Faculty of Engineering, Department of Electrical Engineering, Communication and Electronics Section, El Fayoum University, Fayoum 63514, Egypt.
Comput Methods Programs Biomed. 2013 Dec;112(3):640-8. doi: 10.1016/j.cmpb.2013.07.014. Epub 2013 Aug 23.
Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide. New insights into the pathogenesis of this lethal disease are urgently needed. Chromosomal copy number alterations (CNAs) can lead to activation of oncogenes and inactivation of tumor suppressors in human cancers. Thus, identification of cancer-specific CNAs will not only provide new insight into understanding the molecular basis of tumor genesis but also facilitate the identification of HCC biomarkers using CNA. This paper presents the TMT-HCC system; it is a tool for text mining the biomedical literature for hepatocellular carcinoma (HCC) biomarkers identification. TMT-HCC provides researchers with a powerful way to identify and discern molecular biomarkers of HCC to inform diagnosis, prognosis, and treatment driver genes with causal roles in carcinogenesis is to detect genomic regions that under frequent alterations in cancers (CNAs). TMT-HCC also extracts protein-protein interactions from the full text of the scientific papers. The results provided that the integration of genomic and transcriptional data offers powerful potential for identifying novel cancer genes in HCC pathogenesis.
肝细胞癌（HCC）是全球癌症相关死亡的第三大主要原因。迫切需要深入了解这种致命疾病的发病机制。染色体拷贝数改变（CNAs）可导致人类癌症中癌基因的激活和肿瘤抑制基因的失活。因此，鉴定癌症特异性 CNA 不仅将为理解肿瘤发生的分子基础提供新的见解，而且还将有助于使用 CNA 鉴定 HCC 生物标志物。本文介绍了 TMT-HCC 系统；这是一种用于从生物医学文献中挖掘肝细胞癌（HCC）生物标志物的文本挖掘工具。TMT-HCC 为研究人员提供了一种强大的方法，可识别和区分 HCC 的分子生物标志物，以告知诊断、预后和治疗与致癌作用有关的关键基因，方法是检测癌症中频繁改变的基因组区域（CNAs）。TMT-HCC 还从科学论文的全文中提取蛋白质-蛋白质相互作用。结果表明，基因组和转录组数据的整合为鉴定 HCC 发病机制中的新型癌症基因提供了强大的潜力。
