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Lu L, Ma D, Xi Z. Coexpression of TP53, BIM, and PTEN Enhances the Therapeutic Efficacy of Non-Small-Cell Lung Cancer. Biomacromolecules 2024; 25:792-808. [PMID: 38237562 DOI: 10.1021/acs.biomac.3c00988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2024]
Abstract
For non-small-cell lung cancer (NSCLC), the ubiquitous occurrence of concurrent multiple genomic alterations poses challenges to single-gene therapy. To increase therapeutic efficacy, we used the branch-PCR method to develop a multigene nanovector, NP-TP53-BIM-PTEN, that carried three therapeutic gene expression cassettes for coexpression. NP-TP53-BIM-PTEN exhibited a uniform size of 104.8 ± 24.2 nm and high serum stability. In cell transfection tests, NP-TP53-BIM-PTEN could coexpress TP53, BIM, and PTEN in NCI-H1299 cells and induce cell apoptosis with a ratio of up to 94.9%. Furthermore, NP-TP53-BIM-PTEN also inhibited cell proliferation with a ratio of up to 42%. In a mouse model bearing an NCI-H1299 xenograft tumor, NP-TP53-BIM-PTEN exhibited a stronger inhibitory effect on the NCI-H1299 xenograft tumor than the other test vectors without any detectable side effects. These results exhibited the potential of NP-TP53-BIM-PTEN as an effective and safe multigene nanovector to enhance NSCLC therapy efficacy, which will provide a framework for genome therapy with multigene combinations.
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Affiliation(s)
- Liqing Lu
- State Key Laboratory of Elemento-Organic Chemistry and Department of Chemical Biology, National Engineering Research Center of Pesticide (Tianjin), College of Chemistry, Nankai University, Tianjin 300071, China
| | - Dejun Ma
- State Key Laboratory of Elemento-Organic Chemistry and Department of Chemical Biology, National Engineering Research Center of Pesticide (Tianjin), College of Chemistry, Nankai University, Tianjin 300071, China
| | - Zhen Xi
- State Key Laboratory of Elemento-Organic Chemistry and Department of Chemical Biology, National Engineering Research Center of Pesticide (Tianjin), College of Chemistry, Nankai University, Tianjin 300071, China
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Chen HO, Lin PC, Liu CR, Wang CS, Chiang JH. Contextualizing Genes by Using Text-Mined Co-Occurrence Features for Cancer Gene Panel Discovery. Front Genet 2021; 12:771435. [PMID: 34759963 PMCID: PMC8573063 DOI: 10.3389/fgene.2021.771435] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 10/11/2021] [Indexed: 12/13/2022] Open
Abstract
Developing a biomedical-explainable and validatable text mining pipeline can help in cancer gene panel discovery. We create a pipeline that can contextualize genes by using text-mined co-occurrence features. We apply Biomedical Natural Language Processing (BioNLP) techniques for literature mining in the cancer gene panel. A literature-derived 4,679 × 4,630 gene term-feature matrix was built. The EGFR L858R and T790M, and BRAF V600E genetic variants are important mutation term features in text mining and are frequently mutated in cancer. We validate the cancer gene panel by the mutational landscape of different cancer types. The cosine similarity of gene frequency between text mining and a statistical result from clinical sequencing data is 80.8%. In different machine learning models, the best accuracy for the prediction of two different gene panels, including MSK-IMPACT (Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets), and Oncomine cancer gene panel, is 0.959, and 0.989, respectively. The receiver operating characteristic (ROC) curve analysis confirmed that the neural net model has a better prediction performance (Area under the ROC curve (AUC) = 0.992). The use of text-mined co-occurrence features can contextualize each gene. We believe the approach is to evaluate several existing gene panels, and show that we can use part of the gene panel set to predict the remaining genes for cancer discovery.
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Affiliation(s)
- Hui-O Chen
- Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Cheng Kung University, Tainan, Taiwan.,Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan
| | - Peng-Chan Lin
- Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Cheng Kung University, Tainan, Taiwan.,Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan.,Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Genomic Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chen-Ruei Liu
- Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Cheng Kung University, Tainan, Taiwan.,Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan
| | - Chi-Shiang Wang
- Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Cheng Kung University, Tainan, Taiwan.,Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan
| | - Jung-Hsien Chiang
- Department of Computer Science and Information Engineering, College of Electrical Engineering and Computer Science, National Cheng Kung University, Tainan, Taiwan.,Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan
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