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Role of Bioinformatics Analysis in Early Differential Diagnosis of Ovarian Cancer

In order to solve the problem of early differential diagnosis of ovarian cancer, this paper proposes the role of bioinformatics analysis in early differential diagnosis of ovarian cancer. This method uses bioinformatics methods to mine the existing data in the tumor database and obtain tumor-related...

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Detalles Bibliográficos
Autores principales: Zhang, Lihua, Zhao, Yuanyuan, Li, Li, Xin, Huadong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9507672/
https://www.ncbi.nlm.nih.gov/pubmed/36185577
http://dx.doi.org/10.1155/2022/6129817
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author Zhang, Lihua
Zhao, Yuanyuan
Li, Li
Xin, Huadong
author_facet Zhang, Lihua
Zhao, Yuanyuan
Li, Li
Xin, Huadong
author_sort Zhang, Lihua
collection PubMed
description In order to solve the problem of early differential diagnosis of ovarian cancer, this paper proposes the role of bioinformatics analysis in early differential diagnosis of ovarian cancer. This method uses bioinformatics methods to mine the existing data in the tumor database and obtain tumor-related molecules. It is an efficient method to obtain effective biomarkers, screen signal pathway molecules, and reveal the internal mechanism of tumor occurrence and development. Using this method can greatly improve the efficiency and reliability of screening diagnosis, prognosis, and treatment targets. The results showed that 5821 new lncRNA transcripts and 4611 new lncRNA genes were identified by lncScore from the assembled transcripts. 10 new lncRNA transcripts and 174 new lncRNA genes were found to be differentially expressed in ovarian cancer.
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spelling pubmed-95076722022-09-29 Role of Bioinformatics Analysis in Early Differential Diagnosis of Ovarian Cancer Zhang, Lihua Zhao, Yuanyuan Li, Li Xin, Huadong Contrast Media Mol Imaging Research Article In order to solve the problem of early differential diagnosis of ovarian cancer, this paper proposes the role of bioinformatics analysis in early differential diagnosis of ovarian cancer. This method uses bioinformatics methods to mine the existing data in the tumor database and obtain tumor-related molecules. It is an efficient method to obtain effective biomarkers, screen signal pathway molecules, and reveal the internal mechanism of tumor occurrence and development. Using this method can greatly improve the efficiency and reliability of screening diagnosis, prognosis, and treatment targets. The results showed that 5821 new lncRNA transcripts and 4611 new lncRNA genes were identified by lncScore from the assembled transcripts. 10 new lncRNA transcripts and 174 new lncRNA genes were found to be differentially expressed in ovarian cancer. Hindawi 2022-09-16 /pmc/articles/PMC9507672/ /pubmed/36185577 http://dx.doi.org/10.1155/2022/6129817 Text en Copyright © 2022 Lihua Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Lihua
Zhao, Yuanyuan
Li, Li
Xin, Huadong
Role of Bioinformatics Analysis in Early Differential Diagnosis of Ovarian Cancer
title Role of Bioinformatics Analysis in Early Differential Diagnosis of Ovarian Cancer
title_full Role of Bioinformatics Analysis in Early Differential Diagnosis of Ovarian Cancer
title_fullStr Role of Bioinformatics Analysis in Early Differential Diagnosis of Ovarian Cancer
title_full_unstemmed Role of Bioinformatics Analysis in Early Differential Diagnosis of Ovarian Cancer
title_short Role of Bioinformatics Analysis in Early Differential Diagnosis of Ovarian Cancer
title_sort role of bioinformatics analysis in early differential diagnosis of ovarian cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9507672/
https://www.ncbi.nlm.nih.gov/pubmed/36185577
http://dx.doi.org/10.1155/2022/6129817
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