Excel spreadsheet containing the tables of the supplementary material for the research work.

Differentially expressed genes analyzed in GEO2R in the contrasts: DENV Acute x DENV convalescent and ZIKV acute x ZIKV convalescent.

Datasets containing the results of gene expression analysis using GEO2R for different HPV cancer cell lines and normal samples.

Information for selected microarray datasets.

Genes with logFC> 0.1 are marked in bold typeface and significance is indicated as follows:*p

The sensitivity and specificity of the 10 hub genes in detecting DM.

Top CpGs and associated genes from GEO2R across 8 independent folds.

Using GEO2R, GSE28988, patients with primary and secondary dengue were compared during defervescence and convalescence.

Supplementary Code 1. Geo2R. The Geo2R script, used to compute p-values for probes, is available as part of the online supplementary materials. The process starts by downloading MILE data from GEO. (TXT 5.91 kb)

Clinical data characteristics of the experimental group and control group.

Additional file 3: Supplementary File 1. Lists of differentially expressed genes (DEGs) from comparisons of symptomatic and asymptomatic HCMV infections to control samples, from GEO2R analysis of BioProject dataset PRJNA422858 ( https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE108211 ). Sheets A, B and C show GEO2R lists of DEGs from symptomatic vs control, asymptomatic vs control, and symptomatic vs asymptomatic HCMV samples comparisons. Sheets D and E show results of Reactome pathways overrepresentation analyses for significant (FDR

ObjectiveTo explore the shared gene signatures and potential molecular mechanisms of polyarticular juvenile idiopathic arthritis (pJIA) and autoimmune uveitis (AU).MethodThe microarray data of pJIA and AU from the Gene Expression Omnibus (GEO) database were downloaded and analyzed. The GEO2R tool was used to identify the shared differentially expressed genes (DEGs) and genes of extracellular proteins were identified among them. Then, weighted gene co-expression network analysis (WGCNA) was used to identify the shared immune-related genes (IRGs) related to pJIA and AU. Moreover, the shared transcription factors (TFs) and microRNAs (miRNAs) in pJIA and AU were acquired by comparing data from HumanTFDB, hTFtarget, GTRD, HMDD, and miRTarBase. Finally, Metascape and g: Profiler were used to carry out function enrichment analyses of previously identified gene sets.ResultsWe found 40 up-regulated and 15 down-regulated shared DEGs via GEO2R. Then 24 shared IRGs in positivity-related modules, and 18 shared IRGs in negatively-related modules were found after WGCNA. After that, 3 shared TFs (ARID1A, SMARCC2, SON) were screened. And the constructed TFs-shared DEGs network indicates a central role of ARID1A. Furthermore, hsa-miR-146 was found important in both diseases. The gene sets enrichment analyses suggested up-regulated shared DEGs, TFs targeted shared DEGs, and IRGs positivity-correlated with both diseases mainly enriched in neutrophil degranulation process, IL-4, IL-13, and cytokine signaling pathways. The IRGs negatively correlated with pJIA and AU mainly influence functions of the natural killer cell, cytotoxicity, and glomerular mesangial cell proliferation. The down-regulated shared DEGs and TFs targeted shared DEGs did not show particular functional enrichment.ConclusionOur study fully demonstrated the flexibility and complexity of the immune system disorders involved in pJIA and AU. Neutrophil degranulation may be considered the shared pathogenic mechanism, and the roles of ARID1A and MiR-146a are worthy of further in-depth study. Other than that, the importance of periodic inspection of kidney function is also noteworthy.

Additional file 1: Supplementary Material Figures 1-5: The assessment results about sample distribution and data reliability for GSE59867 dataset, GSE76591 dataset, GSE97320 dataset, GSE168149 dataset and GSE66360 dataset. (A): Box Plots of GEO2R show the distribution of values for each sample in the dataset to assess the sample quality and to exclude significantly discrete samples; (B): Expression Density Plots of GEO2R are complementary to box plots. By observing whether the normalized data in the Expression Density Plot matches the normal distribution, the suitability of the data for differential expression analysis is determined, which in turn ensures the reliability of the final data analysis results; (C): Plot of sample quartiles: the points in the plot are distributed along a straight line, indicating that the values of the moderated t-statistic calculated from the sample data during testing follow their theoretical predicted distribution.

