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Recent Advances in Cancer Diagnostics and Therapy: A Nano-Based Approach

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This book provides information about different types and stages of cancer and their subtypes with their respective molecular mechanisms, etiology, histopathology, and cellular origins. This book also provides detailed information about cancer incidence, mortality, and different types of technologies both bio and nano employed in cancer diagnosis and screening, and their applications in cancer therapies. This book informs readers about molecular mechanisms of cancer, diagnosis, and therapies along with different computational techniques used on a single platform.

The chapters include a broad and integrated perspective on cancer-related topics. This book covers both conventional and emerging techniques employed in cancer screening and diagnosis, including imaging, biomarker, and electrochemical nanosensor-based approaches with detailed information on sensor development. Similarly, this book also covers the mechanisms of different conventional and emerging herbal and nano therapies used in cancer treatment. The authors discuss applications of different computational and mathematical tools, such as machine-learning methods, that can be employed in cancer diagnosis and therapy at the level of personalized medicine.

Features:

    • Offers an integrated approach to provide information about all aspects of cancer biology, diagnosis, and therapy

    • Focuses on both conventional and emerging tools/techniques applicable in cancer screening and diagnosis

    • Covers the mechanisms of conventional and emerging anticancer drugs and therapies

    • Provides insights about a personalized medicine-based approach in cancer diagnosis and therapy

    This book is essential for university students, course lecturers, researchers, and industrialists working in the fields of cancer biology, medicine, and pharmacology.

    Author(s): Anjana Pandey, Saumya Srivastava
    Publisher: CRC Press
    Year: 2022

