Evidence for an association between thyroid-stimulating hormone and insulin-like growth factor 1 receptors: a tale of two antigens implicated in Graves' disease

Thyroid-stimulating hormone receptor (TSHR) plays a central role in regulating thyroid function and is targeted by IgGs in Graves' disease (GD-IgG). Whether TSHR is involved in the pathogenesis of thyroid-associated ophthalmopathy (TAO), the orbital manifestation of GD, remains uncertain. TSHR signaling overlaps with that of insulin-like grow factor 1 receptor (IGF-1R). GD-IgG can activate fibroblasts derived from donors with GD to synthesize T cell chemoattractants and hyaluronan, actions mediated through IGF-1R. In this study, we compare levels of IGF-1R and TSHR on the surfaces of TAO and control orbital fibroblasts and thyrocytes and explore the physical and functional relationship between the two receptors. TSHR levels are 11-fold higher on thyrocytes than on TAO or control fibroblasts. In contrast, IGF-1R levels are 3-fold higher on TAO vs control fibroblasts. In pull-down studies using fibroblasts, thyrocytes, and thyroid tissue, Abs directed specifically against either IGF-1Rbeta or TSHR bring both proteins out of solution. Moreover, IGF-1Rbeta and TSHR colocalize to the perinuclear and cytoplasmic compartments in fibroblasts and thyrocytes by confocal microscopy. Examination of orbital tissue from patients with TAO reveals similar colocalization to cell membranes. Treatment of primary thyrocytes with recombinant human TSH results in rapid ERK phosphorylation which can be blocked by an IGF-1R-blocking mAb. Our findings suggest that IGF-1R might mediate some TSH-provoked signaling. Furthermore, they indicate that TSHR levels on orbital fibroblasts are considerably lower than those on thyrocytes and that this receptor associates with IGF-1R in situ and together may comprise a functional complex in thyroid and orbital tissue.     

A novel prognostic signature of immune‐related genes for patients with colorectal cancer

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers with an estimated 1.8 million new cases worldwide and associated with high mortality rates of 881 000 CRC‐related deaths in 2018. Screening programs and new therapies have only marginally improved the survival of CRC patients. Immune‐related genes (IRGs) have attracted attention in recent years as therapeutic targets. The aim of this study was to identify an immune‐related prognostic signature for CRC. To this end, we combined gene expression and clinical data from the CRC data sets of The Cancer Genome Atlas (TCGA) into an integrated immune landscape profile. We identified a total of 476 IRGs that were differentially expressed in CRC vs normal tissues, of which 18 were survival related according to univariate Cox analysis. Stepwise multivariate Cox proportional hazards analysis established an immune‐related prognostic signature consisting of SLC10A2, FGF2, CCL28, NDRG1, ESM1, UCN, UTS2 and TRDC. The predictive ability of this signature for 3‐ and 5‐year overall survival was determined using receiver operating characteristics (ROC), and the respective areas under the curve (AUC) were 79.2% and 76.6%. The signature showed moderate predictive accuracy in the validation and GSE38832 data sets as well. Furthermore, the 8‐IRG signature correlated significantly with tumour stage, invasion, lymph node metastasis and distant metastasis by univariate Cox analysis, and was established an independent prognostic factor by multivariate Cox regression analysis for CRC. Gene set enrichment analysis (GSEA) revealed a relationship between the IRG prognostic signature and various biological pathways. Focal adhesions and ECM‐receptor interactions were positively correlated with the risk scores, while cytosolic DNA sensing and metabolism‐related pathways were negatively correlated. Finally, the bioinformatics results were validated by real‐time RT?qPCR. In conclusion, we identified and validated a novel, immune‐related prognostic signature for patients with CRC, and this signature reflects the dysregulated tumour immune microenvironment and has a potential for better CRC patient management.     