Background: Pancreatic ductal adenocarcinoma (PDAC) develops rapidly and has a poor prognosis. It has been demonstrated that pancreatic ductal adenocarcinoma and chronic pancreatitis (CP) have a close connection. However, the underlying mechanisms for chronic pancreatitis transforming into pancreatic ductal adenocarcinoma are still unclear. The purpose of this study was to identify real hub genes in the development of chronic pancreatitis and pancreatic ductal adenocarcinoma.Methods: RNA-seq data of chronic pancreatitis and pancreatic ductal adenocarcinoma were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between chronic pancreatitis and pancreatic ductal adenocarcinoma. GEO2R and a Venn diagram were used to identify differentially expressed genes. Then visualized networks were constructed with ClueGO, and modules of PPI network were calculated by MCODE plugin. Further validation of the results was carried out in two additional cohorts. Analyses of CEL-coexpressed genes and regulators including miRNAs and transcription factors were performed by using the corresponding online web tool. Finally, the influence of CEL in the tumor immune microenvironment (TIME) was assessed by immune contextual analysis.Results: With the help of WGCNA and GEO2R, four co-expression modules and six hub genes were identified, respectively. ClueGO enrichment analysis and MCODE cluster analysis revealed that the dysfunctional transport of nutrients and trace elements might contribute to chronic pancreatitis and pancreatic ductal adenocarcinoma development. The real hub gene CEL was identified with a markedly low expression in pancreatic ductal adenocarcinoma in external validation sets. According to the miRNA-gene network construction, hsa-miR-198 may be the key miRNA. A strong correlation exists between CEL and TIME after an evaluation of the influence of CEL in TIME.Conclusion: Our study revealed the dysfunctional transport of nutrients and trace elements may be common pathogenesis of pancreatic ductal adenocarcinoma and chronic pancreatitis. Examination on these common pathways and real hub genes may shed light on the underlying mechanism.

Background: Pancreatic ductal adenocarcinoma (PDAC) develops rapidly and has a poor prognosis. It has been demonstrated that pancreatic ductal adenocarcinoma and chronic pancreatitis (CP) have a close connection. However, the underlying mechanisms for chronic pancreatitis transforming into pancreatic ductal adenocarcinoma are still unclear. The purpose of this study was to identify real hub genes in the development of chronic pancreatitis and pancreatic ductal adenocarcinoma.Methods: RNA-seq data of chronic pancreatitis and pancreatic ductal adenocarcinoma were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between chronic pancreatitis and pancreatic ductal adenocarcinoma. GEO2R and a Venn diagram were used to identify differentially expressed genes. Then visualized networks were constructed with ClueGO, and modules of PPI network were calculated by MCODE plugin. Further validation of the results was carried out in two additional cohorts. Analyses of CEL-coexpressed genes and regulators including miRNAs and transcription factors were performed by using the corresponding online web tool. Finally, the influence of CEL in the tumor immune microenvironment (TIME) was assessed by immune contextual analysis.Results: With the help of WGCNA and GEO2R, four co-expression modules and six hub genes were identified, respectively. ClueGO enrichment analysis and MCODE cluster analysis revealed that the dysfunctional transport of nutrients and trace elements might contribute to chronic pancreatitis and pancreatic ductal adenocarcinoma development. The real hub gene CEL was identified with a markedly low expression in pancreatic ductal adenocarcinoma in external validation sets. According to the miRNA-gene network construction, hsa-miR-198 may be the key miRNA. A strong correlation exists between CEL and TIME after an evaluation of the influence of CEL in TIME.Conclusion: Our study revealed the dysfunctional transport of nutrients and trace elements may be common pathogenesis of pancreatic ductal adenocarcinoma and chronic pancreatitis. Examination on these common pathways and real hub genes may shed light on the underlying mechanism.

