ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network
Abstract Background Sepsis is a life-threatening multi-organ dysfunction caused by the dysregulated host response to infection. Sepsis remains a major global concern with high mortality and morbidity, while management of sepsis patients relies heavily on early recognition and rapid stratification. T...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-06-01
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| Series: | Hereditas |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41065-022-00240-1 |
| _version_ | 1818545891979558912 |
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| author | Jing-Xiang Zhang Wei-Heng Xu Xin-Hao Xing Lin-Lin Chen Qing-Jie Zhao Yan Wang |
| author_facet | Jing-Xiang Zhang Wei-Heng Xu Xin-Hao Xing Lin-Lin Chen Qing-Jie Zhao Yan Wang |
| author_sort | Jing-Xiang Zhang |
| collection | DOAJ |
| description | Abstract Background Sepsis is a life-threatening multi-organ dysfunction caused by the dysregulated host response to infection. Sepsis remains a major global concern with high mortality and morbidity, while management of sepsis patients relies heavily on early recognition and rapid stratification. This study aims to identify the crucial genes and biomarkers for sepsis which could guide clinicians to make rapid diagnosis and prognostication. Methods Preliminary analysis of multiple global datasets, including 170 samples from patients with sepsis and 110 healthy control samples, revealed common differentially expressed genes (DEGs) in peripheral blood of patients with sepsis. After Gene Oncology (GO) and pathway analysis, the Weighted Gene Correlation Network Analysis (WGCNA) was used to screen for genes most related with clinical diagnosis. Also, the Protein-Protein Interaction Network (PPI Network) was constructed based on the DEGs and the hub genes were found. The results of WGCNA and PPI network were compared and one shared gene was discovered. Then more datasets of 728 experimental samples and 355 control samples were used to prove the diagnostic and prognostic value of this gene. Last, we used real-time PCR to confirm the bioinformatic results. Results Four hundred forty-four common differentially expressed genes in the blood of sepsis patients from different ethnicities were identified. Fifteen genes most related with clinical diagnosis were found by WGCNA, and 24 hub genes with most node degrees were identified by PPI network. ARG1 turned out to be the unique overlapped gene. Further analysis using more datasets showed that ARG1 was not only sharply up-regulated in sepsis than in healthy controls, but also significantly high-expressed in septic shock than in non-septic shock, significantly high-expressed in severe or lethal sepsis than in uncomplicated sepsis, and significantly high-expressed in non-responders than in responders upon early treatment. These all demonstrate the performance of ARG1 as a key biomarker. Last, the up-regulation of ARG1 in the blood was confirmed experimentally. Conclusions We identified crucial genes that may play significant roles in sepsis by WGCNA and PPI network. ARG1 was the only overlapped gene in both results and could be used to make an accurate diagnosis, discriminate the severity and predict the treatment response of sepsis. |
| first_indexed | 2024-12-12T07:45:55Z |
| format | Article |
| id | doaj.art-8c29248e17df454797002a70d719786b |
| institution | Directory Open Access Journal |
| issn | 1601-5223 |
| language | English |
| last_indexed | 2024-12-12T07:45:55Z |
| publishDate | 2022-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Hereditas |
| spelling | doaj.art-8c29248e17df454797002a70d719786b2022-12-22T00:32:35ZengBMCHereditas1601-52232022-06-01159111310.1186/s41065-022-00240-1ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI networkJing-Xiang Zhang0Wei-Heng Xu1Xin-Hao Xing2Lin-Lin Chen3Qing-Jie Zhao4Yan Wang5School of Pharmacy, Second Military Medical UniversitySchool of Pharmacy, Second Military Medical UniversitySchool of Pharmacy, Second Military Medical UniversitySchool of Pharmacy, Second Military Medical UniversitySchool of Pharmacy, Second Military Medical UniversitySchool of Pharmacy, Second Military Medical UniversityAbstract Background Sepsis is a life-threatening multi-organ dysfunction caused by the dysregulated host response to infection. Sepsis remains a major global concern with high mortality and morbidity, while management of sepsis patients relies heavily on early recognition and rapid stratification. This study aims to identify the crucial genes and biomarkers for sepsis which could guide clinicians to make rapid diagnosis and prognostication. Methods Preliminary analysis of multiple global datasets, including 170 samples from patients with sepsis and 110 healthy control samples, revealed common differentially expressed genes (DEGs) in peripheral blood of patients with sepsis. After Gene Oncology (GO) and pathway analysis, the Weighted Gene Correlation Network Analysis (WGCNA) was used to screen for genes most related with clinical diagnosis. Also, the Protein-Protein Interaction Network (PPI Network) was constructed based on the DEGs and the hub genes were found. The results of WGCNA and PPI network were compared and one shared gene was discovered. Then more datasets of 728 experimental samples and 355 control samples were used to prove the diagnostic and prognostic value of this gene. Last, we used real-time PCR to confirm the bioinformatic results. Results Four hundred forty-four common differentially expressed genes in the blood of sepsis patients from different ethnicities were identified. Fifteen genes most related with clinical diagnosis were found by WGCNA, and 24 hub genes with most node degrees were identified by PPI network. ARG1 turned out to be the unique overlapped gene. Further analysis using more datasets showed that ARG1 was not only sharply up-regulated in sepsis than in healthy controls, but also significantly high-expressed in septic shock than in non-septic shock, significantly high-expressed in severe or lethal sepsis than in uncomplicated sepsis, and significantly high-expressed in non-responders than in responders upon early treatment. These all demonstrate the performance of ARG1 as a key biomarker. Last, the up-regulation of ARG1 in the blood was confirmed experimentally. Conclusions We identified crucial genes that may play significant roles in sepsis by WGCNA and PPI network. ARG1 was the only overlapped gene in both results and could be used to make an accurate diagnosis, discriminate the severity and predict the treatment response of sepsis.https://doi.org/10.1186/s41065-022-00240-1SepsisBioinformatical analysisDifferentially expressed genesWGCNAARG1 |
| spellingShingle | Jing-Xiang Zhang Wei-Heng Xu Xin-Hao Xing Lin-Lin Chen Qing-Jie Zhao Yan Wang ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network Hereditas Sepsis Bioinformatical analysis Differentially expressed genes WGCNA ARG1 |
| title | ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network |
| title_full | ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network |
| title_fullStr | ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network |
| title_full_unstemmed | ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network |
| title_short | ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network |
| title_sort | arg1 as a promising biomarker for sepsis diagnosis and prognosis evidence from wgcna and ppi network |
| topic | Sepsis Bioinformatical analysis Differentially expressed genes WGCNA ARG1 |
| url | https://doi.org/10.1186/s41065-022-00240-1 |
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