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Query: UMLS:C0699790 (colon cancer)
 28,837 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Colon cancer is the third most common cancer and one of the leading causes of cancer-related death in the world. Therefore, identification of biomarkers with potential in recognizing the biological characteristics is a key problem for early diagnosis of colon cancer patients. In this study, we used a random forest approach to discover biomarkers based on a set of oligonucleotide microarray data of colon cancer. Real-time PCR was used to validate the related expression levels of biomarkers selected by our approach. Furthermore, ROC curves were used to analyze the sensitivity and specificity of each biomarker in both training and test sample sets. Finally, we analyzed the clinical significance of each biomarker based on their differential expression. A single classifier consisting of 4 genes (IL8, WDR77, MYL9 and VIP) was selected by random forests with an average sensitivity and specificity of 83.75 and 76.15%. The differential expression levels of each biomarker was validated by real-time PCR in 48 test colon cancer samples compared to the matched normal tissues. Patients with high expression of IL8 and WDR77, and low expression of MYL9 and VIP had a significantly reduced median survival rate compared to colon cancer patients. The results indicate that our approach can be employed for biomarker identification based on microarray data. These 4 genes identified by our approach have the potential to act as clinical biomarkers for the early diagnosis of colon cancer.
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PMID:Identification of candidate colon cancer biomarkers by applying a random forest approach on microarray data. 2275 57

Aspirin prevents cardiovascular disease and colon cancer; however aspirin's inhibition of platelet COX-1 only partially explains its diverse effects. We previously identified an aspirin response signature (ARS) in blood consisting of 62 co-expressed transcripts that correlated with aspirin's effects on platelets and myocardial infarction (MI). Here we report that 60% of ARS transcripts are regulated by RUNX1 - a hematopoietic transcription factor - and 48% of ARS gene promoters contain a RUNX1 binding site. Megakaryocytic cells exposed to aspirin and its metabolite (salicylic acid, a weak COX-1 inhibitor) showed up regulation in the RUNX1 P1 isoform and MYL9, which is transcriptionally regulated by RUNX1. In human subjects, RUNX1 P1 expression in blood and RUNX1-regulated platelet proteins, including MYL9, were aspirin-responsive and associated with platelet function. In cardiovascular disease patients RUNX1 P1 expression was associated with death or MI. RUNX1 acts as a tumor suppressor gene in gastrointestinal malignancies. We show that RUNX1 P1 expression is associated with colon cancer free survival suggesting a role for RUNX1 in aspirin's protective effect in colon cancer. Our studies reveal an effect of aspirin on RUNX1 and gene expression that may additionally explain aspirin's effects in cardiovascular disease and cancer.
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PMID:Systems Pharmacogenomics Finds RUNX1 Is an Aspirin-Responsive Transcription Factor Linked to Cardiovascular Disease and Colon Cancer. 2756 55

It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Information Gain-Support Vector Machine (IG-SVM) in this study. IG was initially employed to filter irrelevant and redundant genes. Then, further removal of redundant genes was performed using SVM to eliminate the noise in the datasets more effectively. Finally, the informative genes selected by IG-SVM served as the input for the LIBSVM classifier. Compared to other related algorithms, IG-SVM showed the highest classification accuracy and superior performance as evaluated using five cancer gene expression datasets based on a few selected genes. As an example, IG-SVM achieved a classification accuracy of 90.32% for colon cancer, which is difficult to be accurately classified, only based on three genes including CSRP1, MYL9, and GUCA2B.
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PMID:Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification. 2924 19