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Three-gene risk model in papillary renal cell carcinoma: a robust likelihood-based survival analysis

Background: Papillary renal cell carcinoma (PRCC) accounts for 15% of all renal cell carcinomas. The molecular mechanisms of renal papillary cell carcinoma remain unclear, and treatments for advanced disease are limited. Result: We built the computing model as follows: Risk score = 1.806 * TPX2 - 0....

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Autores principales: Wang, Yutao, Yan, Kexin, Lin, Jiaxing, Wang, Jianfeng, Zheng, Zhenhua, Li, Xinxin, Hua, Zhixiong, Bu, Yuepeng, Shi, Jianxiu, Sun, Siqing, Li, Xuejie, Liu, Yang, Bi, Jianbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7695399/
https://www.ncbi.nlm.nih.gov/pubmed/33154194
http://dx.doi.org/10.18632/aging.104001
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author Wang, Yutao
Yan, Kexin
Lin, Jiaxing
Wang, Jianfeng
Zheng, Zhenhua
Li, Xinxin
Hua, Zhixiong
Bu, Yuepeng
Shi, Jianxiu
Sun, Siqing
Li, Xuejie
Liu, Yang
Bi, Jianbin
author_facet Wang, Yutao
Yan, Kexin
Lin, Jiaxing
Wang, Jianfeng
Zheng, Zhenhua
Li, Xinxin
Hua, Zhixiong
Bu, Yuepeng
Shi, Jianxiu
Sun, Siqing
Li, Xuejie
Liu, Yang
Bi, Jianbin
author_sort Wang, Yutao
collection PubMed
description Background: Papillary renal cell carcinoma (PRCC) accounts for 15% of all renal cell carcinomas. The molecular mechanisms of renal papillary cell carcinoma remain unclear, and treatments for advanced disease are limited. Result: We built the computing model as follows: Risk score = 1.806 * TPX2 - 0.355 * TXNRD2 - 0.805 * SLC6A20. The 3-year AUC of overall survival was 0.917 in the training set (147 PRCC samples) and 0.760 in the test set (142 PRCC samples). Based on the robust model, M2 macrophages showed positive correlation with risk score, while M1 macrophages were the opposite. PRCC patients with low risk score showed higher tumor mutation burden. TPX2 is a risk factor, and co-expression factors were enriched in cell proliferation and cancer-related pathways. Finally, the proliferation and invasion of PRCC cell line were decreased in the TPX2 reduced group, and the differential expression was identified. TPX2 is a potential risk biomarker which involved in cell proliferation in PRCC. Conclusion: We conducted a study to develop a three gene model for predicting prognosis in patients with papillary renal cell carcinoma. Our findings may provide candidate biomarkers for prognosis that have important implications for understanding the therapeutic targets of papillary renal cell carcinoma. Method: Gene expression matrix and clinical data were obtained from TCGA (The Cancer Genome Atlas), GSE26574, GSE2048, and GSE7023. Prognostic factors were identified using “survival” and “rbsurv” packages, and a risk score was constructed using Multivariate Cox regression analysis. The co-expression networks of the factors in model were constructed using the “WGCNA” package. The co-expression genes of factors were enriched and displayed the biological process. Based on this robust risk model, immune cells infiltration proportions and tumor mutation burdens were compared between risk groups. Subsequently, using the PRCC cell line, the role of TPX2 was determined by Cell proliferation assay, 5-Ethynyl-20-deoxyuridine assay and Transwell assay.
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spelling pubmed-76953992020-12-04 Three-gene risk model in papillary renal cell carcinoma: a robust likelihood-based survival analysis Wang, Yutao Yan, Kexin Lin, Jiaxing Wang, Jianfeng Zheng, Zhenhua Li, Xinxin Hua, Zhixiong Bu, Yuepeng Shi, Jianxiu Sun, Siqing Li, Xuejie Liu, Yang Bi, Jianbin Aging (Albany NY) Research Paper Background: Papillary renal cell carcinoma (PRCC) accounts for 15% of all renal cell carcinomas. The molecular mechanisms of renal papillary cell carcinoma remain unclear, and treatments for advanced disease are limited. Result: We built the computing model as follows: Risk score = 1.806 * TPX2 - 0.355 * TXNRD2 - 0.805 * SLC6A20. The 3-year AUC of overall survival was 0.917 in the training set (147 PRCC samples) and 0.760 in the test set (142 PRCC samples). Based on the robust model, M2 macrophages showed positive correlation with risk score, while M1 macrophages were the opposite. PRCC patients with low risk score showed higher tumor mutation burden. TPX2 is a risk factor, and co-expression factors were enriched in cell proliferation and cancer-related pathways. Finally, the proliferation and invasion of PRCC cell line were decreased in the TPX2 reduced group, and the differential expression was identified. TPX2 is a potential risk biomarker which involved in cell proliferation in PRCC. Conclusion: We conducted a study to develop a three gene model for predicting prognosis in patients with papillary renal cell carcinoma. Our findings may provide candidate biomarkers for prognosis that have important implications for understanding the therapeutic targets of papillary renal cell carcinoma. Method: Gene expression matrix and clinical data were obtained from TCGA (The Cancer Genome Atlas), GSE26574, GSE2048, and GSE7023. Prognostic factors were identified using “survival” and “rbsurv” packages, and a risk score was constructed using Multivariate Cox regression analysis. The co-expression networks of the factors in model were constructed using the “WGCNA” package. The co-expression genes of factors were enriched and displayed the biological process. Based on this robust risk model, immune cells infiltration proportions and tumor mutation burdens were compared between risk groups. Subsequently, using the PRCC cell line, the role of TPX2 was determined by Cell proliferation assay, 5-Ethynyl-20-deoxyuridine assay and Transwell assay. Impact Journals 2020-11-05 /pmc/articles/PMC7695399/ /pubmed/33154194 http://dx.doi.org/10.18632/aging.104001 Text en Copyright: © 2020 Wang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/) (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Paper
Wang, Yutao
Yan, Kexin
Lin, Jiaxing
Wang, Jianfeng
Zheng, Zhenhua
Li, Xinxin
Hua, Zhixiong
Bu, Yuepeng
Shi, Jianxiu
Sun, Siqing
Li, Xuejie
Liu, Yang
Bi, Jianbin
Three-gene risk model in papillary renal cell carcinoma: a robust likelihood-based survival analysis
title Three-gene risk model in papillary renal cell carcinoma: a robust likelihood-based survival analysis
title_full Three-gene risk model in papillary renal cell carcinoma: a robust likelihood-based survival analysis
title_fullStr Three-gene risk model in papillary renal cell carcinoma: a robust likelihood-based survival analysis
title_full_unstemmed Three-gene risk model in papillary renal cell carcinoma: a robust likelihood-based survival analysis
title_short Three-gene risk model in papillary renal cell carcinoma: a robust likelihood-based survival analysis
title_sort three-gene risk model in papillary renal cell carcinoma: a robust likelihood-based survival analysis
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7695399/
https://www.ncbi.nlm.nih.gov/pubmed/33154194
http://dx.doi.org/10.18632/aging.104001
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