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Yu S, Liu L, Wang H, Yan S, Zheng S, Ning J, Luo R, Fu X, Deng X. AtML: An Arabidopsis thaliana root cell identity recognition tool for medicinal ingredient accumulation. Methods 2024:S1046-2023(24)00208-1. [PMID: 39293728 DOI: 10.1016/j.ymeth.2024.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2024] [Revised: 08/05/2024] [Accepted: 09/12/2024] [Indexed: 09/20/2024] Open
Abstract
Arabidopsis thaliana synthesizes various medicinal compounds, and serves as a model plant for medicinal plant research. Single-cell transcriptomics technologies are essential for understanding the developmental trajectory of plant roots, facilitating the analysis of synthesis and accumulation patterns of medicinal compounds in different cell subpopulations. Although methods for interpreting single-cell transcriptomics data are rapidly advancing in Arabidopsis, challenges remain in precisely annotating cell identity due to the lack of marker genes for certain cell types. In this work, we trained a machine learning system, AtML, using sequencing datasets from six cell subpopulations, comprising a total of 6000 cells, to predict Arabidopsis root cell stages and identify biomarkers through complete model interpretability. Performance testing using an external dataset revealed that AtML achieved 96.50% accuracy and 96.51% recall. Through the interpretability provided by AtML, our model identified 160 important marker genes, contributing to the understanding of cell type annotations. In conclusion, we trained AtML to efficiently identify Arabidopsis root cell stages, providing a new tool for elucidating the mechanisms of medicinal compound accumulation in Arabidopsis roots.
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Affiliation(s)
- Shicong Yu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Rice Research Institute, Sichuan Agricultural University, Chengdu 611130, China
| | - Lijia Liu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Hao Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Shen Yan
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Shuqin Zheng
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Rice Research Institute, Sichuan Agricultural University, Chengdu 611130, China
| | - Jing Ning
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Rice Research Institute, Sichuan Agricultural University, Chengdu 611130, China
| | - Ruxian Luo
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Rice Research Institute, Sichuan Agricultural University, Chengdu 611130, China
| | - Xiangzheng Fu
- Research Institute of Hunan University in Chongqing, Chongqing 401120, China.
| | - Xiaoshu Deng
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Rice Research Institute, Sichuan Agricultural University, Chengdu 611130, China; Chongqing Academy of Chinese Matera Medica, Chongqing 400065, China.
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2
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Chakraborty C, Bhattacharya M, Lee SS, Wen ZH, Lo YH. The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges. MOLECULAR THERAPY. NUCLEIC ACIDS 2024; 35:102295. [PMID: 39257717 PMCID: PMC11386122 DOI: 10.1016/j.omtn.2024.102295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of de novo drug, prediction of drug toxicity, estimation of absorption, distribution, metabolism, excretion, toxicity; and estimation of drug-drug interaction. Moreover, we highlighted the success stories of AI-driven drug discovery and discussed several collaboration and the challenges in this area. The discussions in the article will enrich the pharmaceutical industry.
