapy for enhancement in immunosurveillance |
| topic | Endocervical adenocarcinoma Molecular docking and dynamics simulation Hub genes MTT Assay Functional enrichment analysis Tumor immunosurveillance |
| url | https://doi.org/10.1007/s12672-025-02834-3 |
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