An In‐Silico Study to Identify Relevant Biomarkers in Sepsis Applying Integrated Bulk RNA Sequencing and Single‐Cell RNA Sequencing Analyses
Abstract This study aims to discover sepsis‐related biomarkers via in‐silico analyses. The single‐cell sequencing RNA (sc‐RNA) data and metabolism‐related genes are obtained from public databases and previous studies, respectively. Cell subpopulations are identified and annotated, followed by perfor...
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Wiley
2025-04-01
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| Series: | Global Challenges |
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| Online Access: | https://doi.org/10.1002/gch2.202400321 |
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| author | Qile Ye Yuhang Dong Jingting Liang Jingyao Lv Rong Tang Shuai Zhao Guiying Hou |
| author_facet | Qile Ye Yuhang Dong Jingting Liang Jingyao Lv Rong Tang Shuai Zhao Guiying Hou |
| author_sort | Qile Ye |
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| description | Abstract This study aims to discover sepsis‐related biomarkers via in‐silico analyses. The single‐cell sequencing RNA (sc‐RNA) data and metabolism‐related genes are obtained from public databases and previous studies, respectively. Cell subpopulations are identified and annotated, followed by performing single‐sample geneset enrichment analysis (ssGSEA and identification of differentially expressed genes (DEGs). Weighted gene co‐expression network analysis (WGCNA) is applied to classify specific gene modules, and the key module is subjected to immune infiltration analysis. The communication between the subclusters of monocytes is visualized. Five cell subpopulations (subcluster C1‐5) containing a relatively higher percentage of monocytes are identified, with subcluster C4 having the lowest enrichment score of metabolism‐related genes. Genes with a higher expression in the subclusters are enriched for antigen processing and presentation of exogenous antigen, lymphocyte differentiation, and leukocyte activation. Subcluster C5 affected other subclusters through galectin 9 (LGALS9)‐CD45 and LGALS9‐CD44, while other subclusters affected subcluster C5 through MIF‐(CD74+C‐X‐C motif chemokine receptor 4 (CXCR4)) and MIF‐(CD74+CD44). Six genes (F‐Box Protein 4, FBXO4; Forkhead Box K1, FOXK1; MSH2 with MutS Homolog 2, MSH2; Nop‐7‐associated 2, NSA2; Transmembrane Protein 128, TMEM128; and SBDS) are determined as the hub genes for sepsis. The 6 hub genes are positively correlated with, among others, monocytes and NK cells, but negatively correlated with neutrophils. This study identifies accurate biomarkers for sepsis, contributing to the diagnosis and treatment of the disease. |
| format | Article |
| id | doaj-art-0dcdfecfde5244c7a108cd575b1fb441 |
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| issn | 2056-6646 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
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| series | Global Challenges |
| spelling | doaj-art-0dcdfecfde5244c7a108cd575b1fb4412025-08-20T02:12:50ZengWileyGlobal Challenges2056-66462025-04-0194n/an/a10.1002/gch2.202400321An In‐Silico Study to Identify Relevant Biomarkers in Sepsis Applying Integrated Bulk RNA Sequencing and Single‐Cell RNA Sequencing AnalysesQile Ye0Yuhang Dong1Jingting Liang2Jingyao Lv3Rong Tang4Shuai Zhao5Guiying Hou6Department of Critical Care Medicine The Second Affiliated Hospital of Harbin Medical University Harbin 150001 ChinaDepartment of Critical Care Medicine The Fourth Affiliated Hospital of Harbin Medical University Harbin 150001 ChinaDepartment of Neurology Beidahuang Industry Group General Hospital Harbin 150088 ChinaCollege of Basic Medicine Qiqihar Medical University Qiqihar 161006 ChinaIntensive Care Unit Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine Nanning 530011 ChinaDepartment of Respiratory and Critical Care Medicine The Second Affiliated Hospital of Harbin Medical University Harbin 150001 ChinaDepartment of Critical Care Medicine The Second Affiliated Hospital of Harbin Medical University Harbin 150001 ChinaAbstract This study aims to discover sepsis‐related biomarkers via in‐silico analyses. The single‐cell sequencing RNA (sc‐RNA) data and metabolism‐related genes are obtained from public databases and previous studies, respectively. Cell subpopulations are identified and annotated, followed by performing single‐sample geneset enrichment analysis (ssGSEA and identification of differentially expressed genes (DEGs). Weighted gene co‐expression network analysis (WGCNA) is applied to classify specific gene modules, and the key module is subjected to immune infiltration analysis. The communication between the subclusters of monocytes is visualized. Five cell subpopulations (subcluster C1‐5) containing a relatively higher percentage of monocytes are identified, with subcluster C4 having the lowest enrichment score of metabolism‐related genes. Genes with a higher expression in the subclusters are enriched for antigen processing and presentation of exogenous antigen, lymphocyte differentiation, and leukocyte activation. Subcluster C5 affected other subclusters through galectin 9 (LGALS9)‐CD45 and LGALS9‐CD44, while other subclusters affected subcluster C5 through MIF‐(CD74+C‐X‐C motif chemokine receptor 4 (CXCR4)) and MIF‐(CD74+CD44). Six genes (F‐Box Protein 4, FBXO4; Forkhead Box K1, FOXK1; MSH2 with MutS Homolog 2, MSH2; Nop‐7‐associated 2, NSA2; Transmembrane Protein 128, TMEM128; and SBDS) are determined as the hub genes for sepsis. The 6 hub genes are positively correlated with, among others, monocytes and NK cells, but negatively correlated with neutrophils. This study identifies accurate biomarkers for sepsis, contributing to the diagnosis and treatment of the disease.https://doi.org/10.1002/gch2.202400321bulk RNA sequencing analysiscell–cell communicationmonocytessepsissingle‐cell RNA sequencing analysis |
| spellingShingle | Qile Ye Yuhang Dong Jingting Liang Jingyao Lv Rong Tang Shuai Zhao Guiying Hou An In‐Silico Study to Identify Relevant Biomarkers in Sepsis Applying Integrated Bulk RNA Sequencing and Single‐Cell RNA Sequencing Analyses Global Challenges bulk RNA sequencing analysis cell–cell communication monocytes sepsis single‐cell RNA sequencing analysis |
| title | An In‐Silico Study to Identify Relevant Biomarkers in Sepsis Applying Integrated Bulk RNA Sequencing and Single‐Cell RNA Sequencing Analyses |
| title_full | An In‐Silico Study to Identify Relevant Biomarkers in Sepsis Applying Integrated Bulk RNA Sequencing and Single‐Cell RNA Sequencing Analyses |
| title_fullStr | An In‐Silico Study to Identify Relevant Biomarkers in Sepsis Applying Integrated Bulk RNA Sequencing and Single‐Cell RNA Sequencing Analyses |
| title_full_unstemmed | An In‐Silico Study to Identify Relevant Biomarkers in Sepsis Applying Integrated Bulk RNA Sequencing and Single‐Cell RNA Sequencing Analyses |
| title_short | An In‐Silico Study to Identify Relevant Biomarkers in Sepsis Applying Integrated Bulk RNA Sequencing and Single‐Cell RNA Sequencing Analyses |
| title_sort | in silico study to identify relevant biomarkers in sepsis applying integrated bulk rna sequencing and single cell rna sequencing analyses |
| topic | bulk RNA sequencing analysis cell–cell communication monocytes sepsis single‐cell RNA sequencing analysis |
| url | https://doi.org/10.1002/gch2.202400321 |
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