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Affiliation(s)
- Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal 700126, India
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha 756020, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon, Gangwon-Do 24252, Republic of Korea
| | - Zhi-Hong Wen
- Department of Marine Biotechnology and Resources, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
| | - Yi-Hao Lo
- Department of Family Medicine, Zuoying Armed Forces General Hospital, Kaohsiung 813204, Taiwan
- Shu-Zen Junior College of Medicine and Management, Kaohsiung 821004, Taiwan
- Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 804201, Taiwan
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3
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Singh S, Kaur N, Gehlot A. Application of artificial intelligence in drug design: A review. Comput Biol Med 2024; 179:108810. [PMID: 38991316 DOI: 10.1016/j.compbiomed.2024.108810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/31/2024] [Accepted: 06/24/2024] [Indexed: 07/13/2024]
Abstract
Artificial intelligence (AI) is a field of computer science that involves acquiring information, developing rule bases, and mimicking human behaviour. The fundamental concept behind AI is to create intelligent computer systems that can operate with minimal human intervention or without any intervention at all. These rule-based systems are developed using various machine learning and deep learning models, enabling them to solve complex problems. AI is integrated with these models to learn, understand, and analyse provided data. The rapid advancement of Artificial Intelligence (AI) is reshaping numerous industries, with the pharmaceutical sector experiencing a notable transformation. AI is increasingly being employed to automate, optimize, and personalize various facets of the pharmaceutical industry, particularly in pharmacological research. Traditional drug development methods areknown for being time-consuming, expensive, and less efficient, often taking around a decade and costing billions of dollars. The integration of artificial intelligence (AI) techniques addresses these challenges by enabling the examination of compounds with desired properties from a vast pool of input drugs. Furthermore, it plays a crucial role in drug screening by predicting toxicity, bioactivity, ADME properties (absorption, distribution, metabolism, and excretion), physicochemical properties, and more. AI enhances the drug design process by improving the efficiency and accuracy of predicting drug behaviour, interactions, and properties. These approaches further significantly improve the precision of drug discovery processes and decrease clinical trial costs leading to the development of more effective drugs.
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Affiliation(s)
- Simrandeep Singh
- Department of Electronics & Communication Engineering, UCRD, Chandigarh University, Gharuan, Punjab, India.
| | - Navjot Kaur
- Department of Pharmacognosy, Amar Shaheed Baba Ajit Singh Jujhar Singh Memorial College of Pharmacy, Bela, Ropar, India
| | - Anita Gehlot
- Uttaranchal Institute of technology, Uttaranchal University, Dehradun, India
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4
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Zhang Y, Yang Y, Ren L, Ning L, Zou Q, Luo N, Zhang Y, Liu R. RDscan: Extracting RNA-disease relationship from the literature based on pre-training model. Methods 2024; 228:48-54. [PMID: 38789016 DOI: 10.1016/j.ymeth.2024.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 05/02/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024] Open
Abstract
With the rapid advancements in molecular biology and genomics, a multitude of connections between RNA and diseases has been unveiled, making the efficient and accurate extraction of RNA-disease (RD) relationships from extensive biomedical literature crucial for advancing research in this field. This study introduces RDscan, a novel text mining method developed based on the pre-training and fine-tuning strategy, aimed at automatically extracting RD-related information from a vast corpus of literature using pre-trained biomedical large language models (LLM). Initially, we constructed a dedicated RD corpus by manually curating from literature, comprising 2,082 positive and 2,000 negative sentences, alongside an independent test dataset (comprising 500 positive and 500 negative sentences) for training and evaluating RDscan. Subsequently, by fine-tuning the Bioformer and BioBERT pre-trained models, RDscan demonstrated exceptional performance in text classification and named entity recognition (NER) tasks. In 5-fold cross-validation, RDscan significantly outperformed traditional machine learning methods (Support Vector Machine, Logistic Regression and Random Forest). In addition, we have developed an accessible webserver that assists users in extracting RD relationships from text. In summary, RDscan represents the first text mining tool specifically designed for RD relationship extraction, and is poised to emerge as an invaluable tool for researchers dedicated to exploring the intricate interactions between RNA and diseases. Webserver of RDscan is free available at https://cellknowledge.com.cn/RDscan/.
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Affiliation(s)
- Yang Zhang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Healthcare Technology, Chengdu Neusoft University, Chengdu 611844, China.
| | - Yu Yang
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu 611844, China
| | - Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu 611844, China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu 611844, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Nanchao Luo
- School of Computer Science and Technology, Aba Teachers College, WenChuan, Sichuan, 623002, China
| | - Yinghui Zhang
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu 611844, China.
| | - Ruijun Liu
- School of Software, Beihang University, Beijing 100191, China.
