Zheng Liu, Tuulikki Sokka, Kevin Maas, Nancy J Olsen, Thomas M Aune
{"title":"基因表达谱预测早期类风湿关节炎患者疾病严重程度","authors":"Zheng Liu, Tuulikki Sokka, Kevin Maas, Nancy J Olsen, Thomas M Aune","doi":"10.4061/2009/484351","DOIUrl":null,"url":null,"abstract":"<p><p>In order to test the ability of peripheral blood gene expression profiles to predict future disease severity in patients with early rheumatoid arthritis (RA), a group of 17 patients (1 ± 0.2 years disease duration) was evaluated at baseline for gene expression profiles. Disease status was evaluated after a mean of 5 years using an index combining pain, global and recoded MHAQ scores. Unsupervised and supervised algorithms identified \"predictor genes\" whose combined expression levels correlated with follow-up disease severity scores. Unsupervised clustering algorithms separated patients into two branches. The only significant difference between these two groups was the disease severity score; demographic variables and medication usage were not different. Supervised T-Test analysis identified 19 \"predictor genes\" of future disease severity. Results were validated in an independent cohort of subjects of established RA with using Support Vector Machines and K-Nearest-Neighbor Classification. Our study demonstrates that peripheral blood gene expression profiles may be a useful tool to predict future disease severity in patients with early and established RA.</p>","PeriodicalId":88887,"journal":{"name":"Human genomics and proteomics : HGP","volume":"2009 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2009-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4061/2009/484351","citationCount":"29","resultStr":"{\"title\":\"Prediction of disease severity in patients with early rheumatoid arthritis by gene expression profiling.\",\"authors\":\"Zheng Liu, Tuulikki Sokka, Kevin Maas, Nancy J Olsen, Thomas M Aune\",\"doi\":\"10.4061/2009/484351\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In order to test the ability of peripheral blood gene expression profiles to predict future disease severity in patients with early rheumatoid arthritis (RA), a group of 17 patients (1 ± 0.2 years disease duration) was evaluated at baseline for gene expression profiles. Disease status was evaluated after a mean of 5 years using an index combining pain, global and recoded MHAQ scores. Unsupervised and supervised algorithms identified \\\"predictor genes\\\" whose combined expression levels correlated with follow-up disease severity scores. Unsupervised clustering algorithms separated patients into two branches. The only significant difference between these two groups was the disease severity score; demographic variables and medication usage were not different. Supervised T-Test analysis identified 19 \\\"predictor genes\\\" of future disease severity. Results were validated in an independent cohort of subjects of established RA with using Support Vector Machines and K-Nearest-Neighbor Classification. Our study demonstrates that peripheral blood gene expression profiles may be a useful tool to predict future disease severity in patients with early and established RA.</p>\",\"PeriodicalId\":88887,\"journal\":{\"name\":\"Human genomics and proteomics : HGP\",\"volume\":\"2009 \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-04-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.4061/2009/484351\",\"citationCount\":\"29\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Human genomics and proteomics : HGP\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4061/2009/484351\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human genomics and proteomics : HGP","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4061/2009/484351","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of disease severity in patients with early rheumatoid arthritis by gene expression profiling.
In order to test the ability of peripheral blood gene expression profiles to predict future disease severity in patients with early rheumatoid arthritis (RA), a group of 17 patients (1 ± 0.2 years disease duration) was evaluated at baseline for gene expression profiles. Disease status was evaluated after a mean of 5 years using an index combining pain, global and recoded MHAQ scores. Unsupervised and supervised algorithms identified "predictor genes" whose combined expression levels correlated with follow-up disease severity scores. Unsupervised clustering algorithms separated patients into two branches. The only significant difference between these two groups was the disease severity score; demographic variables and medication usage were not different. Supervised T-Test analysis identified 19 "predictor genes" of future disease severity. Results were validated in an independent cohort of subjects of established RA with using Support Vector Machines and K-Nearest-Neighbor Classification. Our study demonstrates that peripheral blood gene expression profiles may be a useful tool to predict future disease severity in patients with early and established RA.