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Selvaraj S, Alsayed AO, Ismail NA, Kavin BP, Onyema EM, Seng GH, Uchechi AQ. Super learner model for classifying leukemia through gene expression monitoring. Discov Oncol 2024; 15:499. [PMID: 39331180 PMCID: PMC11436508 DOI: 10.1007/s12672-024-01337-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Accepted: 09/11/2024] [Indexed: 09/28/2024] Open
Abstract
Leukemia is a form of cancer that affects the bone marrow and lymphatic system, and it requires complex treatment strategies that vary with each subtype. Due to the subtle morphological differences among these types, monitoring gene expressions is crucial for accurate classification. Manual or pathological testing can be time-consuming and expensive. Therefore, data-driven methods and machine learning algorithms offer an efficient alternative for leukemia classification. This study introduced a novel super learning model that leverages heterogeneous machine learning models to analyze gene expression data and classify leukemia cells. The proposed approach incorporates an entropy-based feature importance technique to identify the gene profiles most significant to the labeling process. The strength of this super learning model lies in its final super learner, Random Forest, which effectively classifies cross-validated data from the candidate learners. Validation on a gene expression monitoring dataset demonstrates that this model outperforms other state-of-the-art models in predictive accuracy. The study contributes to the knowledge regarding the use of advanced machine learning techniques to improve the accuracy and reliability of leukemia classification using gene expression data, addressing the challenges of traditional methods that rely on clinical features and morphological examination.
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Affiliation(s)
- Sharanya Selvaraj
- Department of Data Science and Business Systems, SRM Institute of Science and Technology, Kattankulathur, Chennai, India, 603203
| | | | - Nor Azman Ismail
- Faculty of Computing, UniversitiTeknologi Malaysia, Johor Bahru, Malaysia
| | - Balasubramanian Prabhu Kavin
- Department of Data Science and Business Systems, SRM Institute of Science and Technology, Kattankulathur, Chennai, India, 603203
| | - Edeh Michael Onyema
- Department of Mathematics and Computer Science, Coal City University, Enugu, Nigeria.
- Adjunct Faculty, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India.
| | - Gan Hong Seng
- School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, People's Republic of China, 215400
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Wang Y, Du Y. Graph neural network model GGDisnet for identifying genes in gastrointestinal cancer and single-cell analysis. Comput Biol Med 2024; 172:108285. [PMID: 38503088 DOI: 10.1016/j.compbiomed.2024.108285] [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: 01/17/2024] [Revised: 02/22/2024] [Accepted: 03/12/2024] [Indexed: 03/21/2024]
Abstract
Gastrointestinal cancer, a highly prevalent form of cancer, has been the subject of extensive research resulting in the identification of numerous pathogenic genes. However, validation and exploration of these findings often require traditional biological experiments, which are time-consuming and limit the ability to make extensive assessments promptly. To address this challenge, this paper introduces GGDisnet, a novel model for identifying genes associated with gastrointestinal cancer. GGDisnet efficiently screens human genes, providing a set of genes with a high correlation to gastrointestinal cancer for reference. Comparative analysis with other models demonstrates GGDisnet's superior performance. Furthermore, we conducted enrichment and single-cell analyses based on GGDisnet-predicted genes, offering valuable clinical insights.
