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Identification of a Genomic Instability-Related Long Noncoding RNA Prognostic Model in Colorectal Cancer Based on Bioinformatic Analysis

BACKGROUND: In recent years, a growing body of research has revealed that long noncoding RNAs (lncRNAs) participate in regulating genomic instability. MATERIALS AND METHODS: We obtained RNA expression profiles, somatic mutation profiles, clinical information, and pathological features of colorectal...

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Autores principales: Liang, Yu, Sun, Hong-Xu, Ma, Bin, Meng, Qing-Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9197617/
https://www.ncbi.nlm.nih.gov/pubmed/35711569
http://dx.doi.org/10.1155/2022/4556585
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author Liang, Yu
Sun, Hong-Xu
Ma, Bin
Meng, Qing-Kai
author_facet Liang, Yu
Sun, Hong-Xu
Ma, Bin
Meng, Qing-Kai
author_sort Liang, Yu
collection PubMed
description BACKGROUND: In recent years, a growing body of research has revealed that long noncoding RNAs (lncRNAs) participate in regulating genomic instability. MATERIALS AND METHODS: We obtained RNA expression profiles, somatic mutation profiles, clinical information, and pathological features of colorectal cancer (CRC) from The Cancer Genome Atlas project. We divided the cohort into two groups based on mutation frequency and identified genomic instability-related lncRNAs (GI-lncRNAs) using R software. We further analyzed the function of identified GI-lncRNAs and established a prognostic model through Cox regression. Using the established prognostic model, we divided the cohort into the high- and low-risk groups and further verified the prognostic differences between the two groups as well as the predictive power of prognosis-related lncRNAs in the genomic instability of CRC. RESULTS: We identified a total of 143 GI-lncRNAs that were differentially expressed between the higher mutation frequency group and the lower mutation frequency group. According to Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analyses, a series of cancer-associated terms were enriched. We further constructed a prognostic model that included five GI-lncRNAs (lncRNA PTPRD-AS1, lncRNA AC009237.14, lncRNA LINC00543, lncRNA AP003555.1, and lncRNA AL109615.3). We confirmed that the expression of the five GI-lncRNAs was associated with prognosis and the mutation of critical genes in the CRC patient cohort. CONCLUSIONS: The present research further confirmed the vital function of GI-lncRNAs in the genomic instability of CRC. The five GI-lncRNAs identified in our study are potential biomarkers and need to be studied in more depth.
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spelling pubmed-91976172022-06-15 Identification of a Genomic Instability-Related Long Noncoding RNA Prognostic Model in Colorectal Cancer Based on Bioinformatic Analysis Liang, Yu Sun, Hong-Xu Ma, Bin Meng, Qing-Kai Dis Markers Research Article BACKGROUND: In recent years, a growing body of research has revealed that long noncoding RNAs (lncRNAs) participate in regulating genomic instability. MATERIALS AND METHODS: We obtained RNA expression profiles, somatic mutation profiles, clinical information, and pathological features of colorectal cancer (CRC) from The Cancer Genome Atlas project. We divided the cohort into two groups based on mutation frequency and identified genomic instability-related lncRNAs (GI-lncRNAs) using R software. We further analyzed the function of identified GI-lncRNAs and established a prognostic model through Cox regression. Using the established prognostic model, we divided the cohort into the high- and low-risk groups and further verified the prognostic differences between the two groups as well as the predictive power of prognosis-related lncRNAs in the genomic instability of CRC. RESULTS: We identified a total of 143 GI-lncRNAs that were differentially expressed between the higher mutation frequency group and the lower mutation frequency group. According to Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analyses, a series of cancer-associated terms were enriched. We further constructed a prognostic model that included five GI-lncRNAs (lncRNA PTPRD-AS1, lncRNA AC009237.14, lncRNA LINC00543, lncRNA AP003555.1, and lncRNA AL109615.3). We confirmed that the expression of the five GI-lncRNAs was associated with prognosis and the mutation of critical genes in the CRC patient cohort. CONCLUSIONS: The present research further confirmed the vital function of GI-lncRNAs in the genomic instability of CRC. The five GI-lncRNAs identified in our study are potential biomarkers and need to be studied in more depth. Hindawi 2022-06-07 /pmc/articles/PMC9197617/ /pubmed/35711569 http://dx.doi.org/10.1155/2022/4556585 Text en Copyright © 2022 Yu Liang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liang, Yu
Sun, Hong-Xu
Ma, Bin
Meng, Qing-Kai
Identification of a Genomic Instability-Related Long Noncoding RNA Prognostic Model in Colorectal Cancer Based on Bioinformatic Analysis
title Identification of a Genomic Instability-Related Long Noncoding RNA Prognostic Model in Colorectal Cancer Based on Bioinformatic Analysis
title_full Identification of a Genomic Instability-Related Long Noncoding RNA Prognostic Model in Colorectal Cancer Based on Bioinformatic Analysis
title_fullStr Identification of a Genomic Instability-Related Long Noncoding RNA Prognostic Model in Colorectal Cancer Based on Bioinformatic Analysis
title_full_unstemmed Identification of a Genomic Instability-Related Long Noncoding RNA Prognostic Model in Colorectal Cancer Based on Bioinformatic Analysis
title_short Identification of a Genomic Instability-Related Long Noncoding RNA Prognostic Model in Colorectal Cancer Based on Bioinformatic Analysis
title_sort identification of a genomic instability-related long noncoding rna prognostic model in colorectal cancer based on bioinformatic analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9197617/
https://www.ncbi.nlm.nih.gov/pubmed/35711569
http://dx.doi.org/10.1155/2022/4556585
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