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开发一个可访问的基因表达生物信息学管道,以研究结直肠癌的驱动突变。

Development of an Accessible Gene Expression Bioinformatics Pipeline to Study Driver Mutations of Colorectal Cancer.

机构信息

Strathclyde Institute of Pharmacy and Biomedical Sciences, 3527University of Strathclyde, Glasgow, UK.

Yale School of Public Health, 5755Yale University, New Haven, CT, USA.

出版信息

Altern Lab Anim. 2022 Jul;50(4):282-292. doi: 10.1177/02611929221107546. Epub 2022 Jun 28.

DOI:10.1177/02611929221107546
PMID:35765262
Abstract

Colorectal cancer (CRC) is a global cause of cancer-related mortality driven by genetic and environmental factors which influence therapeutic outcomes. The emergence of next-generation sequencing technologies enables the rapid and extensive collection and curation of genetic data for each cancer type into clinical gene expression biobanks. We report the application of bioinformatics tools for investigating the expression patterns and prognostic significance of three genes that are commonly dysregulated in colon cancer: adenomatous polyposis coli (); B-Raf proto-oncogene (); and Kirsten rat sarcoma viral oncogene homologue (). Through the use of bioinformatics tools, we show the patterns of , and genetic alterations and their role in patient prognosis. Our results show mutation types, the frequency of mutations, tumour anatomical location and differential expression patterns for and for colorectal tumour and matched healthy tissue. The prognostic value of , and genetic alterations was investigated as a function of their expression levels in CRC. In the era of precision medicine, with significant advancements in biobanking and data curation, there is significant scope to use existing clinical data sets for evaluating the role of mutational drivers in carcinogenesis. This approach offers the potential for studying combinations of less well-known genes and the discovery of novel biomarkers, or for studying the association between various effector proteins and pathways.

摘要

结直肠癌(CRC)是由遗传和环境因素共同作用导致的全球癌症相关死亡率的主要原因,这些因素影响治疗效果。新一代测序技术的出现使得对每种癌症类型的遗传数据进行快速和广泛的收集和整理成为可能,并将其纳入临床基因表达生物库中。我们报告了生物信息学工具在研究三种常见于结肠癌的基因表达模式和预后意义中的应用:结肠腺瘤性息肉病基因();B-Raf 原癌基因();和 Kirsten 大鼠肉瘤病毒致癌基因同源物()。通过使用生物信息学工具,我们展示了、和基因改变的模式及其在患者预后中的作用。我们的研究结果显示了结直肠癌肿瘤和匹配的健康组织中、和的突变类型、突变频率、肿瘤解剖位置和差异表达模式。我们还研究了 CRC 中基因改变的预后价值,作为其表达水平的函数。在精准医学时代,随着生物库和数据管理的显著进步,有很大的空间可以利用现有的临床数据集来评估突变驱动因素在癌症发生中的作用。这种方法为研究不太知名基因的组合和发现新的生物标志物提供了可能,或者为研究各种效应蛋白和途径之间的关联提供了可能。

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