Bioinformatics-based gene set enrichment and immune cell infiltration analysis of psoriasis

Objective:Based on bioinformatics,gene set enrichment analysis(GSEA)and immune infiltration analysis were carried out on the microarray data of psoriasis expression profile to further understand the pathogenesis of psoriasis.Methods:GSE6710 chip data were obtained from gene expression database(GEO),and gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were performed using GSEA software.22 kinds of immune cell gene expression matrices and R packages were downloaded from CIBERSOFT official website,and the immune cell infiltration matrix was obtained by R software and related graphs were drawn.Results:The pathways related to cell proliferation and innate immunity were highly expressed in psoriatic lesions,and some cancer-related pathways were highly expressed in psoriatic lesions.Immunized cell infiltration analysis showed that activated memory T cells,follicular helper T cells,M0 macrophages and activated dendritic cells were up-regulated in psoriatic skin lesion group,and inactive mast cells were down-regulated in psoriatic skin lesion group.Activated dendritic cells are positively correlated with follicular helper T cells,activated mast cells are positively correlated with M0 macrophages.Inactivated mast cells are negatively correlated with activated memory T cells,M1 macrophages are negatively correlated with regulatory T cells,M0 macrophages are negatively correlated with inactive mast cells.Conclusion:Cell proliferation and innate immunity are of great significance in the pathogenesis of psoriasis.Immune cell infiltration analysis is generally consistent with the current psoriasis pathogenesis model.Macrophages and mast cells also play a certain role in psoriasis.

CGPS:A machine learning-based approach integrating multiple gene set analysis tools for better prioritization of biologically relevant pathways

Gene set enrichment（GSE） analyses play an important role in the interpretation of large-scale transcriptome datasets. Multiple GSE tools can be integrated into a single method as obtaining optimal results is challenging due to the plethora of GSE tools and their discrepant performances. Several existing ensemble methods lead to different scores in sorting pathways as integrated results; furthermore, it is difficult for users to choose a single ensemble score to obtain optimal final results. Here, we develop an ensemble method using a machine learning approach called Combined Gene set analysis incorporating Prioritization and Sensitivity（CGPS） that integrates the results provided by nine prominent GSE tools into a single ensemble score（R score） to sort pathways as integrated results. Moreover, to the best of our knowledge, CGPS is the first GSE ensemble method built based on a priori knowledge of pathways and phenotypes. Compared with 10 widely used individual methods and five types of ensemble scores from two ensemble methods, we demonstrate that sorting pathways based on the R score can better prioritize relevant pathways, as established by an evaluation of 120 simulated datasets and 45 real datasets.Additionally, CGPS is applied to expression data involving the drug panobinostat, which is an anticancer treatment against multiple myeloma. The results identify cell processes associated with cancer, such as the p53 signaling pathway（hsa04115）; by contrast, according to two ensemble methods（EnrichmentBrowser and EGSEA）, this pathway has a rank higher than 20, which may cause users to miss the pathway in their analyses. We show that this method, which is based on a priori knowledge, can capture valuable biological information from numerous types of gene set collections, such as KEGG pathways, GO terms, Reactome, and BioCarta. CGPS is publicly available as a standalone source code at ftp：//ftp.cbi.pku.edu.cn/pub/CGPS_download/cgps-1.0.0.tar.gz.

Comparative transcriptomic analysis of rat versus mouse cerebral cortex after traumatic brain injury

The heterogeneity of traumatic brain injury(TBI)-induced secondary injury has greatly hampered the development of effective treatments for TBI patients.Targeting common processes across species may be an innovative strategy to combat debilitating TBI.In the present study, a cross-species transcriptome comparison was performed for the first time to determine the fundamental processes of secondary brain injury in Sprague-Dawley rat and C57/BL6 mouse models of TBI, caused by acute controlled cortical impact.The RNA sequencing data from the mouse model of TBI were downloaded from the Gene Expression Omnibus(ID: GSE79441) at the National Center for Biotechnology Information.For the rat data, peri-injury cerebral cortex samples were collected for transcriptomic analysis 24 hours after TBI.Differentially expressed gene-based functional analysis revealed that common features between the two species were mainly involved in the regulation and activation of the innate immune response, including complement cascades as well as Toll-like and nucleotide oligomerization domain-like receptor pathways.These findings were further corroborated by gene set enrichment analysis.Moreover, transcription factor analysis revealed that the families of signal transducers and activators of transcription(STAT), basic leucine zipper(BZIP), Rel homology domain(RHD), and interferon regulatory factor(IRF) transcription factors play vital regulatory roles in the pathophysiological processes of TBI, and are also largely associated with inflammation.These findings suggest that targeting the common innate immune response might be a promising therapeutic approach for TBI.The animal experimental procedures were approved by the Beijing Neurosurgical Institute Animal Care and Use Committee(approval No.201802001) on June 6, 2018.

