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A Five-Genes-Based Prognostic Signature for Cervical Cancer Overall Survival Prediction
Aims. This study is aimed at identifying a prognostic signature for cervical cancer. Main Methods. The gene expression data and clinical information of cervical cancer and normal cervical tissues were acquired from The Cancer Genome Atlas and from three datasets of the Gene Expression Omnibus databa...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7136791/ https://www.ncbi.nlm.nih.gov/pubmed/32300605 http://dx.doi.org/10.1155/2020/8347639 |
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author | Zhao, Menghuang Huang, Wenbin Zou, Shuangwei Shen, Qi Zhu, Xueqiong |
author_facet | Zhao, Menghuang Huang, Wenbin Zou, Shuangwei Shen, Qi Zhu, Xueqiong |
author_sort | Zhao, Menghuang |
collection | PubMed |
description | Aims. This study is aimed at identifying a prognostic signature for cervical cancer. Main Methods. The gene expression data and clinical information of cervical cancer and normal cervical tissues were acquired from The Cancer Genome Atlas and from three datasets of the Gene Expression Omnibus database. DESeq2 and Limma were employed to screen differentially expressed genes (DEGs). The overlapping DEGs among all datasets were considered the final DEGs. Then, the functional enrichment analysis was performed. Moreover, the Cox proportional hazards regression was performed to establish a prognostic signature of the DEGs. The Kaplan-Meier analysis was applied to test the model. Relationships between gene expression and clinicopathological parameters in cervical cancer, including age, HPV status, histology, stage, and lymph node metastasis, were analysed by the chi-square test. The somatic mutations of these prognostic genes were assessed through cBioPortal. The robustness of the model was verified in another two independent validation cohorts. Key Findings. In total, 169 overlapping upregulated genes and 29 overlapping downregulated genes were identified in cervical cancer compared with normal cervical tissues. Functional enrichment analysis indicated that the DEGs were mainly enriched in DNA replication, the cell cycle, and the p53 signalling pathway. Finally, a 5-gene- (ITM2A, DSG2, SPP1, EFNA1, and MMP1) based prognostic signature was built. According to this model, each patient was given a prognostic-related risk value. The Kaplan-Meier analysis showed that a higher risk was related to worse overall survival in cervical cancer, with an area under the receiver operating characteristic curve of 0.811 for 15 years. The validity of this model in the prediction of cervical cancer outcome was verified in another two independent datasets. In addition, our study also found that the low expression of ITM2A was associated with cervical adenocarcinoma. Interestingly, DSG2 was associated with the HPV status of cervical cancer. Significance. Our study constructed a prognostic model in cervical cancer and discovered two novel genes, ITM2A and DSG2, associated with cervical carcinogenesis and survival. |
format | Online Article Text |
id | pubmed-7136791 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-71367912020-04-16 A Five-Genes-Based Prognostic Signature for Cervical Cancer Overall Survival Prediction Zhao, Menghuang Huang, Wenbin Zou, Shuangwei Shen, Qi Zhu, Xueqiong Int J Genomics Research Article Aims. This study is aimed at identifying a prognostic signature for cervical cancer. Main Methods. The gene expression data and clinical information of cervical cancer and normal cervical tissues were acquired from The Cancer Genome Atlas and from three datasets of the Gene Expression Omnibus database. DESeq2 and Limma were employed to screen differentially expressed genes (DEGs). The overlapping DEGs among all datasets were considered the final DEGs. Then, the functional enrichment analysis was performed. Moreover, the Cox proportional hazards regression was performed to establish a prognostic signature of the DEGs. The Kaplan-Meier analysis was applied to test the model. Relationships between gene expression and clinicopathological parameters in cervical cancer, including age, HPV status, histology, stage, and lymph node metastasis, were analysed by the chi-square test. The somatic mutations of these prognostic genes were assessed through cBioPortal. The robustness of the model was verified in another two independent validation cohorts. Key Findings. In total, 169 overlapping upregulated genes and 29 overlapping downregulated genes were identified in cervical cancer compared with normal cervical tissues. Functional enrichment analysis indicated that the DEGs were mainly enriched in DNA replication, the cell cycle, and the p53 signalling pathway. Finally, a 5-gene- (ITM2A, DSG2, SPP1, EFNA1, and MMP1) based prognostic signature was built. According to this model, each patient was given a prognostic-related risk value. The Kaplan-Meier analysis showed that a higher risk was related to worse overall survival in cervical cancer, with an area under the receiver operating characteristic curve of 0.811 for 15 years. The validity of this model in the prediction of cervical cancer outcome was verified in another two independent datasets. In addition, our study also found that the low expression of ITM2A was associated with cervical adenocarcinoma. Interestingly, DSG2 was associated with the HPV status of cervical cancer. Significance. Our study constructed a prognostic model in cervical cancer and discovered two novel genes, ITM2A and DSG2, associated with cervical carcinogenesis and survival. Hindawi 2020-03-25 /pmc/articles/PMC7136791/ /pubmed/32300605 http://dx.doi.org/10.1155/2020/8347639 Text en Copyright © 2020 Menghuang Zhao et al. http://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 Zhao, Menghuang Huang, Wenbin Zou, Shuangwei Shen, Qi Zhu, Xueqiong A Five-Genes-Based Prognostic Signature for Cervical Cancer Overall Survival Prediction |
title | A Five-Genes-Based Prognostic Signature for Cervical Cancer Overall Survival Prediction |
title_full | A Five-Genes-Based Prognostic Signature for Cervical Cancer Overall Survival Prediction |
title_fullStr | A Five-Genes-Based Prognostic Signature for Cervical Cancer Overall Survival Prediction |
title_full_unstemmed | A Five-Genes-Based Prognostic Signature for Cervical Cancer Overall Survival Prediction |
title_short | A Five-Genes-Based Prognostic Signature for Cervical Cancer Overall Survival Prediction |
title_sort | five-genes-based prognostic signature for cervical cancer overall survival prediction |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7136791/ https://www.ncbi.nlm.nih.gov/pubmed/32300605 http://dx.doi.org/10.1155/2020/8347639 |
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