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5
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Ye DX, Yu JW, Li R, Hao YD, Wang TY, Yang H, Ding H. The Prediction of Recombination Hotspot Based on Automated Machine Learning. J Mol Biol 2024:168653. [PMID: 38871176 DOI: 10.1016/j.jmb.2024.168653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 05/12/2024] [Accepted: 06/06/2024] [Indexed: 06/15/2024]
Abstract
Meiotic recombination plays a pivotal role in genetic evolution. Genetic variation induced by recombination is a crucial factor in generating biodiversity and a driving force for evolution. At present, the development of recombination hotspot prediction methods has encountered challenges related to insufficient feature extraction and limited generalization capabilities. This paper focused on the research of recombination hotspot prediction methods. We explored deep learning-based recombination hotspot prediction and scrutinized the shortcomings of prevalent models in addressing the challenge of recombination hotspot prediction. To addressing these deficiencies, an automated machine learning approach was utilized to construct recombination hotspot prediction model. The model combined sequence information with physicochemical properties by employing TF-IDF-Kmer and DNA composition components to acquire more effective feature data. Experimental results validate the effectiveness of the feature extraction method and automated machine learning technology used in this study. The final model was validated on three distinct datasets and yielded accuracy rates of 97.14%, 79.71%, and 98.73%, surpassing the current leading models by 2%, 2.56%, and 4%, respectively. In addition, we incorporated tools such as SHAP and AutoGluon to analyze the interpretability of black-box models, delved into the impact of individual features on the results, and investigated the reasons behind misclassification of samples. Finally, an application of recombination hotspot prediction was established to facilitate easy access to necessary information and tools for researchers. The research outcomes of this paper underscore the enormous potential of automated machine learning methods in gene sequence prediction.
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Affiliation(s)
- Dong-Xin Ye
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jun-Wen Yu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Rui Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yu-Duo Hao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Tian-Yu Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Yang
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China.
| | - Hui Ding
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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Xie W, Yu J, Huang L, For LS, Zheng Z, Chen X, Wang Y, Liu Z, Peng C, Wong KC. DeepSeq2Drug: An expandable ensemble end-to-end anti-viral drug repurposing benchmark framework by multi-modal embeddings and transfer learning. Comput Biol Med 2024; 175:108487. [PMID: 38653064 DOI: 10.1016/j.compbiomed.2024.108487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 03/26/2024] [Accepted: 04/15/2024] [Indexed: 04/25/2024]
Abstract
Drug repurposing is promising in multiple scenarios, such as emerging viral outbreak controls and cost reductions of drug discovery. Traditional graph-based drug repurposing methods are limited to fast, large-scale virtual screens, as they constrain the counts for drugs and targets and fail to predict novel viruses or drugs. Moreover, though deep learning has been proposed for drug repurposing, only a few methods have been used, including a group of pre-trained deep learning models for embedding generation and transfer learning. Hence, we propose DeepSeq2Drug to tackle the shortcomings of previous methods. We leverage multi-modal embeddings and an ensemble strategy to complement the numbers of drugs and viruses and to guarantee the novel prediction. This framework (including the expanded version) involves four modal types: six NLP models, four CV models, four graph models, and two sequence models. In detail, we first make a pipeline and calculate the predictive performance of each pair of viral and drug embeddings. Then, we select the best embedding pairs and apply an ensemble strategy to conduct anti-viral drug repurposing. To validate the effect of the proposed ensemble model, a monkeypox virus (MPV) case study is conducted to reflect the potential predictive capability. This framework could be a benchmark method for further pre-trained deep learning optimization and anti-viral drug repurposing tasks. We also build software further to make the proposed model easier to reuse. The code and software are freely available at http://deepseq2drug.cs.cityu.edu.hk.
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Affiliation(s)
- Weidun Xie
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Jixiang Yu
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Lei Huang
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Lek Shyuen For
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Zetian Zheng
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Xingjian Chen
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yuchen Wang
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Zhichao Liu
- Sir William Dunn School of Pathology, University of Oxford, UK
| | - Chengbin Peng
- College of Information Science and Engineering, Ningbo University, Ningbo, China
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China; Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China; Hong Kong Institute for Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China.