    Language: English
    Pages: 218
    City: Boca Raton

    Cover
    Half Title
    Title Page
    Copyright Page
    Contents
    Preface
    Authors
    1. Introduction to Cancer
    1.1 Introduction
    1.2 Features of Cancer
    1.2.1 Self-Sufficient Signals for Growth
    1.2.2 Insensitivity to Antigrowth Signals
    1.2.3 Programmed Cell Death Evasion
    1.2.4 Unlimited Replicative Skills
    1.2.5 Prolonged Angiogenesis
    1.2.6 Metastasis
    1.3 Initiation of Metastasis
    1.3.1 Dissemination
    1.3.2 Intravasation
    1.3.3 Survival in the Circulatory System
    1.3.4 Extravasation
    1.4 Classification and Types of Cancer
    1.4.1 Carcinoma
    1.4.2 Sarcoma
    1.4.3 Myeloma
    1.4.4 Lymphoma
    1.5 Staging of Cancer
    1.5.1 TNM Staging
    1.5.1.1 Stages of Cancer
    1.6 Cancer from the Molecular Perspective
    1.7 Cancer Etiology
    1.7.1 Environment and Lifestyle
    1.7.2 Age
    1.8 Cancer Histopathology and Identification
    1.9 Cancer Epidemiology
    References
    2. Cancer Incidence and Mortality: In India and Worldwide
    2.1 Introduction
    2.2 Sources and Methods of Data
    2.3 Worldwide Distribution of Cases and Deaths by Cancer Types
    2.4 Worldwide Cancer Patterns Based on Sex
    2.5 Cancer Distribution in India
    2.6 Human Development Index and Site-Specific Cancer
    2.6.1 Colorectal Cancer
    2.6.2 Breast Cancer
    2.6.2.1 Incidence
    2.6.2.2 Mortality
    2.6.3 Prostate Cancer
    2.6.3.1 Incidence
    2.6.3.2 Mortality
    2.6.4 Cervical Cancer
    2.6.4.1 Incidence
    2.6.5 Lung Cancer
    2.6.5.1 Incidence
    2.6.5.2 Risk Factors
    2.6.6 Ovarian Cancer
    2.6.6.1 Incidence
    2.6.6.2 Risk Factors
    References
    3. Trends in Cancer Screening: Different Diagnostic Approaches
    3.1 Introduction
    3.2 Existing Diagnostic Techniques
    3.2.1 Other Screening Tests
    3.3 Markers-Based Diagnosis of Cancer
    3.3.1 Tumor Markers Currently in Practice for Cancer Detection
    3.3.1.1 Tissue-Derived Tumor Markers
    3.3.1.2 21-Gene Signature (Oncotype DX)
    3.3.1.3 Anaplastic Lymphoma Kinase (ALK)
    3.3.1.4 BRAF
    3.3.1.5 Epidermal Growth Factor Receptor (EGFR)
    3.3.1.6 HER2
    3.3.1.7 Hormone Receptors
    3.3.1.8 KIT
    3.3.1.9 KRAS
    3.3.1.10 Urokinase Plasminogen Activator (uPA) and Plasminogen Activator Inhibitor (PAI-1)
    3.3.1.11 Tumor Markers Detectable in Blood or Fluids
    3.3.1.11.1 5-Protein Signature (Ova1)
    3.3.1.11.2 Alpha-fetoprotein (AFP)
    3.3.1.11.3 BCR-ABL
    3.3.1.11.4 CA 15-3
    3.3.1.11.5 CA 19-9
    3.3.1.11.6 CA 125
    3.3.1.11.7 Carcinoembryonic Antigen (CEA)
    3.3.1.11.8 Chromogranin A (CgA)
    3.3.1.11.9 Lactate Dehydrogenase (LDH)
    3.3.1.11.10 Prostate-Specific Antigen (PSA)
    3.3.2 Molecular Markers-Based Technologies
    3.3.2.1 Nucleic-Acid-Based Markers
    3.3.2.2 Protein Markers
    3.3.2.2.1 Advantages of Molecular Screening
    3.3.2.3 Robust Circulating Markers
    3.3.2.3.1 Enzymes and Serum Proteins
    3.3.2.3.2 Carcinoembryonic Proteins
    3.3.2.3.3 Circulating Nucleic Acid Markers
    3.3.2.3.4 Circulating DNA
    3.3.2.3.5 Detection Methods and Sensitivity
    3.3.2.3.6 Circulating RNA
    3.3.2.3.7 Types of Circulating Cell-Free RNA: Non-Coding RNA
    3.3.2.3.8 Relation of miRNAs Alterations to Cancer
    3.3.2.3.9 Circulating miRNAs: Diagnostic and Predictive Markers for Cancers
    3.3.2.3.10 miRNA Expression Profiles in Cancer Tissues
    3.4 Integrative Techniques
    3.4.1 Methods for Detection of Circulating miRNA
    3.4.1.1 Non-Sensor Techniques
    3.4.1.1.1 Quantitative PCR (qPCR)
    3.4.1.1.2 Digital PCR (dPCR)
    3.4.1.1.3 miRNA Microarrays
    3.4.1.1.4 Small RNA Sequencing (sRNA-Seq)
    3.4.1.2 Biosensor-Based Techniques
    3.4.1.2.1 Electrochemical Biosensors
    3.4.1.2.2 Nanopore-Based Biosensors
    3.4.1.2.3 Optical Biosensors
    References
    4. Biosensor Development: A Way to Achieve a Milestone for Cancer Detection
    4.1 Introduction
    4.2 Synthesis of Nanoparticles
    4.2.1 Bottom-Up Method
    4.2.2 Top-Down Method
    4.3 Types of Nanoparticles
    4.4 Characterization of Nanoparticles
    4.4.1 Particle Size
    4.4.2 Dynamic Light Scattering (DLS) Technique
    4.4.3 Scanning Electron Microscopy (SEM)
    4.4.4 Transmission Electron Microscope (TEM)
    4.4.5 Atomic Force Microscopy (AFM)
    4.4.6 Surface Charge
    4.4.7 UV-Visible Absorption Spectroscopy
    4.4.8 X-Ray Diffraction (XRD)
    4.4.9 Fourier Transform Infrared (FTIR) Spectroscopy
    4.4.10 Nuclear Magnetic Resonance (NMR) Spectroscopy
    4.4.11 Photoluminescence (PL) Spectroscopy
    4.5 Preparation of a Sensor Device
    4.5.1 Bio-Transducer Elements
