The pathogenic mechanisms of prostate cancer (PCa) remain to be defined. In this study, we utilized the Robust Rank Aggregation (RRA) method to integrate 10 eligible PCa microarray datasets from the GEO and identified a set of significant differentially expressed genes (DEGs) between tumor samples and normal, matched specimens. To explore potential associations between gene sets and PCa clinical features and to identify hub genes, we utilized WGCNA to construct gene co-expression networks incorporating the DEGs screened with the use of RRA. From the key module, we selected LMNB1, TK1, ZWINT, and RACGAP1 for validation. We found that these genes were up-regulated in PCa samples, and higher expression levels were associated with higher Gleason scores and tumor grades. Moreover, ROC and K-M plots indicated these genes had good diagnostic and prognostic value for PCa. On the other hand, methylation analyses suggested that the abnormal up-regulation of these four genes likely resulted from hypomethylation, while GSEA and GSVA for single hub gene revealed they all had a close association with proliferation of PCa cells. These findings provide new insight into PCa pathogenesis, and identify LMNB1, TK1, RACGAP1 and ZWINT as candidate biomarkers for diagnosis and prognosis of PCa.

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Purpose: Sjögren’s syndrome (SS) is an autoimmune disease characterized by dry mouth and eyes. To date, the exact molecular mechanisms of its etiology are still largely unknown. The aim of this study was to identify SS related key genes and functionally enriched pathways using the weighted gene co-expression network analysis (WGCNA).Materials and Methods: We downloaded the microarray data of 190 SS patients and 32 controls from Gene Expression Omnibus (GEO). Gene network was constructed and genes were classified into different modules using WGCNA. In addition, for the hub genes in the most related module to SS, gene ontology analysis was applied. The expression profile and diagnostic capacity (ROC curve) of interested hub genes were verified using a dataset from the GEO. Moreover, gene set enrichment analysis (GSEA) was also performed.Results: A total of 1483 differentially expressed genes were filtered. Weighted gene coexpression network was constructed and genes were classified into 17 modules. Among them, the turquoise module was most closely associated with SS, which contained 278 genes. These genes were significantly enriched in 10 Gene Ontology terms, such as response to virus, immune response, defense response, response to cytokine stimulus, and the inflammatory response. A total of 19 hub genes (GBP1, PARP9, EPSTI1, LOC400759, STAT1, STAT2, IFIH1, EIF2AK2, TDRD7, IFI44, PARP12, FLJ20035, PARP14, ISGF3G, XAF1, RSAD2,LY6E, IFI44L, and DDX58) were identified. The expression levels of the five interested genes including EIF2AK2, GBP1, PARP12, PARP14, and TDRD7 were also confirmed. ROC curve analysis determined that the above five genes’ expression can distinguish SS from controls (the area under the curve is all greater than 0.7). GSEA suggests that the SS samples with highly expressed EIF2AK2 or TDRD7 genes are correlated with inflammatory response, interferon α response, and interferon γ response.Conclusion: The present study applied WGCNA to generate a holistic view of SS and provide a basis for the identification of potential pathways and hub genes that may be involved in the development of SS.

Partially aligned polyacrylonitrile (PAN)-based nanofibers were electrospun from PAN and PAN/single-walled carbon nanotubes (SWNTs) in a solution of dimethylformamide (DMF) to make the nanofiber composites. The as-spun nanofibers were then hot-stretched in the oven to enhance its orientation and crystallinity. With the introduction of SWNTs and by the hot-stretched process, the mechanical properties will be enhanced correspondingly. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray scattering (XRD), differential scanning calorimetry (DSC), and the tensile test were used to characterize the microstructure and performances of the nanofibers. The orientation and crystallinity of the as-spun and hot-stretched nanofibers confirmed by X-ray have increased. Differential scanning calorimetry showed that the glass transition temperature of PAN increased about 3 °C by an addition of 0.75 wt% SWNTs indicating a strong interfacial interaction between PAN and SWNTs. The tensile strength and the modulus of the nanofibers increased revealing significant load transfer across the nanotube-matrix interface. For PAN nanofibers, the improved fiber alignment, orientation and crystallinity resulted in enhanced mechanical properties, such as the tensile strength and modulus of the nanofibers. It was concluded that the hot-stretched nanofiber and the PAN/SWNTs nanofibers can be used as a potential precursor to produce high-performance nanocomposites.