The present study was designed to detect possible biomarkers associated with Type 1 diabetes mellitus (T1DM) incidence in an effort to develop novel treatments for this condition. Three mRNA expression datasets of peripheral blood mononuclear cells (PBMCs) were obtained from the GEO database. Differentially expressed genes (DEGs) between T1DM patients and healthy controls were identified by Limma package in R, and using the DEGs to conduct GO and DO pathway enrichment. The LASSO-SVM were used to screen the hub genes. We performed immune correlation analysis of hub genes and established a T1DM prognosis model. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. The correlation of the hub genes and immune cells was analyzed by Spearman. ROC curves were used to assess the diagnostic value of genes in T1DM. A total of 60 immune related DEGs were obtained from the T1DM and normal samples. Then, DEGs were further screened to obtain 3 hub genes, ANP32A-IT1, ESCO2 and NBPF1. CIBERSORT analysis revealed the percentage of immune cells in each sample, indicating that there was significant difference in monocytes, T cells CD8+, gamma delta T cells, naive CD4+ T cells and activated memory CD4+ T cells between T1DM and normal samples. The area under curve (AUC) of ESCO2, ANP32A-IT1 and NBPF1 were all greater than 0.8, indicating that these three genes have high diagnostic value for T1DM. Together, the findings of these bioinformatics analyses thus identified key hub genes associated with T1DM development.
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http://dx.doi.org/10.1507/endocrj.EJ22-0178 | DOI Listing |
Int J Biol Markers
January 2025
Department of Respiratory and Critical Care Medicine, Anyue County People's Hospital, Anyue, China.
Purpose: To detect the prognostic importance of liquid-liquid phase separation (LLPS) in lung adenocarcinoma.
Methods: The gene expression files, copy number variation data, and clinical data were downloaded from The Cancer Genome Atlas cohort. LLPS-related genes were acquired from the DrLLPS website.
Endocr Metab Immune Disord Drug Targets
January 2025
Department of Orthopaedic Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China.
Background: Osteoporosis (OP) is a skeletal condition characterized by increased susceptibility to fractures. Programmed cell death (PCD) is the orderly process of cells ending their own life that has not been thoroughly explored in relation to OP.
Objective: This study is to investigate PCD-related genes in OP, shedding light on potential mechanisms underlying the disease.
Sex Med
December 2024
Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China.
Background: Diabetic erectile dysfunction (DMED) has a high incidence and is poorly treated.
Aim: This study investigates fibrosis's genetic profiling and explores potential mechanisms for DMED.
Methods: The DMED model was constructed in rats using streptozotocin.
PeerJ
January 2025
Department of Urology, First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
Background: Plasma membrane tension-related genes (MTRGs) are known to play a crucial role in tumor progression by influencing cell migration and adhesion. However, their specific mechanisms in bladder cancer (BLCA) remain unclear.
Methods: Transcriptomic, clinical and mutation data from BLCA patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases.
Objectives: To identify cuproptosis- and ferroptosis-related genes involved in nonalcoholic fatty liver disease and to determine the diagnostic value of hub genes.
Methods: The gene expression dataset GSE89632 was retrieved from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) between the non-alcoholic steatohepatitis (NASH) group and the healthy group using the 'limma' package in R software and weighted gene co-expression network analysis. Gene ontology, kyoto encyclopedia of genes and genomes pathway, and single-sample gene set enrichment analyses were performed to identify functional enrichment of DEGs.
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