Overexpression of oncostatin M receptor regulates local immune response in glioblastoma

Glioblastoma (GBM) is the most lethal cancer in central nervous system. It is urgently needed to look for novel therapeutics for GBM. Oncostatin M receptor (OSMR) is a cytokine receptor gene of IL-6 family and has been reported to be involved in regulating GBM tumorigenesis. However, the role of OSMR regulating the disrupted immune response in GBM need to be further investigated. Three gene expression profiles, Chinese Glioma Genome Atlas (CGGA), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) data set (GSE16011), were enrolled in our study and used for OSMR expression and survival analysis. The expression of OSMR was further verified with immunohistochemistry and western blot analysis in glioma tissues. Microenvironment cell populations-counter (MCP-counter) was applied for analyzing the relationship between OSMR expression and nontumor cells. The functions of OSMR in GBM was investigated by Gene Ontology, Gene set enrichment analysis (GSEA), gene set variation analysis and so on. The analysis of cytokine receptor activity-related genes in glioma identifies OSMR as a gene with an independent predictive factor for progressive malignancy in GBM. Furthermore, OSMR expression is a prognostic marker in the response prediction to radiotherapy and chemotherapy. OSMR contributes to the regulation of local immune response and extracellular matrix process in GBM. Our findings define an important role of OSMR in the regulation of local immune response in GBM, which may suggest OSMR as a possible biomarker in developing new therapeutic immune strategies in GBM.     

Expression and distribution of S-100 protein, CD83 and apoptosis-related proteins (Fas, FasL and Bcl-2) in thyroid tissues of autoimmune thyroid diseases

Previous studies have shown that dendritic cells (DCs) and apoptosis-related proteins play a critical role in the pathogenesis of autoimmune thyroid diseases (ATD).This study was designed to investigate the expression and distribution of S-100 protein, CD83 and apoptosis-related proteins (Fas, FasL and Bcl-2) in the thyroid tissues of ATD and their role in ATD pathogenesis as determined by immunochemical staing techniques and other methods. Pathological tissues of 30 patients with Hashimoto's thyroiditis (HT), 30 patients with Graves' disease (GD) and 30 cases of thyroid follicular adenoma (TFA, as control) were used for this study. A higher expression of S-100 in HT (4.2+/-3.1%) and GD (3.9+/-2.8%) vs TFA (0.95+/-0.64%) (p<0.001). was observed as well as a higher expression of CD83 in HT (22.58+/-13.96% and GD (29.92+/-14.43%) vs TFA (5.19+/-8.08%) (p<0.001). HT thyrocytes adjacent to thyroid infiltrating lymphocytes (TILs) showed greater increases in the levels of Fas and FasL than did the GD thyrocytes while HT TILs exhibited lower expression of Fas and FasL than did the GD TILs. GD thyrocytes expressed increased levels of the antiapoptotic protein Bcl-2 as compared to the low levels detected in HT thyrocytes. An opposite pattern was observed in the TILs in GD (low expression of Bcl-2) and HT (high expression of Bcl-2). The findings suggest that the high expression of DC markers is related to the pathogenesis of HT and GD. Up-regulation of both the number and matured functions of DCs may lead to the presentation of more antigens to lymphocytes which are related to the development of autoimmune thyroid diseases. The regulation of Fas/FasL/Bcl-2 in GD favors apoptosis of infiltrating lymphocytes and thyrocyte survival. The regulation of Fas/FasL/Bcl-2 in HT may promote thyrocyte apoptosis leading to hypothyroidism.     

Leptin Responsiveness to Energy Restriction: Genetic Variation in the Leptin Receptor Gene

Serum leptin concentrations are an important afferent signal in energy balance homeostasis. It has been speculated that the leptin responsiveness to energy restriction is affected by the functionality of the leptin receptor. The purpose of this analysis was to explore the effect of polymorphisms in the LEPR gene on the acute decline in leptin after 4 days of 65% energy restriction. Leptin concentrations of the study group (n = 44; all men) declined by 2.3 ± 1.5 μg/L [–39.4% (95% confidence interval: ?43.6 to ?34.9)]. Leptin responses did not statistically differ between noncarriers and carriers of three mutant variants of the polymorphisms: Lys109/Lys109 (?41.4%) vs. Arg109/+ (?37.0%) (p = 0.33); Gln223/Gln223 (?41.5%) vs. Arg223/+ (?37.8%) (p = 0.40); Lys656/Lys656 (?39.5%) vs. Asn656/+ (?39.3%) (p = 0.96). No effect of the assessed polymorphisms in the LEPR gene on the acute decline in leptin after energy restriction was observed. Power calculations are provided for future studies on the leptin responsiveness to energy restriction.     

PGE2 induces IL-6 in orbital fibroblasts through EP2 receptors and increased gene promoter activity: implications to thyroid-associated ophthalmopathy

Background

IL-6 plays an important role in the pathogenesis of Graves'' disease and its orbital component, thyroid-associated ophthalmopathy (TAO). Orbital tissues become inflamed in TAO, a process in which prostanoids have been implicated. Orbital fibroblasts both generate and respond to PGE₂, underlying the inflammatory phenotype of these cells.