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7
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Khan MK, Raza M, Shahbaz M, Hussain I, Khan MF, Xie Z, Shah SSA, Tareen AK, Bashir Z, Khan K. The recent advances in the approach of artificial intelligence (AI) towards drug discovery. Front Chem 2024; 12:1408740. [PMID: 38882215 PMCID: PMC11176507 DOI: 10.3389/fchem.2024.1408740] [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: 03/29/2024] [Accepted: 04/26/2024] [Indexed: 06/18/2024] Open
Abstract
Artificial intelligence (AI) has recently emerged as a unique developmental influence that is playing an important role in the development of medicine. The AI medium is showing the potential in unprecedented advancements in truth and efficiency. The intersection of AI has the potential to revolutionize drug discovery. However, AI also has limitations and experts should be aware of these data access and ethical issues. The use of AI techniques for drug discovery applications has increased considerably over the past few years, including combinatorial QSAR and QSPR, virtual screening, and denovo drug design. The purpose of this survey is to give a general overview of drug discovery based on artificial intelligence, and associated applications. We also highlighted the gaps present in the traditional method for drug designing. In addition, potential strategies and approaches to overcome current challenges are discussed to address the constraints of AI within this field. We hope that this survey plays a comprehensive role in understanding the potential of AI in drug discovery.
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Affiliation(s)
- Mahroza Kanwal Khan
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, China
| | - Mohsin Raza
- Additive Manufacturing Institute, Shenzhen University, Shenzhen, China
| | - Muhammad Shahbaz
- Additive Manufacturing Institute, Shenzhen University, Shenzhen, China
| | - Iftikhar Hussain
- Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
- A. J. Drexel Nanomaterials Institute and Department of Materials Science and Engineering, Drexel University, Philadelphia, PA, United States
| | - Muhammad Farooq Khan
- Department of Electrical Engineering, Sejong University, Seoul, Republic of Korea
| | - Zhongjian Xie
- Shenzhen Children's Hospital, Clinical Medical College of Southern University of Science and Technology, Shenzhen, China
| | - Syed Shoaib Ahmad Shah
- Department of Chemistry, School of Natural Sciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Ayesha Khan Tareen
- School of Mechanical Engineering, Dongguan University of Technology, Dongguan, China
| | - Zoobia Bashir
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, China
| | - Karim Khan
- Additive Manufacturing Institute, Shenzhen University, Shenzhen, China
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8
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Cheng L. Attention mechanism models for precision medicine. Brief Bioinform 2024; 25:bbae156. [PMID: 38811359 PMCID: PMC11136616 DOI: 10.1093/bib/bbae156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 03/21/2024] [Indexed: 05/31/2024] Open
Abstract
The development of deep learning models plays a crucial role in advancing precision medicine. These models enable personalized medical treatments and interventions based on the unique genetic, environmental and lifestyle factors of individual patients, and the promotion of precision medicine is achieved mainly through genomic data analysis, variant annotation and interpretation, pharmacogenomics research, biomarker discovery, disease typing, clinical decision support and disease mechanism interpretation. Extensive research has been conducted to address precision medicine challenges using attention mechanism models such as SAN, GAT and transformers. Especially, the recent popularity of ChatGPT has significantly propelled the application of this model type to a new height. Therefore, I propose a Special Issue for Briefings in Bioinformatics about the topic 'Attention Mechanism Models for Precision Medicine'. This Special Issue aims to provide a comprehensive overview and presentation of innovative researches on the application of graph attention mechanism models in precision medicine.
