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Landscape of PCOS co-expression gene and its role in predicting prognosis and assisting immunotherapy in endometrial cancer

BACKGROUND: Endometrial carcinoma (EC) is the sixth most frequent malignancy in women and is often linked to high estrogen exposure. Polycystic ovarian syndrome (PCOS) is a known risk factor for EC, but the underlying mechanisms remain unclear. METHODS: We investigated shared gene signals and potent...

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Autores principales: Zhang, Yun, Hu, Yifang, Yu, Jian, Xie, Xiaoyan, Jiang, Feng, Wu, Chuyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10315044/
https://www.ncbi.nlm.nih.gov/pubmed/37393293
http://dx.doi.org/10.1186/s13048-023-01201-6
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author Zhang, Yun
Hu, Yifang
Yu, Jian
Xie, Xiaoyan
Jiang, Feng
Wu, Chuyan
author_facet Zhang, Yun
Hu, Yifang
Yu, Jian
Xie, Xiaoyan
Jiang, Feng
Wu, Chuyan
author_sort Zhang, Yun
collection PubMed
description BACKGROUND: Endometrial carcinoma (EC) is the sixth most frequent malignancy in women and is often linked to high estrogen exposure. Polycystic ovarian syndrome (PCOS) is a known risk factor for EC, but the underlying mechanisms remain unclear. METHODS: We investigated shared gene signals and potential biological pathways to identify effective therapy options for PCOS- and EC-related malignancies. Weighted gene expression network analysis (WGCNA) was used to identify genes associated with PCOS and EC using gene expression data from the Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) datasets. Enrichment analysis using Cluego software revealed that the steroid hormone biosynthetic process was a critical feature in both PCOS and EC. A predictive signature encompassing genes involved in steroid hormone production was developed using multivariate and least absolute shrinkage and selection operator (LASSO) regression analysis to predict the prognosis of EC. Then, we conducted further experimental verification. RESULTS: Patients in the TCGA cohort with high predictive scores had poorer outcomes than those with low scores. We also investigated the relationship between tumor microenvironment (TME) features and predictive risk rating and found that patients with low-risk scores had higher levels of inflammatory and inhibitory immune cells. Also, we found that immunotherapy against anti-CTLA4 and anti-PD-1/PD-L1 was successful in treating individuals with low risk. Low-risk individuals were more responsive to crizotinib therapy, according to further research performed using the “pRRophetic” R package. We further confirmed that IGF2 expression was associated with tumor cell migration, proliferation, and invasion in EC cells. CONCLUTIONS: By uncovering the pathways and genes linking PCOS and EC, our findings may provide new therapeutic strategies for patients with PCOS-related EC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-023-01201-6.
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spelling pubmed-103150442023-07-03 Landscape of PCOS co-expression gene and its role in predicting prognosis and assisting immunotherapy in endometrial cancer Zhang, Yun Hu, Yifang Yu, Jian Xie, Xiaoyan Jiang, Feng Wu, Chuyan J Ovarian Res Research BACKGROUND: Endometrial carcinoma (EC) is the sixth most frequent malignancy in women and is often linked to high estrogen exposure. Polycystic ovarian syndrome (PCOS) is a known risk factor for EC, but the underlying mechanisms remain unclear. METHODS: We investigated shared gene signals and potential biological pathways to identify effective therapy options for PCOS- and EC-related malignancies. Weighted gene expression network analysis (WGCNA) was used to identify genes associated with PCOS and EC using gene expression data from the Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) datasets. Enrichment analysis using Cluego software revealed that the steroid hormone biosynthetic process was a critical feature in both PCOS and EC. A predictive signature encompassing genes involved in steroid hormone production was developed using multivariate and least absolute shrinkage and selection operator (LASSO) regression analysis to predict the prognosis of EC. Then, we conducted further experimental verification. RESULTS: Patients in the TCGA cohort with high predictive scores had poorer outcomes than those with low scores. We also investigated the relationship between tumor microenvironment (TME) features and predictive risk rating and found that patients with low-risk scores had higher levels of inflammatory and inhibitory immune cells. Also, we found that immunotherapy against anti-CTLA4 and anti-PD-1/PD-L1 was successful in treating individuals with low risk. Low-risk individuals were more responsive to crizotinib therapy, according to further research performed using the “pRRophetic” R package. We further confirmed that IGF2 expression was associated with tumor cell migration, proliferation, and invasion in EC cells. CONCLUTIONS: By uncovering the pathways and genes linking PCOS and EC, our findings may provide new therapeutic strategies for patients with PCOS-related EC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-023-01201-6. BioMed Central 2023-07-01 /pmc/articles/PMC10315044/ /pubmed/37393293 http://dx.doi.org/10.1186/s13048-023-01201-6 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
Zhang, Yun
Hu, Yifang
Yu, Jian
Xie, Xiaoyan
Jiang, Feng
Wu, Chuyan
Landscape of PCOS co-expression gene and its role in predicting prognosis and assisting immunotherapy in endometrial cancer
title Landscape of PCOS co-expression gene and its role in predicting prognosis and assisting immunotherapy in endometrial cancer
title_full Landscape of PCOS co-expression gene and its role in predicting prognosis and assisting immunotherapy in endometrial cancer
title_fullStr Landscape of PCOS co-expression gene and its role in predicting prognosis and assisting immunotherapy in endometrial cancer
title_full_unstemmed Landscape of PCOS co-expression gene and its role in predicting prognosis and assisting immunotherapy in endometrial cancer
title_short Landscape of PCOS co-expression gene and its role in predicting prognosis and assisting immunotherapy in endometrial cancer
title_sort landscape of pcos co-expression gene and its role in predicting prognosis and assisting immunotherapy in endometrial cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10315044/
https://www.ncbi.nlm.nih.gov/pubmed/37393293
http://dx.doi.org/10.1186/s13048-023-01201-6
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