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A prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles
Even after debulking surgery combined with chemotherapy or new adjuvant chemotherapy paired with internal surgery, the average year of disease free survival in advanced ovarian cancer was approximately 1.7 years(1). The development of a molecular predictor of early recurrence would allow for the ide...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632395/ https://www.ncbi.nlm.nih.gov/pubmed/37940688 http://dx.doi.org/10.1038/s41598-023-45410-x |
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author | Hua, Yanjiao Cai, Du Shirley, Cole Andrea Mo, Sien Chen, Ruyun Gao, Feng Chen, Fangying |
author_facet | Hua, Yanjiao Cai, Du Shirley, Cole Andrea Mo, Sien Chen, Ruyun Gao, Feng Chen, Fangying |
author_sort | Hua, Yanjiao |
collection | PubMed |
description | Even after debulking surgery combined with chemotherapy or new adjuvant chemotherapy paired with internal surgery, the average year of disease free survival in advanced ovarian cancer was approximately 1.7 years(1). The development of a molecular predictor of early recurrence would allow for the identification of ovarian cancer (OC) patients with high risk of relapse. The Ovarian Cancer Disease Free Survival Predictor (ODFSP), a predictive model constructed from a special set of 1580 OC tumors in which gene expression was assessed using both microarray and sequencing platforms, was created by our team. To construct gene expression barcodes that were resistant to biases caused by disparate profiling platforms and batch effects, we employed a meta-analysis methodology that was based on the binary gene pair technique. We demonstrate that ODFSP is a reliable single-sample predictor of early recurrence (1 year or less) using the largest pool of OC transcriptome data sets available to date. The ODFSP model showed significantly high prognostic value for binary recurrence prediction unaffected by clinicopathologic factors, with a meta-estimate of the area under the receiver operating curve of 0.64 (P = 4.6E-05) and a D-index (robust hazard ratio) of 1.67 (P = 9.2E-06), respectively. GO analysis of ODFSP’s 2040 gene pairs (collapsed to 886 distinct genes) revealed the involvement in small molecular catabolic process, sulfur compound metabolic process, organic acid catabolic process, sulfur compound biosynthetic process, glycosaminoglycan metabolic process and aminometabolic process. Kyoto encyclopedia of genes and genomes pathway analysis of ODFSP’s signature genes identified prominent pathways that included cAMP signaling pathway and FoxO signaling pathway. By identifying individuals who might benefit from a more aggressive treatment plan or enrolment in a clinical trial but who will not benefit from standard surgery or chemotherapy, ODFSP could help with treatment decisions. |
format | Online Article Text |
id | pubmed-10632395 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106323952023-11-10 A prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles Hua, Yanjiao Cai, Du Shirley, Cole Andrea Mo, Sien Chen, Ruyun Gao, Feng Chen, Fangying Sci Rep Article Even after debulking surgery combined with chemotherapy or new adjuvant chemotherapy paired with internal surgery, the average year of disease free survival in advanced ovarian cancer was approximately 1.7 years(1). The development of a molecular predictor of early recurrence would allow for the identification of ovarian cancer (OC) patients with high risk of relapse. The Ovarian Cancer Disease Free Survival Predictor (ODFSP), a predictive model constructed from a special set of 1580 OC tumors in which gene expression was assessed using both microarray and sequencing platforms, was created by our team. To construct gene expression barcodes that were resistant to biases caused by disparate profiling platforms and batch effects, we employed a meta-analysis methodology that was based on the binary gene pair technique. We demonstrate that ODFSP is a reliable single-sample predictor of early recurrence (1 year or less) using the largest pool of OC transcriptome data sets available to date. The ODFSP model showed significantly high prognostic value for binary recurrence prediction unaffected by clinicopathologic factors, with a meta-estimate of the area under the receiver operating curve of 0.64 (P = 4.6E-05) and a D-index (robust hazard ratio) of 1.67 (P = 9.2E-06), respectively. GO analysis of ODFSP’s 2040 gene pairs (collapsed to 886 distinct genes) revealed the involvement in small molecular catabolic process, sulfur compound metabolic process, organic acid catabolic process, sulfur compound biosynthetic process, glycosaminoglycan metabolic process and aminometabolic process. Kyoto encyclopedia of genes and genomes pathway analysis of ODFSP’s signature genes identified prominent pathways that included cAMP signaling pathway and FoxO signaling pathway. By identifying individuals who might benefit from a more aggressive treatment plan or enrolment in a clinical trial but who will not benefit from standard surgery or chemotherapy, ODFSP could help with treatment decisions. Nature Publishing Group UK 2023-11-08 /pmc/articles/PMC10632395/ /pubmed/37940688 http://dx.doi.org/10.1038/s41598-023-45410-x 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/) . |
spellingShingle | Article Hua, Yanjiao Cai, Du Shirley, Cole Andrea Mo, Sien Chen, Ruyun Gao, Feng Chen, Fangying A prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles |
title | A prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles |
title_full | A prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles |
title_fullStr | A prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles |
title_full_unstemmed | A prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles |
title_short | A prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles |
title_sort | prognostic model for ovarian neoplasms established by an integrated analysis of 1580 transcriptomic profiles |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632395/ https://www.ncbi.nlm.nih.gov/pubmed/37940688 http://dx.doi.org/10.1038/s41598-023-45410-x |
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