KEGG Pathway enriched (using Enrichr website) for C-network (genes with conserved co-expression). Gene expression data from microarray, comparing AD and normal.

The file contains 642,389 gene sets extracted from supporting materials of 121,237 articles listed on PubMed Central. The file is part of a project called Rummagene. The Rummagene web server application is available at: https://rummagene.com.

This signature contains 61 downregulated and 39 upregulated RNA transcripts in early stage in comparison to late stage of LIHC).

DisGeNET analysis of calumenin correlated diseases.

Diagnostic performance of DEGs in TCGA-COAD and LUAD datasets.

BackgroundImmune infiltration plays an important role in the course of ischemic stroke (IS) progression. Cuproptosis is a newly discovered form of programmed cell death. To date, no studies on the mechanisms by which cuproptosis-related genes regulate immune infiltration in IS have been reported.MethodsIS-related microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database and standardized. Immune infiltration was extracted and quantified based on the processed gene expression matrix. The differences between the IS group and the normal group as well as the correlation between the infiltrating immune cells and their functions were analyzed. The cuproptosis-related DEGs most related to immunity were screened out, and the risk model was constructed. Finally, Gene Ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and drug target were performed using the Enrichr website database. miRNAs were predicted using FunRich software. Finally, cuproptosis-related differentially expressed genes (DEGs) in IS samples were typed, and Gene Set Variation Analysis (GSVA) was used to analyze the differences in biological functions among the different types.ResultsSeven Cuproptosis-related DEGs were obtained by merging the GSE16561 and GSE37587 datasets. Correlation analysis of the immune cells showed that NLRP3, NFE2L2, ATP7A, LIPT1, GLS, and MTF1 were significantly correlated with immune cells. Subsequently, these six genes were included in the risk study, and the risk prediction model was constructed to calculate the total score to analyze the risk probability of the IS group. KEGG analysis showed that the genes were mainly enriched in the following two pathways: D-glutamine and D-glutamate metabolism; and lipids and atherosclerosis. Drug target prediction found that DMBA CTD 00007046 and Lithocholate TTD 00009000 were predicted to have potential therapeutic effects of candidate molecules. GSVA showed that the TGF-β signaling pathway and autophagy regulation pathways were upregulated in the subgroup with high expression of cuproptosis-related DEGs.ConclusionsNLRP3, NFE2L2, ATP7A, LIPT1, GLS and MTF1 may serve as predictors of cuproptosis and play an important role in the pathogenesis of immune infiltration in IS.

Clinical features of the colon cancer group.

Clinical features of the lung cancer group.

Background: Breast carcinoma is well recognized to be having the highest global occurrence rate among all cancers, being the leading cause of cancer mortality in females. The aim of this study was to elucidate breast cancer at the genomic and transcriptomic levels in different subtypes so that we can develop more personalized treatments and precision medicine to obtain better outcomes.Method: In this study, an expression profiling dataset downloaded from the Gene Expression Omnibus database, GSE45827, was re-analyzed to compare the expression profiles of breast cancer samples in the different subtypes. Using the GEO2R tool, different expression genes were identified. Using the STRING online tool, the protein–protein interaction networks were conducted. Using the Cytoscape software, we found modules, seed genes, and hub genes and performed pathway enrichment analysis. The Kaplan–Meier plotter was used to analyze the overall survival. MicroRNAs and transcription factors targeted different expression genes and were predicted by the Enrichr web server.Result: The analysis of these elements implied that the carcinogenesis and development of triple-negative breast cancer were the most important and complicated in breast carcinoma, occupying the most different expression genes, modules, seed genes, hub genes, and the most complex protein–protein interaction network and signal pathway. In addition, the luminal A subtype might occur in a completely different way from the other three subtypes as the pathways enriched in the luminal A subtype did not overlap with the others. We identified 16 hub genes that were related to good prognosis in triple-negative breast cancer. Moreover, SRSF1 was negatively correlated with overall survival in the Her2 subtype, while in the luminal A subtype, it showed the opposite relationship. Also, in the luminal B subtype, CCNB1 and KIF23 were associated with poor prognosis. Furthermore, new transcription factors and microRNAs were introduced to breast cancer which would shed light upon breast cancer in a new way and provide a novel therapeutic strategy.Conclusion: We preliminarily delved into the potentially comprehensive molecular mechanisms of breast cancer by creating a holistic view at the genomic and transcriptomic levels in different subtypes using computational tools. We also introduced new prognosis-related genes and novel therapeutic strategies and cast new light upon breast cancer.

Significantly enriched common GO-terms (BPs, MFs, and CCs) that might be associated with SARS-CoV-2 infections and some lung diseases identified from two online web-tools DAVID and Enrichr (adjusted p-value < 0.05).

Understanding the mechanisms that drive HIV expression and latency is a key goal for achieving an HIV cure. Here we investigate the role of the SETD2 histone methyltransferase, which deposits H3K36 trimethylation (H3K36me3), in HIV infection. We show that prevention of H3K36me3 by a potent and selective inhibitor of SETD2 (EPZ-719) leads to reduced post-integration viral gene expression and accelerated emergence of latently infected cells. CRISPR/Cas9-mediated knockout of SETD2 in primary CD4 T cells confirmed the role of SETD2 in HIV expression. Transcriptomic profiling of EPZ-719-exposed HIV-infected cells identified numerous pathways impacted by EPZ-719. Notably, depletion of H3K36me3 prior to infection did not prevent HIV integration but resulted in a shift of integration sites from highly transcribed genes to quiescent chromatin regions and to polycomb repressed regions. We also observed that SETD2 inhibition did not apparently affect HIV RNA levels, indicating a post-transcriptional mechanism affecting HIV expression. Viral RNA splicing was modestly reduced in the presence of EPZ-719. Intriguingly, EPZ-719 exposure enhanced responsiveness of latent HIV to the HDAC inhibitor vorinostat, suggesting that H3K36me3 can contribute to a repressive chromatin state at the HIV locus. These results identify SETD2 and H3K36me3 as novel regulators of HIV integration, expression and latency.

