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Ferroptosis-related genes participate in the microglia-induced neuroinflammation of spinal cord injury via NF-κB signaling: evidence from integrated single-cell and spatial transcriptomic analysis.
J Transl Med
January 2025
Division of Spine, Department of Orthopedics, Tongji Hospital affiliated to Tongji University, Tongji University School of Medicine, Shanghai, 200065, China.
Background: Ferroptosis and immune responses are critical pathological events in spinal cord injury (SCI), whereas relative molecular and cellular mechanisms remain unclear.
Methods: Micro-array datasets (GSE45006, GSE69334), RNA sequencing (RNA-seq) dataset (GSE151371), spatial transcriptome datasets (GSE214349, GSE184369), and single cell RNA sequencing (scRNA-seq) datasets (GSE162610, GSE226286) were available from the Gene Expression Omnibus (GEO) database. Through weighted gene co-expression network analysis and differential expression analysis in GSE45006, we identified differentially expressed time- and immune-related genes (DETIRGs) associated with chronic SCI and differentially expressed ferroptosis- and immune-related genes (DEFIRGs), which were validated in GSE151371.
Comprehensive analysis pinpoints CCNA2 as a prognostic and immunological biomarker in non-small cell lung cancer.
BMC Pulm Med
January 2025
Medical Research Center, Affiliated Hospital of Jining Medical University, Jining Medical University, 89 Guhuai Road, Jining, Shandong Province, 272029, PR China.
Background: Lung cancer is a leading cause of morbidity and mortality globally. Despite advances in targeted and immunotherapies, overall survival (OS) rates remain suboptimal. Cyclin-A2 (CCNA2), known for its upregulation in various tumors and role in tumorigenesis, has an undefined function in non-small cell lung cancer (NSCLC).
View Article and Find Full Text PDFThe role of mitophagy-related genes in prognosis and immunotherapy of cutaneous melanoma: a comprehensive analysis based on single-cell RNA sequencing and machine learning.
Immunol Res
January 2025
Department of Dermatology, Shaanxi Provincial People's Hospital, Xi'an, 710068, China.
Mitophagy, the selective degradation of mitochondria by autophagy, plays a crucial role in cancer progression and therapy response. This study aims to elucidate the role of mitophagy-related genes (MRGs) in cutaneous melanoma (CM) through single-cell RNA sequencing (scRNA-seq) and machine learning approaches, ultimately developing a predictive model for patient prognosis. The scRNA-seq data, bulk transcriptomic data, and clinical data of CM were obtained from publicly available databases.
View Article and Find Full Text PDFIntegrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma.
Sci Rep
January 2025
Department of Urology, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further research is required to identify how metabolic reprogramming affects interpatient heterogeneity and prognosis in clear cell renal cell carcinoma (ccRCC). In this work, single-cell RNA sequencing (scRNA-seq) based deconvolution was utilized to create a malignant cell hierarchy with metabolic differences and to investigate the relationship between metabolic biomarkers and prognosis.
View Article and Find Full Text PDFSupervised analysis of alternative polyadenylation from single-cell and spatial transcriptomics data with spvAPA.
Brief Bioinform
November 2024
Cancer Institute, Suzhou Medical College, Soochow University, NO. 199 Ren-ai Road, SIP, Suzhou 215000, China.
Alternative polyadenylation (APA) is an important driver of transcriptome diversity that generates messenger RNA isoforms with distinct 3' ends. The rapid development of single-cell and spatial transcriptomic technologies opened up new opportunities for exploring APA data to discover hidden cell subpopulations invisible in conventional gene expression analysis. However, conventional gene-level analysis tools are not fully applicable to APA data, and commonly used unsupervised dimensionality reduction methods often disregard experimentally derived annotations such as cell type identities.
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