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Assessing of programmed cell death gene signature for predicting ovarian cancer prognosis and treatment response

BACKGROUND: Programmed cell death (PCD) is an overwhelming factor affecting tumor cell metastasis, but the mechanism of PCD in ovarian cancer (OV) is still uncertain. METHODS: To define the molecular subtypes of OV, we performed unsupervised clustering based on the expression level of prognosis rela...

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Autores principales: Lian, Xin, Liu, Bing, Wang, Caixia, Wang, Shuang, Zhuang, Yuan, Li, Xiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10277615/
https://www.ncbi.nlm.nih.gov/pubmed/37342266
http://dx.doi.org/10.3389/fendo.2023.1182776
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author Lian, Xin
Liu, Bing
Wang, Caixia
Wang, Shuang
Zhuang, Yuan
Li, Xiao
author_facet Lian, Xin
Liu, Bing
Wang, Caixia
Wang, Shuang
Zhuang, Yuan
Li, Xiao
author_sort Lian, Xin
collection PubMed
description BACKGROUND: Programmed cell death (PCD) is an overwhelming factor affecting tumor cell metastasis, but the mechanism of PCD in ovarian cancer (OV) is still uncertain. METHODS: To define the molecular subtypes of OV, we performed unsupervised clustering based on the expression level of prognosis related PCD genes in the Cancer Genome Atlas (TCGA)-OV. COX and least absolute shrinkage and selection operator (LASSO) COX analysis were used to identify the OV prognostic related PCD genes, and the genes identified according to the minimum Akaike information criterion (AIC) were the OV prognostic characteristic genes. According to the regression coefficient in the multivariate COX analysis and gene expression data, the Risk Score of OV prognosis was constructed. Kaplan-Meier analysis was conducted to assess the prognostic status of OV patients, and receiver operating characteristic (ROC) curves were conducted to assess the clinical value of Risk Score. Moreover, RNA-Seq date of OV patient derived from Gene Expression Omnibus (GEO, GSE32062) and the International Cancer Genome Consortium (ICGC) database (ICGC-AU), verifying the robustness of the Risk Score via Kaplan-Meier and ROC analysis.Pathway features were performed by gene set enrichment analysis and single sample gene set enrichment analysis. Finally, Risk Score in terms of chemotherapy drug sensitivity and immunotherapy suitability was also evaluated in different groups. RESULTS: 9-gene composition Risk Score system was finally determined by COX and LASSO COX analysis. Patients in the low Risk Score group possessed improved prognostic status, immune activity. PI3K pathway activity was increased in the high Risk Score group. In the chemotherapy drug sensitivity analysis, we found that the high Risk Score group might be more suitable for treatment with PI3K inhibitors Taselisib and Pictilisib. In addition, we found that patients in the low-risk group responded better to immunotherapy. CONCLUSION: Risk Score of 9-gene composition of PCD signature possesses promising clinical potential in OV prognosis, immunotherapy, immune microenvironment activity, and chemotherapeutic drug selection, and our study provides the basis for an in-depth investigation of the PCD mechanism in OV.
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spelling pubmed-102776152023-06-20 Assessing of programmed cell death gene signature for predicting ovarian cancer prognosis and treatment response Lian, Xin Liu, Bing Wang, Caixia Wang, Shuang Zhuang, Yuan Li, Xiao Front Endocrinol (Lausanne) Endocrinology BACKGROUND: Programmed cell death (PCD) is an overwhelming factor affecting tumor cell metastasis, but the mechanism of PCD in ovarian cancer (OV) is still uncertain. METHODS: To define the molecular subtypes of OV, we performed unsupervised clustering based on the expression level of prognosis related PCD genes in the Cancer Genome Atlas (TCGA)-OV. COX and least absolute shrinkage and selection operator (LASSO) COX analysis were used to identify the OV prognostic related PCD genes, and the genes identified according to the minimum Akaike information criterion (AIC) were the OV prognostic characteristic genes. According to the regression coefficient in the multivariate COX analysis and gene expression data, the Risk Score of OV prognosis was constructed. Kaplan-Meier analysis was conducted to assess the prognostic status of OV patients, and receiver operating characteristic (ROC) curves were conducted to assess the clinical value of Risk Score. Moreover, RNA-Seq date of OV patient derived from Gene Expression Omnibus (GEO, GSE32062) and the International Cancer Genome Consortium (ICGC) database (ICGC-AU), verifying the robustness of the Risk Score via Kaplan-Meier and ROC analysis.Pathway features were performed by gene set enrichment analysis and single sample gene set enrichment analysis. Finally, Risk Score in terms of chemotherapy drug sensitivity and immunotherapy suitability was also evaluated in different groups. RESULTS: 9-gene composition Risk Score system was finally determined by COX and LASSO COX analysis. Patients in the low Risk Score group possessed improved prognostic status, immune activity. PI3K pathway activity was increased in the high Risk Score group. In the chemotherapy drug sensitivity analysis, we found that the high Risk Score group might be more suitable for treatment with PI3K inhibitors Taselisib and Pictilisib. In addition, we found that patients in the low-risk group responded better to immunotherapy. CONCLUSION: Risk Score of 9-gene composition of PCD signature possesses promising clinical potential in OV prognosis, immunotherapy, immune microenvironment activity, and chemotherapeutic drug selection, and our study provides the basis for an in-depth investigation of the PCD mechanism in OV. Frontiers Media S.A. 2023-06-05 /pmc/articles/PMC10277615/ /pubmed/37342266 http://dx.doi.org/10.3389/fendo.2023.1182776 Text en Copyright © 2023 Lian, Liu, Wang, Wang, Zhuang and Li https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Lian, Xin
Liu, Bing
Wang, Caixia
Wang, Shuang
Zhuang, Yuan
Li, Xiao
Assessing of programmed cell death gene signature for predicting ovarian cancer prognosis and treatment response
title Assessing of programmed cell death gene signature for predicting ovarian cancer prognosis and treatment response
title_full Assessing of programmed cell death gene signature for predicting ovarian cancer prognosis and treatment response
title_fullStr Assessing of programmed cell death gene signature for predicting ovarian cancer prognosis and treatment response
title_full_unstemmed Assessing of programmed cell death gene signature for predicting ovarian cancer prognosis and treatment response
title_short Assessing of programmed cell death gene signature for predicting ovarian cancer prognosis and treatment response
title_sort assessing of programmed cell death gene signature for predicting ovarian cancer prognosis and treatment response
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10277615/
https://www.ncbi.nlm.nih.gov/pubmed/37342266
http://dx.doi.org/10.3389/fendo.2023.1182776
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