Identification of New Players in Hepatocarcinogenesis: Limits and Opportunities of Using Tissue Microarray (TMA).

Luca Quagliata, Manuel Schlageter, Cristina Quintavalle, Luigi Tornillo, Luigi M Terracciano
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引用次数: 12

Abstract

Liver tumours are among the leading causes of cancer-related death worldwide and hepatocellular carcinoma (HCC) accounts for the vast majority of liver tumours. When detected at an early stage of disease, patients might still be eligible for surgical-based curative treatments. However, currently only small portion of HCC affected patients are diagnosed at an early stage. For late stage HCC no treatment option exists beside the multi-tyrosine kinase inhibitor Sorafenib. Thus new molecular targets and treatment options for HCC are urgently needed. Nevertheless, despite some improvements in diagnosis and patient management, the biology of liver tumour remains inadequately understood, mainly because these tumours have shown to harbour a highly complex genomic landscape. In addition, one major obstacle delaying the identification of new molecular targets in biomedical research is the necessity to validate them using a large collection of tissue specimens. Tissue microarray (TMA) technology allows the prompt molecular profiling of multiple tissue specimens and is therefore ideal to analyze presumptive candidate biomarkers in a fast an effective manner. The use of TMA has substantial benefits over standard techniques and represents a significant advancement in molecular pathology. For example, TMA technology reduces laboratory work, offers a high level of experimental uniformity and provides a judicious use of precious tissue. On the other hand, one potential limitation of using TMA is that the small cores sampled may not be representative of whole tumors. This issue is very critical in particularly heterogeneous cancers such as HCC. For liver focused studies, it is ideal to evaluate the staining patters of a determined marker over the structure of an entire acinus and to define staining in as many as possible anatomical regions. In this review we analyze the limits and opportunities offered by the usage of TMA technology in HCC research. In summary, TMA has revolutionized the histopathological analysis and will be of great help to further advance the knowledge in the field of hepatocarcinogenesis research.

Abstract Image

确定肝癌发生的新参与者:使用组织微阵列(TMA)的限制和机会。
肝肿瘤是世界范围内癌症相关死亡的主要原因之一,肝细胞癌(HCC)占绝大多数肝肿瘤。当在疾病的早期阶段发现时,患者可能仍然有资格接受手术治疗。然而,目前只有一小部分HCC患者在早期得到诊断。对于晚期HCC,除了多酪氨酸激酶抑制剂索拉非尼之外,没有其他治疗选择。因此,迫切需要新的肝癌分子靶点和治疗方案。然而,尽管在诊断和患者管理方面取得了一些进步,但肝脏肿瘤的生物学仍然没有得到充分的了解,主要是因为这些肿瘤已经显示出高度复杂的基因组景观。此外,在生物医学研究中,延迟识别新分子靶点的一个主要障碍是必须使用大量组织标本来验证它们。组织微阵列(TMA)技术允许对多个组织标本进行快速分子分析,因此是快速有效地分析推定候选生物标志物的理想方法。与标准技术相比,TMA的使用具有实质性的好处,并代表了分子病理学的重大进步。例如,TMA技术减少了实验室工作,提供了高水平的实验一致性,并提供了宝贵组织的明智使用。另一方面,使用TMA的一个潜在限制是采样的小核心可能不能代表整个肿瘤。这个问题在异质癌症如HCC中尤为重要。对于肝脏聚焦的研究,理想的做法是评估一个确定的标记物在整个腺泡结构上的染色模式,并在尽可能多的解剖区域定义染色。在这篇综述中,我们分析了TMA技术在HCC研究中的局限性和机会。总之,TMA已经彻底改变了组织病理学分析,并将对进一步推进肝癌发生研究领域的知识有很大帮助。
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来源期刊
自引率
0.00%
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0
审稿时长
11 weeks
期刊介绍: High-Throughput (formerly Microarrays, ISSN 2076-3905) is a multidisciplinary peer-reviewed scientific journal that provides an advanced forum for the publication of studies reporting high-dimensional approaches and developments in Life Sciences, Chemistry and related fields. Our aim is to encourage scientists to publish their experimental and theoretical results based on high-throughput techniques as well as computational and statistical tools for data analysis and interpretation. The full experimental or methodological details must be provided so that the results can be reproduced. There is no restriction on the length of the papers. High-Throughput invites submissions covering several topics, including, but not limited to: Microarrays, DNA Sequencing, RNA Sequencing, Protein Identification and Quantification, Cell-based Approaches, Omics Technologies, Imaging, Bioinformatics, Computational Biology/Chemistry, Statistics, Integrative Omics, Drug Discovery and Development, Microfluidics, Lab-on-a-chip, Data Mining, Databases, Multiplex Assays.
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