Genome-wide interrogation of transfer RNA-derived small RNAs in a mouse model of traumatic brain injury

Transfer RNA(t RNA)-derived small RNAs(ts RNAs) are a recently established family of regulatory small non-coding RNAs that modulate diverse biological processes. Growing evidence indicates that ts RNAs are involved in neurological disorders and play a role in the pathogenesis of neurodegenerative disease. However, whether ts RNAs are involved in traumatic brain injury-induced secondary injury remains poorly understood. In this study, a mouse controlled cortical impact model of traumatic brain injury was established, and integrated ts RNA and messenger RNA(m RNA) transcriptome sequencing were used. The results revealed that 103 ts RNAs were differentially expressed in the mouse model of traumatic brain injury at 72 hours, of which 56 ts RNAs were upregulated and 47 ts RNAs were downregulated. Based on micro RNA-like seed matching and Pearson correlation analysis, 57 differentially expressed ts RNA-m RNA interaction pairs were identified, including 29 ts RNAs and 26 m RNAs. Moreover, Gene Ontology annotation of target genes revealed that the significantly enriched terms were primarily associated with inflammation and synaptic function. Collectively, our findings suggest that ts RNAs may be associated with traumatic brain injury-induced secondary brain injury, and are thus a potential therapeutic target for traumatic brain injury. The study was approved by the Beijing Neurosurgical Institute Animal Care and Use Committee(approval No. 20190411) on April 11, 2019.

LncRNA SNHG15 predicts poor prognosis in uveal melanoma and its potential pathways

AIM:To evaluate the role of long noncoding RNA(lncR NA)SNHG15 and its potential pathways in uveal melanoma(UM).METHODS:The SNHG15 mRNA expression level and corresponding clinicopathological characteristics of 80 patients with UM were obtained from the Cancer Genome Atlas(TCGA)database and further analyzed.The SPSS 24.0 statistical software package was used for statistical analyses.To investigate the potential function of SNHG15 in UM,we conducted in-depth research on Gene Set Enrichment Analysis(GSEA).RESULTS:The univariate analysis revealed that the age,tumor diameter,pathological type,extrascleral extension,cancer status,and high expression of SNHG15 were statistical risk factors for death from all causes.The multivariate analysis suggested that the mR NA expression level of SNHG15 was an independent risk factor for death from all causes,as was age and pathological type.KaplanMeier survival analysis confirmed that UM patients with high SNHG15 expression might have a poor prognosis.In addition,SNHG15 was significantly differentially expressed in the different groups of tumor pathologic stage,metastasis and living status.Besides,the logistic regression analysis indicated that high SNHG15 expression group in UM was significantly associated with cancer status,pathologic stage,metastasis,and living status.Moreover,the GSEA indicated the potential pathways regulated by SNHG15 in UM.CONCLUSION:Our research suggests that SNHG15 may play a vital role as a potential marker in UM that predicts poor prognosis.Besides,GSEA indicates the underlying signaling pathways enriched differentially in SNHG15 high expression phenotype.

TMEM158 May Serve as a Diagnostic Biomarker for Anaplastic Thyroid Carcinoma:An Integrated Bioinformatic Analysis

Anaplastic thyroid carcinoma(ATC)is a rare but extremely lethal malignancy.However,little is known about the pathogenesis of ATC.Given its high mortality,it is critical to improve our understanding of ATC pathogenesis and to find new diagnostic biomarkers.In the present study,two gene microarray profiles(GSE53072 and GSE65144),which included 17 ATC and 17 adjacent non-tumorous tissues,were obtained.Bioinformatic analyses were then performed.Immunohistochemistry(IHC)and receiver operating characteristic(ROC)curves were then used to detect transmembrane protein 158(TMEM158)expression and to assess diagnostic sensitivity.A total of 372 differentially expressed genes(DEGs)were identified.Through protein-protein interaction(PPI)analysis,we identified a significant module with 37 upregulated genes.Most of the genes in this module were related to cell-cycle processes.After co-expression analysis,132 hub genes were selected for further study.Nine genes were identified as both DEGs and genes of interest in the weighted gene co-expression network analysis(WGCNA).IHC and ROC curves confirmed that TMEM158 was overexpressed in ATC tissue as compared with other types of thyroid cancer and normal tissue samples.We identified 8 KEGG pathways that were associated with high expression of TMEM158,including aminoacyl-tRNA biosynthesis and DNA replication.Our results suggest that TMEM158 may be a potential oncogene and serve as a diagnostic indicator for ATC.

ASpediaFI:Functional Interaction Analysis of Alternative Splicing Events

Alternative splicing(AS)regulates biological processes governing phenotypes and diseases.Differential AS(DAS)gene test methods have been developed to investigate important exonic expression from high-throughput datasets.However,the DAS events extracted using statistical tests are insufficient to delineate relevant biological processes.In this study,we developed a novel application,Alternative Splicing Encyclopedia:Functional Interaction(ASpediaFI),to systemically identify DAS events and co-regulated genes and pathways.ASpediaFI establishes a heterogeneous interaction network of genes and their feature nodes(i.e.,AS events and pathways)connected by coexpression or pathway gene set knowledge.Next,ASpediaFI explores the interaction network using the random walk with restart algorithm and interrogates the proximity from a query gene set.Finally,ASpediaFI extracts significant AS events,genes,and pathways.To evaluate the performance of our method,we simulated RNA sequencing(RNA-seq)datasets to consider various conditions of sequencing depth and sample size.The performance was compared with that of other methods.Additionally,we analyzed three public datasets of cancer patients or cell lines to evaluate how well ASpediaFI detects biologically relevant candidates.ASpediaFI exhibits strong performance in both simulated and public datasets.Our integrative approach reveals that DAS events that recognize a global co-expression network and relevant pathways determine the functional importance of spliced genes in the subnetwork.ASpediaFI is publicly available at https://bioconductor.org/packages/ASpediaFI.