Cargando…
Identification of modules and novel prognostic biomarkers in liver cancer through integrated bioinformatics analysis
Liver cancer is a common malignant tumor with poor prognosis. Due to the lack of specific clinical manifestations at early stages, most patients are already at advanced stages of the disease by the time of diagnosis. Identification of novel biomarkers for liver cancer may thus enable earlier detecti...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7609804/ https://www.ncbi.nlm.nih.gov/pubmed/32961635 http://dx.doi.org/10.1002/2211-5463.12983 |
_version_ | 1783605071184920576 |
---|---|
author | Shen, Bo Li, Kun Zhang, Yuting |
author_facet | Shen, Bo Li, Kun Zhang, Yuting |
author_sort | Shen, Bo |
collection | PubMed |
description | Liver cancer is a common malignant tumor with poor prognosis. Due to the lack of specific clinical manifestations at early stages, most patients are already at advanced stages of the disease by the time of diagnosis. Identification of novel biomarkers for liver cancer may thus enable earlier detection, improving outcome. MicroRNAs (miRNAs) are small endogenous noncoding RNAs of 18–22 nucleotides in length, which have a regulatory role in the expression of target proteins. Increased evidence suggests that miRNAs are abnormally expressed in a variety of cancer malignancies. Here, we combined RNA sequencing data and clinical information from The Cancer Genome Atlas Liver Hepatocellular Carcinoma database for weighted gene coexpression network analysis to identify potential miRNA prognostic biomarkers. We constructed nine coexpression modules, allowing us to identify that miR‐105‐5p, miR‐767‐5p, miR‐1266‐5p, miR‐4746‐5p, miR‐500a‐3p, miR‐1180‐3p and miR‐139‐5p are significantly associated with liver cancer prognosis. We found that these miRNAs exhibit significant association with prognosis of patients with liver cancer and confirmed the expression of these miRNAs in liver cancer tissues. Multivariate Cox regression analysis showed that miR‐105‐5p and miR‐139‐5p may be considered as independent factors. In summary, here we report that seven miRNAs have potential value as prognostic biomarkers of liver cancer. |
format | Online Article Text |
id | pubmed-7609804 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-76098042020-11-06 Identification of modules and novel prognostic biomarkers in liver cancer through integrated bioinformatics analysis Shen, Bo Li, Kun Zhang, Yuting FEBS Open Bio Research Articles Liver cancer is a common malignant tumor with poor prognosis. Due to the lack of specific clinical manifestations at early stages, most patients are already at advanced stages of the disease by the time of diagnosis. Identification of novel biomarkers for liver cancer may thus enable earlier detection, improving outcome. MicroRNAs (miRNAs) are small endogenous noncoding RNAs of 18–22 nucleotides in length, which have a regulatory role in the expression of target proteins. Increased evidence suggests that miRNAs are abnormally expressed in a variety of cancer malignancies. Here, we combined RNA sequencing data and clinical information from The Cancer Genome Atlas Liver Hepatocellular Carcinoma database for weighted gene coexpression network analysis to identify potential miRNA prognostic biomarkers. We constructed nine coexpression modules, allowing us to identify that miR‐105‐5p, miR‐767‐5p, miR‐1266‐5p, miR‐4746‐5p, miR‐500a‐3p, miR‐1180‐3p and miR‐139‐5p are significantly associated with liver cancer prognosis. We found that these miRNAs exhibit significant association with prognosis of patients with liver cancer and confirmed the expression of these miRNAs in liver cancer tissues. Multivariate Cox regression analysis showed that miR‐105‐5p and miR‐139‐5p may be considered as independent factors. In summary, here we report that seven miRNAs have potential value as prognostic biomarkers of liver cancer. John Wiley and Sons Inc. 2020-10-27 /pmc/articles/PMC7609804/ /pubmed/32961635 http://dx.doi.org/10.1002/2211-5463.12983 Text en © 2020 The Authors. Published by FEBS Press and John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Shen, Bo Li, Kun Zhang, Yuting Identification of modules and novel prognostic biomarkers in liver cancer through integrated bioinformatics analysis |
title | Identification of modules and novel prognostic biomarkers in liver cancer through integrated bioinformatics analysis |
title_full | Identification of modules and novel prognostic biomarkers in liver cancer through integrated bioinformatics analysis |
title_fullStr | Identification of modules and novel prognostic biomarkers in liver cancer through integrated bioinformatics analysis |
title_full_unstemmed | Identification of modules and novel prognostic biomarkers in liver cancer through integrated bioinformatics analysis |
title_short | Identification of modules and novel prognostic biomarkers in liver cancer through integrated bioinformatics analysis |
title_sort | identification of modules and novel prognostic biomarkers in liver cancer through integrated bioinformatics analysis |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7609804/ https://www.ncbi.nlm.nih.gov/pubmed/32961635 http://dx.doi.org/10.1002/2211-5463.12983 |
work_keys_str_mv | AT shenbo identificationofmodulesandnovelprognosticbiomarkersinlivercancerthroughintegratedbioinformaticsanalysis AT likun identificationofmodulesandnovelprognosticbiomarkersinlivercancerthroughintegratedbioinformaticsanalysis AT zhangyuting identificationofmodulesandnovelprognosticbiomarkersinlivercancerthroughintegratedbioinformaticsanalysis |