Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis

Sepsis-induced cardiomyopathy (SIC), with a possibly reversible cardiac dysfunction, is a potential complication of septic shock. Despite quite a few mechanisms including the inflammatory mediator, exosomes, and mitochondrial dysfunction, having been confirmed in the existing research studies we still find it obscure about the overall situation of gene co-expression that how they can affect the pathological process of SIC. Thus, we intended to find out the crucial hub genes, biological signaling pathways, and infiltration of immunocytes underlying SIC. It was weighted gene co-expression network analysis that worked as our major method on the ground of the gene expression profiles: hearts of those who died from sepsis were compared to hearts donated by non-failing humans which could not be transplanted for technical reasons (GSE79962). The top 25 percent of variant genes were abstracted to identify 10 co-expression modules. In these modules, brown and green modules showed the strongest negative and positive correlation with SIC, which were primarily enriched in the bioenergy metabolism, immunoreaction, and cell death. Next, nine genes (LRRC39, COQ10A, FSD2, PPP1R3A, TNFRSF11B, IL1RAP, DGKD, POR, and THBS1) including two downregulated and seven upregulated genes which were chosen as hub genes that meant the expressive level of which was higher than the counterparts in control groups. Then, the gene set enrichment analysis (GSEA) demonstrated a close relationship of hub genes to the cardiac metabolism and the necroptosis and apoptosis of cells in SIC. Concerning immune cells infiltration, a higher level of neutrophils and B cells native and a lower level of mast cells resting and plasma cells had been observed in patients with SIC. In general, nine candidate biomarkers were authenticated as a reliable signature for deeper exploration of basic and clinical research studies on SIC.

Download Full-text

Transcriptome Research of Key Modules and Hub Genes Associated with Primary Myelofibrosis by Weighted Co-expression Network Analysis.

10.21203/rs.3.rs-41776/v1 ◽

2020 ◽

Author(s):

Weihang Li ◽

Bin Yuan ◽

Shilei Zhang ◽

Ziyi Ding ◽

Yingjing Zhao ◽

...

Keyword(s):

Transcription Factor ◽

Network Analysis ◽

Early Stage ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Enrichment Analysis ◽

Primary Myelofibrosis ◽

Gene Set Enrichment Analysis ◽

Hub Genes

Abstract Background: This study aimed to identify novel targets of diagnosis, therapy as well as prognosis for primary myelofibrosis (PMF).Methods: The gene expression profiles of GSE26049 was obtained from GEO dataset, weighted gene co-expression network analysis (WGCNA) was then performed to identify the most related modules with PMF. Subsequently, GO (Gene Ontology), KEGG (Kyoto Encyclopedia Genes and Genomes), GSEA (Gene Set Enrichment Analysis) and PPI (Protein-Protein Interaction) network were conducted to fully understand the detailed information of the green module.Results: Green module was strongly correlated with PMF disease after WGCNA analysis. 20 genes in green module were identified as hub genes responsible for the progression of PMF. Functional annotation and pathway analysis revealed that these hub genes were primarily enriched in erythrocyte differentiation, transcription factor binding, hemoglobin complex, transcription factor complex and cell cycle et al. Of which, EPB42, CALR, SLC4A1 and MPL had the most correlations with PMF.Conclusions: This study elucidated that genes EPB42, CALR, SLC4A1 and MPL were significantly more highly expressed in PMF samples than in normal samples. These four genes may be considered candidate prognostic biomarkers and potential therapeutic targets for early stage of PMF. Meanwhile, EPB42 and SLC4A1 were firstly found to be highly correlated with the progression of PMF.

Download Full-text

Identification of potential therapeutic targets for atherosclerosis by analysing the gene signature related to different immune cells and immune regulators in atheromatous plaques

BMC Medical Genomics ◽

10.1186/s12920-021-00991-2 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Yang Shen ◽

Li-rong Xu ◽

Xiao Tang ◽

Chang-po Lin ◽

Dong Yan ◽

...