Methodology/Principal Findings

Using cultured orbital and dermal fibroblasts, we characterized the effects of PGE₂ on IL-6 expression. We found that the prostanoid provokes substantially greater cytokine synthesis in orbital fibroblasts, effects that are mediated through cell-surface EP₂ receptors and increased steady-state IL-6 mRNA levels. The pre-translational up-regulation of IL-6 results from increased gene promoter activity and can be reproduced with the PKA agonist, Sp-cAMP and blocked by interrupting the PKA pathway. PGE₂-induced production of cAMP in orbital fibroblasts was far greater than that in dermal fibroblasts, resulting from higher levels of adenylate cyclase. PGE₂ provokes CREB phosphorylation, increases the pCREB/CREB ratio, and initiates nuclear localization of the pCREB/CREB binding protein/p300 complex (CBP) preferentially in orbital fibroblasts. Transfection with siRNAs targeting either CREB or CBP blunts the induction of IL-6 gene expression. PGE₂ promotes the binding of pCREB to its target DNA sequence which is substantially greater in orbital fibroblasts.

Conclusion/Significance

These results identify the mechanism underlying the exaggerated induction of IL-6 in orbital fibroblasts and tie together two proinflammatory pathways involved in the pathogenesis of TAO. Moreover, they might therefore define an attractive therapeutic target for the treatment of TAO.     

Outcome of array CGH analysis for 255 subjects with intellectual disability and search for candidate genes using bioinformatics

Array CGH enables the detection of pathogenic copy number variants (CNVs) in 5–15% of individuals with intellectual disability (ID), making it a promising tool for uncovering ID candidate genes. However, most CNVs encompass multiple genes, making it difficult to identify key disease gene(s) underlying ID etiology. Using array CGH we identified 47 previously unreported unique CNVs in 45/255 probands. We prioritized ID candidate genes using five bioinformatic gene prioritization web tools. Gene priority lists were created by comparing integral genes from each CNV from our ID cohort with sets of training genes specific either to ID or randomly selected. Our findings suggest that different training sets alter gene prioritization only moderately; however, only the ID gene training set resulted in significant enrichment of genes with nervous system function (19%) in prioritized versus non-prioritized genes from the same de novo CNVs (7%, p < 0.05). This enrichment further increased to 31% when the five web tools were used in concert and included genes within mitogen-activated protein kinase (MAPK) and neuroactive ligand-receptor interaction pathways. Gene prioritization web tools enrich for genes with relevant function in ID and more readily facilitate the selection of ID candidate genes for functional studies, particularly for large CNVs.     

Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer

Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein-protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan-Meier plot demonstrated that low-immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. -, P < .001). A total of 540 genes were screened as DEGs with 539 up-regulated genes and 1 down-regulated gene. In addition, 60 DEGs were identified associated with overall survival. Functional enrichment analyses and PPI networks showed that the DEGs are mainly participated in immune process, and cytokine-cytokine receptor interaction. Finally, 19 prognostic genes were verified by GSE17536 and GSE17537 from GEO, and five genes (ADAM23, ARHGAP20, ICOS, IRF4, MMRN1) were significantly different in tumour tissues compared with normal tissues at the protein level. In summary, our study demonstrated the associations between TME and prognosis as well as clinical characteristics of rectal cancer. Moreover, we explored and verified microenvironment-related genes, which may be the potential key prognostic genes of rectal cancer. Further clinical samples and functional studies are needed to validate this finding.     

Age-related DNA methylation in normal breast tissue and its relationship with invasive breast tumor methylation

Age is a key risk factor for breast cancer and epigenetic alterations may contribute to age-related increases in breast cancer risk, though the relation of age-related methylation in normal breast tissues with altered methylation in breast tumors is unclear. We investigated the relation of age with DNA methylation in normal breast tissues genome-wide using two data sets from the Gene Expression Omnibus (GEO) database (GSE32393 and GSE31979). We validated our observations in an independent set of normal breast tissues, examined age-related methylation in normal breast for enrichment of genomic features, and compared age-related methylation in normal tissue with methylation alterations in breast tumors. Between the two array-based methylation data sets, there were 204 CpG loci with significant (P < 0.05) and consistent age-related methylation, 97% of which were increases in methylation. Our validation sets confirmed the direction of age-related DNA methylation changes in all measured regions. Among the 204 age-related CpG loci, we observed a significant enrichment for CpG islands (P = 8.7E-6) and polycomb group protein target genes (P = 0.03). In addition, 24 of the 204 CpGs with age-related methylation in normal breast were significantly differentially methylated between normal and breast tumor tissues. We identified consistent age-related methylation changes in normal breast tissue that are further altered in breast tumors and may represent early events contributing to breast carcinogenesis. This work identifies age-related methylation in normal breast tissue and begins to deconstruct the contribution of aging to epigenetic alterations present in breast tumors.     

