Cargando…

Identification of Molecular Targets for Predicting Colon Adenocarcinoma

BACKGROUND: Colon adenocarcinoma mostly happens at the junction of the rectum and is a common gastrointestinal malignancy. Accumulated evidence has indicated that colon adenocarcinoma develops by genetic alterations and is a complicated disease. The aim of this study was to screen differentially exp...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Yansheng, Zhang, Jun, Li, Li, Xu, Xin, Zhang, Yong, Teng, Zhaowei, Wu, Feihu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scientific Literature, Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4754092/
https://www.ncbi.nlm.nih.gov/pubmed/26868022
http://dx.doi.org/10.12659/MSM.895881
_version_ 1782415969530937344
author Wang, Yansheng
Zhang, Jun
Li, Li
Xu, Xin
Zhang, Yong
Teng, Zhaowei
Wu, Feihu
author_facet Wang, Yansheng
Zhang, Jun
Li, Li
Xu, Xin
Zhang, Yong
Teng, Zhaowei
Wu, Feihu
author_sort Wang, Yansheng
collection PubMed
description BACKGROUND: Colon adenocarcinoma mostly happens at the junction of the rectum and is a common gastrointestinal malignancy. Accumulated evidence has indicated that colon adenocarcinoma develops by genetic alterations and is a complicated disease. The aim of this study was to screen differentially expressed miRNAs (DEMs) and genes with diagnostic and prognostic potentials in colon adenocarcinoma. MATERIAL/METHODS: In this study we screened DEMs and their target genes (DEGs) between 100 colon adenocarcinoma and normal samples in The Cancer Genome Atlas (TCGA) database by using the DEseq toolkit in Bioconductor. Then Go enrichment and KEGG pathway analysis were performed on the selected differential genes by use of the DAVID online tool. A regulation network of miRNA-gene was constructed and analyzed by Cytoscape. Finally, we performed ROC analysis of 8 miRNAs and ROC curves were drawn. RESULTS: A total of 159 DEMs and 1921 DEGs were screened, and 1881 pairs of miRNA-target genes with significant negative correlations were also obtained. A regulatory network of miRNA-gene, including 60 cancer-related genes and 47 miRNAs, was successfully constructed. In addition, 5 clusters with several miRNAs regulating a set of target genes simultaneously were identified through cluster analysis. There were 8 miRNAs involved in these 5 clusters, and these miRNAs could serve as molecular biomarkers to distinguish colon adenocarcinoma and normal samples indicated by ROC analysis. CONCLUSIONS: The identified 8 miRNAs were closely associated with colon adenocarcinoma, which may have great clinical value as diagnostic and prognostic biomarkers and provide new ideas for targeted therapy.
format Online
Article
Text
id pubmed-4754092
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher International Scientific Literature, Inc.
record_format MEDLINE/PubMed
spelling pubmed-47540922016-02-26 Identification of Molecular Targets for Predicting Colon Adenocarcinoma Wang, Yansheng Zhang, Jun Li, Li Xu, Xin Zhang, Yong Teng, Zhaowei Wu, Feihu Med Sci Monit Molecular Biology BACKGROUND: Colon adenocarcinoma mostly happens at the junction of the rectum and is a common gastrointestinal malignancy. Accumulated evidence has indicated that colon adenocarcinoma develops by genetic alterations and is a complicated disease. The aim of this study was to screen differentially expressed miRNAs (DEMs) and genes with diagnostic and prognostic potentials in colon adenocarcinoma. MATERIAL/METHODS: In this study we screened DEMs and their target genes (DEGs) between 100 colon adenocarcinoma and normal samples in The Cancer Genome Atlas (TCGA) database by using the DEseq toolkit in Bioconductor. Then Go enrichment and KEGG pathway analysis were performed on the selected differential genes by use of the DAVID online tool. A regulation network of miRNA-gene was constructed and analyzed by Cytoscape. Finally, we performed ROC analysis of 8 miRNAs and ROC curves were drawn. RESULTS: A total of 159 DEMs and 1921 DEGs were screened, and 1881 pairs of miRNA-target genes with significant negative correlations were also obtained. A regulatory network of miRNA-gene, including 60 cancer-related genes and 47 miRNAs, was successfully constructed. In addition, 5 clusters with several miRNAs regulating a set of target genes simultaneously were identified through cluster analysis. There were 8 miRNAs involved in these 5 clusters, and these miRNAs could serve as molecular biomarkers to distinguish colon adenocarcinoma and normal samples indicated by ROC analysis. CONCLUSIONS: The identified 8 miRNAs were closely associated with colon adenocarcinoma, which may have great clinical value as diagnostic and prognostic biomarkers and provide new ideas for targeted therapy. International Scientific Literature, Inc. 2016-02-12 /pmc/articles/PMC4754092/ /pubmed/26868022 http://dx.doi.org/10.12659/MSM.895881 Text en © Med Sci Monit, 2016 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License
spellingShingle Molecular Biology
Wang, Yansheng
Zhang, Jun
Li, Li
Xu, Xin
Zhang, Yong
Teng, Zhaowei
Wu, Feihu
Identification of Molecular Targets for Predicting Colon Adenocarcinoma
title Identification of Molecular Targets for Predicting Colon Adenocarcinoma
title_full Identification of Molecular Targets for Predicting Colon Adenocarcinoma
title_fullStr Identification of Molecular Targets for Predicting Colon Adenocarcinoma
title_full_unstemmed Identification of Molecular Targets for Predicting Colon Adenocarcinoma
title_short Identification of Molecular Targets for Predicting Colon Adenocarcinoma
title_sort identification of molecular targets for predicting colon adenocarcinoma
topic Molecular Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4754092/
https://www.ncbi.nlm.nih.gov/pubmed/26868022
http://dx.doi.org/10.12659/MSM.895881
work_keys_str_mv AT wangyansheng identificationofmoleculartargetsforpredictingcolonadenocarcinoma
AT zhangjun identificationofmoleculartargetsforpredictingcolonadenocarcinoma
AT lili identificationofmoleculartargetsforpredictingcolonadenocarcinoma
AT xuxin identificationofmoleculartargetsforpredictingcolonadenocarcinoma
AT zhangyong identificationofmoleculartargetsforpredictingcolonadenocarcinoma
AT tengzhaowei identificationofmoleculartargetsforpredictingcolonadenocarcinoma
AT wufeihu identificationofmoleculartargetsforpredictingcolonadenocarcinoma