Keyword(s):

Immune Cells ◽

Plasma Cells ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gamma Delta ◽

Hub Genes ◽

Gene Set Enrichment ◽

Bioinformatics Analyses ◽

Immune Infiltration ◽

Atheromatous Plaques

Abstract Background Atherosclerosis is a chronic inflammatory disease that affects multiple arteries. Numerous studies have shown the inherent immune diversity in atheromatous plaques and suggest that the dysfunction of different immune cells plays an important role in atherosclerosis. However, few comprehensive bioinformatics analyses have investigated the potential coordinators that might orchestrate different immune cells to exacerbate atherosclerosis. Methods Immune infiltration of 69 atheromatous plaques from different arterial beds in GSE100927 were explored by single-sample-gene-set enrichment analysis (presented as ssGSEA scores), ESTIMATE algorithm (presented as immune scores) and CIBERSORT algorithm (presented as relative fractions of 22 types of immune cells) to divide these plaques into ImmuneScoreL cluster (of low immune infiltration) and ImmuneScoreH cluster (of high immune infiltration). Subsequently, comprehensive bioinformatics analyses including differentially-expressed-genes (DEGs) analysis, protein–protein interaction networks analysis, hub genes analysis, Gene-Ontology-terms and KEGG pathway enrichment analysis, gene set enrichment analysis, analysis of expression profiles of immune-related genes, correlation analysis between DEGs and hub genes and immune cells were conducted. GSE28829 was analysed to cross-validate the results in GSE100927. Results Immune-related pathways, including interferon-related pathways and PD-1 signalling, were highly enriched in the ImmuneScoreH cluster. HLA-related (except for HLA-DRB6) and immune checkpoint genes (IDO1, PDCD-1, CD274(PD-L1), CD47), RORC, IFNGR1, STAT1 and JAK2 were upregulated in the ImmuneScoreH cluster, whereas FTO, CRY1, RORB, and PER1 were downregulated. Atheromatous plaques in the ImmuneScoreH cluster had higher proportions of M0 macrophages and gamma delta T cells but lower proportions of plasma cells and monocytes (p < 0.05). CAPG, CECR1, IL18, IGSF6, FBP1, HLA-DPA1 and MMP7 were commonly related to these immune cells. In addition, the advanced-stage carotid plaques in GSE28829 exhibited higher immune infiltration than early-stage carotid plaques. Conclusions Atheromatous plaques with higher immune scores were likely at a more clinically advanced stage. The progression of atherosclerosis might be related to CAPG, IGSF6, IL18, CECR1, FBP1, MMP7, FTO, CRY1, RORB, RORC, PER1, HLA-DPA1 and immune-related pathways (IFN-γ pathway and PD-1 signalling pathway). These genes and pathways might play important roles in regulating immune cells such as M0 macrophages, gamma delta T cells, plasma cells and monocytes and might serve as potential therapeutic targets for atherosclerosis.

Download Full-text

Identification of IL10RA by Weighted Correlation Network Analysis and in vitro Validation of Its Association With Prognosis of Metastatic Melanoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2020.630790 ◽

2021 ◽

Vol 8 ◽

Author(s):

Si Cheng ◽

Zhe Li ◽

Wenhao Zhang ◽

Zhiqiang Sun ◽

Zhigang Fan ◽

...

Keyword(s):

Network Analysis ◽

Metastatic Melanoma ◽

Large Scale ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Migration And Invasion

Skin cutaneous melanoma (SKCM) is the major cause of death for skin cancer patients, its high metastasis often leads to poor prognosis of patients with malignant melanoma. However, the molecular mechanisms underlying metastatic melanoma remain to be elucidated. In this study we aim to identify and validate prognostic biomarkers associated with metastatic melanoma. We first construct a co-expression network using large-scale public gene expression profiles from GEO, from which candidate genes are screened out using weighted gene co-expression network analysis (WGCNA). A total of eight modules are established via the average linkage hierarchical clustering, and 111 hub genes are identified from the clinically significant modules. Next, two other datasets from GEO and TCGA are used for further screening of biomarker genes related to prognosis of metastatic melanoma, and identified 11 key genes via survival analysis. We find that IL10RA has the highest correlation with clinically important modules among all identified biomarker genes. Further in vitro biochemical experiments, including CCK8 assays, wound-healing assays and transwell assays, have verified that IL10RA can significantly inhibit the proliferation, migration and invasion of melanoma cells. Furthermore, gene set enrichment analysis shows that PI3K-AKT signaling pathway is significantly enriched in metastatic melanoma with highly expressed IL10RA, indicating that IL10RA mediates in metastatic melanoma via PI3K-AKT pathway.

