Background: Psoriasis is a common inflammatory skin disease that has a great impact on patients' physical and mental health. However, the causes and underlying molecular mechanisms of psoriasis are still largely unknown.
Methods: The expression profiles of genes from psoriatic lesion samples and skin samples from healthy controls were integrated the sva software package, and differentially expressed genes (DEGs) between psoriasis and healthy skin were screened by the limma package. Furthermore, GO and KEGG pathway enrichments for the DEGs were performed using the Clusterprofiler package. Protein-protein interaction (PPI) networks for the DEGs were then constructed to identify hub genes. scGESA analysis was performed on a single-cell RNA sequencing dataset irGSEA. In order to find the cytokines correlated with the hub genes expression, single cell weighted gene co-expression network analyses (scWGCNA) were utilized to build a gene co-expression network. Furthermore, the featured genes of psoriasis found in suprabasal keratinocytes were intersected with hub genes. We then analyzed the expression of the intersection genes and cytokines in the integrated dataset. After that, we used other datasets to reveal the changes in the intersection genes' expression levels during biological therapy. The relationship between intersection genes and PASI scores was also explored.
Results: We identified 148 DEGs between psoriatic and healthy samples. GO and KEGG pathway enrichment analysis suggested that DEGs are mainly involved in the defense response to other organisms. The PPI network showed that 11 antiviral proteins (AVPs) were hub genes. scGSEA analysis in the single-cell transcriptome dataset showed that those hub genes are highly expressed in keratinocytes, especially in suprabasal keratinocytes. ISG15, MX1, IFI44L, and IFI27 were the characteristic genes of psoriasis in suprabasal keratinocytes. scWGCNA showed that three cytokines-IL36G, MIF, and IL17RA-were co-expressed in the turquoise module. Only interleukin-36 gamma (IL36G) was positively correlated with AVPs in the integrated dataset. IL36G and AVPs were found co-expressed in a substantial number of suprabasal keratinocytes. Furthermore, we found that the expression levels of IL36G and the 4 AVPs showed positive correlation with PASI score in patients with psoriasis, and that these levels decreased significantly during treatment with biological therapies, but not with methotrexate.
Conclusion: IL36G and antiviral proteins may be closely related with the pathogenesis of psoriasis, and they may represent new candidate molecular markers for the occurrence and severity of psoriasis.
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http://dx.doi.org/10.3389/fimmu.2022.971071 | DOI Listing |
Curr Pharm Biotechnol
January 2025
Department of Intensive Care Unit, Affiliated Hospital of Guangdong Medical University, 524000 Zhanjiang, China.
Objectives: This study aimed to comprehensively investigate the molecular landscape of gastric cancer (GC) by integrating various bioinformatics tools and experimental validations.
Methodology: GSE79973 dataset, limma package, STRING, UALCAN, GEPIA, OncoDB, cBioPortal, DAVID, TISIDB, Gene Set Cancer Analysis (GSCA), tissue samples, RT-qPCR, and cell proliferation assay were employed in this study.
Results: Analysis of the GSE79973 dataset identified 300 differentially expressed genes (DEGs), from which COL1A1, COL1A2, CHN1, and FN1 emerged as pivotal hub genes using protein-protein interaction network analysis.
BMC Med Inform Decis Mak
January 2025
Department of Pharmacy, Hangzhou Third People's Hospital, Hangzhou Third Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.
Background: Alopecia areata (AA) is a common non-scarring hair loss disorder associated with autoimmune conditions. However, the pathobiology of AA is not well understood, and there is no targeted therapy available for AA. METHODS: In this study, differential gene expression analysis, immune status assessment, weighted correlation network analysis (WGCNA), and functional enrichment analysis were performed to identify shared genes associated with both immunological response and AA.
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.
View Article and Find Full Text PDFLife Sci
January 2025
Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China. Electronic address:
Aims: Clear cell renal cell carcinoma (ccRCC) shows considerable variation within and between tumors, presents varying treatment responses among patients, possibly due to molecular distinctions. This study utilized a multi-center and multi-omics analysis to establish and validate a prognosis and treatment vulnerability signature (PTVS) capable of effectively predicting patient prognosis and drug responsiveness.
Materials And Methods: To address this complexity, we constructed an integrative multi-omics analysis using 10 clustering algorithms on ccRCC patient data.
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