Weighted gene co-expression network analysis identifies RHOH and TRAF1 as key candidate genes for psoriatic arthritis.

BACKGROUND: Psoriatic arthritis (PsA) is inflammatory arthritis associated with psoriasis, which involves the axial joint and the distal interphalangeal joints. Its clinical features are varied, often resulting in delayed diagnosis and treatment. Improved knowledge about disease mechanisms will catalyze the rapid development of effective targeted therapies for this disease. The perturbations in the gene co-expression network may not be detected by the differential expression analysis of the microarray. This study aims to identify key modules and hub genes in psoriatic arthritis-applied WGCNA (weighted gene co-expression network analysis) on a microarray. METHODS: This study downloaded the array data of GSE61281 from the gene expression overview (GEO) database, which includes 20 psoriatic arthritis samples and 12 healthy controls. The analysis was performed with the WGCNA package. Gene ontology (GO) annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on these key modules. Candidate hub genes were identified using GS and MM measures, Cytoscape, and the online database STRING. RESULTS: A total of 10 co-expression modules were constructed. The lightcyan module was identified as the key module. GO and KEGG pathway analyses were mainly enriched in dephosphorylation, regulation of small GTPase-mediated signal transduction, Ras signaling pathway, MAPK signaling pathway, and vascular smooth muscle contraction. Two hub genes, RHOH/TRAF1, were selected. CONCLUSIONS: This finding may indicate that RHOH/TRAF1 play a critical role in the pathogenesis of PsA. This is one of the first studies in PsA using WGCNA, which may provide a new research direction for further understanding of the molecular mechanism and clinical application of PsA. Key points • The WGCNA method was applied to the expression profile microarray of psoriatic arthritis and the co-expression module was constructed. • Identify the key modules by combining the onset time of psoriasis in patients with psoriatic arthritis. • Three screening methods are used to identify and verify hub genes of key modules.

LUM is the hub gene of advanced fibrosis in nonalcoholic fatty liver disease patients.

INTRODUCTION: Advanced fibrosis in nonalcoholic fatty liver disease (NAFLD) is associated with a poor prognosis. The genetic factors contributing to fibrosis in NAFLD have been described. However, the genetic mechanism and hub genes of advanced fibrosis have not been elucidated to date. In this study, we performed a weighted gene coexpression network analysis (WGCNA) to identify the hub genes related to advanced fibrosis in NAFLD. MATERIALS AND METHODS: The datasets GSE89632 and GSE31803 of NAFLD patients were selected from the Gene Expression Omnibus (GEO) database of NCBI and analyzed by WGCNA. The hub genes were selected in the GSE31803 dataset and verified in the GSE31803 dataset. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the dataset were also performed. RESULTS: The gene LUM was identified as the hub gene in the datasets GSE89632 and GSE31803 according to three different algorithms (gene significance and module membership, the pathways of the genes, and protein expressed by the genes). The functional enrichment analysis shows that the identified module is related to the extracellular matrix, regulation of cell proliferation, and the inflammatory response. The metabolic pathway analysis identified metabolic pathways and focal adhesion as the most important pathways. CONCLUSION: By a variety of methods, LUM was identified as the hub gene of advanced fibrosis in patients with NAFLD. Therefore, further research on the LUM gene is warranted.

Investigation of the gene co-expression network and hub genes associated with acute mountain sickness.

BACKGROUND: Acute mountain sickness has become a heavily researched topic in recent years. However, the genetic mechanism and effects have not been elucidated. Our goal is to construct a gene co-expression network to identify the key modules and hub genes associated with high altitude hypoxia. RESULTS: The GSE46480 dataset of rapidly transported healthy adults with acute mountain sickness was selected and analyzed by weighted gene co-expression network analysis (WGCNA) to construct a co-expression network. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the data set were carried out using Database for Annotation Visualization and Integrated Discovery (DAVID), and the hub genes were selected. We found that the turquoise module was most significantly correlated with acute mountain sickness. The functional enrichment analysis showed that the turquoise module was related to the apoptotic process, protein transport, and translation processes. The metabolic pathway analysis identified hsa03010:ribosome and hsa04144:endocytosis as the most important pathways in the turquoise module. Ten top 10 hub genes (MRPL3, PSMC6, AIMP1, HAT1, DPY30, ATP5L, COX7B, UQCRB, DPM1, and COMMD6) for acute mountain sickness were identified. CONCLUSION: One module and 10 hub genes were identified, which were related to acute mountain sickness. The reference provided by this module may help to elucidate the mechanism of acute mountain sickness. In addition, the hub genes may be used in the future as a biomarker and therapeutic target for accurate diagnosis and treatment.