Download Full-text

Identification of Key Genes and Pathways in Hypertrophic Cardiomyopathy via Weighted Gene Co-Expression Network Analysis

10.21203/rs.3.rs-723383/v1 ◽

2021 ◽

Author(s):

Yang Hua ◽

Jin-Yu Sun ◽

Ziling Xin ◽

Wei Sun ◽

Yanhui Sheng ◽

...

Keyword(s):

Hypertrophic Cardiomyopathy ◽

Network Analysis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Enrichment Analysis ◽

Actin Binding ◽

Inherited Disease ◽

Hub Genes ◽

Underlying Mechanisms

Abstract BackgroundHypertrophic cardiomyopathy (HCM) is a prevalent cardiovascular disease characterized as asymmetric hypertrophy of ventricular muscles. Cardiac morphological abnormality may result in slight or severe cardiopulmonary symptoms, arrhythmia, heart failure, and even sudden death. Previous studies have shown that HCM was an inherited disease where sixty percent carry mutations in genes encoding sarcomere proteins. However, considering heterogeneous phenotype or prognosis, the underlying mechanisms remain unclear.MethodsThe gene expression profiles of GSE36961 and GSE160997 were analyzed by ‘limma’ and ‘weighted gene co-expression network analysis (WGCNA)’ package in R to identify differentially expressed genes (DEGs) and key modules, respectively. Then, enrichment analysis was performed based on Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Protein-protein interaction network was constructed based on the overlapped genes of DEGs and key modules, and we identified the top 4 hub genes using ‘cytohubba’ according to inner connectivity.ResultsWe identified the red and brown modules as the key modules. Enrichment analysis showed that cellular divalent inorganic cation homeostasis, collagen−containing extracellular matrix, and actin binding were significantly enriched. VSIG4, CD163, FCER1G, and LAPTM5 were identified as hub genes.ConclusionsThis study suggested that VSIG4, CD163, FCER1G, and LAPTM5 might be hub genes associated with the progression of HCM. Further studies are required to elucidate the underlying mechanisms and provide potential therapeutic targets.

Download Full-text

Identification of Candidate Biomarkers Correlated Poorer Prognosis of Breast Cancer via Bioinformatics Method

10.21203/rs.3.rs-402557/v1 ◽

2021 ◽

Author(s):

Gang Chen ◽

Mingwei Yu ◽

Jianqiao Cao ◽

Huishan Zhao ◽

Yuanping Dai ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Kegg Pathway ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

Molecular Therapy ◽

Hub Genes

Abstract Background: Breast cancer (BC) is a malignancy with a high incidence among women in the world, and it is very urgent to identify significant biomarkers and molecular therapy methods.Methods: Total 58 normal tissues and 203 cancer tissues were collected from three Gene Expression Omnibus (GEO) gene expression profiles, and the differential expressed genes (DEGs) were identified. Subsequently, the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway were analyzed. Additionally, hub genes were screened by constructing a protein-protein interaction (PPI) network. Then, we explored the prognostic values and molecular mechanism of these hub genes Kaplan-Meier (KM) curve and Gene Set Enrichment Analysis (GSEA). Results: 42 up-regulated and 82 down-regulated DEGs were screened out from GEO datasets. GO and KEGG pathway analysis revealed that DEGs were mainly related to cell cycles and cell proliferation. Furthermore, 12 hub genes (FN1, AURKA, CCNB1, BUB1B, PRC1, TPX2, NUSAP1, TOP2A, KIF20A, KIF2C, RRM2, ASPM) with a high degree of genes were selected, among which, 11 hub gene were significantly correlated with the prognosis of patients with BC. From GSEA reviewed correlated with KEGG_CELL_CYCLE and HALLMARK_P53_PATHWAY. Conclusion: this study identified 11 key genes as BC potential prognosis biomarkers on the basis of integrated bioinformatics analysis. This finding will improve our knowledge of the BC progress and mechanisms.

