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Profiles of prognostic alternative splicing signature in hepatocellular carcinoma
Previous studies have demonstrated the role of abnormal alternative splicing (AS) in tumor progression. This study examines the prognostic index (PI) of alternative splices (ASs) in patients with hepatocellular carcinoma (HCC). The clinical features and splicing events of patients with HCC were down...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064038/ https://www.ncbi.nlm.nih.gov/pubmed/31975560 http://dx.doi.org/10.1002/cam4.2875 |
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author | Wu, Fangming Chen, Qifeng Liu, Chaojun Duan, Xiaoran Hu, Jinlong Liu, Jian Cao, Huicun Li, Wang Li, Hui |
author_facet | Wu, Fangming Chen, Qifeng Liu, Chaojun Duan, Xiaoran Hu, Jinlong Liu, Jian Cao, Huicun Li, Wang Li, Hui |
author_sort | Wu, Fangming |
collection | PubMed |
description | Previous studies have demonstrated the role of abnormal alternative splicing (AS) in tumor progression. This study examines the prognostic index (PI) of alternative splices (ASs) in patients with hepatocellular carcinoma (HCC). The clinical features and splicing events of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed AS (DEAS) were compared between HCC and adjacent normal samples. Univariate Cox regression analysis was used to determine changes in DEAS associated with overall survival (OS). A PI was generated from OS‐associated DEASs using Kaplan‐Meier curves, receiver operating characteristic (ROC) curves, multivariate Cox regression, and cluster analysis. Then, the correlation between DEASs and splicing factors was assessed, followed by functional and pathway enrichment analysis. We identified 34 163 ASs of 8985 genes in HCC, and 153 OS‐ASs were identified using univariate Cox regression analysis. Low‐ and high‐PI groups were determined based on the median “PI‐ALL” value according to significantly different survival (P = 2.2e − 16). The ROC curve of all PI (PI‐ALL) had an area under the curve (AUC) of 0.993 for survival status in patients with HCC. A potential regulatory network associated with prognosis of patients with HCC was established. Enrichment analysis also resulted in the identification of several pathways potentially associated with carcinogenesis and progression of HCC. Four clusters were identified that were associated with clinical features and prognosis. Our study generated comprehensive profiles of ASs in HCC. The interaction network and functional connections were used to elucidate the underlying mechanisms of AS in HCC. |
format | Online Article Text |
id | pubmed-7064038 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-70640382020-03-16 Profiles of prognostic alternative splicing signature in hepatocellular carcinoma Wu, Fangming Chen, Qifeng Liu, Chaojun Duan, Xiaoran Hu, Jinlong Liu, Jian Cao, Huicun Li, Wang Li, Hui Cancer Med Cancer Biology Previous studies have demonstrated the role of abnormal alternative splicing (AS) in tumor progression. This study examines the prognostic index (PI) of alternative splices (ASs) in patients with hepatocellular carcinoma (HCC). The clinical features and splicing events of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed AS (DEAS) were compared between HCC and adjacent normal samples. Univariate Cox regression analysis was used to determine changes in DEAS associated with overall survival (OS). A PI was generated from OS‐associated DEASs using Kaplan‐Meier curves, receiver operating characteristic (ROC) curves, multivariate Cox regression, and cluster analysis. Then, the correlation between DEASs and splicing factors was assessed, followed by functional and pathway enrichment analysis. We identified 34 163 ASs of 8985 genes in HCC, and 153 OS‐ASs were identified using univariate Cox regression analysis. Low‐ and high‐PI groups were determined based on the median “PI‐ALL” value according to significantly different survival (P = 2.2e − 16). The ROC curve of all PI (PI‐ALL) had an area under the curve (AUC) of 0.993 for survival status in patients with HCC. A potential regulatory network associated with prognosis of patients with HCC was established. Enrichment analysis also resulted in the identification of several pathways potentially associated with carcinogenesis and progression of HCC. Four clusters were identified that were associated with clinical features and prognosis. Our study generated comprehensive profiles of ASs in HCC. The interaction network and functional connections were used to elucidate the underlying mechanisms of AS in HCC. John Wiley and Sons Inc. 2020-01-24 /pmc/articles/PMC7064038/ /pubmed/31975560 http://dx.doi.org/10.1002/cam4.2875 Text en © 2020 The Authors. Cancer Medicine published by 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 | Cancer Biology Wu, Fangming Chen, Qifeng Liu, Chaojun Duan, Xiaoran Hu, Jinlong Liu, Jian Cao, Huicun Li, Wang Li, Hui Profiles of prognostic alternative splicing signature in hepatocellular carcinoma |
title | Profiles of prognostic alternative splicing signature in hepatocellular carcinoma |
title_full | Profiles of prognostic alternative splicing signature in hepatocellular carcinoma |
title_fullStr | Profiles of prognostic alternative splicing signature in hepatocellular carcinoma |
title_full_unstemmed | Profiles of prognostic alternative splicing signature in hepatocellular carcinoma |
title_short | Profiles of prognostic alternative splicing signature in hepatocellular carcinoma |
title_sort | profiles of prognostic alternative splicing signature in hepatocellular carcinoma |
topic | Cancer Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064038/ https://www.ncbi.nlm.nih.gov/pubmed/31975560 http://dx.doi.org/10.1002/cam4.2875 |
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