An Overview: Genetic Tumor Markers for Early Detection and Current Gene Therapy Strategies.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Reeshan Ul Quraish, Tetsuyuki Hirahata, Afraz Ul Quraish, Shahan Ul Quraish
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引用次数: 0

Abstract

Genomic instability is considered a fundamental factor involved in any neoplastic disease. Consequently, the genetically unstable cells contribute to intratumoral genetic heterogeneity and phenotypic diversity of cancer. These genetic alterations can be detected by several diagnostic techniques of molecular biology and the detection of alteration in genomic integrity may serve as reliable genetic molecular markers for the early detection of cancer or cancer-related abnormal changes in the body cells. These genetic molecular markers can detect cancer earlier than any other method of cancer diagnosis, once a tumor is diagnosed, then replacement or therapeutic manipulation of these cancer-related abnormal genetic changes can be possible, which leads toward effective and target-specific cancer treatment and in many cases, personalized treatment of cancer could be performed without the adverse effects of chemotherapy and radiotherapy. In this review, we describe how these genetic molecular markers can be detected and the possible ways for the application of this gene diagnosis for gene therapy that can attack cancerous cells, directly or indirectly, which lead to overall improved management and quality of life for a cancer patient.

综述:肿瘤基因标志物的早期检测和目前的基因治疗策略。
基因组不稳定性被认为是任何肿瘤疾病的基本因素。因此,遗传不稳定的细胞有助于肿瘤内遗传异质性和表型多样性。这些遗传改变可以通过分子生物学的几种诊断技术检测到,基因组完整性改变的检测可以作为早期检测癌症或体细胞中与癌症相关的异常变化的可靠遗传分子标记。这些遗传分子标记可以比任何其他癌症诊断方法更早地检测到癌症,一旦肿瘤被诊断出来,就可以替代或治疗这些与癌症相关的异常基因变化,从而导致有效的和靶向特异性的癌症治疗,在许多情况下,癌症的个性化治疗可以在没有化疗和放疗的不利影响的情况下进行。在这篇综述中,我们描述了如何检测这些遗传分子标记,以及将这些基因诊断应用于基因治疗的可能方法,这些基因治疗可以直接或间接地攻击癌细胞,从而全面改善癌症患者的管理和生活质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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