临床资料分析显示胃癌可分为三种亚型

Xinxin Wang, Zhana Duren, Chao Zhang, Lin Chen, Yong Wang
{"title":"临床资料分析显示胃癌可分为三种亚型","authors":"Xinxin Wang, Zhana Duren, Chao Zhang, Lin Chen, Yong Wang","doi":"10.1109/ISB.2012.6314156","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":224011,"journal":{"name":"2012 IEEE 6th International Conference on Systems Biology (ISB)","volume":"11 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Clinical data analysis reveals three subytpes of gastric cancer\",\"authors\":\"Xinxin Wang, Zhana Duren, Chao Zhang, Lin Chen, Yong Wang\",\"doi\":\"10.1109/ISB.2012.6314156\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":224011,\"journal\":{\"name\":\"2012 IEEE 6th International Conference on Systems Biology (ISB)\",\"volume\":\"11 3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE 6th International Conference on Systems Biology (ISB)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISB.2012.6314156\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 6th International Conference on Systems Biology (ISB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISB.2012.6314156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

胃癌是全球第四大常见癌症,也是导致癌症相关死亡的第二大原因。目前大量临床资料的积累,使得临床病理检查能够识别临床因素,揭示其可能的相关性,挖掘胃癌可能的临床模式。本文对2006 ~ 2010年经病理诊断和治疗的1500余例胃癌患者的临床资料进行分析。具体来说,我们通过从临床背景、免疫组化数据和癌症分期信息三大类中提取14个可用的临床因素来收集和预处理数据。然后对这些因素进行量化,计算显著因子及其相关性。重要的是,我们通过他们的临床因素概况相似度来定义两个患者之间的距离,并将所有患者聚类到亚组中。我们发现大多数患者可分为三大类,我们将其定义为胃癌的三种亚型。每个亚型都有其自身的重要因素和相关性来分析和表征。我们的分析可能为胃癌的分类和诊断提供重要的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A fixed-point blind source extraction algorithm and its application to ECG data analysis Comparing two models based on the transcriptional regulation by KaiC of cyanobacteria rhythm Predicting protein complexes via the integration of multiple biological information Effective clustering of microRNA sequences by N-grams and feature weighting RNA-seq coverage effects on biological pathways and GO tag clouds
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1