How to decide which are the most pertinent overly-represented features during gene set enrichment analysis

Background  

The search for enriched features has become widely used to characterize a set of genes or proteins. A key aspect of this technique is its ability to identify correlations amongst heterogeneous data such as Gene Ontology annotations, gene expression data and genome location of genes. Despite the rapid growth of available data, very little has been proposed in terms of formalization and optimization. Additionally, current methods mainly ignore the structure of the data which causes results redundancy. For example, when searching for enrichment in GO terms, genes can be annotated with multiple GO terms and should be propagated to the more general terms in the Gene Ontology. Consequently, the gene sets often overlap partially or totally, and this causes the reported enriched GO terms to be both numerous and redundant, hence, overwhelming the researcher with non-pertinent information. This situation is not unique, it arises whenever some hierarchical clustering is performed (e.g. based on the gene expression profiles), the extreme case being when genes that are neighbors on the chromosomes are considered.     

Profiles of immune-related genes and immune cell infiltration in the tumor microenvironment of diffuse lower-grade gliomas

The tumor microenvironment is highly correlated with tumor occurrence, progress, and prognosis. We aimed to investigate the immune-related gene (IRG) expression and immune infiltration pattern in the tumor microenvironment of lower-grade glioma (LGG). We employed the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm to calculate immune and stromal scores and identify prognostic IRG based on The Cancer Genome Atlas data set. The potential molecular functions of these genes were explored with the help of functional enrichment analysis and the protein–protein interaction network. Remarkably, three cohorts that were downloaded from the Chinese Glioma Genome Atlas database were analyzed to further verify the prognostic values of these genes. Moreover, the Tumor IMmune Estimation Resource (TIMER) algorithm was used to estimate the abundance of infiltrating immune cells and explore the immune infiltration pattern in LGG. And unsupervised cluster analysis determined three clusters of the immune infiltration pattern and indicated that CD8⁺ T cells and macrophages were significantly associated with LGG outcomes. Altogether, our study identified a list of prognostic IRGs and provided a perspective to explore the immune infiltration pattern in LGG.     

ABCC9, NKAPL,and TMEM132C are potential diagnostic and prognostic markers in triple‐negative breast cancer

Triple‐negative breast cancer (TNBC) is a highly heterogeneous disease. The aim of this study is to identify the diagnostic and poor prognostic signatures in TNBC by exploring the aberrant DNA methylation and gene expression. Differential expression and methylation analysis of the TNBC and paracancer samples from The Cancer Genome Atlas were performed. Gene set enrichment and protein–protein interaction (PPI) network analysis was used to explore the mechanisms of TNBC. Methylation‐gene expression correlation analysis was performed, and multivariate Cox analysis and receiver operating characteristics analysis were used to further screen the hub genes for TNBC. We identified 1,525 differentially expressed genes and 150 differentially methylated genes between TNBC and paracancer samples. About 96.64% of the methylation sites were located on the CpG island. A total of 17 Gene Ontology biological process terms and 18 signal pathways were significantly enriched. GNG4, GNG11, PENK, MAOA, and AOX1 were identified as the core genes of the PPI network. Methylation‐expression correlations revealed that ABCC9 (cg06951626), NKAPL (cg18675097, cg01031101, and cg17384889), and TMEM132C (cg03530754) showed promise as diagnostic and prognostic markers in TNBC. ABCC9 (cg06951626), NKAPL (cg18675097, cg01031101, and cg17384889), and TMEM132C (cg03530754) were potential diagnostic and prognostic markers in TNBC.     

榕小蜂基因共表达研究

榕小蜂的雌雄个体之间存在很大表型差异,以至于很难将雌雄个体彼此联系在一起.以对叶榕传粉榕小蜂作为材料,利用"加权基因共表达网络分析"软件(WGCNA),对榕小蜂的基因组和转录组进行分析,结果发现,5个基因共表达模块,分别用蓝色、蓝绿色、棕色、绿色和黄色标识,其中2个模块偏爱在雌蜂中表达,3个模块偏爱在蛹中表达.基因本体(GO)分析发现在蓝绿色和黄色表达模块中发现3个功能富集的基因集合.在蓝绿色基因表达模块中发现2个基因集合,分别与细胞周期和核苷酸结合活性有关;在黄色基因表达模块中发现1个基因结合,与细胞分化有关,尤其是与神经发育有关.     

