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Mitophagy-related long non-coding RNA signature predicts prognosis and drug response in Ovarian Cancer

BACKGROUND: Ovarian cancer (OC) is the most malignant tumor with the worst prognosis in female reproductive system. Mitophagy and long non-coding RNAs (lncRNAs) play pivotal roles in tumorigenesis, development, and drug resistance. The effects of mitophagy-related lncRNAs on OC prognosis and therape...

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Autores principales: Wang, Jiao, Zhang, Xiaocui, Zheng, Fei, Yang, Qing, Bi, Fangfang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463594/
https://www.ncbi.nlm.nih.gov/pubmed/37633972
http://dx.doi.org/10.1186/s13048-023-01247-6
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author Wang, Jiao
Zhang, Xiaocui
Zheng, Fei
Yang, Qing
Bi, Fangfang
author_facet Wang, Jiao
Zhang, Xiaocui
Zheng, Fei
Yang, Qing
Bi, Fangfang
author_sort Wang, Jiao
collection PubMed
description BACKGROUND: Ovarian cancer (OC) is the most malignant tumor with the worst prognosis in female reproductive system. Mitophagy and long non-coding RNAs (lncRNAs) play pivotal roles in tumorigenesis, development, and drug resistance. The effects of mitophagy-related lncRNAs on OC prognosis and therapeutic response remain unelucidated. METHODS: We retrieved OC-related RNA sequence, copy number variation, somatic mutation, and clinicopathological information from The Cancer Genome Atlas database and mitophagy-related gene sets from the Reactome database. Pearson’s correlation analysis was used to distinguish mitophagy-related lncRNAs. A prognostic lncRNA signature was constructed using UniCox, LASSO, and forward stepwise regression analysis. Individuals with a risk score above or below the median were classified as high- or low-risk groups, respectively. The risk model was analyzed using the Kaplan–Meier estimator, receiver operating characteristic curve, decision curve analysis, and Cox regression analysis and validated using an internal dataset. LINC00174 was validated in clinical samples and OC cell lines. We also reviewed reports on the role of LINC00174 in cancer. Subsequently, a nomogram model was constructed. Furthermore, the Genomics of Drug Sensitivity in Cancer database was used to explore the relationship between the risk model and anti-tumor drug sensitivity. Gene set variation analysis was performed to assess potential differences in biological functions between the two groups. Finally, a lncRNA prognostic signature-related competing endogenous RNA (ceRNA) network was constructed. RESULTS: The prognostic signature showed that patients in the high-risk group had a poorer prognosis. The nomogram exhibited satisfactory accuracy and predictive potential. LINC00174 mainly acts as an oncogene in cancer and is upregulated in OC; its knockdown inhibited the proliferation and migration, and promoted apoptosis of OC cells. High-risk patients were more insensitive to cisplatin and olaparib than low-risk patients. The ceRNA network may help explore the potential regulatory mechanisms of lncRNAs. CONCLUSION: The mitophagy-related lncRNA signature can help estimate the survival and drug sensitivity, the ceRNA network may provide novel therapeutic targets for patients with OC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-023-01247-6.
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spelling pubmed-104635942023-08-30 Mitophagy-related long non-coding RNA signature predicts prognosis and drug response in Ovarian Cancer Wang, Jiao Zhang, Xiaocui Zheng, Fei Yang, Qing Bi, Fangfang J Ovarian Res Research BACKGROUND: Ovarian cancer (OC) is the most malignant tumor with the worst prognosis in female reproductive system. Mitophagy and long non-coding RNAs (lncRNAs) play pivotal roles in tumorigenesis, development, and drug resistance. The effects of mitophagy-related lncRNAs on OC prognosis and therapeutic response remain unelucidated. METHODS: We retrieved OC-related RNA sequence, copy number variation, somatic mutation, and clinicopathological information from The Cancer Genome Atlas database and mitophagy-related gene sets from the Reactome database. Pearson’s correlation analysis was used to distinguish mitophagy-related lncRNAs. A prognostic lncRNA signature was constructed using UniCox, LASSO, and forward stepwise regression analysis. Individuals with a risk score above or below the median were classified as high- or low-risk groups, respectively. The risk model was analyzed using the Kaplan–Meier estimator, receiver operating characteristic curve, decision curve analysis, and Cox regression analysis and validated using an internal dataset. LINC00174 was validated in clinical samples and OC cell lines. We also reviewed reports on the role of LINC00174 in cancer. Subsequently, a nomogram model was constructed. Furthermore, the Genomics of Drug Sensitivity in Cancer database was used to explore the relationship between the risk model and anti-tumor drug sensitivity. Gene set variation analysis was performed to assess potential differences in biological functions between the two groups. Finally, a lncRNA prognostic signature-related competing endogenous RNA (ceRNA) network was constructed. RESULTS: The prognostic signature showed that patients in the high-risk group had a poorer prognosis. The nomogram exhibited satisfactory accuracy and predictive potential. LINC00174 mainly acts as an oncogene in cancer and is upregulated in OC; its knockdown inhibited the proliferation and migration, and promoted apoptosis of OC cells. High-risk patients were more insensitive to cisplatin and olaparib than low-risk patients. The ceRNA network may help explore the potential regulatory mechanisms of lncRNAs. CONCLUSION: The mitophagy-related lncRNA signature can help estimate the survival and drug sensitivity, the ceRNA network may provide novel therapeutic targets for patients with OC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-023-01247-6. BioMed Central 2023-08-26 /pmc/articles/PMC10463594/ /pubmed/37633972 http://dx.doi.org/10.1186/s13048-023-01247-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Wang, Jiao
Zhang, Xiaocui
Zheng, Fei
Yang, Qing
Bi, Fangfang
Mitophagy-related long non-coding RNA signature predicts prognosis and drug response in Ovarian Cancer
title Mitophagy-related long non-coding RNA signature predicts prognosis and drug response in Ovarian Cancer
title_full Mitophagy-related long non-coding RNA signature predicts prognosis and drug response in Ovarian Cancer
title_fullStr Mitophagy-related long non-coding RNA signature predicts prognosis and drug response in Ovarian Cancer
title_full_unstemmed Mitophagy-related long non-coding RNA signature predicts prognosis and drug response in Ovarian Cancer
title_short Mitophagy-related long non-coding RNA signature predicts prognosis and drug response in Ovarian Cancer
title_sort mitophagy-related long non-coding rna signature predicts prognosis and drug response in ovarian cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463594/
https://www.ncbi.nlm.nih.gov/pubmed/37633972
http://dx.doi.org/10.1186/s13048-023-01247-6
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