This book presents the applications of systems biology and synthetic biology in cancer medicine. It highlights the use of computational and mathematical models to decipher the complexity of cancer heterogeneity. The book emphasizes the modeling approaches for predicting behavior of cancer cells, tissues in context of drug response, and angiogenesis. It introduces cell-based therapies for the treatment of various cancers and reviews the role of neural networks for drug response prediction. Further, it examines the system biology approaches for the identification of medicinal plants in cancer drug discovery. It explores the opportunities for metabolic engineering in the realm of cancer research towards development of new cancer therapies based on metabolically derived targets. Lastly, it discusses the applications of data mining techniques in cancer research. This book is an excellent guide for oncologists and researchers who are involved in the latest cancer research.
Author(s): Shailza Singh
Publisher: Springer
Year: 2022
Language: English
Pages: 169
City: Singapore
Preface
Acknowledgments
Contents
About the Editor
1: Medicinal Plants for Indigenous Cancer Drug Discovery: Current to Future
1.1 Introduction
1.2 Anticancer Potential of Peganum harmala (PG)
1.3 Anticancer Potential of Melissa officinalis (MA)
1.4 Anticancer Effect of Galls of Quercus infectoria (GQI)
1.5 Anticancer Potential of Plumbago with Focus on Plumbagin (PU)
1.6 Computational Approach Toward Natural Products and Drug Discovery
1.7 Therapeutic Natural Products Divided into Several Categories
1.8 Natural Products and Cheminformatics
1.8.1 QSAR Analysis of Natural Products
1.8.2 Molecular Docking and Dynamics
1.8.3 Library Construction
1.9 Conclusion and Perspectives
References
2: Artificial Intelligence and Machine Learning Techniques Using Omics Data for Cancer Diagnosis and Treatment
2.1 Introduction
2.2 Omics Data in Cancer Research
2.2.1 Genomic Data
2.2.1.1 Genomic Variation Data
2.2.2 Epigenomic Data
2.2.2.1 DNA Methylation Data
2.2.2.2 Histone Modification Data
2.2.3 Transcriptomics Data
2.2.3.1 Transcript Profiling Data
2.3 Machine Learning Approaches
2.3.1 Feature Selection Methods
2.3.2 Dimension Reduction Methods
2.3.3 Overview of Machine Learning Algorithms
2.3.3.1 Supervised Machine Learning Algorithms
Support Vector Machine (SVM)
Naive Bayes (NB)
Logistic Regression Classifier
Artificial Neural Networks (ANNs)
k-Nearest Neighbors (KNNs)
Decision Trees (DTs)
Random Forest (RF)
CN2 Classifier
2.3.3.2 Semi-Supervised Classifier
Laplacian SVM
2.3.4 Model Performance Evaluation
2.4 Application of AI and Machine Learning Techniques in Cancer
2.4.1 Cancer Classification
2.4.2 Anti Cancer Drug Response Prediction
2.4.3 Survival Prediction
2.4.4 Metastasis Prediction
2.4.5 Biomarker Prediction
2.5 Conclusion and Future Directions
References
3: Cancer Biomarkers in the Era of Systems Biology
3.1 Introduction
3.2 Categorization of Cancer Biomarkers
3.2.1 Classification of Cancer Biomarkers Based on Biomolecules
3.2.1.1 DNA Cancer Biomarkers
3.2.1.2 RNA Cancer Biomarkers
