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100 Clinical Results associated with RGCC

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100 Translational Medicine associated with RGCC

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0 Patents (Medical) associated with RGCC

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Literatures (Medical) associated with RGCC

01 Aug 2024·Heliyon

Prediction and verification of benignancy and malignancy of pulmonary nodules based on inflammatory related biological markers

Article

Author: Zhang, Zexin ; Lin, Siqi ; Wu, Wenfeng ; Lin, Lizhu ; Zhang, Haibo ; Lei, Qiwei ; Yu, Ling ; Lin, Jietao ; Li, Xuewei ; Sun, Lingling

ObjectiveInflammation plays an important role in the transformation of pulmonary nodules (PNs) from benign to malignant. Prediction of benignancy and malignancy of PNs is still lacking efficacy methods. Although Mayo or Brock model have been widely applied in clinical practices, their application conditions are limited. This study aims to construct a diagnostic model of PNs by machine learning using inflammation-related biological markers (IRBMs).MethodsInflammatory related genes (IRGs) were first extracted from GSE135304 chip data. Then, differentially expressed genes (DEGs) and infiltrating immune cells were screened between malignant pulmonary nodules (MN) and benign pulmonary nodule (BN). Correlation analysis was performed on DEGs and infiltrating immune cells. Molecular modules of IRGs were identified through Consistency cluster analysis. Subsequently, IRBMs in IRGs modules were filtered through Weighted gene co-expression network analysis (WGCNA). An optimal diagnostic model was established using machine learning methods. Finally, external dataset GSE108375 was used to verify this result.Results4 hub IRGs and 3 immune cells showed significantly difference between MN and BN, C1 and C2 module, namely PRTN3, ELANE, NFKB1 and CTLA4, T cells CD4 naïve, NK cells activated and Monocytes. IRBMs were screened from black module and yellowgreen module through WGCNA analysis. The Support vector machines (SVM) was identified as the optimal model with the Area Under Curve (AUC) was 0.753. A nomogram was established based on 5 hub IRBMs, namely HS.137078, KLC3, C13ORF15, STOM and KCTD13. Finally, external dataset GSE108375 verified this result, with the AUC was 0.718.ConclusionSVM model established by 5 hub IRBMs was able to effectively identify MN or BN. Accumulating inflammation and immune dysfunction were important to the transformation from BN to MN.

01 Aug 2024·Heliyon

Prediction and verification of benignancy and malignancy of pulmonary nodules based on inflammatory related biological markers

Article

Author: Zhang, Zexin ; Lin, Siqi ; Wu, Wenfeng ; Lin, Lizhu ; Zhang, Haibo ; Lei, Qiwei ; Yu, Ling ; Lin, Jietao ; Li, Xuewei ; Sun, Lingling

ObjectiveInflammation plays an important role in the transformation of pulmonary nodules (PNs) from benign to malignant. Prediction of benignancy and malignancy of PNs is still lacking efficacy methods. Although Mayo or Brock model have been widely applied in clinical practices, their application conditions are limited. This study aims to construct a diagnostic model of PNs by machine learning using inflammation-related biological markers (IRBMs).MethodsInflammatory related genes (IRGs) were first extracted from GSE135304 chip data. Then, differentially expressed genes (DEGs) and infiltrating immune cells were screened between malignant pulmonary nodules (MN) and benign pulmonary nodule (BN). Correlation analysis was performed on DEGs and infiltrating immune cells. Molecular modules of IRGs were identified through Consistency cluster analysis. Subsequently, IRBMs in IRGs modules were filtered through Weighted gene co-expression network analysis (WGCNA). An optimal diagnostic model was established using machine learning methods. Finally, external dataset GSE108375 was used to verify this result.Results4 hub IRGs and 3 immune cells showed significantly difference between MN and BN, C1 and C2 module, namely PRTN3, ELANE, NFKB1 and CTLA4, T cells CD4 naïve, NK cells activated and Monocytes. IRBMs were screened from black module and yellowgreen module through WGCNA analysis. The Support vector machines (SVM) was identified as the optimal model with the Area Under Curve (AUC) was 0.753. A nomogram was established based on 5 hub IRBMs, namely HS.137078, KLC3, C13ORF15, STOM and KCTD13. Finally, external dataset GSE108375 verified this result, with the AUC was 0.718.ConclusionSVM model established by 5 hub IRBMs was able to effectively identify MN or BN. Accumulating inflammation and immune dysfunction were important to the transformation from BN to MN.

01 Aug 2024·Clinical Immunology

RGC-32 mediates proinflammatory and profibrotic pathways in immune-mediated kidney disease

Article

Author: Papadimitriou, John ; Rus, Horea G ; Nguyen, Vinh ; Badea, Tudor C ; Atamas, Sergei P ; Rus, Violeta ; Vlaicu, Sonia ; Tatomir, Alexandru ; Luzina, Irina G ; Drachenberg, Cinthia

Systemic lupus erythematosus is an autoimmune disease that results in immune-mediated damage to kidneys and other organs. We investigated the role of response gene to complement-32 (RGC-32), a proinflammatory and profibrotic mediator induced by TGFβ and C5b-9, in nephrotoxic nephritis (NTN), an experimental model that mimics human lupus nephritis. Proteinuria, loss of renal function and kidney histopathology were attenuated in RGC-32 KO NTN mice. RGC-32 KO NTN mice displayed downregulation of the CCL20/CCR6 and CXCL9/CXCR3 ligand/receptor pairs resulting in decreased renal recruitment of IL-17⁺ and IFNγ⁺ cells and subsequent decrease in the influx of innate immune cells. RGC-32 deficiency attenuated renal fibrosis as demonstrated by decreased deposition of collagen I, III and fibronectin. Thus, RGC-32 is a unique mediator shared by the Th17 and Th1 dependent proinflammatory and profibrotic pathways and a potential novel therapeutic target in the treatment of immune complex mediated glomerulonephritis such as lupus nephritis.

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