LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer

The aim of this study is to identify potential biomarkers for early diagnosis of gynecologic cancer in order to improve survival. Cervical cancer (CC) and endometrial cancer (EC) are the most common malignant tumors of gynecologic cancer among women in the world. As the underlying molecular mechanis...

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Autores principales:	Shao-Hua Yu, Jia-Hua Cai, De-Lun Chen, Szu-Han Liao, Yi-Zhen Lin, Yu-Ting Chung, Jeffrey J. P. Tsai, Charles C. N. Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	cervical cancer endometrial cancer bioinformatics LASSO regression prognostic biomarkers Medicine R
Acceso en línea:	https://doaj.org/article/6a69dd96293d49c99567d9a6e781230f
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spelling	oai:doaj.org-article:6a69dd96293d49c99567d9a6e781230f2021-11-25T18:07:48ZLASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer10.3390/jpm111111772075-4426https://doaj.org/article/6a69dd96293d49c99567d9a6e781230f2021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4426/11/11/1177https://doaj.org/toc/2075-4426The aim of this study is to identify potential biomarkers for early diagnosis of gynecologic cancer in order to improve survival. Cervical cancer (CC) and endometrial cancer (EC) are the most common malignant tumors of gynecologic cancer among women in the world. As the underlying molecular mechanisms in both cervical and endometrial cancer remain unclear, a comprehensive and systematic bioinformatics analysis is required. In our study, gene expression profiles of GSE9750, GES7803, GES63514, GES17025, GES115810, and GES36389 downloaded from Gene Expression Omnibus (GEO) were utilized to analyze differential gene expression between cancer and normal tissues. A total of 78 differentially expressed genes (DEGs) common to CC and EC were identified to perform the functional enrichment analyses, including gene ontology and pathway analysis. KEGG pathway analysis of 78 DEGs indicated that three main types of pathway participate in the mechanism of gynecologic cancer such as drug metabolism, signal transduction, and tumorigenesis and development. Furthermore, 20 diagnostic signatures were confirmed using the least absolute shrink and selection operator (LASSO) regression with 10-fold cross validation. Finally, we used the GEPIA2 online tool to verify the expression of 20 genes selected by the LASSO regression model. Among them, the expression of PAMR1 and SLC24A3 in tumor tissues was downregulated significantly compared to the normal tissue, and found to be statistically significant in survival rates between the CC and EC of patients (<i>p</i> < 0.05). The two genes have their function: (1.) PAMR1 is a tumor suppressor gene, and many studies have proven that overexpression of the gene markedly suppresses cell growth, especially in breast cancer and polycystic ovary syndrome; (2.) SLC24A3 is a sodium–calcium regulator of cells, and high SLC24A3 levels are associated with poor prognosis. In our study, the gene signatures can be used to predict CC and EC prognosis, which could provide novel clinical evidence to serve as a potential biomarker for future diagnosis and treatment.Shao-Hua YuJia-Hua CaiDe-Lun ChenSzu-Han LiaoYi-Zhen LinYu-Ting ChungJeffrey J. P. TsaiCharles C. N. WangMDPI AGarticlecervical cancerendometrial cancerbioinformaticsLASSO regressionprognostic biomarkersMedicineRENJournal of Personalized Medicine, Vol 11, Iss 1177, p 1177 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cervical cancer endometrial cancer bioinformatics LASSO regression prognostic biomarkers Medicine R
spellingShingle	cervical cancer endometrial cancer bioinformatics LASSO regression prognostic biomarkers Medicine R Shao-Hua Yu Jia-Hua Cai De-Lun Chen Szu-Han Liao Yi-Zhen Lin Yu-Ting Chung Jeffrey J. P. Tsai Charles C. N. Wang LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer
description	The aim of this study is to identify potential biomarkers for early diagnosis of gynecologic cancer in order to improve survival. Cervical cancer (CC) and endometrial cancer (EC) are the most common malignant tumors of gynecologic cancer among women in the world. As the underlying molecular mechanisms in both cervical and endometrial cancer remain unclear, a comprehensive and systematic bioinformatics analysis is required. In our study, gene expression profiles of GSE9750, GES7803, GES63514, GES17025, GES115810, and GES36389 downloaded from Gene Expression Omnibus (GEO) were utilized to analyze differential gene expression between cancer and normal tissues. A total of 78 differentially expressed genes (DEGs) common to CC and EC were identified to perform the functional enrichment analyses, including gene ontology and pathway analysis. KEGG pathway analysis of 78 DEGs indicated that three main types of pathway participate in the mechanism of gynecologic cancer such as drug metabolism, signal transduction, and tumorigenesis and development. Furthermore, 20 diagnostic signatures were confirmed using the least absolute shrink and selection operator (LASSO) regression with 10-fold cross validation. Finally, we used the GEPIA2 online tool to verify the expression of 20 genes selected by the LASSO regression model. Among them, the expression of PAMR1 and SLC24A3 in tumor tissues was downregulated significantly compared to the normal tissue, and found to be statistically significant in survival rates between the CC and EC of patients (<i>p</i> < 0.05). The two genes have their function: (1.) PAMR1 is a tumor suppressor gene, and many studies have proven that overexpression of the gene markedly suppresses cell growth, especially in breast cancer and polycystic ovary syndrome; (2.) SLC24A3 is a sodium–calcium regulator of cells, and high SLC24A3 levels are associated with poor prognosis. In our study, the gene signatures can be used to predict CC and EC prognosis, which could provide novel clinical evidence to serve as a potential biomarker for future diagnosis and treatment.
format	article
author	Shao-Hua Yu Jia-Hua Cai De-Lun Chen Szu-Han Liao Yi-Zhen Lin Yu-Ting Chung Jeffrey J. P. Tsai Charles C. N. Wang
author_facet	Shao-Hua Yu Jia-Hua Cai De-Lun Chen Szu-Han Liao Yi-Zhen Lin Yu-Ting Chung Jeffrey J. P. Tsai Charles C. N. Wang
author_sort	Shao-Hua Yu
title	LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer
title_short	LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer
title_full	LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer
title_fullStr	LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer
title_full_unstemmed	LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer
title_sort	lasso and bioinformatics analysis in the identification of key genes for prognostic genes of gynecologic cancer
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6a69dd96293d49c99567d9a6e781230f
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LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer

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