Identification and Verification of Biomarker in Clear Cell Renal Cell Carcinoma via Bioinformatics and Neural Network Model

Background. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, which represents the 9th most frequently diagnosed cancer. However, the molecular mechanism of occurrence and development of ccRCC is indistinct. Therefore, the research aims to identify the hub biomarke...

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Main Authors: Bin Liu, Yu Xiao, Hao Li, Ai-li Zhang, Ling-bing Meng, Lu Feng, Zhi-hong Zhao, Xiao-chen Ni, Bo Fan, Xiao-yu Zhang, Shi-bin Zhao, Yi-bo Liu
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:BioMed Research International
Online Access:http://dx.doi.org/10.1155/2020/6954793
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spelling doaj-94453e0c2fb5456b93ff3b82133b40322020-11-25T03:23:45ZengHindawi LimitedBioMed Research International2314-61332314-61412020-01-01202010.1155/2020/69547936954793Identification and Verification of Biomarker in Clear Cell Renal Cell Carcinoma via Bioinformatics and Neural Network ModelBin Liu0Yu Xiao1Hao Li2Ai-li Zhang3Ling-bing Meng4Lu Feng5Zhi-hong Zhao6Xiao-chen Ni7Bo Fan8Xiao-yu Zhang9Shi-bin Zhao10Yi-bo Liu11Department of Urinary Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, 050000, ChinaSchool of Basic Medicine, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, ChinaDepartment of Oncology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Urinary Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, 050000, ChinaSchool of Basic Medical Sciences, Hebei Medical University, Shijiazhuang, Hebei, ChinaMOH Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Beijing, ChinaDepartment of Urinary Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, 050000, ChinaDepartment of Urinary Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, 050000, ChinaDepartment of Urinary Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, 050000, ChinaDepartment of Urinary Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, 050000, ChinaDepartment of Reproductive Medicine, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, 050000, ChinaDepartment of Urinary Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, 050000, ChinaBackground. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, which represents the 9th most frequently diagnosed cancer. However, the molecular mechanism of occurrence and development of ccRCC is indistinct. Therefore, the research aims to identify the hub biomarkers of ccRCC using numerous bioinformatics tools and functional experiments. Methods. The public data was downloaded from the Gene Expression Omnibus (GEO) database, and the differently expressed genes (DEGs) between ccRCC and normal renal tissues were identified with GEO2R. Protein-protein interaction (PPI) network of the DEGs was constructed, and hub genes were screened with cytoHubba. Then, ten ccRCC tumor samples and ten normal kidney tissues were obtained to verify the expression of hub genes with the RT-qPCR. Finally, the neural network model was constructed to verify the relationship among the genes. Results. A total of 251 DEGs and ten hub genes were identified. AURKB, CCNA2, TPX2, and NCAPG were highly expressed in ccRCC compared with renal tissue. With the increasing expression of AURKB, CCNA2, TPX2, and NCAPG, the pathological stage of ccRCC increased gradually (P<0.05). Patients with high expression of AURKB, CCNA2, TPX2, and NCAPG have a poor overall survival. After the verification of RT-qPCR, the expression of hub genes was same as the public data. And there were strong correlations between the AURKB, CCNA2, TPX2, and NCAPG with the verification of the neural network model. Conclusion. After the identification and verification, AURKB, CCNA2, TPX2, and NCAPG might be related to the occurrence and malignant progression of ccRCC.http://dx.doi.org/10.1155/2020/6954793
collection DOAJ
language English
format Article
sources DOAJ
author Bin Liu
Yu Xiao
Hao Li
Ai-li Zhang
Ling-bing Meng
Lu Feng
Zhi-hong Zhao
Xiao-chen Ni
Bo Fan
Xiao-yu Zhang
Shi-bin Zhao
Yi-bo Liu
spellingShingle Bin Liu
Yu Xiao
Hao Li
Ai-li Zhang
Ling-bing Meng
Lu Feng
Zhi-hong Zhao
Xiao-chen Ni
Bo Fan
Xiao-yu Zhang
Shi-bin Zhao
Yi-bo Liu
Identification and Verification of Biomarker in Clear Cell Renal Cell Carcinoma via Bioinformatics and Neural Network Model
BioMed Research International
author_facet Bin Liu
Yu Xiao
Hao Li
Ai-li Zhang
Ling-bing Meng
Lu Feng
Zhi-hong Zhao
Xiao-chen Ni
Bo Fan
Xiao-yu Zhang
Shi-bin Zhao
Yi-bo Liu
author_sort Bin Liu
title Identification and Verification of Biomarker in Clear Cell Renal Cell Carcinoma via Bioinformatics and Neural Network Model
title_short Identification and Verification of Biomarker in Clear Cell Renal Cell Carcinoma via Bioinformatics and Neural Network Model
title_full Identification and Verification of Biomarker in Clear Cell Renal Cell Carcinoma via Bioinformatics and Neural Network Model
title_fullStr Identification and Verification of Biomarker in Clear Cell Renal Cell Carcinoma via Bioinformatics and Neural Network Model
title_full_unstemmed Identification and Verification of Biomarker in Clear Cell Renal Cell Carcinoma via Bioinformatics and Neural Network Model
title_sort identification and verification of biomarker in clear cell renal cell carcinoma via bioinformatics and neural network model
publisher Hindawi Limited
series BioMed Research International
issn 2314-6133
2314-6141
publishDate 2020-01-01
description Background. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, which represents the 9th most frequently diagnosed cancer. However, the molecular mechanism of occurrence and development of ccRCC is indistinct. Therefore, the research aims to identify the hub biomarkers of ccRCC using numerous bioinformatics tools and functional experiments. Methods. The public data was downloaded from the Gene Expression Omnibus (GEO) database, and the differently expressed genes (DEGs) between ccRCC and normal renal tissues were identified with GEO2R. Protein-protein interaction (PPI) network of the DEGs was constructed, and hub genes were screened with cytoHubba. Then, ten ccRCC tumor samples and ten normal kidney tissues were obtained to verify the expression of hub genes with the RT-qPCR. Finally, the neural network model was constructed to verify the relationship among the genes. Results. A total of 251 DEGs and ten hub genes were identified. AURKB, CCNA2, TPX2, and NCAPG were highly expressed in ccRCC compared with renal tissue. With the increasing expression of AURKB, CCNA2, TPX2, and NCAPG, the pathological stage of ccRCC increased gradually (P<0.05). Patients with high expression of AURKB, CCNA2, TPX2, and NCAPG have a poor overall survival. After the verification of RT-qPCR, the expression of hub genes was same as the public data. And there were strong correlations between the AURKB, CCNA2, TPX2, and NCAPG with the verification of the neural network model. Conclusion. After the identification and verification, AURKB, CCNA2, TPX2, and NCAPG might be related to the occurrence and malignant progression of ccRCC.
url http://dx.doi.org/10.1155/2020/6954793
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