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Affiliation(s)
- Liang Cheng
- Corresponding author: Liang Cheng, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150001, Heilongjiang, China. E-mail:
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Khorsandi D, Rezayat D, Sezen S, Ferrao R, Khosravi A, Zarepour A, Khorsandi M, Hashemian M, Iravani S, Zarrabi A. Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like? J Mater Chem B 2024; 12:4584-4612. [PMID: 38686396 DOI: 10.1039/d4tb00310a] [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: 05/02/2024]
Abstract
The application of three- and four-dimensional (3D/4D) printing in cancer research represents a significant advancement in understanding and addressing the complexities of cancer biology. 3D/4D materials provide more physiologically relevant environments compared to traditional two-dimensional models, allowing for a more accurate representation of the tumor microenvironment that enables researchers to study tumor progression, drug responses, and interactions with surrounding tissues under conditions similar to in vivo conditions. The dynamic nature of 4D materials introduces the element of time, allowing for the observation of temporal changes in cancer behavior and response to therapeutic interventions. The use of 3D/4D printing in cancer research holds great promise for advancing our understanding of the disease and improving the translation of preclinical findings to clinical applications. Accordingly, this review aims to briefly discuss 3D and 4D printing and their advantages and limitations in the field of cancer. Moreover, new techniques such as 5D/6D printing and artificial intelligence (AI) are also introduced as methods that could be used to overcome the limitations of 3D/4D printing and opened promising ways for the fast and precise diagnosis and treatment of cancer.
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Affiliation(s)
- Danial Khorsandi
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90024, USA
| | - Dorsa Rezayat
- Center for Global Design and Manufacturing, College of Engineering and Applied Science, University of Cincinnati, 2901 Woodside Drive, Cincinnati, OH 45221, USA
| | - Serap Sezen
- Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla 34956 Istanbul, Türkiye
- Nanotechnology Research and Application Center, Sabanci University, Tuzla 34956 Istanbul, Türkiye
| | - Rafaela Ferrao
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA, 90024, USA
- University of Coimbra, Institute for Interdisciplinary Research, Doctoral Programme in Experimental Biology and Biomedicine (PDBEB), Portugal
| | - Arezoo Khosravi
- Department of Genetics and Bioengineering, Faculty of Engineering and Natural Sciences, Istanbul Okan University, Istanbul 34959, Türkiye
| | - Atefeh Zarepour
- Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai - 600 077, India
| | - Melika Khorsandi
- Department of Cellular and Molecular Biology, Najafabad Branch, Islamic Azad University, Isfahan, Iran
| | - Mohammad Hashemian
- Department of Cellular and Molecular Biology, Najafabad Branch, Islamic Azad University, Isfahan, Iran
| | - Siavash Iravani
- Independent Researcher, W Nazar ST, Boostan Ave, Isfahan, Iran.
| | - Ali Zarrabi
- Department of Biomedical Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul 34396, Türkiye.
- Graduate School of Biotechnology and Bioengineering, Yuan Ze University, Taoyuan 320315, Taiwan
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10
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Ren L, Huang D, Liu H, Ning L, Cai P, Yu X, Zhang Y, Luo N, Lin H, Su J, Zhang Y. Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review). Oncol Lett 2024; 27:152. [PMID: 38406595 PMCID: PMC10885005 DOI: 10.3892/ol.2024.14285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/19/2024] [Indexed: 02/27/2024] Open
Abstract
Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.
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Affiliation(s)
- Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Danni Huang
- Department of Radiology, Central South University Xiangya School of Medicine Affiliated Haikou People's Hospital, Haikou, Hainan 570208, P.R. China
| | - Hongjiang Liu
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Peiling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, Sichuan 610106, P.R. China
| | - Xiaolong Yu
- Hainan Yazhou Bay Seed Laboratory, Sanya Nanfan Research Institute, Material Science and Engineering Institute of Hainan University, Sanya, Hainan 572025, P.R. China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Nanchao Luo
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. China
| | - Jinsong Su
- Research Institute of Integrated Traditional Chinese Medicine and Western Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Yinghui Zhang
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
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