Background: Pancreatic ductal adenocarcinoma (PDAC) develops rapidly and has a poor prognosis. It has been demonstrated that pancreatic ductal adenocarcinoma and chronic pancreatitis (CP) have a close connection. However, the underlying mechanisms for chronic pancreatitis transforming into pancreatic ductal adenocarcinoma are still unclear. The purpose of this study was to identify real hub genes in the development of chronic pancreatitis and pancreatic ductal adenocarcinoma.Methods: RNA-seq data of chronic pancreatitis and pancreatic ductal adenocarcinoma were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between chronic pancreatitis and pancreatic ductal adenocarcinoma. GEO2R and a Venn diagram were used to identify differentially expressed genes. Then visualized networks were constructed with ClueGO, and modules of PPI network were calculated by MCODE plugin. Further validation of the results was carried out in two additional cohorts. Analyses of CEL-coexpressed genes and regulators including miRNAs and transcription factors were performed by using the corresponding online web tool. Finally, the influence of CEL in the tumor immune microenvironment (TIME) was assessed by immune contextual analysis.Results: With the help of WGCNA and GEO2R, four co-expression modules and six hub genes were identified, respectively. ClueGO enrichment analysis and MCODE cluster analysis revealed that the dysfunctional transport of nutrients and trace elements might contribute to chronic pancreatitis and pancreatic ductal adenocarcinoma development. The real hub gene CEL was identified with a markedly low expression in pancreatic ductal adenocarcinoma in external validation sets. According to the miRNA-gene network construction, hsa-miR-198 may be the key miRNA. A strong correlation exists between CEL and TIME after an evaluation of the influence of CEL in TIME.Conclusion: Our study revealed the dysfunctional transport of nutrients and trace elements may be common pathogenesis of pancreatic ductal adenocarcinoma and chronic pancreatitis. Examination on these common pathways and real hub genes may shed light on the underlying mechanism.

Background: Pancreatic ductal adenocarcinoma (PDAC) develops rapidly and has a poor prognosis. It has been demonstrated that pancreatic ductal adenocarcinoma and chronic pancreatitis (CP) have a close connection. However, the underlying mechanisms for chronic pancreatitis transforming into pancreatic ductal adenocarcinoma are still unclear. The purpose of this study was to identify real hub genes in the development of chronic pancreatitis and pancreatic ductal adenocarcinoma.Methods: RNA-seq data of chronic pancreatitis and pancreatic ductal adenocarcinoma were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between chronic pancreatitis and pancreatic ductal adenocarcinoma. GEO2R and a Venn diagram were used to identify differentially expressed genes. Then visualized networks were constructed with ClueGO, and modules of PPI network were calculated by MCODE plugin. Further validation of the results was carried out in two additional cohorts. Analyses of CEL-coexpressed genes and regulators including miRNAs and transcription factors were performed by using the corresponding online web tool. Finally, the influence of CEL in the tumor immune microenvironment (TIME) was assessed by immune contextual analysis.Results: With the help of WGCNA and GEO2R, four co-expression modules and six hub genes were identified, respectively. ClueGO enrichment analysis and MCODE cluster analysis revealed that the dysfunctional transport of nutrients and trace elements might contribute to chronic pancreatitis and pancreatic ductal adenocarcinoma development. The real hub gene CEL was identified with a markedly low expression in pancreatic ductal adenocarcinoma in external validation sets. According to the miRNA-gene network construction, hsa-miR-198 may be the key miRNA. A strong correlation exists between CEL and TIME after an evaluation of the influence of CEL in TIME.Conclusion: Our study revealed the dysfunctional transport of nutrients and trace elements may be common pathogenesis of pancreatic ductal adenocarcinoma and chronic pancreatitis. Examination on these common pathways and real hub genes may shed light on the underlying mechanism.