    4.5.1.1 Electrochemical Sensors
    4.5.1.2 Optical Sensors
    4.5.1.3 Piezoelectric Sensors
    4.5.1.4 Calorimetric Sensors
    4.5.2 New Generation Biosensors: Nanobiosensors
    4.5.2.1 Quantum Dots (QDs)
    4.5.2.2 Graphene Biosensors
    4.5.2.3 Carbon Nanotubes (CNTs)
    4.5.2.4 Lab-on-a-Chip
    4.5.3 Construction of Biosensor Platforms
    4.5.4 The Immobilization of Sensitive Elements onto Biosensor Film
    4.5.5 Types of Electrodes for Biosensor Fabrication
    4.5.5.1 Mercury Electrodes
    4.5.5.2 Gold and Silver Electrodes
    4.5.5.2.1 Gold Nanoparticle-Modified Electrodes
    4.5.5.2.2 Bismuth Film Electrodes
    4.5.5.2.3 Antimony Film Electrodes
    4.5.5.3 Bore-Doped Diamond (BDD)
    4.5.5.4 Diamond-Like Carbon
    4.5.6 Strategy for Improvement of Biosensor Sensitivity
    4.5.7 Enzymatic Amplification
    4.5.8 Amplification via Nanoparticles
    4.5.8.1 Optical Transducers
    4.5.8.2 Mass-Based Transducers
    4.5.8.3 Calorimetric Biosensors
    References
    5. Recent Advances in Diagnosis: Nano-Based Approach
    5.1 Introduction
    5.2 Amperometric Biosensors
    5.3 Potentiometric Biosensors
    5.4 Impedimetric Biosensors
    5.5 Different Types of Electrochemical Sensors
    5.5.1 Amperometry
    5.5.1.1 Electrochemical Sensors for Cancer Biomarkers Detection Based on Amperometry
    5.5.1.1.1 Embryonic Antigen Biomarkers
    5.5.1.1.2 Carbohydrate Antigen Biomarkers
    5.5.1.1.3 Enzyme and Isozyme Biomarkers
    5.5.1.1.4 Protein Biomarkers
    5.5.1.1.5 Oncogene and Oncogenic Production
    5.5.1.1.6 Hormone Biomarkers
    5.5.2 Chronoamperometry
    5.5.3 Potentiometry
    5.5.4 Chronopotentiometry
    5.5.5 Voltammetry
    5.5.6 Impedimetric Sensing
    5.5.7 Conductometric Sensing
    References
    6. Mechanisms of Different Anticancer Drugs
    6.1 Introduction
    6.2 Cancer Research and Anticancer Drug History
    6.3 Mustard Gas
    6.4 Synthetic Agents
    6.5 Natural Products
    6.6 Targeting the Growth of Tumors
    6.6.1 Inhibition of Cancer Proliferation
    6.6.2 Targeting the Suppressors Responsible for Tumor Growth
    6.6.3 Inhibiting Antiapoptotic Behavior of Tumor
    6.6.4 Targeting Ability of Tumors to Divide Indefinitely by Extending Telomeres
    6.6.5 Targeting Angiogenesis Property of Cancer Cells
    6.6.6 Targeting Inflammation-Promoting Tumors
    6.7 Different Classes of Anticancer Drugs
    6.7.1 Chemical Structure-Based Classification
    6.7.2 Classification Based on Mechanism of Action
    6.7.3 Common Classification Based on Different Groups
    6.7.3.1 Antimetabolites
    6.7.3.1.1 Methotrexate
    6.7.3.1.2 5-Fluorouracil
    6.7.3.1.3 Azacytidine and Azauridine
    6.7.3.1.4 6-Mercaptopurine (6-Mp)
    6.7.3.1.5 Azathioprine
    6.7.3.1.6 6-Thioguanine (6-TG)
    6.7.3.2 Alkylating Agents
    6.7.3.2.1 Cyclophosphamide
    6.7.3.2.2 Chlorambucil
    6.7.3.2.3 Melphalan
    6.7.3.2.4 Nitrogen mustard
    6.7.3.3 Natural Products
    6.7.3.3.1 Mitotic Inhibitors
    6.7.3.3.2 Vincristine and vinblastine
    6.7.3.3.3 Antibiotics
    6.7.3.3.4 Enzymes
    6.7.3.4 Steroid Hormones
    References
    7. In Silico Approach to Cancer Therapy
    7.1 Introduction
    7.2 Machine Learning (ML) Approaches in Breast Cancer Prognosis Prediction
    7.2.1 Methodological Outline for Machine Learning Methods
    7.2.1.1 Preprocessing
    7.2.1.2 The Requirement of Balanced Data Sets
    7.2.1.3 Features Selection
    7.2.1.4 Model-Based Estimation of Performances
    7.3 Enzyme-Mediated Cancer Imaging and Therapy (EMCIT) Concept
    7.4 Tools and Databases for the Study of Cancer Pathways
    7.4.1 Target Pathways for Treatment of Cancer
    7.4.2 Databases Involving Molecular Interaction
    7.4.2.1 BioCyc
    7.4.2.2 KEGG
    7.4.2.3 Reactome
    7.4.2.4 ConsensusPathDB
    7.4.2.5 TRANSPATHR
    7.4.3 Annotation Tools
    7.4.4 Modeling Tools
    7.4.5 Computational Models for Processes Associated with Cancer
    7.4.5.1 BioModels Database
    7.4.5.2 Detailed Kinetic Models: Cancer
    7.5 In Silico Repurposing of Cancer Drugs
    7.5.1 In Silico Drug Repurposing and Personalized Medicine in Cancer
    7.5.2 Computational Tools for In Silico Drug Repurposing Related to Cancer
    7.5.2.1 Resources Based on the Nature of Drugs
    7.5.2.2 Resources Based on Effects of Drug Treatment
    7.5.2.3 Disease-Data-Based Resources
    7.6 Target Linked Data Modeling Related to Oncogenic Pathways
    7.7 Technologies for Cancer Immunity Investigation
    7.7.1 Single-Cell Omics Study of Isolated Cells
    7.7.2 Computational Tools for the Prediction of Neoantigens
    7.7.3 Characterization of Tumor-Infiltrating Immune Cells
    References
    Index