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Yu L, Lin N, Ye Y, Zhou S, Xu Y, Chen J, Zhuang W, Wang Q. Prognostic and chemotherapeutic response prediction by proliferation essential gene signature: Investigating POLE2 in bladder cancer progression and cisplatin resistance. J Cancer 2024; 15:1734-1749. [PMID: 38370377 PMCID: PMC10869977 DOI: 10.7150/jca.93023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 01/16/2024] [Indexed: 02/20/2024] Open
Abstract
Background: Bladder cancer (BLCA) is the most common genitourinary malignancy. Proliferation essential genes (PEGs) are crucial to the survival of cancer cells. This study aimed to build a PEG signature to predict BLCA prognosis and treatment efficacy. Methods: BLCA PEGs and differentially expressed PEGs were identified using DepMap and TCGA-BLCA datasets, respectively. Based on the prognostic analysis of the differentially expressed PEGs, a PEG model was constructed. Subsequently, we analyzed the relationship between the PEG signature and prognosis of BLCA patients as well as their response to chemotherapy. Finally, we performed random forest analysis to target and functional experiments to validate the most significant PEG which is associated with BLCA progression. CCK-8, invasion, migration, and chemosensitivity assays were performed to assess effects of gene knockdown on BLCA cell proliferation, invasion and migration abilities, and cisplatin chemosensitivity. Results: We screened 10 prognostic PEGs from 201 differentially expressed PEGs and used them to construct a PEG signature model. Patients with high PEG signature score (PEGs-high) exhibited worse OS and lower sensitivity to chemotherapy than those with PEGs-low. We also found significant correlations between the PEG score and previously defined BLCA molecular subtypes. This suggests that the PEG score may effectively predict the molecular subtypes which have distinct clinical outcomes. Random forest analysis revealed that POLE2 (DNA polymerase epsilon subunit 2) was the most significant PEG differentiating BLCA tissue and normal tissue. Bioinformatic analysis and an immunohistochemistry staining assay confirmed that POLE2 was significantly up-regulated in tumor tissues and was associated with poor survival in BLCA patients. Moreover, POLE2 knockdown inhibited the ability of cell clone formation, proliferation, invasion, immigration and IC50 of cisplatin. Conclusion: The PEG signature acts as a potential predictor for prognosis and chemotherapy response in BLCA patients. POLE2 is a key PEG and plays a remarkable role in promoting the malignant progression and cisplatin resistance of BLCA.
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Affiliation(s)
- Liying Yu
- Central Laboratory, the Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Na Lin
- Department of Pathology, the Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Yan Ye
- Ganzhou Key Laboratory of Molecular Medicine, the Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, Jiangxi, 341000, China
| | - Shuang Zhou
- The Second Clinical Medical School of Fujian Medical University, Quanzhou, Fujian Province, 362000, China
| | - Yanjuan Xu
- Department of Pathology, the Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Jiabi Chen
- Department of Urology, the Second Affiliated Hospital of Fujian Medical University, No. 34 Zhongshan North Road, Quanzhou 362000, Fujian
| | - Wei Zhuang
- Department of Urology, the Second Affiliated Hospital of Fujian Medical University, No. 34 Zhongshan North Road, Quanzhou 362000, Fujian
| | - Qingshui Wang
- The Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine, Fujian-Macao Science and Technology Cooperation Base of Traditional Chinese Medicine-Oriented Chronic Disease Prevention and Treatment, Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, Fuzhou, 361000, China
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Dickson KA, Field N, Blackman T, Ma Y, Xie T, Kurangil E, Idrees S, Rathnayake SNH, Mahbub RM, Faiz A, Marsh DJ. CRISPR single base-editing: in silico predictions to variant clonal cell lines. Hum Mol Genet 2023; 32:2704-2716. [PMID: 37369005 DOI: 10.1093/hmg/ddad105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
Engineering single base edits using CRISPR technology including specific deaminases and single-guide RNA (sgRNA) is a rapidly evolving field. Different types of base edits can be constructed, with cytidine base editors (CBEs) facilitating transition of C-to-T variants, adenine base editors (ABEs) enabling transition of A-to-G variants, C-to-G transversion base editors (CGBEs) and recently adenine transversion editors (AYBE) that create A-to-C and A-to-T variants. The base-editing machine learning algorithm BE-Hive predicts which sgRNA and base editor combinations have the strongest likelihood of achieving desired base edits. We have used BE-Hive and TP53 mutation data from The Cancer Genome Atlas (TCGA) ovarian cancer cohort to predict which mutations can be engineered, or reverted to wild-type (WT) sequence, using CBEs, ABEs or CGBEs. We have developed and automated a ranking system to assist in selecting optimally designed sgRNA that considers the presence of a suitable protospacer adjacent motif (PAM), the frequency of predicted bystander edits, editing efficiency and target base change. We have generated single constructs containing ABE or CBE editing machinery, an sgRNA cloning backbone and an enhanced green fluorescent protein tag (EGFP), removing the need for co-transfection of multiple plasmids. We have tested our ranking system and new plasmid constructs to engineer the p53 mutants Y220C, R282W and R248Q into WT p53 cells and shown that these mutants cannot activate four p53 target genes, mimicking the behaviour of endogenous p53 mutations. This field will continue to rapidly progress, requiring new strategies such as we propose to ensure desired base-editing outcomes.
