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Identification of LINC00654-NINL Regulatory Axis in Diffuse Large B-Cell Lymphoma In Silico Analysis
BACKGROUND: The long non-coding RNA (lncRNA)-mRNA regulation network plays an important role in the development of diffuse large B-cell lymphoma (DLBCL). This study uses bioinformatics to find an innovative regulation axis in DLBCL that will provide a positive reference for defining the mechanism of...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204339/ https://www.ncbi.nlm.nih.gov/pubmed/35719990 http://dx.doi.org/10.3389/fonc.2022.883301 |
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author | Chen, Yinchu Li, Chen Wang, Nana Wu, Zhenghao Zhang, Jin Yan, Jiawei Wei, Yuanfeng Peng, Qunlong Qi, Jing |
author_facet | Chen, Yinchu Li, Chen Wang, Nana Wu, Zhenghao Zhang, Jin Yan, Jiawei Wei, Yuanfeng Peng, Qunlong Qi, Jing |
author_sort | Chen, Yinchu |
collection | PubMed |
description | BACKGROUND: The long non-coding RNA (lncRNA)-mRNA regulation network plays an important role in the development of diffuse large B-cell lymphoma (DLBCL). This study uses bioinformatics to find an innovative regulation axis in DLBCL that will provide a positive reference for defining the mechanism of disease progression. METHODS: Batch Cox regression was used to screen prognosis-related lncRNAs, and a random forest model was used to identify hub lncRNA. The clinical value of the lncRNA was evaluated and Spearman correlation analysis was used to predict the candidate target genes. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were used to define the biological function of the lncRNA. A batch Cox regression model, expression validation, and Spearman correlation analysis were used to select the best downstream target genes. The expression and prognostic value validation of this gene was conducted using public data. Gene Set Enrichment Analysis (GSEA) was performed to explore potential mechanisms for this gene in DLBCL. RESULTS: LINC00654 was identified as the hub lncRNA and 1443 mRNAs were selected as downstream target genes of the lncRNA. The target genes were enriched in the regulation of GTPase and Notch signaling pathways. After validation, the ninein-like (NINL) gene was selected as the potential target of LINC00654 and the LINC00654-NINL axis was constructed. Patients with better responses to therapy were shown to have high NINL gene expression (p-value = 0.036). NINL also had high expression in the DB cell line and low expression in the OCILY3 cell line. Survival analysis showed that high NINL expression was a risk factor for overall survival (OS) and disease-specific survival (DSS) within older patients and those with advanced-stage cancer. GSEA results showed that NINL may be involved in neutrophil-mediated immunity and NF-κB signaling. CONCLUSION: This study identified a novel LncRNA00654-NINL regulatory axis in DLBCL, which could provide a favorable reference for exploring the possible mechanisms of disease progression. |
format | Online Article Text |
id | pubmed-9204339 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92043392022-06-18 Identification of LINC00654-NINL Regulatory Axis in Diffuse Large B-Cell Lymphoma In Silico Analysis Chen, Yinchu Li, Chen Wang, Nana Wu, Zhenghao Zhang, Jin Yan, Jiawei Wei, Yuanfeng Peng, Qunlong Qi, Jing Front Oncol Oncology BACKGROUND: The long non-coding RNA (lncRNA)-mRNA regulation network plays an important role in the development of diffuse large B-cell lymphoma (DLBCL). This study uses bioinformatics to find an innovative regulation axis in DLBCL that will provide a positive reference for defining the mechanism of disease progression. METHODS: Batch Cox regression was used to screen prognosis-related lncRNAs, and a random forest model was used to identify hub lncRNA. The clinical value of the lncRNA was evaluated and Spearman correlation analysis was used to predict the candidate target genes. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were used to define the biological function of the lncRNA. A batch Cox regression model, expression validation, and Spearman correlation analysis were used to select the best downstream target genes. The expression and prognostic value validation of this gene was conducted using public data. Gene Set Enrichment Analysis (GSEA) was performed to explore potential mechanisms for this gene in DLBCL. RESULTS: LINC00654 was identified as the hub lncRNA and 1443 mRNAs were selected as downstream target genes of the lncRNA. The target genes were enriched in the regulation of GTPase and Notch signaling pathways. After validation, the ninein-like (NINL) gene was selected as the potential target of LINC00654 and the LINC00654-NINL axis was constructed. Patients with better responses to therapy were shown to have high NINL gene expression (p-value = 0.036). NINL also had high expression in the DB cell line and low expression in the OCILY3 cell line. Survival analysis showed that high NINL expression was a risk factor for overall survival (OS) and disease-specific survival (DSS) within older patients and those with advanced-stage cancer. GSEA results showed that NINL may be involved in neutrophil-mediated immunity and NF-κB signaling. CONCLUSION: This study identified a novel LncRNA00654-NINL regulatory axis in DLBCL, which could provide a favorable reference for exploring the possible mechanisms of disease progression. Frontiers Media S.A. 2022-05-26 /pmc/articles/PMC9204339/ /pubmed/35719990 http://dx.doi.org/10.3389/fonc.2022.883301 Text en Copyright © 2022 Chen, Li, Wang, Wu, Zhang, Yan, Wei, Peng and Qi https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Chen, Yinchu Li, Chen Wang, Nana Wu, Zhenghao Zhang, Jin Yan, Jiawei Wei, Yuanfeng Peng, Qunlong Qi, Jing Identification of LINC00654-NINL Regulatory Axis in Diffuse Large B-Cell Lymphoma In Silico Analysis |
title | Identification of LINC00654-NINL Regulatory Axis in Diffuse Large B-Cell Lymphoma In Silico Analysis |
title_full | Identification of LINC00654-NINL Regulatory Axis in Diffuse Large B-Cell Lymphoma In Silico Analysis |
title_fullStr | Identification of LINC00654-NINL Regulatory Axis in Diffuse Large B-Cell Lymphoma In Silico Analysis |
title_full_unstemmed | Identification of LINC00654-NINL Regulatory Axis in Diffuse Large B-Cell Lymphoma In Silico Analysis |
title_short | Identification of LINC00654-NINL Regulatory Axis in Diffuse Large B-Cell Lymphoma In Silico Analysis |
title_sort | identification of linc00654-ninl regulatory axis in diffuse large b-cell lymphoma in silico analysis |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204339/ https://www.ncbi.nlm.nih.gov/pubmed/35719990 http://dx.doi.org/10.3389/fonc.2022.883301 |
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