Identification of key candidate biomarkers for severe influenza infection by integrated bioinformatical analysis and initial clinical validation

One of the key barriers for early identification and intervention of severe influenza cases is a lack of reliable immunologic indicators. In this study, we utilized differentially expressed genes screening incorporating weighted gene co-expression network analysis in one eligible influenza GEO data set ( GSE111368 ) to identify hub genes associated with clinical severity. A total of 10 genes (PBI, MMP8, TCN1, RETN, OLFM4, ELANE, LTF, LCN2, DEFA4 and HP) were identified. Gene set enrichment analysis (GSEA) for single hub gene revealed that these genes had a close association with antimicrobial response and neutrophils activity. To further evaluate these genes' ability for diagnosis/prognosis of disease developments, we adopted double validation with (a) another new independent data set ( GSE101702 ); and (b) plasma samples collected from hospitalized influenza patients. We found that 10 hub genes presented highly correlation with disease severity. In particular, BPI and MMP8 encoding proteins in plasma achieved higher expression in severe and dead cases, which indicated an adverse disease development and suggested a frustrating prognosis. These findings provide new insight into severe influenza pathogenesis and identify two significant candidate genes that were superior to the conventional clinical indicators. These candidate genes or encoding proteins could be biomarker for clinical diagnosis and therapeutic targets for severe influenza infection.     

Ion channels and schizophrenia: a gene set-based analytic approach to GWAS data for biological hypothesis testing

Schizophrenia is a complex genetic disorder. Gene set-based analytic (GSA) methods have been widely applied for exploratory analyses of large, high-throughput datasets, but less commonly employed for biological hypothesis testing. Our primary hypothesis is that variation in ion channel genes contribute to the genetic susceptibility to schizophrenia. We applied Exploratory Visual Analysis (EVA), one GSA application, to analyze European-American (EA) and African-American (AA) schizophrenia genome-wide association study datasets for statistical enrichment of ion channel gene sets, comparing GSA results derived under three SNP-to-gene mapping strategies: (1) GENIC; (2) 500-Kb; (3) 2.5-Mb and three complimentary SNP-to-gene statistical reduction methods: (1) minimum p value (pMIN); (2) a novel method, proportion of SNPs per Gene with p values below a pre-defined α-threshold (PROP); and (3) the truncated product method (TPM). In the EA analyses, ion channel gene set(s) were enriched under all mapping and statistical approaches. In the AA analysis, ion channel gene set(s) were significantly enriched under pMIN for all mapping strategies and under PROP for broader mapping strategies. Less extensive enrichment in the AA sample may reflect true ethnic differences in susceptibility, sampling or case ascertainment differences, or higher dimensionality relative to sample size of the AA data. More consistent findings under broader mapping strategies may reflect enhanced power due to increased SNP inclusion, enhanced capture of effects over extended haplotypes or significant contributions from regulatory regions. While extensive pMIN findings may reflect gene size bias, the extent and significance of PROP and TPM findings suggest that common variation at ion channel genes may capture some of the heritability of schizophrenia.     

基于单细胞转录组的多级别胶质瘤异质性及免疫微环境分析揭示了潜在的预后生物标志物

脑胶质瘤(glioma)是中枢神经系统最常见的内在肿瘤,具有发病率高、预后较差等特点。本研究旨在鉴定多形性胶质母细胞瘤(glioblastoma multiforme,GBM)和低级别胶质瘤(lower-grade gliomas, LGG)之间的差异表达基因(differentially expressed genes, DEGs),以探讨不同级别胶质瘤的预后影响因素。从NCBI基因表达综合数据库中收集了胶质瘤的单细胞转录组测序数据,其中包括来自3个数据集的共29 097个细胞样本。对于不同分级的人脑胶质瘤进行分析,经过滤得到21 071个细胞,通过基因本体分析、京都基因与基因组百科全书途径分析,从差异表达基因中筛选出70个基因,我们通过查阅文献,聚焦到delta样典型Notch配体3 (delta like canonical Notch ligand 3,DLL3)这个基因。基于TCGA的基因表达谱交互分析(gene expression profiling interactive analysis, GEPIA)数据库用于探索LGG和GBM中DLL3基因的表达差异,采用基因表达...     