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Affiliation(s)
- Kristie-Ann Dickson
- Translational Oncology Group, Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Natisha Field
- Translational Oncology Group, Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Tiane Blackman
- Translational Oncology Group, Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Yue Ma
- Translational Oncology Group, Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Tao Xie
- Translational Oncology Group, Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Ecem Kurangil
- Translational Oncology Group, Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Sobia Idrees
- Faculty of Science, School of Life Sciences, Centre for Inflammation, Centenary Institute and the University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Senani N H Rathnayake
- Respiratory Bioinformatics and Molecular Biology (RBMB), Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Rashad M Mahbub
- Respiratory Bioinformatics and Molecular Biology (RBMB), Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Alen Faiz
- Respiratory Bioinformatics and Molecular Biology (RBMB), Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Deborah J Marsh
- Translational Oncology Group, Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
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Barrena N, Valcárcel LV, Olaverri-Mendizabal D, Apaolaza I, Planes FJ. Synthetic lethality in large-scale integrated metabolic and regulatory network models of human cells. NPJ Syst Biol Appl 2023; 9:32. [PMID: 37454223 DOI: 10.1038/s41540-023-00296-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 07/04/2023] [Indexed: 07/18/2023] Open
Abstract
Synthetic lethality (SL) is a promising concept in cancer research. A wide array of computational tools has been developed to predict and exploit synthetic lethality for the identification of tumour-specific vulnerabilities. Previously, we introduced the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to SL developed for genome-scale metabolic networks. The major challenge in our gMCS framework is to go beyond metabolic networks and extend existing algorithms to more complex protein-protein interactions. In this article, we take a step further and incorporate linear regulatory pathways into our gMCS approach. Extensive algorithmic modifications to compute gMCSs in integrated metabolic and regulatory models are presented in detail. Our extended approach is applied to calculate gMCSs in integrated models of human cells. In particular, we integrate the most recent genome-scale metabolic network, Human1, with 3 different regulatory network databases: Omnipath, Dorothea and TRRUST. Based on the computed gMCSs and transcriptomic data, we discovered new essential genes and their associated synthetic lethal for different cancer cell lines. The performance of the different integrated models is assessed with available large-scale in-vitro gene silencing data. Finally, we discuss the most relevant gene essentiality predictions based on published literature in cancer research.
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Affiliation(s)
- Naroa Barrena
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Luis V Valcárcel
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain
| | - Danel Olaverri-Mendizabal
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
| | - Iñigo Apaolaza
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain
| | - Francisco J Planes
- University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal 13, 20018, San Sebastián, Spain.
- University of Navarra, Biomedical Engineering Center, Campus Universitario, 31009, Pamplona, Navarra, Spain.
- University of Navarra, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Campus Universitario, 31080, Pamplona, Spain.
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Gimeno M, San José-Enériz E, Villar S, Agirre X, Prosper F, Rubio A, Carazo F. Explainable artificial intelligence for precision medicine in acute myeloid leukemia. Front Immunol 2022; 13:977358. [PMID: 36248800 PMCID: PMC9556772 DOI: 10.3389/fimmu.2022.977358] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 09/13/2022] [Indexed: 12/02/2022] Open
Abstract
Artificial intelligence (AI) can unveil novel personalized treatments based on drug screening and whole-exome sequencing experiments (WES). However, the concept of “black box” in AI limits the potential of this approach to be translated into the clinical practice. In contrast, explainable AI (XAI) focuses on making AI results understandable to humans. Here, we present a novel XAI method -called multi-dimensional module optimization (MOM)- that associates drug screening with genetic events, while guaranteeing that predictions are interpretable and robust. We applied MOM to an acute myeloid leukemia (AML) cohort of 319 ex-vivo tumor samples with 122 screened drugs and WES. MOM returned a therapeutic strategy based on the FLT3, CBFβ-MYH11, and NRAS status, which predicted AML patient response to Quizartinib, Trametinib, Selumetinib, and Crizotinib. We successfully validated the results in three different large-scale screening experiments. We believe that XAI will help healthcare providers and drug regulators better understand AI medical decisions.
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Affiliation(s)
- Marian Gimeno
- Departamento de Ingeniería Biomédica y Ciencias, TECNUN, Universidad de Navarra, San Sebastián, Spain
| | - Edurne San José-Enériz
- Programa Hemato-Oncología, Centro de Investigación Médica Aplicada, Instituto de Investigación Sanitaria de Navarra (IDISNA), Universidad de Navarra, Pamplona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Sara Villar
- Departamento de Hematología and CCUN (Cancer Center University of Navarra), Clínica Universidad de Navarra, Universidad de Navarra, Pamplona, Spain
| | - Xabier Agirre
- Programa Hemato-Oncología, Centro de Investigación Médica Aplicada, Instituto de Investigación Sanitaria de Navarra (IDISNA), Universidad de Navarra, Pamplona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Felipe Prosper
- Programa Hemato-Oncología, Centro de Investigación Médica Aplicada, Instituto de Investigación Sanitaria de Navarra (IDISNA), Universidad de Navarra, Pamplona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
- Departamento de Hematología and CCUN (Cancer Center University of Navarra), Clínica Universidad de Navarra, Universidad de Navarra, Pamplona, Spain
| | - Angel Rubio
- Departamento de Ingeniería Biomédica y Ciencias, TECNUN, Universidad de Navarra, San Sebastián, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Universidad de Navarra, Pamplona, Spain
- *Correspondence: Angel Rubio, ; Fernando Carazo,
| | - Fernando Carazo
- Departamento de Ingeniería Biomédica y Ciencias, TECNUN, Universidad de Navarra, San Sebastián, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), Universidad de Navarra, Pamplona, Spain
- *Correspondence: Angel Rubio, ; Fernando Carazo,
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