Identification of hub genes and prediction of the ceRNA network in adult sepsis
Background Sepsis refers to a dysregulated host immune response to infection. It carries a high risk of morbidity and mortality, and its pathogenesis has yet to be fully elucidated. The main aim of this study was to identify prognostic hub genes for sepsis and to predict a competitive endogenous RNA...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-08-01
|
| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/19619.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849769377742716928 |
|---|---|
| author | Kangyi Xue Kan Wu Haoxian Luo Haihua Luo Zhaoqian Zhong Fen Li Lei Li Li Chen |
| author_facet | Kangyi Xue Kan Wu Haoxian Luo Haihua Luo Zhaoqian Zhong Fen Li Lei Li Li Chen |
| author_sort | Kangyi Xue |
| collection | DOAJ |
| description | Background Sepsis refers to a dysregulated host immune response to infection. It carries a high risk of morbidity and mortality, and its pathogenesis has yet to be fully elucidated. The main aim of this study was to identify prognostic hub genes for sepsis and to predict a competitive endogenous RNA (ceRNA) network that regulates the hub genes. Methods Six transcriptome datasets from the peripheral blood of septic patients were retrieved from the Gene Expression Omnibus (GEO) database. The robust rank aggregation (RRA) method was used to screen differentially expressed genes (DEGs) across these datasets. A comprehensive bioinformatics investigation was conducted, encompassing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using the “clusterProfiler” package in R, as well as gene set enrichment analysis (GSEA) to further elucidate the biological functions and pathways associated with the DEGs. Weighted gene co-expression network analysis (WGCNA) was performed to identify a module significantly associated with sepsis. Integration of this module with protein–protein interaction (PPI) network analysis facilitated the identification of five hub genes. These hub genes were subsequently validated using an independent dataset and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis of peripheral blood samples from septic patients. The prognostic values of these hub genes were assessed via receiver operating characteristic (ROC) curve analysis. Finally, a ceRNA network regulating the prognostic hub genes was constructed by integrating data from a literature review as well as five online databases. Results RRA analysis identified 164 DEGs across six training cohorts. Bioinformatics analyses revealed concurrent hyperinflammation and immunosuppression in sepsis patients. Five hub genes were identified via WGCNA and PPI network analysis, and their differential expression was verified by the validation dataset (GSE28750) and RT-qPCR analysis in the peripheral blood of septic patients. ROC analysis confirmed four hub genes with prognostic value, and a ceRNA network was predicted to elucidate their regulatory mechanisms. Conclusion This study identified four hub genes (CLEC4D, GPR84, S100A12, and HK3) with significant prognostic value in sepsis and predicted a ceRNA network (NEAT1-hsa-miR-495-3p-ELF1) regulating their expression. The integrated analysis reconfirmed the concurrent presence of hyperinflammation and immunosuppression in hospitalized sepsis patients. These findings enhance the understanding of sepsis pathogenesis and identify potential therapeutic targets. |
| format | Article |
| id | doaj-art-a16e72909f894bbf95335678d0b5ea80 |
| institution | DOAJ |
| issn | 2167-8359 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj-art-a16e72909f894bbf95335678d0b5ea802025-08-20T03:03:25ZengPeerJ Inc.PeerJ2167-83592025-08-0113e1961910.7717/peerj.19619Identification of hub genes and prediction of the ceRNA network in adult sepsisKangyi Xue0Kan Wu1Haoxian Luo2Haihua Luo3Zhaoqian Zhong4Fen Li5Lei Li6Li Chen7Department of Urology, The Third Affiliated Hospital of Southern Medical University, Southern Medical University, Guangzhou, Guangdong Province, ChinaGuangdong Provincial Key Laboratory of Proteomics, State Key Laboratory of Organ Failure Research, Department of Pathophysiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong Province, ChinaDepartment of Anesthesiology, The Third Affiliated Hospital of Southern Medical University, Southern Medical University, Guangzhou, Guangdong Province, ChinaGuangdong Provincial Key Laboratory of Proteomics, State Key Laboratory of Organ Failure Research, Department of Pathophysiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong Province, ChinaGuangdong Provincial Key Laboratory of Proteomics, State Key Laboratory of Organ Failure Research, Department of Pathophysiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong Province, ChinaDepartment