Dataset Information

A long non-coding RNA signature to improve prognosis prediction of colorectal cancer.

ABSTRACT: Increasing evidence suggests long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers, however, few related lncRNA signatures have been established for prediction of cancer prognosis. We aimed to develop a lncRNA signature to improve prognosis prediction of colorectal cancer (CRC). Using a lncRNA-mining approach, we performed lncRNA expression profiling in large CRC cohorts from Gene Expression Ominus (GEO), including GSE39582 test series(N=436), internal validation series (N=117); and two independent validation series GSE14333 (N=197) and GSE17536(N=145). We established a set of six lncRNAs that were significantly correlated with the disease free survival (DFS) in the test series. Based on this six-lncRNA signature, the test series patients could be classified into high-risk and low-risk subgroups with significantly different DFS (HR=2.670; P<0.0001). The prognostic value of this six-lncRNA signature was confirmed in the internal validation series and another two independent CRC sets. Gene set enrichment analysis (GSEA) analysis suggested that risk score positively correlated with several cancer metastasis related pathways. Functional experiments demonstrated three dysregulated lncRNAs, AK123657, BX648207 and BX649059 were required for efficient invasion and proliferation suppression in CRC cell lines. Our results might provide an efficient classification tool for clinical prognosis evaluation of CRC.

SUBMITTER: Hu Y

PROVIDER: S-EPMC4039159 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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A long non-coding RNA signature to improve prognosis prediction of colorectal cancer.

Hu Ye Y Chen Hao-Yan HY Yu Chen-Yang CY Xu Jie J Wang Ji-Lin JL Qian Jin J Zhang Xi X Fang Jing-Yuan JY

Oncotarget 20140401 8

Increasing evidence suggests long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers, however, few related lncRNA signatures have been established for prediction of cancer prognosis. We aimed to develop a lncRNA signature to improve prognosis prediction of colorectal cancer (CRC). Using a lncRNA-mining approach, we performed lncRNA expression profiling in large CRC cohorts from Gene Expression Ominus (GEO), including GSE39582 test series(N=436), internal validation series ( ...[more]

PMID: 24809982

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Project description:BackgroundHeterogeneity in colorectal cancer (CRC) patients provides novel strategies in clinical decision-making. Identifying distinctive subgroups in patients can improve the screening of CRC and reduce the cost of tests. Metabolism-related long non-coding RNA (lncRNA) can help detection of tumorigenesis and development for CRC patients.MethodsRNA sequencing and clinical data of CRC patients which extracted and integrated from public databases including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were set as training cohort and validation cohort. Metabolism-related genes were acquired from Kyoto Encyclopedia of Genes and Genomes (KEGG) and the metabolism-related lncRNAs were filtered using correlation analysis. The risk score was calculated based on lncRNAs with prognostic value and verified through survival curve, receiver operating characteristic (ROC) curve and risk curve. Prognostic factors of CRC patients were also analyzed. Nomogram was constructed based on the results of cox regression analyses. The different immune status was observed in the single sample Gene Set Enrichment Analysis (ssGSEA).ResultsThe training cohort and the validation cohort enrolled 432 and 547 CRC patients respectively. A total of 23 metabolism-related lncRNAs with prognostic value were screened out and 10 of which were significantly differentially expressed between tumour and normal tissues. Finally, 8 lncRNAs were used to establish a risk score (DICER1-AS1, PCAT6, GAS5, PRR7-AS1, MCM3AP-AS1, GAS6-AS1, LINC01082 and ADIRF-AS1). Patients were divided into high-risk and low-risk groups according to the median of risk scores in training cohort and the survival curves indicated that the survival prognosis was significantly different. The area under curve (AUC) of the ROC curve in two cohorts were both greater than 0.6. The age, tumour stage and risk score were selected as independent factors and used to construct a nomogram to predict CRC patients' survival rate with the c-index of 0.806. The ssGSEA indicated that the risk score was associated with immune cells and functions.ConclusionsOur systematic study established a metabolism-related lncRNA signature to predict outcomes of CRC patients which may contribute to individual prevention and treatment.

Dataset Information

A long non-coding RNA signature to improve prognosis prediction of colorectal cancer.

Publications

A long non-coding RNA signature to improve prognosis prediction of colorectal cancer.

Similar Datasets

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