Download Full-text

Connectivity Map Analysis Indicates PI3K/Akt/mTOR Inhibitors as Potential Anti-Hypoxia Drugs in Neuroblastoma

Cancers ◽

10.3390/cancers13112809 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2809

Author(s):

Paolo Uva ◽

Maria Carla Bosco ◽

Alessandra Eva ◽

Massimo Conte ◽

Alberto Garaventa ◽

...

Keyword(s):

Expression Profiles ◽

Gene Expression Profiles ◽

Drug Repurposing ◽

Enrichment Analysis ◽

Mtor Inhibitors ◽

Gene Set Enrichment Analysis ◽

Low Oxygen ◽

Connectivity Map ◽

Hypoxic Conditions ◽

Connectivity Score

Neuroblastoma (NB) is one of the deadliest pediatric cancers, accounting for 15% of deaths in childhood. Hypoxia is a condition of low oxygen tension occurring in solid tumors and has an unfavorable prognostic factor for NB. In the present study, we aimed to identify novel promising drugs for NB treatment. Connectivity Map (CMap), an online resource for drug repurposing, was used to identify connections between hypoxia-modulated genes in NB tumors and compounds. Two sets of 34 and 21 genes up- and down-regulated between hypoxic and normoxic primary NB tumors, respectively, were analyzed with CMap. The analysis reported a significant negative connectivity score across nine cell lines for 19 compounds mainly belonging to the class of PI3K/Akt/mTOR inhibitors. The gene expression profiles of NB cells cultured under hypoxic conditions and treated with the mTORC complex inhibitor PP242, referred to as the Mohlin dataset, was used to validate the CMap findings. A heat map representation of hypoxia-modulated genes in the Mohlin dataset and the gene set enrichment analysis (GSEA) showed an opposite regulation of these genes in the set of NB cells treated with the mTORC inhibitor PP242. In conclusion, our analysis identified inhibitors of the PI3K/Akt/mTOR signaling pathway as novel candidate compounds to treat NB patients with hypoxic tumors and a poor prognosis.

Download Full-text

Identification of key genes in calcific aortic valve disease via weighted gene co-expression network analysis

BMC Medical Genomics ◽

10.1186/s12920-021-00989-w ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Jin-Yu Sun ◽

Yang Hua ◽

Hui Shen ◽

Qiang Qu ◽

Jun-Yan Kan ◽

...

Keyword(s):

Extracellular Matrix ◽

Network Analysis ◽

Aortic Valve ◽

Aortic Valve Disease ◽

Expression Profiles ◽

Enrichment Analysis ◽

Valve Disease ◽

Hub Genes ◽

Calcific Aortic Valve Disease ◽

Underlying Mechanisms

Abstract Background Calcific aortic valve disease (CAVD) is the most common subclass of valve heart disease in the elderly population and a primary cause of aortic valve stenosis. However, the underlying mechanisms remain unclear. Methods The gene expression profiles of GSE83453, GSE51472, and GSE12644 were analyzed by ‘limma’ and ‘weighted gene co-expression network analysis (WGCNA)’ package in R to identify differentially expressed genes (DEGs) and key modules associated with CAVD, respectively. Then, enrichment analysis was performed based on Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, DisGeNET, and TRRUST database. Protein–protein interaction network was constructed using the overlapped genes of DEGs and key modules, and we identified the top 5 hub genes by mixed character calculation. Results We identified the blue and yellow modules as the key modules. Enrichment analysis showed that leukocyte migration, extracellular matrix, and extracellular matrix structural constituent were significantly enriched. SPP1, TNC, SCG2, FAM20A, and CD52 were identified as hub genes, and their expression levels in calcified or normal aortic valve samples were illustrated, respectively. Conclusions This study suggested that SPP1, TNC, SCG2, FAM20A, and CD52 might be hub genes associated with CAVD. Further studies are required to elucidate the underlying mechanisms and provide potential therapeutic targets.

Download Full-text

Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽

10.1186/s12885-021-08412-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Baojie Wu ◽

Shuyi Xi

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Molecular Targets ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

Download Full-text

Relationship Between San-Huang-Xie-Xin-Tang and Its Herbal Components on the Gene Expression Profiles in HepG2 Cells

The American Journal of Chinese Medicine ◽

10.1142/s0192415x08006235 ◽

2008 ◽

Vol 36 (04) ◽

pp. 783-797 ◽

Cited By ~ 43

Author(s):

Wen-Yu Cheng ◽

Shih-Lu Wu ◽

Chien-Yun Hsiang ◽

Chia-Cheng Li ◽

Tung-Yuan Lai ◽

...

