{"title":"Identification of a seven autophagy-related gene pairs signature for the diagnosis of colorectal cancer using the RankComp algorithm.","authors":"Qi-Shi Song, Hai-Jun Wu, Qian Lin, Yu-Kai Tang","doi":"10.1142/S0219720023500129","DOIUrl":null,"url":null,"abstract":"<p><p>Based on the colorectal cancer microarray sets gene expression data series (GSE) GSE10972 and GSE74602 in colon cancer and 222 autophagy-related genes, the differential signature in colorectal cancer and paracancerous tissues was analyzed by RankComp algorithm, and a signature consisting of seven autophagy-related reversal gene pairs with stable relative expression orderings (REOs) was obtained. Scoring based on these gene pairs could significantly distinguish colorectal cancer samples from adjacent noncancerous samples, with an average accuracy of 97.5% in two training sets and 90.25% in four independent validation GSE21510, GSE37182, GSE33126, and GSE18105. Scoring based on these gene pairs also accurately identifies 99.85% of colorectal cancer samples in seven other independent datasets containing a total of 1406 colorectal cancer samples.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"21 3","pages":"2350012"},"PeriodicalIF":0.9000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Bioinformatics and Computational Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1142/S0219720023500129","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Based on the colorectal cancer microarray sets gene expression data series (GSE) GSE10972 and GSE74602 in colon cancer and 222 autophagy-related genes, the differential signature in colorectal cancer and paracancerous tissues was analyzed by RankComp algorithm, and a signature consisting of seven autophagy-related reversal gene pairs with stable relative expression orderings (REOs) was obtained. Scoring based on these gene pairs could significantly distinguish colorectal cancer samples from adjacent noncancerous samples, with an average accuracy of 97.5% in two training sets and 90.25% in four independent validation GSE21510, GSE37182, GSE33126, and GSE18105. Scoring based on these gene pairs also accurately identifies 99.85% of colorectal cancer samples in seven other independent datasets containing a total of 1406 colorectal cancer samples.
期刊介绍：
The Journal of Bioinformatics and Computational Biology aims to publish high quality, original research articles, expository tutorial papers and review papers as well as short, critical comments on technical issues associated with the analysis of cellular information.
The research papers will be technical presentations of new assertions, discoveries and tools, intended for a narrower specialist community. The tutorials, reviews and critical commentary will be targeted at a broader readership of biologists who are interested in using computers but are not knowledgeable about scientific computing, and equally, computer scientists who have an interest in biology but are not familiar with current thrusts nor the language of biology. Such carefully chosen tutorials and articles should greatly accelerate the rate of entry of these new creative scientists into the field.
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