The aim of this study was to investigate the disease-associated genes in periodontitis. In the present experiments, the topological analysis of the differential co-expression network was proposed. Using the GSE16134 dataset downloaded from the European Molecular Biology Laboratory-European Bioinformatics Institute, a co-expression network was constructed after the differentially expressed genes (DEGs) were identified between the diseased (242 samples) and healthy (69 samples) gingival tissues from periodontitis patients. The topological properties of the modules obtained from the network as well as an analysis of transcription factors (TFs) were used to determine the disease-associated genes. The gene ontology and pathway enrichment analysis was performed to investigate the underlying mechanisms of these disease related genes. A total of 524 DEGs, including 19 TFs were identified and a co-expression network with 2569 edges was obtained. Among the 7 modules gained in the network, the TFs (ZNF215, ZEN273, NFAT5, TRPS1, MEF2C and FLI1) were considered to be important in periodontitis. The functional and pathway enrichment analysis revealed that the DEGs were highly involved in the immune system. The co-expression network analysis and TFs identified in periodontitis may provide opportunities for biomarker development and novel insights into the therapeutics of periodontitis.
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http://dx.doi.org/10.7868/S0026898416010195 | DOI Listing |
Discov Oncol
January 2025
Department of Orthopedics, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China.
Sarcoma (SARC), a diverse group of stromal tumors arising from mesenchymal tissues, is often associated with a poor prognosis. Emerging evidence indicates that senescent cells within the tumor microenvironment (TME) significantly contribute to cancer progression and metastasis. Although the influence of senescence on SARC has been partially acknowledged, it has yet to be fully elucidated.
View Article and Find Full Text PDFBreast Cancer (Dove Med Press)
January 2025
The Second Surgical Department of Breast Cancer, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, People's Republic of China.
Purpose: Cell division cycle protein 45 (CDC45) plays a crucial role in DNA replication. This study investigates its role in breast cancer (BC) and its impact on tumor progression.
Methods: We utilized the GEO database to screen differentially expressed genes (DEGs) and conducted enrichment analysis on these genes.
Commun Biol
January 2025
College of Medical Information and Artificial Intelligence, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, P. R. China.
Digestive and psychiatric disorders tend to co-occur, yet mechanisms remain unclear. Leveraging genetic and transcriptomic data integration, we conduct multi-trait analysis of GWAS (MTAG) and weighted gene co-expression network analysis (WGCNA) to explore shared mechanism between psychiatric and gastrointestinal disorders. Significant genetic correlations were found between these disorders, especially in irritable bowel syndrome (IBS), gastroesophageal reflux disease (GERD), depression (DEP), and neuroticism (NE).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Research, Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway.
Testicular germ cell tumour (TGCT) is a malignancy with known inherited risk factors, affecting young men. We have previously identified several hundred differentially abundant circulating RNAs in pre-diagnostic serum from TGCT cases compared to healthy controls. In this study, we performed Weighted Gene Co-expression Network Analysis (WGCNA) on mRNA and miRNA data from these samples.
View Article and Find Full Text PDFSci Rep
January 2025
Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, 8000, Denmark.
Low fertility in cows leads to early removal from herds. Since reproductive traits are complex and have low heritability, genetic analysis can aid in improving reproduction. This study identified key genes linked to fertility by conducting genome- and transcriptome-wide association studies, RNA-seq analysis, meta-analysis, weighted gene co-expression network analysis, and functional enrichment analysis.
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