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Identifying glycolysis-related LncRNAs for predicting prognosis in breast cancer patients
PURPOSE: Functions associated with glycolysis could serve as targets or biomarkers for therapy cancer. Our purpose was to establish a prognostic model that could evaluate the importance of Glycolysis-related lncRNAs in breast cancer. METHODS: Gene expressions were evaluated for breast cancer through...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
IOS Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198763/ https://www.ncbi.nlm.nih.gov/pubmed/35068448 http://dx.doi.org/10.3233/CBM-210446 |
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author | Zou, Jiayue Gu, Yanlin Zhu, Qi Li, Xiaohua Qin, Lei |
author_facet | Zou, Jiayue Gu, Yanlin Zhu, Qi Li, Xiaohua Qin, Lei |
author_sort | Zou, Jiayue |
collection | PubMed |
description | PURPOSE: Functions associated with glycolysis could serve as targets or biomarkers for therapy cancer. Our purpose was to establish a prognostic model that could evaluate the importance of Glycolysis-related lncRNAs in breast cancer. METHODS: Gene expressions were evaluated for breast cancer through The Cancer Genome Atlas (TCGA) database, and we calculated Pearson correlations to discover potential related lncRNAs. Differentially expressed genes were identified via criteria of FDR [Formula: see text] 0.05 and [Formula: see text] FC [Formula: see text] [Formula: see text] 2. Total samples were separated into training and validating sets randomly. Univariate Cox regression identified 14 prognostic lncRNAs in training set. A prognostic model was constructed to evaluate the accuracy in predicting prognosis. The univariate and multivariate Cox analysis were performed to verify whether lncRNA signature could be an independent prognostic factor The signature was validated in validating set. Immune infiltration levels were assessed. RESULTS: Eighty-nine differentially expressed lncRNAs were identified from 420 Glycolysis-related lncRNAs. 14 lncRNAs were correlated with prognosis in training set and were selected to establish the prognostic model. Low risk group had better prognosis in both training ([Formula: see text] 9.025 e -10) and validating ([Formula: see text] 4.272 e -3) sets. The univariate and multivariate Cox analysis revealed that risk score of glycolysis-related lncRNAs ([Formula: see text] 0.001) was an independent prognostic factor in both training and validating sets. The neutrophils ([Formula: see text] 4.214 e -13, [Formula: see text] [Formula: see text] 0.223), CD4+ T cells ([Formula: see text] 1.833 e -20, [Formula: see text] [Formula: see text] 0.283), CD8+ T cells ([Formula: see text] 7.641 e -12, [Formula: see text] [Formula: see text] 0.211), B cells ([Formula: see text] 2.502 e -10, [Formula: see text] [Formula: see text] 0.195) and dendritic cells ([Formula: see text] 5.14 e -18, [Formula: see text] [Formula: see text] 0.265) were negatively correlated with risk score of prognostic model. The Macrophage ([Formula: see text] 0.016, [Formula: see text] 0.0755) was positively correlated with the risk score. CONCLUSION: Our study indicated that glycolysis-related lncRNAs had a significant role to facilitate the individualized survival prediction in breast cancer patients, which would be a potential therapeutic target. |
format | Online Article Text |
id | pubmed-9198763 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | IOS Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-91987632022-06-16 Identifying glycolysis-related LncRNAs for predicting prognosis in breast cancer patients Zou, Jiayue Gu, Yanlin Zhu, Qi Li, Xiaohua Qin, Lei Cancer Biomark Research Article PURPOSE: Functions associated with glycolysis could serve as targets or biomarkers for therapy cancer. Our purpose was to establish a prognostic model that could evaluate the importance of Glycolysis-related lncRNAs in breast cancer. METHODS: Gene expressions were evaluated for breast cancer through The Cancer Genome Atlas (TCGA) database, and we calculated Pearson correlations to discover potential related lncRNAs. Differentially expressed genes were identified via criteria of FDR [Formula: see text] 0.05 and [Formula: see text] FC [Formula: see text] [Formula: see text] 2. Total samples were separated into training and validating sets randomly. Univariate Cox regression identified 14 prognostic lncRNAs in training set. A prognostic model was constructed to evaluate the accuracy in predicting prognosis. The univariate and multivariate Cox analysis were performed to verify whether lncRNA signature could be an independent prognostic factor The signature was validated in validating set. Immune infiltration levels were assessed. RESULTS: Eighty-nine differentially expressed lncRNAs were identified from 420 Glycolysis-related lncRNAs. 14 lncRNAs were correlated with prognosis in training set and were selected to establish the prognostic model. Low risk group had better prognosis in both training ([Formula: see text] 9.025 e -10) and validating ([Formula: see text] 4.272 e -3) sets. The univariate and multivariate Cox analysis revealed that risk score of glycolysis-related lncRNAs ([Formula: see text] 0.001) was an independent prognostic factor in both training and validating sets. The neutrophils ([Formula: see text] 4.214 e -13, [Formula: see text] [Formula: see text] 0.223), CD4+ T cells ([Formula: see text] 1.833 e -20, [Formula: see text] [Formula: see text] 0.283), CD8+ T cells ([Formula: see text] 7.641 e -12, [Formula: see text] [Formula: see text] 0.211), B cells ([Formula: see text] 2.502 e -10, [Formula: see text] [Formula: see text] 0.195) and dendritic cells ([Formula: see text] 5.14 e -18, [Formula: see text] [Formula: see text] 0.265) were negatively correlated with risk score of prognostic model. The Macrophage ([Formula: see text] 0.016, [Formula: see text] 0.0755) was positively correlated with the risk score. CONCLUSION: Our study indicated that glycolysis-related lncRNAs had a significant role to facilitate the individualized survival prediction in breast cancer patients, which would be a potential therapeutic target. IOS Press 2022-05-13 /pmc/articles/PMC9198763/ /pubmed/35068448 http://dx.doi.org/10.3233/CBM-210446 Text en © 2022 – The authors. Published by IOS Press. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License (https://creativecommons.org/licenses/by-nc/4.0/) , which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zou, Jiayue Gu, Yanlin Zhu, Qi Li, Xiaohua Qin, Lei Identifying glycolysis-related LncRNAs for predicting prognosis in breast cancer patients |
title | Identifying glycolysis-related LncRNAs for predicting prognosis in breast cancer patients |
title_full | Identifying glycolysis-related LncRNAs for predicting prognosis in breast cancer patients |
title_fullStr | Identifying glycolysis-related LncRNAs for predicting prognosis in breast cancer patients |
title_full_unstemmed | Identifying glycolysis-related LncRNAs for predicting prognosis in breast cancer patients |
title_short | Identifying glycolysis-related LncRNAs for predicting prognosis in breast cancer patients |
title_sort | identifying glycolysis-related lncrnas for predicting prognosis in breast cancer patients |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198763/ https://www.ncbi.nlm.nih.gov/pubmed/35068448 http://dx.doi.org/10.3233/CBM-210446 |
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