Identification of key genes in sepsis by WGCNA

Gao, Xuemeng; Zhou, Xiaofeng; Jia, Meng-Wei; Wang, Xiao-Zhuo; Liú, Dan

doi:10.1016/j.ypmed.2023.107540

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…WGCNA has been used to explore biomarkers related to pathogenesis, diagnosis and prognosis of LIHC. Immune cell infiltration related biomarkers in LIHC including CCL5, CXCR6, CD3E and LCK were identified using WGCNA (Bai et al 2020;Gao et al 2023).…”

Section: Discussionmentioning

confidence: 99%

Identification of Hub Genes in Liver Hepatocellular Carcinoma Based on Weighted Gene Co-expression Network Analysis

Sun,

Zhang,

Cai

et al. 2024

Biochem Genet

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Liver hepatocellular carcinoma (LIHC) is a malignant cancer with high incidence and poor prognosis. To investigate the correlation between hub genes and progression of LIHC and to provided potential prognostic markers and therapy targets for LIHC. Our study mainly used The Cancer Genome Atlas (TCGA) LIHC database and the gene expression profiles of GSE54236 from the Gene Expression Omnibus (GEO) to explore the differential co-expression genes between LIHC and normal tissues. The differential co-expression genes were extracted by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. The Genetic Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to annotate the function of differential genes. Then the hub genes were validated using protein-protein interaction (PPI) network. And the expression level and prognostic analysis were performed. The probable associations between the expression of hub genes and both tumor purity and infiltration of immune cells were explored by TIMER. A total of 68 differential co-expression genes were extracted. These genes were mainly enriched in complement activation (biological process), collagen trimer (cellular component), carbohydrate binding and receptor ligand activity (molecular function) and cytokine − cytokine receptor interaction. Then we demonstrated that the 10 hub genes (CFP, CLEC1B, CLEC4G, CLEC4M, FCN2, FCN3, PAMR1 and TIMD4) were weakly expressed in LIHC tissues, the qRT-PCR results of clinical samples showed that six genes were significantly downregulated in LIHC patients compared with adjacent tissues. Worse overall survival (OS) and disease-free survival (DFS) in LIHC patients were associated with the lower expression of CFP, CLEC1B, FCN3 and TIMD4. Ten hub genes had positive association with tumor purity. CFP, CLEC1B, FCN3 and TIMD4 could serve as novel potential molecular targets for prognosis prediction in LIHC.

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Section: Discussionmentioning

confidence: 99%

Identification of Hub Genes in Liver Hepatocellular Carcinoma Based on Weighted Gene Co-expression Network Analysis

Sun,

Zhang,

Cai

et al. 2024

Biochem Genet

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“…The mRNA expression profiles of 1231 UbRGs were extracted from the GSE76427 and TCGA databases for further analysis. WGCNA has been effectively utilized in numerous biological studies 14 - 16 . The "WGCNA" package was used to transform the similarity matrix into an adjacency matrix using a weight coefficient of 15.…”

Section: Methodsmentioning

confidence: 99%

Prognostic and Immune Landscape Analysis of Ubiquitination-related Genes in Hepatocellular Carcinoma: Based on Bulk and Single-cell RNA Sequencing Data

Yuan,

Zhu,

et al. 2024

J. Cancer

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Background: Despite significant advances in tumor immunotherapy, hepatocellular carcinoma (HCC) remains a malignancy with a challenging prognosis. The increasing research emphasizes the crucial role of ubiquitination in tumor immunotherapy. However, the establishment of prognostic signatures based on ubiquitination-related genes (UbRGs) and their role in immunotherapy are still lacking in HCC. Methods: We employed datasets from TCGA and GEO for transcriptome differential expression analysis and single-cell RNA sequencing analysis. Applying weighted gene co-expression network analysis, cox regression, lasso, selection and visualization of the most relevant features, and gradient boosting machine, we identified hub UbRGs as a gene signature to develop a prognostic model. We evaluated the predictive utility concerning clinical characteristics as well as its role in the immune landscape and immunotherapy potential. Additionally, western blotting, reverse transcription-quantitative PCR, and immunofluorescence were employed to detect the expression and sub-localization of hub genes. Results: Three hub UbRGs (BOP1, CDC20, and UBE2S) were identified as a gene signature. In particular, the high-risk group exhibited notable characteristics, including higher tumor mutation burden, enrichment in immune-related pathways, up-regulation immune checkpoint, and higher immunity scores. Treatment response to immunotherapy varied based on the expression of PD-1 and CTLA-4. Furthermore, single-cell data analysis revealed heterogeneous expression of hub UbRGs across different cell subtypes, while cytological experiments provided additional confirmation of the high expression of hub UbRGs in HCC. Conclusion: Our study provides valuable insights into the identification of novel ubiquitination-related biomarkers with potential applications for prognosis, immunotherapy prediction, and drug sensitivity in HCC.

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“…Finally, the expression profiles of each module were summarized using module eigengenes (ME), and the correlation between ME and specific traits was computed. Modules that exhibited the highest relevance to the traits of interest were subsequently selected for further analysis [ 17 – 19 ].…”

Section: Methodsmentioning

confidence: 99%

Diagnostic potential of NRG1 in benign nerve sheath tumors and its influence on the PI3K-Akt signaling and tumor immunity

Yan,

Zhao,

Gao

et al. 2024

Diagn Pathol

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Objective Benign nerve sheath tumors (BNSTs) present diagnostic challenges due to their heterogeneous nature. This study aimed to determine the significance of NRG1 as a novel diagnostic biomarker in BNST, emphasizing its involvement in the PI3K-Akt pathway and tumor immune regulation. Methods Differential genes related to BNST were identified from the GEO database. Gene co-expression networks, protein-protein interaction networks, and LASSO regression were utilized to pinpoint key genes. The CIBERSORT algorithm assessed immune cell infiltration differences, and functional enrichment analyses explored BNST signaling pathways. Clinical samples helped establish PDX models, and in vitro cell lines to validate NRG1’s role via the PI3K-Akt pathway. Results Nine hundred eighty-two genes were upregulated, and 375 downregulated in BNST samples. WGCNA revealed the brown module with the most significant difference. Top hub genes included NRG1, which was also determined as a pivotal gene in disease characterization. Immune infiltration showed significant variances in neutrophils and M2 macrophages, with NRG1 playing a central role. Functional analyses confirmed NRG1’s involvement in key pathways. Validation experiments using PDX models and cell lines further solidified NRG1’s role in BNST. Conclusion NRG1 emerges as a potential diagnostic biomarker for BNST, influencing the PI3K-Akt pathway, and shaping the tumor immune microenvironment.

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Identification of key genes in sepsis by WGCNA

Cited by 9 publications

References 27 publications

Identification of Hub Genes in Liver Hepatocellular Carcinoma Based on Weighted Gene Co-expression Network Analysis

Identification of Hub Genes in Liver Hepatocellular Carcinoma Based on Weighted Gene Co-expression Network Analysis

Prognostic and Immune Landscape Analysis of Ubiquitination-related Genes in Hepatocellular Carcinoma: Based on Bulk and Single-cell RNA Sequencing Data

Diagnostic potential of NRG1 in benign nerve sheath tumors and its influence on the PI3K-Akt signaling and tumor immunity

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