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Identification of hub genes in bladder cancer based on weighted gene co‐expression network analysis from TCGA database

BACKGROUND: Muscular invasive bladder cancer (MIBC) is a common malignant tumor in the world. Because of their heterogeneity in prognosis and response to treatment, biomarkers that can predict survival or help make treatment decisions in patients with MIBC are essential for individualized treatment....

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Autores principales: Wang, Lei, Liu, Xudong, Yue, Miao, Liu, Zhe, Zhang, Yu, Ma, Ying, Luo, Jia, Li, Wuling, Bai, Jiangshan, Yao, Hongmei, Chen, Yuxuan, Li, Xiaofeng, Feng, Dayun, Song, Xinqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9458504/
https://www.ncbi.nlm.nih.gov/pubmed/34541834
http://dx.doi.org/10.1002/cnr2.1557
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author Wang, Lei
Liu, Xudong
Yue, Miao
Liu, Zhe
Zhang, Yu
Ma, Ying
Luo, Jia
Li, Wuling
Bai, Jiangshan
Yao, Hongmei
Chen, Yuxuan
Li, Xiaofeng
Feng, Dayun
Song, Xinqiang
author_facet Wang, Lei
Liu, Xudong
Yue, Miao
Liu, Zhe
Zhang, Yu
Ma, Ying
Luo, Jia
Li, Wuling
Bai, Jiangshan
Yao, Hongmei
Chen, Yuxuan
Li, Xiaofeng
Feng, Dayun
Song, Xinqiang
author_sort Wang, Lei
collection PubMed
description BACKGROUND: Muscular invasive bladder cancer (MIBC) is a common malignant tumor in the world. Because of their heterogeneity in prognosis and response to treatment, biomarkers that can predict survival or help make treatment decisions in patients with MIBC are essential for individualized treatment. AIM: We aimed to integrate bioinformatics research methods to identify a set of effective biomarkers capable of predicting, diagnosing, and treating MIBC. To provide a new theoretical basis for the diagnosis and treatment of bladder cancer. METHODS AND RESULTS: Gene expression profiles and clinical data of MIBC were obtained by downloading from the Cancer Genome Atlas database. A dataset of 129 MIBC cases and controls was included. 2084 up‐regulated genes and 2961 down‐regulated genes were identified by differentially expressed gene (DEG) analysis. Then, gene ontology analysis was performed to explore the biological functions of DEGs, respectively. The up‐regulated DEGs are mainly enriched in epidermal cell differentiation, mitotic nuclear division, and so forth. They are also involved in the cell cycle, p53 signaling pathway, PPAR signaling pathway, and so forth. The weighted gene co‐expression network analysis yielded five modules related to pathological stages and grading, of which blue and turquoise were the most relevant modules for MIBC. Next, Using Kaplan–Meier survival analysis to identify further hub genes, the screening criteria at p ≤ .05, we found CNKSR1, HIP1R, CFL2, TPM1, CSRP1, SYNM, POPDC2, PJA2, and RBBP8NL genes associated with the progression and prognosis of MIBC patients. Finally, immunohistochemistry experiments further confirmed that CNKSR1 plays a vital role in the tumorigenic context of MIBC. CONCLUSION: The research suggests that CNKSR1, POPDC2, and PJA2 may be novel biomarkers as therapeutic targets for MIBC, especially we used immunohistochemical further to validate CNKSR1 as a therapeutic target for MIBC which may help to improve the prognosis for MIBC.
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spelling pubmed-94585042022-09-12 Identification of hub genes in bladder cancer based on weighted gene co‐expression network analysis from TCGA database Wang, Lei Liu, Xudong Yue, Miao Liu, Zhe Zhang, Yu Ma, Ying Luo, Jia Li, Wuling Bai, Jiangshan Yao, Hongmei Chen, Yuxuan Li, Xiaofeng Feng, Dayun Song, Xinqiang Cancer Rep (Hoboken) Original Articles BACKGROUND: Muscular invasive bladder cancer (MIBC) is a common malignant tumor in the world. Because of their heterogeneity in prognosis and response to treatment, biomarkers that can predict survival or help make treatment decisions in patients with MIBC are essential for individualized treatment. AIM: We aimed to integrate bioinformatics research methods to identify a set of effective biomarkers capable of predicting, diagnosing, and treating MIBC. To provide a new theoretical basis for the diagnosis and treatment of bladder cancer. METHODS AND RESULTS: Gene expression profiles and clinical data of MIBC were obtained by downloading from the Cancer Genome Atlas database. A dataset of 129 MIBC cases and controls was included. 2084 up‐regulated genes and 2961 down‐regulated genes were identified by differentially expressed gene (DEG) analysis. Then, gene ontology analysis was performed to explore the biological functions of DEGs, respectively. The up‐regulated DEGs are mainly enriched in epidermal cell differentiation, mitotic nuclear division, and so forth. They are also involved in the cell cycle, p53 signaling pathway, PPAR signaling pathway, and so forth. The weighted gene co‐expression network analysis yielded five modules related to pathological stages and grading, of which blue and turquoise were the most relevant modules for MIBC. Next, Using Kaplan–Meier survival analysis to identify further hub genes, the screening criteria at p ≤ .05, we found CNKSR1, HIP1R, CFL2, TPM1, CSRP1, SYNM, POPDC2, PJA2, and RBBP8NL genes associated with the progression and prognosis of MIBC patients. Finally, immunohistochemistry experiments further confirmed that CNKSR1 plays a vital role in the tumorigenic context of MIBC. CONCLUSION: The research suggests that CNKSR1, POPDC2, and PJA2 may be novel biomarkers as therapeutic targets for MIBC, especially we used immunohistochemical further to validate CNKSR1 as a therapeutic target for MIBC which may help to improve the prognosis for MIBC. John Wiley and Sons Inc. 2021-09-20 /pmc/articles/PMC9458504/ /pubmed/34541834 http://dx.doi.org/10.1002/cnr2.1557 Text en © 2021 The Authors. Cancer Reports published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Wang, Lei
Liu, Xudong
Yue, Miao
Liu, Zhe
Zhang, Yu
Ma, Ying
Luo, Jia
Li, Wuling
Bai, Jiangshan
Yao, Hongmei
Chen, Yuxuan
Li, Xiaofeng
Feng, Dayun
Song, Xinqiang
Identification of hub genes in bladder cancer based on weighted gene co‐expression network analysis from TCGA database
title Identification of hub genes in bladder cancer based on weighted gene co‐expression network analysis from TCGA database
title_full Identification of hub genes in bladder cancer based on weighted gene co‐expression network analysis from TCGA database
title_fullStr Identification of hub genes in bladder cancer based on weighted gene co‐expression network analysis from TCGA database
title_full_unstemmed Identification of hub genes in bladder cancer based on weighted gene co‐expression network analysis from TCGA database
title_short Identification of hub genes in bladder cancer based on weighted gene co‐expression network analysis from TCGA database
title_sort identification of hub genes in bladder cancer based on weighted gene co‐expression network analysis from tcga database
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9458504/
https://www.ncbi.nlm.nih.gov/pubmed/34541834
http://dx.doi.org/10.1002/cnr2.1557
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