Review Article

Classification of proteins expression in some popular cancers for protein biomarkers identification

ali farid , kazem Porbadakhshan

ali farid
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kazem Porbadakhshan

Online First: December 29, 2017 | Cite this Article
farid, a., Porbadakhshan, k. 2017. Classification of proteins expression in some popular cancers for protein biomarkers identification. The Cancer Press 3(4). DOI:10.15562/tcp.59

Recognition of the source and stage of cancer has always been one of the Issues of interest to scientists. On the other hand, cancer is the second leading cause of death worldwide after cardiovascular disease. According to the Global Burden of Disease Cancer Report In 2015, there were 17.5 million cancer cases worldwide and over 8.7 million cancer deaths. Based on the same report, breast cancer, TBL (tracheal, bronchus, and lung) cancer and colorectal cancer were the most common incidents. From another perspective, one of the requirements for the treatment of different cancers is early diagnosis in the early stages. With the end of the human genome project, molecular medicine moved to a step beyond the genome called "proteomics". Proteomic ideas play an important role in discovering cancer biomarkers for early diagnosis of disease, prediction and prognosis, identifying new drug goals, monitoring the effectiveness of treatment and personal therapy. Nowadays with new developments in mass spectrometry and bioinformatics, new biomarkers can be identified for different cancers. To analyze a cancer, identifying only one biomarker does not provide enough information for that cancer, but paying attention to changes in the level of expression of various proteins is valuable. In this paper, effective proteins for breast, lung and colorectal cancers, have been identified and classified. Biomarkers sparse in different articles are combined using Text Mining and reviewing articles that introduced a cancer biomarker. In fact, by examining changes in the expression of proteins in the cancerous tissue and considering their significant changes, they are referred to as cancer marker candidates for early diagnosis or even prediction of future illness. This research offers text mining algorithms to collect cancer biomarker's.


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