Cargando…

Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is characterized by excessive liver fat deposition, and progresses to liver cirrhosis, and even hepatocellular carcinoma. However, the invasive diagnosis of NAFLD with histopathological evaluation remains risky. This study investigated potential g...

Descripción completa

Detalles Bibliográficos
Autores principales: Song, Zhengmao, Wang, Yun, Lin, Pingli, Yang, Kaichun, Jiang, Xilin, Dong, Junchen, Xie, Shangjin, Rao, Rong, Cui, Lishan, Liu, Feng, Huang, Xuefeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10364401/
https://www.ncbi.nlm.nih.gov/pubmed/37488473
http://dx.doi.org/10.1186/s12864-023-09458-3
_version_ 1785076837218516992
author Song, Zhengmao
Wang, Yun
Lin, Pingli
Yang, Kaichun
Jiang, Xilin
Dong, Junchen
Xie, Shangjin
Rao, Rong
Cui, Lishan
Liu, Feng
Huang, Xuefeng
author_facet Song, Zhengmao
Wang, Yun
Lin, Pingli
Yang, Kaichun
Jiang, Xilin
Dong, Junchen
Xie, Shangjin
Rao, Rong
Cui, Lishan
Liu, Feng
Huang, Xuefeng
author_sort Song, Zhengmao
collection PubMed
description BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is characterized by excessive liver fat deposition, and progresses to liver cirrhosis, and even hepatocellular carcinoma. However, the invasive diagnosis of NAFLD with histopathological evaluation remains risky. This study investigated potential genes correlated with NAFLD, which may serve as diagnostic biomarkers and even potential treatment targets. METHODS: The weighted gene co-expression network analysis (WGCNA) was constructed based on dataset E-MEXP-3291. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to evaluate the function of genes. RESULTS: Blue module was positively correlated, and turquoise module negatively correlated with the severity of NAFLD. Furthermore, 8 driving genes (ANXA9, FBXO2, ORAI3, NAGS, C/EBPα, CRYAA, GOLM1, TRIM14) were identified from the overlap of genes in blue module and GSE89632. And another 8 driving genes were identified from the overlap of turquoise module and GSE89632. Among these driving genes, C/EBPα (CCAAT/enhancer binding protein α) was the most notable. By validating the expression of C/EBPα in the liver of NAFLD mice using immunohistochemistry, we discovered a significant upregulation of C/EBPα protein in NAFLD. CONCLUSION: we identified two modules and 16 driving genes associated with the progression of NAFLD, and confirmed the protein expression of C/EBPα, which had been paid little attention to in the context of NAFLD, in the present study. Our study will advance the understanding of NAFLD. Moreover, these driving genes may serve as biomarkers and therapeutic targets of NAFLD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09458-3.
format Online
Article
Text
id pubmed-10364401
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-103644012023-07-25 Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis Song, Zhengmao Wang, Yun Lin, Pingli Yang, Kaichun Jiang, Xilin Dong, Junchen Xie, Shangjin Rao, Rong Cui, Lishan Liu, Feng Huang, Xuefeng BMC Genomics Research BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is characterized by excessive liver fat deposition, and progresses to liver cirrhosis, and even hepatocellular carcinoma. However, the invasive diagnosis of NAFLD with histopathological evaluation remains risky. This study investigated potential genes correlated with NAFLD, which may serve as diagnostic biomarkers and even potential treatment targets. METHODS: The weighted gene co-expression network analysis (WGCNA) was constructed based on dataset E-MEXP-3291. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to evaluate the function of genes. RESULTS: Blue module was positively correlated, and turquoise module negatively correlated with the severity of NAFLD. Furthermore, 8 driving genes (ANXA9, FBXO2, ORAI3, NAGS, C/EBPα, CRYAA, GOLM1, TRIM14) were identified from the overlap of genes in blue module and GSE89632. And another 8 driving genes were identified from the overlap of turquoise module and GSE89632. Among these driving genes, C/EBPα (CCAAT/enhancer binding protein α) was the most notable. By validating the expression of C/EBPα in the liver of NAFLD mice using immunohistochemistry, we discovered a significant upregulation of C/EBPα protein in NAFLD. CONCLUSION: we identified two modules and 16 driving genes associated with the progression of NAFLD, and confirmed the protein expression of C/EBPα, which had been paid little attention to in the context of NAFLD, in the present study. Our study will advance the understanding of NAFLD. Moreover, these driving genes may serve as biomarkers and therapeutic targets of NAFLD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09458-3. BioMed Central 2023-07-24 /pmc/articles/PMC10364401/ /pubmed/37488473 http://dx.doi.org/10.1186/s12864-023-09458-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Song, Zhengmao
Wang, Yun
Lin, Pingli
Yang, Kaichun
Jiang, Xilin
Dong, Junchen
Xie, Shangjin
Rao, Rong
Cui, Lishan
Liu, Feng
Huang, Xuefeng
Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis
title Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis
title_full Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis
title_fullStr Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis
title_full_unstemmed Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis
title_short Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis
title_sort identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10364401/
https://www.ncbi.nlm.nih.gov/pubmed/37488473
http://dx.doi.org/10.1186/s12864-023-09458-3
work_keys_str_mv AT songzhengmao identificationofkeymodulesanddrivinggenesinnonalcoholicfattyliverdiseasebyweightedgenecoexpressionnetworkanalysis
AT wangyun identificationofkeymodulesanddrivinggenesinnonalcoholicfattyliverdiseasebyweightedgenecoexpressionnetworkanalysis
AT linpingli identificationofkeymodulesanddrivinggenesinnonalcoholicfattyliverdiseasebyweightedgenecoexpressionnetworkanalysis
AT yangkaichun identificationofkeymodulesanddrivinggenesinnonalcoholicfattyliverdiseasebyweightedgenecoexpressionnetworkanalysis
AT jiangxilin identificationofkeymodulesanddrivinggenesinnonalcoholicfattyliverdiseasebyweightedgenecoexpressionnetworkanalysis
AT dongjunchen identificationofkeymodulesanddrivinggenesinnonalcoholicfattyliverdiseasebyweightedgenecoexpressionnetworkanalysis
AT xieshangjin identificationofkeymodulesanddrivinggenesinnonalcoholicfattyliverdiseasebyweightedgenecoexpressionnetworkanalysis
AT raorong identificationofkeymodulesanddrivinggenesinnonalcoholicfattyliverdiseasebyweightedgenecoexpressionnetworkanalysis
AT cuilishan identificationofkeymodulesanddrivinggenesinnonalcoholicfattyliverdiseasebyweightedgenecoexpressionnetworkanalysis
AT liufeng identificationofkeymodulesanddrivinggenesinnonalcoholicfattyliverdiseasebyweightedgenecoexpressionnetworkanalysis
AT huangxuefeng identificationofkeymodulesanddrivinggenesinnonalcoholicfattyliverdiseasebyweightedgenecoexpressionnetworkanalysis