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Analysis and application of RNA binding protein gene pairs to predict the prognosis of gastric cancer

BACKGROUND: RNA-binding proteins (RBPs) are closely related to tumors, but little is known about the mechanism of RBPs in tumorigenesis and progression of gastric cancer (GC). As genes do not usually act alone in the pathway deregulation, gene pair combinations are more likely to become stable and a...

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Autores principales: Ning, Zhi-kun, Tian, Hua-kai, Liu, Jiang, Hu, Ce-gui, Liu, Zi-tao, Li, Hui, Zong, Zhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10393628/
https://www.ncbi.nlm.nih.gov/pubmed/37539127
http://dx.doi.org/10.1016/j.heliyon.2023.e18242
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author Ning, Zhi-kun
Tian, Hua-kai
Liu, Jiang
Hu, Ce-gui
Liu, Zi-tao
Li, Hui
Zong, Zhen
author_facet Ning, Zhi-kun
Tian, Hua-kai
Liu, Jiang
Hu, Ce-gui
Liu, Zi-tao
Li, Hui
Zong, Zhen
author_sort Ning, Zhi-kun
collection PubMed
description BACKGROUND: RNA-binding proteins (RBPs) are closely related to tumors, but little is known about the mechanism of RBPs in tumorigenesis and progression of gastric cancer (GC). As genes do not usually act alone in the pathway deregulation, gene pair combinations are more likely to become stable and accurate biomarkers. The purpose of our research is to establish a novel signature based on RBP gene pairs to predict the prognosis of gastric cancer patients. METHODS: We downloaded genetic and clinical information from the TCGA and GEO database. TCGA and GSE13911 were used for screening differentially expressed genes (DEGs). The RBP genes were gathered from previous studies and employed to screen out DE-RBP genes after intersecting with DEGs. Samples were classified according to the relative expression of each pair of DE-RBP genes. The univariate Cox regression analysis and random forest were used to identify hub gene pairs to construct signature for predicting the prognosis of gastric cancer. Time-dependent ROC curves and KM survival curves were performed to evaluate the signature. GSEA was performed in TCGA training cohort and GSE62254 testing cohort to analyze enrichment pathways. Finally, the influence of these gene pairs on the prognosis of GC patients was further elucidated respectively through the combination of high and low expression of the two genes in each hub gene pair. RESULTS: We screened out 6 hub RBP gene pairs (COL5A2/FEN1, POP1/GFRA1, EXO1/PLEKHS1, SLC39A10/CHI3L1, MMP7/PPP1R1 B and SLC5A6/BYSL) to predict the prognosis of patients with gastric cancer. Using the optimal cut-off value to divide patients into high-risk and low-risk groups in the training and testing cohort, we found that the overall survival (OS) of the low-risk group was higher than that of the high-risk group (P < 0.05). The area under the ROC curves for 1, 3, and 5 years were (0.659, 0.744, 0.758) and (0.624, 0.650, 0.653) in two cohorts. Univariate and multivariate Cox regression analysis showed that 6 RBP gene pairs signature were independent prognostic factors for gastric cancer (P < 0.05). In addition, the prognostic survival analysis showed that COL5A2-high/FEN1-low, POP1-low/GFRA1-high, EXO1-low/PLEKHS1-low,SLC39A10-high/CHI3L1-low, MMP7-high/PPP1R1 B-low, SLC5A6-low/BYSL-low had worse OS (P < 0.05). And the gene correlation analysis showed that there was no obvious correlation between the genes in each gene pairs except SLC5A6/BYSL and POP1/GFRA1. Finally, GSEA analysis showed that the high-risk group was enriched in tumor migration, invasion and growth-related pathways. CONCLUSION: Our study identified a novel 6 RBP gene pairs signature to predict the prognosis of gastric cancer patients and provide potential targets for clinical gene therapy.