Additional file 2: Table S1. Differentially bound sites (DBSs) obtained from MCF7 cell line treated with 10nM E2 for 45 minutes in GSE94023 study. Table S2. Differentially bound sites (DBSs) obtained from MCF7 cell line treated with 10nM E2 for 45 minutes in GSE99626 study. Table S3. Differentially bound sites (DBSs) obtained from MCF7 cell line treated with 10nM E2 for 45 minutes in GSE67295 study. Table S4. Differentially bound sites (DBSs) obtained from MCF7 cell line treated with 10nM E2 for 45 minutes in GSE115607 study. Table S5. Differentially bound sites (DBSs) obtained from T47D cell line treated with 10nM E2 for 45 minutes in GSE80367 study. Table S6. Differentially bound sites (DBSs) obtained from T47D cell line treated with 100nM E2 for 45 minutes in GSE23893 study. Table S7. Differentially bound sites (DBSs) obtained from MCF7 cell line treated with 100nM E2 for 45 minutes in GSE23893 study. Table S8. Differentially bound sites (DBSs) obtained from MCF7 cell line treated with 100nM E2 for 45 minutes in GSE54855 study. Table S9. Differentially bound sites (DBSs) obtained from MCF7 cell line treated with 100nM E2 for 45 minutes in GSE59530 study. Table S10. Default binding affinity matrix of 6 samples by the 63,612 sites that overlap in at least two of the samples using DiffBind in (GSE94023, GSE99626, GSE67295, & GSE115607) MCF7 cell line treated with 10nM E2 for 45 minutes. Table S11. Default binding affinity matrix of 6 samples by the 23,517 sites that overlap in at least two of the samples using DiffBind in (GSE23893, GSE54855, & GSE59530) MCF7 cell line treated with 100nM E2 for 45 minutes. Table S12. Meta-differentially bound sites (meta-DBSs) obtained from a meta-analysis on (GSE94023, GSE99626, GSE67295, & GSE115607) MCF7 cell line treated with 10nM E2 for 45 minutes. Table S13. Meta-differentially bound sites (meta-DBSs) obtained from a meta-analysis on (GSE23893, GSE54855, & GSE59530) MCF7 cell line treated with 100nM E2 for 45 minutes. Table S14. literature_ChIP-seq. Table S15. Enrichr. Table S16. ARCHS4—Coexpression. Table S17. ENCODE--ChIP-seq. Table S18. ReMap--ChIP-seq. Table S19. GTEx—Coexpression. Table S20. Integrated_topRank. Table S21. Integrated_meanRank. Table S22. Gene Ontology (GO) for 7,308 meta-DBSs related to 617 common genes among MCF7 & T47D cell lines using Cistrome-GO. Table S23. KEGG pathways analysis for 7,308 meta-DBSs related to 617 common genes among MCF7 & T47D cell lines using Cistrome-GO. Table S24. Differentially expressed genes (DEGs) identified from GRO-seq data in the MCF7 cell line treated with 100nM E2 for 40 minutes in the GSE27463 study.

BackgroundHepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Recently, competing endogenous RNAs (ceRNA) have revealed a significant role in the progression of HCC. Herein, we aimed to construct a ceRNA network to identify potential biomarkers and illustrate its correlation with immune infiltration in HCC.MethodsRNA sequencing data and clinical traits of HCC patients were downloaded from TCGA. The limma R package was used to identify differentially expressed (DE) RNAs. The predicted prognostic model was established using univariate and multivariate Cox regression. A K-M curve, TISIDB and GEPIA website were utilized for survival analysis. Functional annotation was determined using Enrichr and Reactome. Protein-to-protein network analysis was implemented using SRTNG and Cytoscape. Hub gene expression was validated by quantitative polymerase chain reaction, Oncomine and the Hunan Protein Atlas database. Immune infiltration was analyzed by TIMMER, and Drugbank was exploited to identify bioactive compounds.ResultsThe predicted model that was established revealed significant efficacy with 3- and 5-years of the area under ROC at 0.804 and 0.744, respectively. Eleven DEmiRNAs were screened out by a K-M survival analysis. Then, we constructed a ceRNA network, including 56 DElncRNAs, 6 DEmiRNAs, and 28 DEmRNAs. The 28 DEmRNAs were enriched in cancer-related pathways, for example, the TNF signaling pathway. Moreover, six hub genes, CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1, were all overexpressed in HCC tissues and independently correlated with survival rate. Furthermore, expression of hub genes was related to immune cell infiltration in HCC, including B cells, CD8+ T cells, CD4+ T cells, monocytes, macrophages, neutrophils, and dendritic cells.ConclusionThe findings from this study demonstrate that CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1 are closely associated with prognosis and immune infiltration, representing potential therapeutic targets or prognostic biomarkers in HCC.