3.2.1.3 Protein Cancer Biomarkers
3.2.1.4 Carbohydrate Cancer Biomarkers
3.2.2 Classification of Cancer Biomarkers Based on Clinical Utility
3.2.2.1 Prediction Cancer Biomarker
3.2.2.2 Detection/Diagnostic Cancer Biomarker
3.2.2.3 Prognostic Cancer Biomarkers
3.2.2.4 Pharmacodynamics Cancer Biomarkers
3.2.3 Classification of Cancer Biomarkers Based on Other Criteria
3.2.3.1 Imaging Cancer Biomarkers
3.2.3.2 Pathological Cancer Biomarkers
3.3 Omics Approaches in Cancer Biomarker Research
3.3.1 Genomics for Cancer Biomarkers
3.3.2 Transcriptomics for Cancer Biomarkers
3.3.3 Proteomics for Cancer Biomarkers
3.3.4 Metabolomics for Cancer Biomarkers
3.3.5 Epigenomics for Cancer Biomarker
3.4 Bioinformatics Analytical Tools for Cancer Biomarker Discovery
3.5 Future Challenges
References
4: The Biology and Chemistry of Microsomal Prostaglandin E Synthase (mPGES) - I Inhibitors for Cancer Biomedicine
4.1 Introduction
4.2 Role of mPGES-1 in Cancer
4.2.1 Binding Mechanism
4.2.2 Crystal Structures of mPGES
4.3 Small Molecule Inhibitors
4.3.1 Imidazoles
4.3.1.1 Phenanthrene Imidazoles
4.3.1.2 2,4-Biarylimidazoles
4.3.1.3 2-Amino Benzimidazoles
4.3.1.4 2-Amino Imidazoles
4.3.1.5 Imidazopyridines
4.3.2 Piperidine Carboxamides
4.3.3 Trisubstituted Ureas
4.3.4 Benzamides
4.3.5 Pirinixic Acids
4.3.6 Triterpene Acids
4.3.7 Indole-Based Carboxylic Acids
4.3.8 Aminobenzothiazoles
4.3.9 Sulfomyl Phenylacetamides
4.3.10 Other Scafolds
4.4 Conclusion
References
5: Emerging Role of Structural and Systems Biology in Anticancer Therapeutics
5.1 Introduction
5.2 Early Development of Structure-Guided Drug Discovery
5.3 Structural Biology and Cancer
5.4 Protein X-ray Crystallography in Drug Discovery
5.5 Protein Crystallography, FBDD, and Cancer
5.6 Structure-Based Approaches in Cancer Therapeutics
5.7 Structural Systems Biology in Cancer Therapeutics
5.8 Conclusion and Future Prospects
References
6: Computational Tools and Databases for Fusion Transcripts: Therapeutic Targets in Cancer
6.1 Introduction
6.1.1 Identification of Chimeric RNA
6.1.1.1 FusionSeq
6.1.1.2 TopHat
6.1.1.3 JAFFA
6.1.1.4 EricScript
6.1.1.5 SOAPfuse
6.1.1.6 STARChip
6.1.1.7 FuSeq
6.2 Fusion Transcripts Databases
6.3 Validation of Transcripts
6.4 Conclusion
References
7: Understanding the Molecular Kinetics in NSCLC Through Computational Method
7.1 Introduction
7.2 Modulation of Immunometabolism in Cancerous Cells
7.3 Treatment Options Presently Offered
7.4 Immunotherapy
7.5 Vaccine Development as a Technique of Immunotherapy
7.6 Whole-Cell Tumor Vaccine
7.6.1 Cell-Based Vaccine
7.6.1.1 Belagenpumatucel-L (Lucanix)
7.6.2 Recombinant Vaccine
7.6.2.1 TG4010 (MVA-MUC1-IL-2) Vaccine
7.6.2.2 CIMAVax EGF
7.6.2.3 GVAX
7.7 Antigen-Specific Vaccines
7.7.1 Peptide- and Protein-Based Vaccines
7.7.1.1 Liposome BLP25 Vaccine (Stimuvax, Tecemotide)
7.7.1.2 Melanoma-Associated Antigen A3 (MAGE-A3)
7.8 Molecular Biology of Non-small Cell Lung Cancer
7.9 Tumor Suppressor Genes
7.10 Genetic Mutations Leading to Cancer Development
7.11 Mathematical Modeling of AKT Signaling Pathway for Cancer Development
References