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spelling pubmed-103936282023-08-03 Analysis and application of RNA binding protein gene pairs to predict the prognosis of gastric cancer Ning, Zhi-kun Tian, Hua-kai Liu, Jiang Hu, Ce-gui Liu, Zi-tao Li, Hui Zong, Zhen Heliyon Research Article BACKGROUND: RNA-binding proteins (RBPs) are closely related to tumors, but little is known about the mechanism of RBPs in tumorigenesis and progression of gastric cancer (GC). As genes do not usually act alone in the pathway deregulation, gene pair combinations are more likely to become stable and accurate biomarkers. The purpose of our research is to establish a novel signature based on RBP gene pairs to predict the prognosis of gastric cancer patients. METHODS: We downloaded genetic and clinical information from the TCGA and GEO database. TCGA and GSE13911 were used for screening differentially expressed genes (DEGs). The RBP genes were gathered from previous studies and employed to screen out DE-RBP genes after intersecting with DEGs. Samples were classified according to the relative expression of each pair of DE-RBP genes. The univariate Cox regression analysis and random forest were used to identify hub gene pairs to construct signature for predicting the prognosis of gastric cancer. Time-dependent ROC curves and KM survival curves were performed to evaluate the signature. GSEA was performed in TCGA training cohort and GSE62254 testing cohort to analyze enrichment pathways. Finally, the influence of these gene pairs on the prognosis of GC patients was further elucidated respectively through the combination of high and low expression of the two genes in each hub gene pair. RESULTS: We screened out 6 hub RBP gene pairs (COL5A2/FEN1, POP1/GFRA1, EXO1/PLEKHS1, SLC39A10/CHI3L1, MMP7/PPP1R1 B and SLC5A6/BYSL) to predict the prognosis of patients with gastric cancer. Using the optimal cut-off value to divide patients into high-risk and low-risk groups in the training and testing cohort, we found that the overall survival (OS) of the low-risk group was higher than that of the high-risk group (P < 0.05). The area under the ROC curves for 1, 3, and 5 years were (0.659, 0.744, 0.758) and (0.624, 0.650, 0.653) in two cohorts. Univariate and multivariate Cox regression analysis showed that 6 RBP gene pairs signature were independent prognostic factors for gastric cancer (P < 0.05). In addition, the prognostic survival analysis showed that COL5A2-high/FEN1-low, POP1-low/GFRA1-high, EXO1-low/PLEKHS1-low,SLC39A10-high/CHI3L1-low, MMP7-high/PPP1R1 B-low, SLC5A6-low/BYSL-low had worse OS (P < 0.05). And the gene correlation analysis showed that there was no obvious correlation between the genes in each gene pairs except SLC5A6/BYSL and POP1/GFRA1. Finally, GSEA analysis showed that the high-risk group was enriched in tumor migration, invasion and growth-related pathways. CONCLUSION: Our study identified a novel 6 RBP gene pairs signature to predict the prognosis of gastric cancer patients and provide potential targets for clinical gene therapy. Elsevier 2023-07-17 /pmc/articles/PMC10393628/ /pubmed/37539127 http://dx.doi.org/10.1016/j.heliyon.2023.e18242 Text en © 2023 Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Ning, Zhi-kun
Tian, Hua-kai
Liu, Jiang
Hu, Ce-gui
Liu, Zi-tao
Li, Hui
Zong, Zhen
Analysis and application of RNA binding protein gene pairs to predict the prognosis of gastric cancer
title Analysis and application of RNA binding protein gene pairs to predict the prognosis of gastric cancer
title_full Analysis and application of RNA binding protein gene pairs to predict the prognosis of gastric cancer
title_fullStr Analysis and application of RNA binding protein gene pairs to predict the prognosis of gastric cancer
title_full_unstemmed Analysis and application of RNA binding protein gene pairs to predict the prognosis of gastric cancer
title_short Analysis and application of RNA binding protein gene pairs to predict the prognosis of gastric cancer
title_sort analysis and application of rna binding protein gene pairs to predict the prognosis of gastric cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10393628/
https://www.ncbi.nlm.nih.gov/pubmed/37539127
http://dx.doi.org/10.1016/j.heliyon.2023.e18242
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