title: Identification of candidate colon cancer biomarkers by applying a random             forest approach on microarray data.
abstract: 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.
authors: Department of Digestive Diseases, Wuhan General Hospital of Guangzhou Command, Wuhan, PR China.
authors: Zhi
authors: Z
authors: Yan
authors: Jiangeng
authors: J
authors: Li
authors: Yimin
authors: Y
authors: Xiong
authors: Weitian
authors: W
authors: Xu
authors: Guorong
authors: G
authors: Zheng
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000235:genetics* / Q000378:metabolism; D016000:Cluster Analysis; D003110:Colonic Neoplasms / Q000378:metabolism*; D003627:Data Interpretation, Statistical; D005260:Female; D015870:Gene Expression; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D016209:Interleukin-8 / Q000235:genetics / Q000378:metabolism; D008297:Male; D008875:Middle Aged; D018994:Myosin Light Chains / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D012372:ROC Curve; D060888:Real-Time Polymerase Chain Reaction; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism; D014660:Vasoactive Intestinal Peptide / Q000235:genetics / Q000378:metabolism
chemical_list: D014408:Biomarkers, Tumor; C508606:CXCL8 protein, human; D016209:Interleukin-8; D018994:Myosin Light Chains; D014157:Transcription Factors; C478359:WDR77 protein, human; D014660:Vasoactive Intestinal Peptide