Cargando…
Comprehensive analyses of glycolysis-related lncRNAs for ovarian cancer patients
BACKGROUND: Not only glycolysis but also lncRNAs play a significant role in the growth, proliferation, invasion and metastasis of of ovarian cancer (OC). However, researches about glycolysis -related lncRNAs (GRLs) remain unclear in OC. Herein, we first constructed a GRL-based risk model for patient...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8464158/ https://www.ncbi.nlm.nih.gov/pubmed/34560889 http://dx.doi.org/10.1186/s13048-021-00881-2 |
_version_ | 1784572564277821440 |
---|---|
author | Zheng, Jianfeng Guo, Jialu Zhu, Linling Zhou, Ying Tong, Jinyi |
author_facet | Zheng, Jianfeng Guo, Jialu Zhu, Linling Zhou, Ying Tong, Jinyi |
author_sort | Zheng, Jianfeng |
collection | PubMed |
description | BACKGROUND: Not only glycolysis but also lncRNAs play a significant role in the growth, proliferation, invasion and metastasis of of ovarian cancer (OC). However, researches about glycolysis -related lncRNAs (GRLs) remain unclear in OC. Herein, we first constructed a GRL-based risk model for patients with OC. METHODS: The processed RNA sequencing (RNA-seq) profiles with clinicopathological data were downloaded from TCGA and glycolysis-related genes (GRGs) were obtained from MSigDB. Pearson correlation coefficient between glycolysis-related genes (GRGs) and annotated lncRNAs (|r| > 0.4 and p < 0.05) were calculated to identify GRLs. After screening prognostic GRLs, a risk model based on five GRLs was constructed using Univariate and Cox regression. The identified risk model was validated by two validation sets. Further, the differences in clinicopathology, biological function, hypoxia score, immune microenvironment, immune checkpoint, immune checkpoint blockade, chemotherapy drug sensitivity, N6-methyladenosine (m6A) regulators, and ferroptosis-related genes between risk groups were explored by abundant algorithms. Finally, we established networks based on co-expression, ceRNA, cis and trans interaction. RESULTS: A total of 535 GRLs were gained and 35 GRLs with significant prognostic value were identified. The prognostic signature containing five GRLs was constructed and validated and can predict prognosis. The nomogram proved the accuracy of the model for predicting prognosis. After computing hypoxia score of each sample by ssGSEA, we found patients with higher risk scores exhibited higher hypoxia score and high hypoxia score was a risk factor. It was revealed that a total of 21 microenvironment cells (such as Central memory CD4 T cell, Neutrophil, Regulatory T cell and so on) and Stromal score had significant differences between the two groups. Four immune checkpoint genes (CD274, LAG3, VTCN1, and CD47) showed disparate expression levels in the two groups. Besides, 16 m6A regulators and 126 ferroptosis-related genes were expressed higher in the low-risk group. GSEA revealed that the risk groups were associated with tumor-related pathways. The two risk groups were confirmed to be sensitive to several chemotherapeutic agents and patients in the low-risk group were more sensitive to ICB therapy. The networks based on co-expression, ceRNA, cis and trans interaction provided insights into the regulatory mechanisms of GRLs. CONCLUSIONS: Our identified and validated risk model based on five GRLs is an independent prognostic factor for OC patients. Through comprehensive analyses, findings of our study uncovered potential biomarker and therapeutic target for the risk model based on the GRLs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-021-00881-2. |
format | Online Article Text |
