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Integrating bioinformatic analysis and detailed experiments reveal an EMT‐related biomarker for clear cell renal cell carcinoma

BACKGROUND: Epithelial–mesenchymal transition (EMT) is associated with early recurrence and a poor prognosis in clear cell renal cell carcinoma (ccRCC). Studies have shown that EMT‐related genes play an important regulatory role in tumor invasion, metastasis, and drug resistance, but the biological...

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Autores principales: Ge, Yue, Ma, Sheng, Zhang, Junbiao, Xiong, Zezhong, Li, Beining, Ma, Siquan, Liu, Bo, Yao, Xiangyang, Wang, Zhihua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557903/
https://www.ncbi.nlm.nih.gov/pubmed/37676078
http://dx.doi.org/10.1002/cam4.6504
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author Ge, Yue
Ma, Sheng
Zhang, Junbiao
Xiong, Zezhong
Li, Beining
Ma, Siquan
Liu, Bo
Yao, Xiangyang
Wang, Zhihua
author_facet Ge, Yue
Ma, Sheng
Zhang, Junbiao
Xiong, Zezhong
Li, Beining
Ma, Siquan
Liu, Bo
Yao, Xiangyang
Wang, Zhihua
author_sort Ge, Yue
collection PubMed
description BACKGROUND: Epithelial–mesenchymal transition (EMT) is associated with early recurrence and a poor prognosis in clear cell renal cell carcinoma (ccRCC). Studies have shown that EMT‐related genes play an important regulatory role in tumor invasion, metastasis, and drug resistance, but the biological functions of EMT‐related genes in ccRCC have not been specifically described. METHODS: The mRNA and clinicopathological data of 532 ccRCC and 72 normal samples were downloaded from The Cancer Genome Atlas as a training set. The gene expression matrix and survival data of 91 and 101 ccRCC samples were obtained from the International Cancer Genome Consortium and the ArrayExpress databases as validation sets, respectively. Univariate Cox analysis was used to identify and cluster prognostic genes, and multivariate Cox was performed to construct a prognostic signature. Moreover, CIBERSORT and CellMiner were used to assess immune cell infiltration and prognostic gene‐drug sensitivity of the signature, respectively. Most importantly, we performed detailed experiments to verify the oncogenic function of a significant gene, OLFML2B, in vitro and in vivo. RESULTS: We constructed a prognostic signature including seven genes and divided patients into high‐risk and low‐risk groups. The prognosis of the high‐risk group was significantly worse than that of the low‐risk group through Kaplan–Meier survival analysis. Interestingly, significant differences were observed in clinical characteristics and immune cell infiltration between the two groups. In addition, a significant correlation was found between the expression of prognostic genes and the sensitivity of tumor cells to chemotherapeutics. Most importantly, OLFML2B was proved to contribute to the proliferation and metastasis of ccRCC through detailed functional experiments in vitro and in vivo, and its prognostic efficacy for ccRCC patients was affirmed. CONCLUSION: We identified the prognostic signature of seven genes based on EMT‐related genes as prognostic biomarkers for ccRCC. Besides, OLFML2B was validated as a potential diagnostic and therapeutic target for ccRCC by our detailed experiments.
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spelling pubmed-105579032023-10-07 Integrating bioinformatic analysis and detailed experiments reveal an EMT‐related biomarker for clear cell renal cell carcinoma Ge, Yue Ma, Sheng Zhang, Junbiao Xiong, Zezhong Li, Beining Ma, Siquan Liu, Bo Yao, Xiangyang Wang, Zhihua Cancer Med Research Articles BACKGROUND: Epithelial–mesenchymal transition (EMT) is associated with early recurrence and a poor prognosis in clear cell renal cell carcinoma (ccRCC). Studies have shown that EMT‐related genes play an important regulatory role in tumor invasion, metastasis, and drug resistance, but the biological functions of EMT‐related genes in ccRCC have not been specifically described. METHODS: The mRNA and clinicopathological data of 532 ccRCC and 72 normal samples were downloaded from The Cancer Genome Atlas as a training set. The gene expression matrix and survival data of 91 and 101 ccRCC samples were obtained from the International Cancer Genome Consortium and the ArrayExpress databases as validation sets, respectively. Univariate Cox analysis was used to identify and cluster prognostic genes, and multivariate Cox was performed to construct a prognostic signature. Moreover, CIBERSORT and CellMiner were used to assess immune cell infiltration and prognostic gene‐drug sensitivity of the signature, respectively. Most importantly, we performed detailed experiments to verify the oncogenic function of a significant gene, OLFML2B, in vitro and in vivo. RESULTS: We constructed a prognostic signature including seven genes and divided patients into high‐risk and low‐risk groups. The prognosis of the high‐risk group was significantly worse than that of the low‐risk group through Kaplan–Meier survival analysis. Interestingly, significant differences were observed in clinical characteristics and immune cell infiltration between the two groups. In addition, a significant correlation was found between the expression of prognostic genes and the sensitivity of tumor cells to chemotherapeutics. Most importantly, OLFML2B was proved to contribute to the proliferation and metastasis of ccRCC through detailed functional experiments in vitro and in vivo, and its prognostic efficacy for ccRCC patients was affirmed. CONCLUSION: We identified the prognostic signature of seven genes based on EMT‐related genes as prognostic biomarkers for ccRCC. Besides, OLFML2B was validated as a potential diagnostic and therapeutic target for ccRCC by our detailed experiments. John Wiley and Sons Inc. 2023-09-07 /pmc/articles/PMC10557903/ /pubmed/37676078 http://dx.doi.org/10.1002/cam4.6504 Text en © 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. 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 Research Articles
Ge, Yue
Ma, Sheng
Zhang, Junbiao
Xiong, Zezhong
Li, Beining
Ma, Siquan
Liu, Bo
Yao, Xiangyang
Wang, Zhihua
Integrating bioinformatic analysis and detailed experiments reveal an EMT‐related biomarker for clear cell renal cell carcinoma
title Integrating bioinformatic analysis and detailed experiments reveal an EMT‐related biomarker for clear cell renal cell carcinoma
title_full Integrating bioinformatic analysis and detailed experiments reveal an EMT‐related biomarker for clear cell renal cell carcinoma
title_fullStr Integrating bioinformatic analysis and detailed experiments reveal an EMT‐related biomarker for clear cell renal cell carcinoma
title_full_unstemmed Integrating bioinformatic analysis and detailed experiments reveal an EMT‐related biomarker for clear cell renal cell carcinoma
title_short Integrating bioinformatic analysis and detailed experiments reveal an EMT‐related biomarker for clear cell renal cell carcinoma
title_sort integrating bioinformatic analysis and detailed experiments reveal an emt‐related biomarker for clear cell renal cell carcinoma
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557903/
https://www.ncbi.nlm.nih.gov/pubmed/37676078
http://dx.doi.org/10.1002/cam4.6504
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