Protein networks have become a popular tool for analyzing and visualizing the often long lists of proteins or genes obtained from proteomics and other high-throughput technologies. One of the most popular sources of such networks is the STRING database, which provides protein networks for more than 2000 organisms, including both physical interactions from experimental data and functional associations from curated pathways, automatic text mining, and prediction methods. However, its web interface is mainly intended for inspection of small networks and their underlying evidence. The Cytoscape software, on the other hand, is much better suited for working with large networks and offers greater flexibility in terms of network analysis, import, and visualization of additional data. To include both resources in the same workflow, we created stringApp, a Cytoscape app that makes it easy to import STRING networks into Cytoscape, retains the appearance and many of the features of STRING, and integrates data from associated databases. Here, we introduce many of the stringApp features and show how they can be used to carry out complex network analysis and visualization tasks on a typical proteomics data set, all through the Cytoscape user interface. stringApp is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/stringapp.

The top 5 GO terms enriched by DEmRNA involved in the ceRNA network.

This study was designed to identify immune-related biomarkers associated with allergic rhinitis (AR) and construct a robust a diagnostic model. Two datasets (GSE5010 and GSE50223) were downloaded from the NCBI GEO database, containing 38 and 84 blood CD4 + T cell samples, respectively. To eliminate batch effects, the surrogate variable analysis (sva) R package (version 3.38.0) was employed, enabling the integration of data for subsequent analysis. Immune cell infiltration profiles were assessed using the Gene Set Variation Analysis (GSVA) R package (version 1.36.3). A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. Protein-protein interaction (PPI) networks were constructed based on the STRING database to highlight key genes. A diagnostic model was subsequently developed utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm and Support Vector Machine (SVM) method, with its discriminative capacity assessed via Receiver Operating Characteristic (ROC) curves. A total of twenty-eight immune cell types were analyzed, revealing significant differences in eight types between the AR and control groups. Through WGCNA, three disease-related modules comprising 4278 candidate genes were identified. Differential expression analysis identified 326 significant DEGs, of which 257 overlapped with WGCNA-selected genes. These genes exhibited significant enrichment in immune-related pathways, including “cytokine-cytokine receptor interaction” and “chemokine signaling pathway.” Gene Set Enrichment Analysis (GSEA) further uncovered 12 KEGG pathways significantly associated with disease risk scores. Drug screening identified 24 small molecule drugs related to key genes. A diagnostic model incorporating five genes (RFC4, LYN, IL3, TNFRSF1B, and RBBP7) was constructed, demonstrating diagnostic efficiencies of 0.843 and 0.739 in the training and validation sets, respectively. An AR mouse model was successfully established, and the expression levels of relevant genes were validated through RT-qPCR experiments. The five-gene diagnostic model established in this study exhibits strong predictive ability in distinguishing AR patients from healthy controls, with potential clinical applications in diagnosing AR and advancing novel diagnostic and therapeutic strategies.

Differentially expressed genes (DEGs) exhibiting both increased and decreased expression levels.

Additional file 2. Summary of Statistical Measures used to Evaluate Gene Expression before and after Minimum Value Adjustment (MVA).

IntroductionAndrogenetic alopecia (AGA) is a common progressive scalp hair loss disorder that leads to baldness. This study aimed to identify core genes and pathways involved in premature AGA through an in-silico approach.MethodsGene expression data (GSE90594) from vertex scalps of men with premature AGA and men without pattern hair loss was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the bald and haired samples were identified using the limma package in R. Gene ontology and Reactome pathway enrichment analyses were conducted separately for the up-regulated and down-regulated genes. The DEGs were annotated with the AGA risk loci, and motif analysis in the promoters of the DEGs was also carried out. STRING Protein-protein interaction (PPI) and Reactome Functional Interaction (FI) networks were constructed using the DEGs, and the networks were analyzed to identify hub genes that play could play crucial roles in AGA pathogenesis.Results and discussionThe in-silico study revealed that genes involved in the structural makeup of the skin epidermis, hair follicle development, and hair cycle are down-regulated, while genes associated with the innate and adaptive immune systems, cytokine signaling, and interferon signaling pathways are up-regulated in the balding scalps of AGA. The PPI and FI network analyses identified 25 hub genes namely CTNNB1, EGF, GNAI3, NRAS, BTK, ESR1, HCK, ITGB7, LCK, LCP2, LYN, PDGFRB, PIK3CD, PTPN6, RAC2, SPI1, STAT3, STAT5A, VAV1, PSMB8, HLA-A, HLA-F, HLA-E, IRF4, and ITGAM that play crucial roles in AGA pathogenesis. The study also implicates that Src family tyrosine kinase genes such as LCK, and LYN in the up-regulation of the inflammatory process in the balding scalps of AGA highlighting their potential as therapeutic targets for future investigations.

Additional file 6. STRING db Analysis of Intra-individual Positional Gene Rankings in 35 Control Samples Based On Range/Median, Range/Q3, Kurtosis and Q4/Q(2 + 3) Slope Calculations.

Example of significant functional enrichment proteins in adult and larvae Ae. aegypti.

aThe significance of the GO biological process derived from the cytosolic protein network was determined by FDR correction (p

Additional file 10. Reference Genes with Raw Counts Greater than 5 and R2 Values > 0.9 in three separate RNA sequencing Studies.

