Project description:We selected humann intervertebral disc samples to perform proteomics analysis. There were 1 case of grade I , 1 case of grade II, 3 cases of grade Ⅲ and 3 cases of grade Ⅳ according to Pfirrmann classfication. RNA seqencing analysis and single-cell RNA sequencing were integrated with proteomics data to identify the hub genes for intervertebral disc degeneration using bioinformatic method.
Project description:Keloids are dermal fibroproliferative skin disorders caused by abnormal wound healing, resulting in impaired skin function and aesthetic defects. Abnormal fibroblast proliferation and excessive collagen deposition are involved in keloid formation. This study investigated the role of fibroblast differentiation in keloid development. Single-cell and bulk RNA sequencing data of keloids were comprehensively analyzed, and 25 clinically relevant differentially expressed fibroblast-differentiation-related genes (DEFDRGs) were identified. Based on DEFDRGs, a keloid diagnostic classification system comprising three subtypes was constructed, indicating that DEFDRGs could serve as therapeutic targets. Additionally, multiple microarray datasets, protein sequencing data, and immunohistochemical analyses of key markers in clinical keloid samples were used for further verification. In conclusion, this study established a molecular classification of keloids based on fibroblast differentiation, contributing to the further understanding of keloid pathogenesis and providing new insights for diagnosis and treatment.
Project description:To identify differentially expressed lipid metabolism-related genes (DE-LMRGs) responsible for immune dysfunction in sepsis. Machine learning algorithms were applied for screening hubgenes related to lipid metabolism. CIBERSORT and Single-sample GSEA were employed for assessing the immune cell infiltration of hubgenes. Next, the immune function of these hubgenes at the single cell level were validated by comparing multiregional immune landscapes between septic patients (SP) and healthy control (HC). Then, support vector machine - recursive feature elimination (SVM-RFE) algorithm was conducted to explore significantly altered metabolites critical to hubgenes using non-targeted liquid chromatography–high resolution mass spectrometry metabolomics from SP and HC. Furthermore, the role of most significant hubgene was verified in sepsis rats and LPS-induced cardiomyoctes, respectively. A total of 508 DE-LMRGs were identified between SP and HC and 5 hubgenes relevant to lipid metabolism-MAPK14, EPHX2, BMX, FCER1A and PAFAH2- were screened via multiple machine learning algorithms. Then, the correlation between hub genes and immune-cell infiltrations was analyzed and suggested an immunosuppressive microenvironment in sepsis. The role of hubgenes in immune cells was further confirmed by single-cell RNA landscape. Moreover, significantly altered metabolites were mainly enriched in lipid metabolism-related signaling pathways and was associated with MAPK14. Finally, inhibiting MAPK14 could decrease the levels of inflammatory cytokines, and improve the survival and myocardial injury of sepsis. This study indicates that these lipid-metabolic hubgenes may has great potential in prognosis prediction and precise treatment of sepsis.