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Affiliation(s)
- Ying Wang
- Department of Endoscopy, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yaqi Du
- Department of Gastroenterology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
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Chang YT, Prompsy P, Kimeswenger S, Tsai YC, Ignatova D, Pavlova O, Iselin C, French LE, Levesque MP, Kuonen F, Bobrowicz M, Brunner PM, Pascolo S, Hoetzenecker W, Guenova E. MHC-I upregulation safeguards neoplastic T cells in the skin against NK cell-mediated eradication in mycosis fungoides. Nat Commun 2024; 15:752. [PMID: 38272918 PMCID: PMC10810852 DOI: 10.1038/s41467-024-45083-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 01/15/2024] [Indexed: 01/27/2024] Open
Abstract
Cancer-associated immune dysfunction is a major challenge for effective therapies. The emergence of antibodies targeting tumor cell-surface antigens led to advancements in the treatment of hematopoietic malignancies, particularly blood cancers. Yet their impact is constrained against tumors of hematopoietic origin manifesting in the skin. In this study, we employ a clonality-supervised deep learning methodology to dissect key pathological features implicated in mycosis fungoides, the most common cutaneous T-cell lymphoma. Our investigations unveil the prominence of the IL-32β-major histocompatibility complex (MHC)-I axis as a critical determinant in tumor T-cell immune evasion within the skin microenvironment. In patients' skin, we find MHC-I to detrimentally impact the functionality of natural killer (NK) cells, diminishing antibody-dependent cellular cytotoxicity and promoting resistance of tumor skin T-cells to cell-surface targeting therapies. Through murine experiments in female mice, we demonstrate that disruption of the MHC-I interaction with NK cell inhibitory Ly49 receptors restores NK cell anti-tumor activity and targeted T-cell lymphoma elimination in vivo. These findings underscore the significance of attenuating the MHC-I-dependent immunosuppressive networks within skin tumors. Overall, our study introduces a strategy to reinvigorate NK cell-mediated anti-tumor responses to overcome treatment resistance to existing cell-surface targeted therapies for skin lymphoma.
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Affiliation(s)
- Yun-Tsan Chang
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Pacôme Prompsy
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Susanne Kimeswenger
- Department of Dermatology and Venerology, Medical Faculty, Johannes Kepler University, Linz, Austria
| | - Yi-Chien Tsai
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Desislava Ignatova
- Department of Dermatology, University Hospital of Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Olesya Pavlova
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Christoph Iselin
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Lars E French
- Department of Dermatology and Allergology, Ludwig-Maximilians-University of Munich, Munich, Germany
- Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Mitchell P Levesque
- Department of Dermatology, University Hospital of Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - François Kuonen
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | | | - Patrick M Brunner
- Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Steve Pascolo
- Department of Dermatology, University Hospital of Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Wolfram Hoetzenecker
- Department of Dermatology and Venerology, Medical Faculty, Johannes Kepler University, Linz, Austria.
| | - Emmanuella Guenova
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
- Department of Dermatology, University Hospital of Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland.
- Department of Dermatology, Hospital 12 de Octubre, Medical School, University Complutense, Madrid, Spain.
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Jain S, Naicker D, Raj R, Patel V, Hu YC, Srinivasan K, Jen CP. Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials. Diagnostics (Basel) 2023; 13:diagnostics13091563. [PMID: 37174954 PMCID: PMC10178016 DOI: 10.3390/diagnostics13091563] [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: 03/04/2023] [Revised: 04/16/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
Cancer is a dangerous and sometimes life-threatening disease that can have several negative consequences for the body, is a leading cause of mortality, and is becoming increasingly difficult to detect. Each form of cancer has its own set of traits, symptoms, and therapies, and early identification and management are important for a positive prognosis. Doctors utilize a variety of approaches to detect cancer, depending on the kind and location of the tumor. Imaging tests such as X-rays, Computed Tomography scans, Magnetic Resonance Imaging scans, and Positron Emission Tomography (PET) scans, which may provide precise pictures of the body's interior structures to spot any abnormalities, are some of the tools that doctors use to diagnose cancer. This article evaluates computational-intelligence approaches and provides a means to impact future work by focusing on the relevance of machine learning and deep learning models such as K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Deep Neural Network, Deep Boltzmann machine, and so on. It evaluates information from 114 studies using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This article explores the advantages and disadvantages of each model and provides an outline of how they are used in cancer diagnosis. In conclusion, artificial intelligence shows significant potential to enhance cancer imaging and diagnosis, despite the fact that there are a number of clinical issues that need to be addressed.
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Affiliation(s)
- Somit Jain
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Dharmik Naicker
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Ritu Raj
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Vedanshu Patel
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Yuh-Chung Hu
- Department of Mechanical and Electromechanical Engineering, National ILan University, Yilan 26047, Taiwan
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Chun-Ping Jen
- School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Department of Mechanical Engineering and Advanced Institute of Manufacturing for High-Tech Innovations, National Chung Cheng University, Chia-Yi 62102, Taiwan
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Alharbi F, Vakanski A. Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review. Bioengineering (Basel) 2023; 10:bioengineering10020173. [PMID: 36829667 PMCID: PMC9952758 DOI: 10.3390/bioengineering10020173] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.
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