id | pubmed-8464158 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84641582021-09-27 Comprehensive analyses of glycolysis-related lncRNAs for ovarian cancer patients Zheng, Jianfeng Guo, Jialu Zhu, Linling Zhou, Ying Tong, Jinyi J Ovarian Res Research BACKGROUND: Not only glycolysis but also lncRNAs play a significant role in the growth, proliferation, invasion and metastasis of of ovarian cancer (OC). However, researches about glycolysis -related lncRNAs (GRLs) remain unclear in OC. Herein, we first constructed a GRL-based risk model for patients with OC. METHODS: The processed RNA sequencing (RNA-seq) profiles with clinicopathological data were downloaded from TCGA and glycolysis-related genes (GRGs) were obtained from MSigDB. Pearson correlation coefficient between glycolysis-related genes (GRGs) and annotated lncRNAs (|r| > 0.4 and p < 0.05) were calculated to identify GRLs. After screening prognostic GRLs, a risk model based on five GRLs was constructed using Univariate and Cox regression. The identified risk model was validated by two validation sets. Further, the differences in clinicopathology, biological function, hypoxia score, immune microenvironment, immune checkpoint, immune checkpoint blockade, chemotherapy drug sensitivity, N6-methyladenosine (m6A) regulators, and ferroptosis-related genes between risk groups were explored by abundant algorithms. Finally, we established networks based on co-expression, ceRNA, cis and trans interaction. RESULTS: A total of 535 GRLs were gained and 35 GRLs with significant prognostic value were identified. The prognostic signature containing five GRLs was constructed and validated and can predict prognosis. The nomogram proved the accuracy of the model for predicting prognosis. After computing hypoxia score of each sample by ssGSEA, we found patients with higher risk scores exhibited higher hypoxia score and high hypoxia score was a risk factor. It was revealed that a total of 21 microenvironment cells (such as Central memory CD4 T cell, Neutrophil, Regulatory T cell and so on) and Stromal score had significant differences between the two groups. Four immune checkpoint genes (CD274, LAG3, VTCN1, and CD47) showed disparate expression levels in the two groups. Besides, 16 m6A regulators and 126 ferroptosis-related genes were expressed higher in the low-risk group. GSEA revealed that the risk groups were associated with tumor-related pathways. The two risk groups were confirmed to be sensitive to several chemotherapeutic agents and patients in the low-risk group were more sensitive to ICB therapy. The networks based on co-expression, ceRNA, cis and trans interaction provided insights into the regulatory mechanisms of GRLs. CONCLUSIONS: Our identified and validated risk model based on five GRLs is an independent prognostic factor for OC patients. Through comprehensive analyses, findings of our study uncovered potential biomarker and therapeutic target for the risk model based on the GRLs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-021-00881-2. BioMed Central 2021-09-24 /pmc/articles/PMC8464158/ /pubmed/34560889 http://dx.doi.org/10.1186/s13048-021-00881-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zheng, Jianfeng Guo, Jialu Zhu, Linling Zhou, Ying Tong, Jinyi Comprehensive analyses of glycolysis-related lncRNAs for ovarian cancer patients |
title | Comprehensive analyses of glycolysis-related lncRNAs for ovarian cancer patients |
title_full | Comprehensive analyses of glycolysis-related lncRNAs for ovarian cancer patients |
title_fullStr | Comprehensive analyses of glycolysis-related lncRNAs for ovarian cancer patients |
title_full_unstemmed | Comprehensive analyses of glycolysis-related lncRNAs for ovarian cancer patients |
title_short | Comprehensive analyses of glycolysis-related lncRNAs for ovarian cancer patients |
title_sort | comprehensive analyses of glycolysis-related lncrnas for ovarian cancer patients |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8464158/ https://www.ncbi.nlm.nih.gov/pubmed/34560889 http://dx.doi.org/10.1186/s13048-021-00881-2 |
work_keys_str_mv | AT zhengjianfeng comprehensiveanalysesofglycolysisrelatedlncrnasforovariancancerpatients AT guojialu comprehensiveanalysesofglycolysisrelatedlncrnasforovariancancerpatients AT zhulinling comprehensiveanalysesofglycolysisrelatedlncrnasforovariancancerpatients AT zhouying comprehensiveanalysesofglycolysisrelatedlncrnasforovariancancerpatients AT tongjinyi comprehensiveanalysesofglycolysisrelatedlncrnasforovariancancerpatients |