ObjectiveTo explore the shared gene signatures and potential molecular mechanisms of polyarticular juvenile idiopathic arthritis (pJIA) and autoimmune uveitis (AU).MethodThe microarray data of pJIA and AU from the Gene Expression Omnibus (GEO) database were downloaded and analyzed. The GEO2R tool was used to identify the shared differentially expressed genes (DEGs) and genes of extracellular proteins were identified among them. Then, weighted gene co-expression network analysis (WGCNA) was used to identify the shared immune-related genes (IRGs) related to pJIA and AU. Moreover, the shared transcription factors (TFs) and microRNAs (miRNAs) in pJIA and AU were acquired by comparing data from HumanTFDB, hTFtarget, GTRD, HMDD, and miRTarBase. Finally, Metascape and g: Profiler were used to carry out function enrichment analyses of previously identified gene sets.ResultsWe found 40 up-regulated and 15 down-regulated shared DEGs via GEO2R. Then 24 shared IRGs in positivity-related modules, and 18 shared IRGs in negatively-related modules were found after WGCNA. After that, 3 shared TFs (ARID1A, SMARCC2, SON) were screened. And the constructed TFs-shared DEGs network indicates a central role of ARID1A. Furthermore, hsa-miR-146 was found important in both diseases. The gene sets enrichment analyses suggested up-regulated shared DEGs, TFs targeted shared DEGs, and IRGs positivity-correlated with both diseases mainly enriched in neutrophil degranulation process, IL-4, IL-13, and cytokine signaling pathways. The IRGs negatively correlated with pJIA and AU mainly influence functions of the natural killer cell, cytotoxicity, and glomerular mesangial cell proliferation. The down-regulated shared DEGs and TFs targeted shared DEGs did not show particular functional enrichment.ConclusionOur study fully demonstrated the flexibility and complexity of the immune system disorders involved in pJIA and AU. Neutrophil degranulation may be considered the shared pathogenic mechanism, and the roles of ARID1A and MiR-146a are worthy of further in-depth study. Other than that, the importance of periodic inspection of kidney function is also noteworthy.

ObjectiveDementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD) are collectively known as Lewy body dementia (LBD). Considering the heterogeneous nature of LBD and the different constellations of symptoms with which patients can present, the exact molecular mechanism underlying the differences between these two isoforms is still unknown. Therefore, this study aimed to explore the biomarkers and potential mechanisms that distinguish between PDD and DLB.MethodsThe mRNA expression profile dataset of GSE150696 was acquired from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between 12 DLB and 12 PDD were identified from Brodmann area 9 of human postmortem brains using GEO2R. A series of bioinformatics methods were applied to identify the potential signaling pathways involved, and a protein–protein interaction (PPI) network was constructed. Weighted gene co-expression network analysis (WGCNA) was used to further investigate the relationship between gene co-expression and different LBD subtypes. Hub genes that are strongly associated with PDD and DLB were obtained from the intersection of DEGs and selected modules by WGCNA.ResultsA total of 1,864 DEGs between PDD and DLB were filtered by the online analysis tool GEO2R. We found that the most significant GO- and KEGG-enriched terms are involved in the establishment of the vesicle localization and pathways of neurodegeneration-multiple diseases. Glycerolipid metabolism and viral myocarditis were enriched in the PDD group. A B-cell receptor signaling pathway and one carbon pool by folate correlated with DLB in the results obtained from the GSEA. We found several clusters of co-expressed genes which we designated by colors in our WGCNA analysis. Furthermore, we identified seven upregulated genes, namely, SNAP25, GRIN2A, GABRG2, GABRA1, GRIA1, SLC17A6, and SYN1, which are significantly correlated with PDD.ConclusionThe seven hub genes and the signaling pathways we identified may be involved in the heterogeneous pathogenesis of PDD and DLB.