Keyword(s):

Gene Expression ◽

Hepg2 Cells ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Major Effect ◽

Gene Set Enrichment Analysis ◽

P53 Signaling ◽

The Relationship ◽

Herbal Components

Traditional Chinese medicine (TCM) has been used for thousands of years. Most Chinese herbal formulae consist of several herbal components and have been used to treat various diseases. However, the mechanisms of most formulae and the relationship between formulae and their components remain to be elucidated. Here we analyzed the putative mechanism of San-Huang-Xie-Xin-Tang (SHXXT) and defined the relationship between SHXXT and its herbal components by microarray technique. HepG2 cells were treated with SHXXT or its components and the gene expression profiles were analyzed by DNA microarray. Gene set enrichment analysis indicated that SHXXT and its components displayed a unique anti-proliferation pattern via p53 signaling, p53 activated, and DNA damage signaling pathways in HepG2 cells. Network analysis showed that most genes were regulated by one molecule, p53. In addition, hierarchical clustering analysis showed that Rhizoma Coptis shared a similar gene expression profile with SHXXT. These findings may explain why Rhizoma Coptis is the principle herb that exerts the major effect in the herbal formula, SHXXT. Moreover, this is the first report to reveal the relationship between formulae and their herbal components in TCM by microarray and bioinformatics tools.

Download Full-text

Identification of Key Immune-Related Genes, Molecular Pathways and Immune Infiltration as Diagnostic and Therapeutic Candidate Targets for RA: an integrated bioinformatics-based analysis

10.21203/rs.3.rs-296982/v1 ◽

2021 ◽

Author(s):

Sheng Fang ◽

Xiao Fang ◽

Xin Xu ◽

Lin Zhong ◽

An-quan Wang ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

Mast Cells ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Systemic Autoimmune Disease ◽

Molecular Pathways ◽

Hub Genes ◽

Immune Infiltration

Abstract Relevance Rheumatoid arthritis (RA) is a systemic autoimmune disease with an aggressive, chronic synovial inflammation as the main pathological change. However, the specific etiology, pathogenesis, and related biomarkers in diagnosis and treatment are still not fully elucidated. This study attempts to provide new perspectives and insights into RA at the genetic, molecular, and cellular levels through the tenet of personalized medicine. Methods Gene expression profiles of four individual knee synovial tissues were downloaded from a comprehensive gene expression database, R language was used to screen for significantly differentially expressed genes (DEGs), Gene Ontology Enrichment Analysis, Kyoto Gene Encyclopedia, and Gene Set Enrichment Analysis were performed to analyze the biological functions and signaling pathways of these DEGs, STRING online database was used to establish protein-protein interaction networks, Cytoscape software to obtain ten hub genes, Goplot to get six inflammatory immune-related hub genes, and CIBERSORT algorithm to impute immune infiltration. Results Molecular pathways that play important roles in RA were obtained: Toll-like receptors, AMPK, MAPK, TNF, FoxO, TGF-beta, PI3K and NF-κB pathways, Ten hub genes: Ccr1, Ccr2, Ccr5, Ccr7, Cxcl5, Cxcl6, Cxcl13, Ccl13, Adcy2, and Pnoc. among which Adcy2 and Pnoc have not been reported in RA studies, suggesting that they may be worthy targets for further study. It was also found that among the synoviocytes in RA, the proportions of plasma cells, CD8 T cells, follicular helper T cells, monocytes, γ delta T cells, and M0 macrophages were higher, while the proportions of CD4 memory resting T cells, regulatory T cells (Tregs), activated NK cells, resting dendritic cells, M1 macrophages, eosinophils, activated mast cells, resting mast cells were lower in proportion, and each cell played an important role in RA. Conclusions This study may help understand the key genes, molecular pathways, the role of inflammatory immune infiltrating cells in RA’s pathogenesis and provide new targets and ideas for the diagnosis and personalized treatment of RA.

Download Full-text