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CircRNA-Based Cervical Cancer Prognosis Model, Immunological Validation and Drug Prediction
Background: Cervical cancer (CC) is a common cancer in female, which is associated with problems like poor prognosis. Circular RNA (circRNA) is a kind of competing endogenous RNA (ceRNA) that has an important role in regulating microRNA (miRNA) in many cancers. The regulatory mechanisms of CC immune...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689098/ https://www.ncbi.nlm.nih.gov/pubmed/36354693 http://dx.doi.org/10.3390/curroncol29110633 |
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author | Guo, Xu Chen, Sui Wang, Sihan Zhang, Hao Yin, Fanxing Guo, Panpan Zhang, Xiaoxu Liu, Xuesong Han, Yanshuo |
author_facet | Guo, Xu Chen, Sui Wang, Sihan Zhang, Hao Yin, Fanxing Guo, Panpan Zhang, Xiaoxu Liu, Xuesong Han, Yanshuo |
author_sort | Guo, Xu |
collection | PubMed |
description | Background: Cervical cancer (CC) is a common cancer in female, which is associated with problems like poor prognosis. Circular RNA (circRNA) is a kind of competing endogenous RNA (ceRNA) that has an important role in regulating microRNA (miRNA) in many cancers. The regulatory mechanisms of CC immune microenvironment and the transcriptome level remain to be fully explored. Methods: In this study, we constructed the ceRNA network through the interaction data and expression matrix of circRNA, miRNA and mRNA. Meanwhile, based on the gene expression matrix, CIBERSORT algorithm was used to reveal contents of tumor-infiltrating immune cells (TIICs). Then, we screened prognostic markers based on ceRNA network and immune infiltration and constructed two nomograms. In order to find immunological differences between the high- and low-risk CC samples, we examined multiple immune checkpoints and predicted the effect of PD-L1 ICI immunotherapy. In addition, the sensitive therapeutics for high-risk patients were screened, and the potential agents with anti-CC activity were predicted by Connective Map (CMap). Results: We mapped a ceRNA network including 5 circRNAs, 17 miRNAs and 129 mRNAs. From the mRNA nodes of the network six genes and two kind of cells were identified as prognostic makers for CC. Among them, there was a significant positive correlation between CD8+ T cells and SNX10 gene. The results of TIDE and single sample GSEA (ssGSEA) showed that T cells CD8 do play a key role in inhibiting tumor progression. Further, our study screened 24 drugs that were more sensitive to high-risk CC patients and several potential therapeutic agents for reference. Conclusions: Our study identified several circRNA-miRNA-mRNA regulatory axes and six prognostic genes based on the ceRNA network. In addition, through TIIC, survival analysis and a series of immunological analyses, T cells were proved to be good prognostic markers, besides play an important role in the immune process. Finally, we screened 24 potentially more effective drugs and multiple potential drug compounds for high- and low-risk patients. |
format | Online Article Text |
id | pubmed-9689098 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96890982022-11-25 CircRNA-Based Cervical Cancer Prognosis Model, Immunological Validation and Drug Prediction Guo, Xu Chen, Sui Wang, Sihan Zhang, Hao Yin, Fanxing Guo, Panpan Zhang, Xiaoxu Liu, Xuesong Han, Yanshuo Curr Oncol Article Background: Cervical cancer (CC) is a common cancer in female, which is associated with problems like poor prognosis. Circular RNA (circRNA) is a kind of competing endogenous RNA (ceRNA) that has an important role in regulating microRNA (miRNA) in many cancers. The regulatory mechanisms of CC immune microenvironment and the transcriptome level remain to be fully explored. Methods: In this study, we constructed the ceRNA network through the interaction data and expression matrix of circRNA, miRNA and mRNA. Meanwhile, based on the gene expression matrix, CIBERSORT algorithm was used to reveal contents of tumor-infiltrating immune cells (TIICs). Then, we screened prognostic markers based on ceRNA network and immune infiltration and constructed two nomograms. In order to find immunological differences between the high- and low-risk CC samples, we examined multiple immune checkpoints and predicted the effect of PD-L1 ICI immunotherapy. In addition, the sensitive therapeutics for high-risk patients were screened, and the potential agents with anti-CC activity were predicted by Connective Map (CMap). Results: We mapped a ceRNA network including 5 circRNAs, 17 miRNAs and 129 mRNAs. From the mRNA nodes of the network six genes and two kind of cells were identified as prognostic makers for CC. Among them, there was a significant positive correlation between CD8+ T cells and SNX10 gene. The results of TIDE and single sample GSEA (ssGSEA) showed that T cells CD8 do play a key role in inhibiting tumor progression. Further, our study screened 24 drugs that were more sensitive to high-risk CC patients and several potential therapeutic agents for reference. Conclusions: Our study identified several circRNA-miRNA-mRNA regulatory axes and six prognostic genes based on the ceRNA network. In addition, through TIIC, survival analysis and a series of immunological analyses, T cells were proved to be good prognostic markers, besides play an important role in the immune process. Finally, we screened 24 potentially more effective drugs and multiple potential drug compounds for high- and low-risk patients. MDPI 2022-10-25 /pmc/articles/PMC9689098/ /pubmed/36354693 http://dx.doi.org/10.3390/curroncol29110633 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Guo, Xu Chen, Sui Wang, Sihan Zhang, Hao Yin, Fanxing Guo, Panpan Zhang, Xiaoxu Liu, Xuesong Han, Yanshuo CircRNA-Based Cervical Cancer Prognosis Model, Immunological Validation and Drug Prediction |
title | CircRNA-Based Cervical Cancer Prognosis Model, Immunological Validation and Drug Prediction |
title_full | CircRNA-Based Cervical Cancer Prognosis Model, Immunological Validation and Drug Prediction |
title_fullStr | CircRNA-Based Cervical Cancer Prognosis Model, Immunological Validation and Drug Prediction |
title_full_unstemmed | CircRNA-Based Cervical Cancer Prognosis Model, Immunological Validation and Drug Prediction |
title_short | CircRNA-Based Cervical Cancer Prognosis Model, Immunological Validation and Drug Prediction |
title_sort | circrna-based cervical cancer prognosis model, immunological validation and drug prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689098/ https://www.ncbi.nlm.nih.gov/pubmed/36354693 http://dx.doi.org/10.3390/curroncol29110633 |
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