Clinical data analysis reveals three subytpes of gastric cancer

Gastric cancer is the fourth most common cancer and second leading cause of cancer-related death worldwide. Nowadays the accumulated large scale clinical data allows the clinicopathlogical review to identify the clinical factors, reveal their possible correlations, and mine the possible clinical patterns for gastric cancer. Here we analyze the clinical data of over 1500 gastric cancer patients histopathologically diagnosed and treated during 2006 to 2010. Specifically, we collect and preprocess the data by extracting 14 available clinical factors from three categories, i.e., the clinical background, immunohistochemistry data, and the caner's stage information. Then these factors are quantized and the significant factors and their correlations are calculated. Importantly, we define a distance between two patients by their clinical factors profile similarity and cluster all the patients into subgroups. We find that most of the patients fall into three major classes and we define them as three subtypes of gastric cancer. Each subtype is analyzed and characterized by its own significant factors and correlations. Our analysis may provide important insights for gastric cancer classification and diagnose.

[1]  Tai-xiang Wu,et al.  Consumption of large amounts of Allium vegetables reduces risk for gastric cancer in a meta-analysis. , 2011, Gastroenterology.

[2]  Chia Huey Ooi,et al.  Intrinsic subtypes of gastric cancer, based on gene expression pattern, predict survival and respond differently to chemotherapy. , 2011, Gastroenterology.

[3]  Ming-Shiang Wu,et al.  Early Helicobacter pylori eradication decreases risk of gastric cancer in patients with peptic ulcer disease. , 2009, Gastroenterology.

[4]  J. T. Jørgensen,et al.  HER2 as a Prognostic Marker in Gastric Cancer - A Systematic Analysis of Data from the Literature , 2012, Journal of Cancer.

[5]  Xiang-Sun Zhang,et al.  A network biology study on circadian rhythm by integrating various omics data. , 2009, Omics : a journal of integrative biology.

[6]  A. Jemal,et al.  The global burden of cancer: priorities for prevention , 2009, Carcinogenesis.

[7]  Kay Washington,et al.  7th Edition of the AJCC Cancer Staging Manual: Stomach , 2010, Annals of surgical oncology.

[8]  Xiang-Sun Zhang,et al.  Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data , 2009, Nucleic acids research.

[9]  R. Fry,et al.  17(lowercase beta)-estradiol and Tamoxifen prevent gastric cancer by modulating leukocyte recruitment and oncogenic pathways in Helicobacter pylori-infected INS-GAS male mice , 2011 .

[10]  Data collection forms in clinical trials , 1991 .

[11]  C. Compton,et al.  The American Joint Committee on Cancer: the 7th Edition of the AJCC Cancer Staging Manual and the Future of TNM , 2010, Annals of Surgical Oncology.

[12]  Joe Gray,et al.  Genomics: The breast cancer landscape , 2012, Nature.

[13]  Luonan Chen,et al.  Biomolecular Networks: Methods and Applications in Systems Biology , 2009 .

[14]  Chen Chen,et al.  Revealing metabolite biomarkers for acupuncture treatment by linear programming based feature selection , 2012, BMC Systems Biology.

[15]  Michael Mitzenmacher,et al.  Detecting Novel Associations in Large Data Sets , 2011, Science.

[16]  R. Fry,et al.  17β-Estradiol and Tamoxifen Prevent Gastric Cancer by Modulating Leukocyte Recruitment and Oncogenic Pathways in Helicobacter Pylori–Infected INS-GAS Male Mice , 2011, Cancer Prevention Research.