Identification of potential long non-coding RNA biomarkers associated with the progression of colon cancer
| Autor: | Binbin Cui, Yanlong Liu, Jun-Li Lv, Peng Han, Jingwen Li, Weinan Xue |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
long non-coding RNA business.industry Colorectal cancer Critical pathways Disease progression biomarkers Pathway enrichment Lncrna expression Bioinformatics medicine.disease Long non-coding RNA 03 medical and health sciences 030104 developmental biology 0302 clinical medicine disease progression Oncology colon cancer 030220 oncology & carcinogenesis Medicine business Survival analysis Research Paper |
| Zdroj: | Oncotarget |
| ISSN: | 1949-2553 |
| Popis: | // Jingwen Li 1, * , Weinan Xue 1, * , Junli Lv 2, * , Peng Han 1 , Yanlong Liu 1 and Binbin Cui 1 1 Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, China 2 Department of Science, Harbin Medical University Cancer Hospital, Harbin 150081, China * These authors contributed equally to this work Correspondence to: Binbin Cui, email: binbincuihrb@126.com Yanlong Liu, email: yanlliu@yahoo.com Keywords: biomarkers, colon cancer, disease progression, long non-coding RNA Received: March 29, 2017 Accepted: May 03, 2017 Published: May 17, 2017 ABSTRACT Increasing evidence has suggested that dysregulated lncRNA expression played important roles in the development and progression of human cancers. Although prognostic roles of lncRNAs have been recognized for colon cancer (CC) patients, the search for novel lncRNA biomarkers potentially involved in CC progression is an urgent and still largely unmet medical need. In this study, we evaluated the lncRNA expression changes during the progression of CC by analyzing two cohorts of previously published expression profiles of CC patients and identified hundreds of differentially expressed lncRNAs. Then we identified eight lncRNAs that closely associated with the progression of CC patients from a large number of significantly altered lncRNAs using random forest supervised classification algorithm. Finally, an SVM-based lncRNA risk classifier was developed to discriminate high-risk CC patients from persons with early-stage and validated in both the training dataset and testing dataset by survival analysis and five-fold cross-validation strategy. Our pathway enrichment analysis based on protein-coding genes that are co-expressed with lncRNAs, suggested that variation in expression of eight lncRNAs biomarkers might affect critical pathways involved in CC progression. With further validation, these eight lncRNAs might have significant implications for the clinical management of CC patients with early stage and improve our understanding of cancer progression. |
| Databáze: | OpenAIRE |
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