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A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer
BACKGROUND: Ovarian cancer (OC) is the most fatal gynaecological malignancy and has a poor prognosis. Glycosylation, the biosynthetic process that depends on specific glycosyltransferases (GTs), has recently attracted increasing importance due to the vital role it plays in cancer. In this study, we...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9826597/ https://www.ncbi.nlm.nih.gov/pubmed/36611197 http://dx.doi.org/10.1186/s13048-022-01088-9 |
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author | Xu, Xuyao Wu, Yue Jia, Genmei Zhu, Qiaoying Li, Dake Xie, Kaipeng |
author_facet | Xu, Xuyao Wu, Yue Jia, Genmei Zhu, Qiaoying Li, Dake Xie, Kaipeng |
author_sort | Xu, Xuyao |
collection | PubMed |
description | BACKGROUND: Ovarian cancer (OC) is the most fatal gynaecological malignancy and has a poor prognosis. Glycosylation, the biosynthetic process that depends on specific glycosyltransferases (GTs), has recently attracted increasing importance due to the vital role it plays in cancer. In this study, we aimed to determine whether OC patients could be stratified by glycosyltransferase gene profiles to better predict the prognosis and efficiency of immune checkpoint blockade therapies (ICBs). METHODS: We retrieved transcriptome data across 420 OC and 88 normal tissue samples using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, respectively. An external validation dataset containing 185 OC samples was downloaded from the Gene Expression Omnibus (GEO) database. Knockdown and pathway prediction of B4GALT5 were conducted to investigate the function and mechanism of B4GALT5 in OC proliferation, migration and invasion. RESULTS: A total of 50 differentially expressed GT genes were identified between OC and normal ovarian tissues. Two clusters were stratified by operating consensus clustering, but no significant prognostic value was observed. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, a 6-gene signature was built that classified OC patients in the TCGA cohort into a low- or high-risk group. Patients with high scores had a worse prognosis than those with low scores. This risk signature was further validated in an external GEO dataset. Furthermore, the risk score was an independent risk predictor, and a nomogram was created to improve the accuracy of prognostic classification. Notably, the low-risk OC patients exhibited a higher degree of antitumor immune cell infiltration and a superior response to ICBs. B4GALT5, one of six hub genes, was identified as a regulator of proliferation, migration and invasion in OC. CONCLUSION: Taken together, we established a reliable GT-gene-based signature to predict prognosis, immune status and identify OC patients who would benefit from ICBs. GT genes might be a promising biomarker for OC progression and a potential therapeutic target for OC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-022-01088-9. |
format | Online Article Text |
id | pubmed-9826597 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-98265972023-01-09 A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer Xu, Xuyao Wu, Yue Jia, Genmei Zhu, Qiaoying Li, Dake Xie, Kaipeng J Ovarian Res Research BACKGROUND: Ovarian cancer (OC) is the most fatal gynaecological malignancy and has a poor prognosis. Glycosylation, the biosynthetic process that depends on specific glycosyltransferases (GTs), has recently attracted increasing importance due to the vital role it plays in cancer. In this study, we aimed to determine whether OC patients could be stratified by glycosyltransferase gene profiles to better predict the prognosis and efficiency of immune checkpoint blockade therapies (ICBs). METHODS: We retrieved transcriptome data across 420 OC and 88 normal tissue samples using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, respectively. An external validation dataset containing 185 OC samples was downloaded from the Gene Expression Omnibus (GEO) database. Knockdown and pathway prediction of B4GALT5 were conducted to investigate the function and mechanism of B4GALT5 in OC proliferation, migration and invasion. RESULTS: A total of 50 differentially expressed GT genes were identified between OC and normal ovarian tissues. Two clusters were stratified by operating consensus clustering, but no significant prognostic value was observed. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, a 6-gene signature was built that classified OC patients in the TCGA cohort into a low- or high-risk group. Patients with high scores had a worse prognosis than those with low scores. This risk signature was further validated in an external GEO dataset. Furthermore, the risk score was an independent risk predictor, and a nomogram was created to improve the accuracy of prognostic classification. Notably, the low-risk OC patients exhibited a higher degree of antitumor immune cell infiltration and a superior response to ICBs. B4GALT5, one of six hub genes, was identified as a regulator of proliferation, migration and invasion in OC. CONCLUSION: Taken together, we established a reliable GT-gene-based signature to predict prognosis, immune status and identify OC patients who would benefit from ICBs. GT genes might be a promising biomarker for OC progression and a potential therapeutic target for OC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-022-01088-9. BioMed Central 2023-01-07 /pmc/articles/PMC9826597/ /pubmed/36611197 http://dx.doi.org/10.1186/s13048-022-01088-9 Text en © The Author(s) 2023 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 Xu, Xuyao Wu, Yue Jia, Genmei Zhu, Qiaoying Li, Dake Xie, Kaipeng A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer |
title | A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer |
title_full | A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer |
title_fullStr | A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer |
title_full_unstemmed | A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer |
title_short | A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer |
title_sort | signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9826597/ https://www.ncbi.nlm.nih.gov/pubmed/36611197 http://dx.doi.org/10.1186/s13048-022-01088-9 |
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