Gene expression and methylation profiles identified CXCL3 and CXCL8 as key genes for diagnosis and prognosis of colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most common malignant tumors with high morbidity and mortality rates worldwide. Due to the poor clinical outcomes, it is indispensable to investigate novel biomarkers for the diagnosis and prognosis of COAD. The aim of this study is to explore key genes as potential biomarkers for the diagnosis and prognosis of COAD for clinical utility. Gene expression profiles (GSE44076 and GSE44861) and gene methylation profile (GSE29490) were analyzed to identify the aberrantly methylated-differentially expressed genes by R language and Perl software. Function enrichments were performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Moreover, hub genes were identified through protein–protein interaction (PPI) network. Besides, key genes were found by the module analysis and The Cancer Genome Atlas (TCGA) survival analysis. Finally, TCGA data and quantitative real-time polymerase chain reaction (RT-qPCR) was used to validate the key genes involved in COAD. Our study found two hypomethylation-high-expression genes (CXCL3 and CXCL8) in COAD tissues compared with the adjacent normal tissues. These results were also confirmed by RT-qPCR with 25 pairs of COAD and adjacent normal tissues. Meanwhile, low expression of the two genes was associated with poor survival in patients with COAD. CXCL3 and CXCL8 may serve as key genes in the diagnosis and prognosis for COAD.     

应用GSEA和WGCNA方法分析细菌性败血症宿主关键差异基因及意义

【目的】采用生物信息学方法分析公共数据库来源的细菌性败血症患者全血转录组学表达谱,探讨细菌败血症相关的宿主关键差异基因及意义。【方法】基于GEO数据库中GSE80496和GSE72829全血转录组基因数据集,采用GEO2R、基因集富集分析(GSEA)联用加权基因共表达网络分析(WGCNA)筛选细菌性败血症患者相比健康人群显著改变的差异基因,通过R软件对交集基因进行GO功能分析和KEGG富集分析。同时,通过String 11.0和Cytoscape分析枢纽基因,验证枢纽基因在数据集GSE72809(Health组52例,Definedsepsis组52例)全血标本中的表达情况,并探讨婴儿性别、月(胎)龄、出生体重、是否接触抗生素等因素与靶基因表达谱间的关系。【结果】分析GSE80496和GSE72829数据集分别筛选得到932个基因和319个基因,联合WGCNA枢纽模块交集得到与细菌性败血症发病相关的10个枢纽基因(MMP9、ITGAM、CSTD、GAPDH、PGLYRP1、FOLR3、OSCAR、TLR5、IL1RN和TIMP1);GSEA分析获得关键通路(氨基酸糖类-核糖代谢、PPAR信号通路、聚糖生物合成通路、自噬调控通路、补体、凝血因子级联反应、尼古丁和烟酰胺代谢、不饱和脂肪酸生物合成和阿尔兹海默症通路)及生物学过程(类固醇激素分泌、腺苷酸环化酶的激活、细胞外基质降解和金属离子运输)。【结论】本项研究通过GEO2R、GSEA联用WGCNA分析,筛选出与细菌性败血症发病相关的2个枢纽模块、10个枢纽基因以及一些关键信号通路和生物学过程,可为后续深入研究细菌性败血症致病机制奠定理论依据。     

TSUNAMI: Translational Bioinformatics Tool Suite for Network Analysis and Mining

Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It enables researchers to discover latent gene/molecule interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus helping to identify disease biomarkers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis, as well as modules that may share common members. To address this need, we developed an online GCN mining tool package: TSUNAMI (Tools SUite for Network Analysis and MIning). TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data (microarray, RNA-seq, or any other numerical omics data), and then performs downstream gene set enrichment analysis for the identified modules. It has several features and advantages: 1) a user-friendly interface and real-time co-expression network mining through a web server; 2) direct access and search of NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, as well as user-input gene expression matrices for GCN module mining; 3) multiple co-expression analysis tools to choose from, all of which are highly flexible in regards to parameter selection options; 4) identified GCN modules are summarized to eigengenes, which are convenient for users to check their correlation with other clinical traits; 5) integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools; and 6) visualization of gene loci by Circos plot in any step of the process. The web service is freely accessible through URL: https://biolearns.medicine.iu.edu/. Source code is available at https://github.com/huangzhii/TSUNAMI/.