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The Value of the Stemness Index in Ovarian Cancer Prognosis

Ovarian cancer (OC) is one of the most common gynecological malignancies. It is associated with a difficult diagnosis and poor prognosis. Our study aimed to analyze tumor stemness to determine the prognosis feature of patients with OC. At this job, we selected the gene expression and the clinical pr...

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Autores principales: Yuan, Hongjun, Yu, Qian, Pang, Jianyu, Chen, Yongzhi, Sheng, Miaomiao, Tang, Wenru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222264/
https://www.ncbi.nlm.nih.gov/pubmed/35741755
http://dx.doi.org/10.3390/genes13060993
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author Yuan, Hongjun
Yu, Qian
Pang, Jianyu
Chen, Yongzhi
Sheng, Miaomiao
Tang, Wenru
author_facet Yuan, Hongjun
Yu, Qian
Pang, Jianyu
Chen, Yongzhi
Sheng, Miaomiao
Tang, Wenru
author_sort Yuan, Hongjun
collection PubMed
description Ovarian cancer (OC) is one of the most common gynecological malignancies. It is associated with a difficult diagnosis and poor prognosis. Our study aimed to analyze tumor stemness to determine the prognosis feature of patients with OC. At this job, we selected the gene expression and the clinical profiles of patients with OC in the TCGA database. We calculated the stemness index of each patient using the one-class logistic regression (OCLR) algorithm and performed correlation analysis with immune infiltration. We used consensus clustering methods to classify OC patients into different stemness subtypes and compared the differences in immune infiltration between them. Finally, we established a prognostic signature by Cox and LASSO regression analysis. We found a significant negative correlation between a high stemness index and immune score. Pathway analysis indicated that the differentially expressed genes (DEGs) from the low- and high-mRNAsi groups were enriched in multiple functions and pathways, such as protein digestion and absorption, the PI3K-Akt signaling pathway, and the TGF-β signaling pathway. By consensus cluster analysis, patients with OC were split into two stemness subtypes, with subtype II having a better prognosis and higher immune infiltration. Furthermore, we identified 11 key genes to construct the prognostic signature for patients with OC. Among these genes, the expression levels of nine, including SFRP2, MFAP4, CCDC80, COL16A1, DUSP1, VSTM2L, TGFBI, PXDN, and GAS1, were increased in the high-risk group. The analysis of the KM and ROC curves indicated that this prognostic signature had a great survival prediction ability and could independently predict the prognosis for patients with OC. We established a stemness index-related risk prognostic module for OC, which has prognostic-independent capabilities and is expected to improve the diagnosis and treatment of patients with OC.
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spelling pubmed-92222642022-06-24 The Value of the Stemness Index in Ovarian Cancer Prognosis Yuan, Hongjun Yu, Qian Pang, Jianyu Chen, Yongzhi Sheng, Miaomiao Tang, Wenru Genes (Basel) Article Ovarian cancer (OC) is one of the most common gynecological malignancies. It is associated with a difficult diagnosis and poor prognosis. Our study aimed to analyze tumor stemness to determine the prognosis feature of patients with OC. At this job, we selected the gene expression and the clinical profiles of patients with OC in the TCGA database. We calculated the stemness index of each patient using the one-class logistic regression (OCLR) algorithm and performed correlation analysis with immune infiltration. We used consensus clustering methods to classify OC patients into different stemness subtypes and compared the differences in immune infiltration between them. Finally, we established a prognostic signature by Cox and LASSO regression analysis. We found a significant negative correlation between a high stemness index and immune score. Pathway analysis indicated that the differentially expressed genes (DEGs) from the low- and high-mRNAsi groups were enriched in multiple functions and pathways, such as protein digestion and absorption, the PI3K-Akt signaling pathway, and the TGF-β signaling pathway. By consensus cluster analysis, patients with OC were split into two stemness subtypes, with subtype II having a better prognosis and higher immune infiltration. Furthermore, we identified 11 key genes to construct the prognostic signature for patients with OC. Among these genes, the expression levels of nine, including SFRP2, MFAP4, CCDC80, COL16A1, DUSP1, VSTM2L, TGFBI, PXDN, and GAS1, were increased in the high-risk group. The analysis of the KM and ROC curves indicated that this prognostic signature had a great survival prediction ability and could independently predict the prognosis for patients with OC. We established a stemness index-related risk prognostic module for OC, which has prognostic-independent capabilities and is expected to improve the diagnosis and treatment of patients with OC. MDPI 2022-05-31 /pmc/articles/PMC9222264/ /pubmed/35741755 http://dx.doi.org/10.3390/genes13060993 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
Yuan, Hongjun
Yu, Qian
Pang, Jianyu
Chen, Yongzhi
Sheng, Miaomiao
Tang, Wenru
The Value of the Stemness Index in Ovarian Cancer Prognosis
title The Value of the Stemness Index in Ovarian Cancer Prognosis
title_full The Value of the Stemness Index in Ovarian Cancer Prognosis
title_fullStr The Value of the Stemness Index in Ovarian Cancer Prognosis
title_full_unstemmed The Value of the Stemness Index in Ovarian Cancer Prognosis
title_short The Value of the Stemness Index in Ovarian Cancer Prognosis
title_sort value of the stemness index in ovarian cancer prognosis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222264/
https://www.ncbi.nlm.nih.gov/pubmed/35741755
http://dx.doi.org/10.3390/genes13060993
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