of ICU, The Third Affiliated Hospital of Southern Medical University, Southern Medical University, Guangzhou, Guangdong Province, ChinaInstitute of Infection and Immunity, Henan Academy of Innovations in Medical Science, Institute of Infection and Immunity, Zhengzhou, Henan, ChinaGuangdong Provincial Key Laboratory of Proteomics, State Key Laboratory of Organ Failure Research, Department of Pathophysiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong Province, ChinaBackground Sepsis refers to a dysregulated host immune response to infection. It carries a high risk of morbidity and mortality, and its pathogenesis has yet to be fully elucidated. The main aim of this study was to identify prognostic hub genes for sepsis and to predict a competitive endogenous RNA (ceRNA) network that regulates the hub genes. Methods Six transcriptome datasets from the peripheral blood of septic patients were retrieved from the Gene Expression Omnibus (GEO) database. The robust rank aggregation (RRA) method was used to screen differentially expressed genes (DEGs) across these datasets. A comprehensive bioinformatics investigation was conducted, encompassing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using the “clusterProfiler” package in R, as well as gene set enrichment analysis (GSEA) to further elucidate the biological functions and pathways associated with the DEGs. Weighted gene co-expression network analysis (WGCNA) was performed to identify a module significantly associated with sepsis. Integration of this module with protein–protein interaction (PPI) network analysis facilitated the identification of five hub genes. These hub genes were subsequently validated using an independent dataset and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis of peripheral blood samples from septic patients. The prognostic values of these hub genes were assessed via receiver operating characteristic (ROC) curve analysis. Finally, a ceRNA network regulating the prognostic hub genes was constructed by integrating data from a literature review as well as five online databases. Results RRA analysis identified 164 DEGs across six training cohorts. Bioinformatics analyses revealed concurrent hyperinflammation and immunosuppression in sepsis patients. Five hub genes were identified via WGCNA and PPI network analysis, and their differential expression was verified by the validation dataset (GSE28750) and RT-qPCR analysis in the peripheral blood of septic patients. ROC analysis confirmed four hub genes with prognostic value, and a ceRNA network was predicted to elucidate their regulatory mechanisms. Conclusion This study identified four hub genes (CLEC4D, GPR84, S100A12, and HK3) with significant prognostic value in sepsis and predicted a ceRNA network (NEAT1-hsa-miR-495-3p-ELF1) regulating their expression. The integrated analysis reconfirmed the concurrent presence of hyperinflammation and immunosuppression in hospitalized sepsis patients. These findings enhance the understanding of sepsis pathogenesis and identify potential therapeutic targets.https://peerj.com/articles/19619.pdfRRAWGCNAPeripheral bloodInflammationPrognostic biomarker |
| spellingShingle | Kangyi Xue Kan Wu Haoxian Luo Haihua Luo Zhaoqian Zhong Fen Li Lei Li Li Chen Identification of hub genes and prediction of the ceRNA network in adult sepsis PeerJ RRA WGCNA Peripheral blood Inflammation Prognostic biomarker |
| title | Identification of hub genes and prediction of the ceRNA network in adult sepsis |
| title_full | Identification of hub genes and prediction of the ceRNA network in adult sepsis |
| title_fullStr | Identification of hub genes and prediction of the ceRNA network in adult sepsis |
| title_full_unstemmed | Identification of hub genes and prediction of the ceRNA network in adult sepsis |
| title_short | Identification of hub genes and prediction of the ceRNA network in adult sepsis |
| title_sort | identification of hub genes and prediction of the cerna network in adult sepsis |
| topic | RRA WGCNA Peripheral blood Inflammation Prognostic biomarker |
| url | https://peerj.com/articles/19619.pdf |
| work_keys_str_mv | AT kangyixue identificationofhubgenesandpredictionofthecernanetworkinadultsepsis AT kanwu identificationofhubgenesandpredictionofthecernanetworkinadultsepsis AT haoxianluo identificationofhubgenesandpredictionofthecernanetworkinadultsepsis AT haihualuo identificationofhubgenesandpredictionofthecernanetworkinadultsepsis AT zhaoqianzhong identificationofhubgenesandpredictionofthecernanetworkinadultsepsis AT fenli identificationofhubgenesandpredictionofthecernanetworkinadultsepsis AT leili identificationofhubgenesandpredictionofthecernanetworkinadultsepsis AT lichen identificationofhubgenesandpredictionofthecernanetworkinadultsepsis |