Dataset Information

MiR-429 identified by dynamic transcriptome analysis is a new candidate biomarker for colorectal cancer prognosis.

ABSTRACT: Colorectal cancer (CRC) is a common malignant gastrointestinal cancer. Efforts for preventive and personalized medicine have intensified in the last decade with attention to novel forms of biomarkers. In the present study, microRNA and genetic analyses were performed in tandem for differential transcriptome profiling between primary tumors with or without nodes or distant metastases. Serial Test Cluster (STC) analysis demonstrated that 20 genes and two microRNAs showed distinctive expression patterns associated with the tumor, node, and metastasis (TNM) stage. The selected target genes were characterized by GO and Pathway analysis. A microRNA-target gene network analysis showed that miR-429 resided in the center of the network, indicating that miR-429 might serve important roles in the development of CRC. Real-time PCR and tissue microarrays showed that miR-429 had a dynamic expression pattern during the CRC progression stage, and was significantly downregulated in stage II and stage III clinical progression. The low expression of miR-429 was correlated with poor prognosis for CRC. Taken together, miR-429 warrant further clinical translation research as a candidate biomarker for CRC prognosis. Additional downstream targets and attendant gene function also need to be discerned to design a sound critical path to personalized medicine for persons susceptible to, or diagnosed with CRC.

SUBMITTER: Sun Y

PROVIDER: S-EPMC3903170 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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miR-429 identified by dynamic transcriptome analysis is a new candidate biomarker for colorectal cancer prognosis.

Sun Yingnan Y Shen Shourong S Tang Hailin H Xiang Juanjuan J Peng Ya Y Tang Anliu A Li Nan N Zhou Weiwei W Wang Zeyou Z Zhang Decai D Xiang Bo B Ge Jie J Li Guiyuan G Wu Minghua M Li Xiayu X

Omics : a journal of integrative biology 20131116 1

Colorectal cancer (CRC) is a common malignant gastrointestinal cancer. Efforts for preventive and personalized medicine have intensified in the last decade with attention to novel forms of biomarkers. In the present study, microRNA and genetic analyses were performed in tandem for differential transcriptome profiling between primary tumors with or without nodes or distant metastases. Serial Test Cluster (STC) analysis demonstrated that 20 genes and two microRNAs showed distinctive expression pat ...[more]

PMID: 24237355

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Project description:Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles from publicly available microarray datasets of cancer (breast, lymphoma and renal) samples linked to clinical information with an iterative machine learning algorithm. ROC curves were used to assess the prediction error of each profile for classification. We compared the prediction error of profiles correlated with molecular phenotype against profiles correlated with relapse-free status. Prediction error of profiles identified with supervised univariate feature selection algorithms were compared to profiles selected randomly from a) all genes on the microarray platform and b) a list of known disease-related genes (a priori selection). We also determined the relevance of expression profiles on test arrays from independent datasets, measured on either the same or different microarray platforms.Highly discriminative expression profiles were produced on both simulated gene expression data and expression data from breast cancer and lymphoma datasets on the basis of ER and BCL-6 expression, respectively. Use of relapse-free status to identify profiles for prognosis prediction resulted in poorly discriminative decision rules. Supervised feature selection resulted in more accurate classifications than random or a priori selection, however, the difference in prediction error decreased as the number of features increased. These results held when decision rules were applied across-datasets to samples profiled on the same microarray platform.Our results show that many gene sets predict molecular phenotypes accurately. Given this, expression profiles identified using different training datasets should be expected to show little agreement. In addition, we demonstrate the difficulty in predicting relapse directly from microarray data using supervised machine learning approaches. These findings are relevant to the use of molecular profiling for the identification of candidate biomarker panels.

Dataset Information

MiR-429 identified by dynamic transcriptome analysis is a new candidate biomarker for colorectal cancer prognosis.

Publications

miR-429 identified by dynamic transcriptome analysis is a new candidate biomarker for colorectal cancer prognosis.

Similar Datasets

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