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Multiple levels of omics data are available due to the development of sequencing technologies. In this study, we proposed an integrative prognostic model for colon cancer based on the integration of clinical and multi-omics data.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>In total, 344 patients were included in this study. Clinical, gene expression, DNA methylation and miRNA expression data were retrieved from The Cancer Genome Atlas (TCGA). To accommodate the high dimensionality of omics data, unsupervised clustering was used as dimension reduction method. The bias-corrected Harrell\u2019s concordance index was used to verify which clustering result provided the best prognostic performance. Finally, we proposed a prognostic prediction model based on the integration of clinical data and multi-omics data. Uno\u2019s concordance index with cross-validation was used to compare the discriminative performance of the prognostic model constructed with different covariates.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Combinations of clinical and multi-omics data can improve prognostic performance, as shown by the increase of the bias-corrected Harrell\u2019s concordance of the prognostic model from 0.7424 (clinical features only) to 0.7604 (clinical features and three types of omics features). Additionally, 2-year, 3-year and 5-year Uno\u2019s concordance statistics increased from 0.7329, 0.7043, and 0.7002 (clinical features only) to 0.7639, 0.7474 and 0.7597 (clinical features and three types of omics features), respectively.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>In conclusion, this study successfully combined clinical and multi-omics data for better prediction of colon cancer prognosis.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-020-1043-1","type":"journal-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T09:04:30Z","timestamp":1581066270000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data"],"prefix":"10.1186","volume":"20","author":[{"given":"Danyang","family":"Tong","sequence":"first","affiliation":[]},{"given":"Yu","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Tianshu","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Qiancheng","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[]},{"given":"Kefeng","family":"Ding","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1064-637X","authenticated-orcid":false,"given":"Jingsong","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,2,7]]},"reference":[{"issue":"3","key":"1043_CR1","doi-asserted-by":"publisher","first-page":"177","DOI":"10.3322\/caac.21395","volume":"67","author":"RL Siegel","year":"2017","unstructured":"Siegel RL, Miller KD, Fedewa SA, Ahnen DJ, Meester RGS, Barzi A, et al. 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We definitely followed the National Institutes of Health Genomic Data Sharing Policy as well as the National Cancer Institution Genomic Data Sharing Policy in this study.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"22"}}