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Identification of the key immune-related genes and immune cell infiltration changes in renal interstitial fibrosis

BACKGROUND: Chronic kidney disease (CKD) is the third-leading cause of premature mortality worldwide. It is characterized by rapid deterioration due to renal interstitial fibrosis (RIF) via excessive inflammatory infiltration. The aim of this study was to discover key immune-related genes (IRGs) to...

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Autores principales: Dong, Zhitao, Chen, Fangzhi, Peng, Shuang, Liu, Xiongfei, Liu, Xingyang, Guo, Lizhe, Wang, E., Chen, Xiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663291/
https://www.ncbi.nlm.nih.gov/pubmed/38027143
http://dx.doi.org/10.3389/fendo.2023.1207444
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author Dong, Zhitao
Chen, Fangzhi
Peng, Shuang
Liu, Xiongfei
Liu, Xingyang
Guo, Lizhe
Wang, E.
Chen, Xiang
author_facet Dong, Zhitao
Chen, Fangzhi
Peng, Shuang
Liu, Xiongfei
Liu, Xingyang
Guo, Lizhe
Wang, E.
Chen, Xiang
author_sort Dong, Zhitao
collection PubMed
description BACKGROUND: Chronic kidney disease (CKD) is the third-leading cause of premature mortality worldwide. It is characterized by rapid deterioration due to renal interstitial fibrosis (RIF) via excessive inflammatory infiltration. The aim of this study was to discover key immune-related genes (IRGs) to provide valuable insights and therapeutic targets for RIF in CKD. MATERIALS AND METHODS: We screened differentially expressed genes (DEGs) between RIF samples from CKD patients and healthy controls from a public database. Least absolute shrinkage and selection operator regression analysis and receiver operating characteristic curve analysis were applied to identify significant key biomarkers. The single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to analyze the infiltration of immune cells between the RIF and control samples. The correlation between biomarkers and immune cell composition was assessed. RESULTS: A total of 928 DEGs between CKD and control samples from six microarray datasets were found, 17 overlapping immune-correlated DEGs were identified by integration with the ImmPort database, and six IRGs were finally identified in the model: apolipoprotein H (APOH), epidermal growth factor (EGF), lactotransferrin (LTF), lysozyme (LYZ), phospholipid transfer protein (PLTP), and secretory leukocyte peptidase inhibitor (SLPI). Two additional datasets and in vivo experiments indicated that the expression levels of APOH and EGF in the fibrosis group were significantly lower than those in the control group, while the expression levels of LTF, LYZ, PLTP, and SLPI were higher (all P < 0.05). These IRGs also showed a significant correlation with renal function impairment. Moreover, four upregulated IRGs were positively associated with various T cell populations, which were enriched in RIF tissues, whereas two downregulated IRGs had opposite results. Several signaling pathways, such as the “T cell receptor signaling pathway” and “positive regulation of NF-κB signaling pathway”, were discovered to be associated not only with immune cell infiltration, but also with the expression levels of six IRGs. CONCLUSION: In summary, six IRGs were identified as key biomarkers for RIF, and exhibited a strong correlation with various T cells and with the NF-κB signaling pathway. All these IRGs and their signaling pathways may evolve as valuable therapeutic targets for RIF in CKD.
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spelling pubmed-106632912023-01-01 Identification of the key immune-related genes and immune cell infiltration changes in renal interstitial fibrosis Dong, Zhitao Chen, Fangzhi Peng, Shuang Liu, Xiongfei Liu, Xingyang Guo, Lizhe Wang, E. Chen, Xiang Front Endocrinol (Lausanne) Endocrinology BACKGROUND: Chronic kidney disease (CKD) is the third-leading cause of premature mortality worldwide. It is characterized by rapid deterioration due to renal interstitial fibrosis (RIF) via excessive inflammatory infiltration. The aim of this study was to discover key immune-related genes (IRGs) to provide valuable insights and therapeutic targets for RIF in CKD. MATERIALS AND METHODS: We screened differentially expressed genes (DEGs) between RIF samples from CKD patients and healthy controls from a public database. Least absolute shrinkage and selection operator regression analysis and receiver operating characteristic curve analysis were applied to identify significant key biomarkers. The single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to analyze the infiltration of immune cells between the RIF and control samples. The correlation between biomarkers and immune cell composition was assessed. RESULTS: A total of 928 DEGs between CKD and control samples from six microarray datasets were found, 17 overlapping immune-correlated DEGs were identified by integration with the ImmPort database, and six IRGs were finally identified in the model: apolipoprotein H (APOH), epidermal growth factor (EGF), lactotransferrin (LTF), lysozyme (LYZ), phospholipid transfer protein (PLTP), and secretory leukocyte peptidase inhibitor (SLPI). Two additional datasets and in vivo experiments indicated that the expression levels of APOH and EGF in the fibrosis group were significantly lower than those in the control group, while the expression levels of LTF, LYZ, PLTP, and SLPI were higher (all P < 0.05). These IRGs also showed a significant correlation with renal function impairment. Moreover, four upregulated IRGs were positively associated with various T cell populations, which were enriched in RIF tissues, whereas two downregulated IRGs had opposite results. Several signaling pathways, such as the “T cell receptor signaling pathway” and “positive regulation of NF-κB signaling pathway”, were discovered to be associated not only with immune cell infiltration, but also with the expression levels of six IRGs. CONCLUSION: In summary, six IRGs were identified as key biomarkers for RIF, and exhibited a strong correlation with various T cells and with the NF-κB signaling pathway. All these IRGs and their signaling pathways may evolve as valuable therapeutic targets for RIF in CKD. Frontiers Media S.A. 2023-11-08 /pmc/articles/PMC10663291/ /pubmed/38027143 http://dx.doi.org/10.3389/fendo.2023.1207444 Text en Copyright © 2023 Dong, Chen, Peng, Liu, Liu, Guo, Wang and Chen https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Dong, Zhitao
Chen, Fangzhi
Peng, Shuang
Liu, Xiongfei
Liu, Xingyang
Guo, Lizhe
Wang, E.
Chen, Xiang
Identification of the key immune-related genes and immune cell infiltration changes in renal interstitial fibrosis
title Identification of the key immune-related genes and immune cell infiltration changes in renal interstitial fibrosis
title_full Identification of the key immune-related genes and immune cell infiltration changes in renal interstitial fibrosis
title_fullStr Identification of the key immune-related genes and immune cell infiltration changes in renal interstitial fibrosis
title_full_unstemmed Identification of the key immune-related genes and immune cell infiltration changes in renal interstitial fibrosis
title_short Identification of the key immune-related genes and immune cell infiltration changes in renal interstitial fibrosis
title_sort identification of the key immune-related genes and immune cell infiltration changes in renal interstitial fibrosis
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10663291/
https://www.ncbi.nlm.nih.gov/pubmed/38027143
http://dx.doi.org/10.3389/fendo.2023.1207444
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