For an enhanced understanding of the biological mechanisms of human disease, it is essential to investigate protein functions. In a previous study, we developed a prediction method of gene ontology (GO) terms by the I-TASSER/COFACTOR result, and we applied this to uPE1 in chromosome 11. Here, to validate the bioinformatics prediction of C11orf52, we utilized affinity purification and mass spectrometry to identify interacting partners of C11orf52. Using immunoprecipitation methods with three different peptide tags (Myc, Flag, and 2B8) in HEK 293T cell lines, we identified 79 candidate proteins that are expected to interact with C11orf52. The results of a pathway analysis of the GO and STRING database with candidate proteins showed that C11orf52 could be related to signaling receptor binding, cell–cell adhesion, and ribosome biogenesis. Then, we selected three partner candidates of DSG1, JUP, and PTPN11 for verification of the interaction with C11orf52 and confirmed them by colocalization at the cell–cell junctions by coimmunofluorescence experiments. On the basis of this study, we expect that C11orf52 is related to the Wnt signaling pathway via DSG1 from the protein–protein interactions, given the results of a comprehensive analysis of the bioinformatic predictions. The data set is available at the ProteomeXchange consortium via PRIDE repository (PXD026986).

This study was designed to identify immune-related biomarkers associated with allergic rhinitis (AR) and construct a robust a diagnostic model. Two datasets (GSE5010 and GSE50223) were downloaded from the NCBI GEO database, containing 38 and 84 blood CD4 + T cell samples, respectively. To eliminate batch effects, the surrogate variable analysis (sva) R package (version 3.38.0) was employed, enabling the integration of data for subsequent analysis. Immune cell infiltration profiles were assessed using the Gene Set Variation Analysis (GSVA) R package (version 1.36.3). A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. Protein-protein interaction (PPI) networks were constructed based on the STRING database to highlight key genes. A diagnostic model was subsequently developed utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm and Support Vector Machine (SVM) method, with its discriminative capacity assessed via Receiver Operating Characteristic (ROC) curves. A total of twenty-eight immune cell types were analyzed, revealing significant differences in eight types between the AR and control groups. Through WGCNA, three disease-related modules comprising 4278 candidate genes were identified. Differential expression analysis identified 326 significant DEGs, of which 257 overlapped with WGCNA-selected genes. These genes exhibited significant enrichment in immune-related pathways, including “cytokine-cytokine receptor interaction” and “chemokine signaling pathway.” Gene Set Enrichment Analysis (GSEA) further uncovered 12 KEGG pathways significantly associated with disease risk scores. Drug screening identified 24 small molecule drugs related to key genes. A diagnostic model incorporating five genes (RFC4, LYN, IL3, TNFRSF1B, and RBBP7) was constructed, demonstrating diagnostic efficiencies of 0.843 and 0.739 in the training and validation sets, respectively. An AR mouse model was successfully established, and the expression levels of relevant genes were validated through RT-qPCR experiments. The five-gene diagnostic model established in this study exhibits strong predictive ability in distinguishing AR patients from healthy controls, with potential clinical applications in diagnosing AR and advancing novel diagnostic and therapeutic strategies.

Additional file 11. Control Reference Data Files.

PurposeIn chronic thromboembolic pulmonary hypertension (CTEPH), fibrosis of thrombi in the lumen of blood vessels and obstruction of blood vessels are important factors in the progression of the disease. Therefore, it is important to explore the key genes that lead to chronic thrombosis in order to understand the development of CTEPH, and at the same time, it is beneficial to provide new directions for early identification, disease prevention, clinical diagnosis and treatment, and development of novel therapeutic agents.MethodsThe GSE130391 dataset was downloaded from the Gene Expression Omnibus (GEO) public database, which includes the full gene expression profiles of patients with CTEPH and Idiopathic Pulmonary Arterial Hypertension (IPAH). Differentially Expressed Genes (DEGs) of CTEPH and IPAH were screened, and then Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) functional enrichment analyses were performed on the DEGs; Weighted Gene Co-Expression Network Analysis (WGCNA) to screen the key gene modules and take the intersection genes of DEGs and the key module genes in WGCNA; STRING database was used to construct the protein-protein interaction (PPI) network; and cytoHubba analysis was performed to identify the hub genes.ResultsA total of 924 DEGs were screened, and the MEturquoise module with the strongest correlation was selected to take the intersection with DEGs A total of 757 intersecting genes were screened. The top ten hub genes were analyzed by cytoHubba: IL-1B, CXCL8, CCL22, CCL5, CCL20, TNF, IL-12B, JUN, EP300, and CCL4.ConclusionIL-1B, CXCL8, CCL22, CCL5, CCL20, TNF, IL-12B, JUN, EP300, and CCL4 have diagnostic and therapeutic value in CTEPH disease, especially playing a role in chronic thrombosis. The discovery of NF-κB, AP-1 transcription factors, and TNF signaling pathway through pivotal genes may be involved in the disease progression process.

Unclassified proteins in the STRING network analysis.

Functional analysis of 18 genes in Module-1 using STRING database.

Proteins differentially expressed during the cortical development and their potential functions in the nervous system.

This study was designed to identify immune-related biomarkers associated with allergic rhinitis (AR) and construct a robust a diagnostic model. Two datasets (GSE5010 and GSE50223) were downloaded from the NCBI GEO database, containing 38 and 84 blood CD4 + T cell samples, respectively. To eliminate batch effects, the surrogate variable analysis (sva) R package (version 3.38.0) was employed, enabling the integration of data for subsequent analysis. Immune cell infiltration profiles were assessed using the Gene Set Variation Analysis (GSVA) R package (version 1.36.3). A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. Protein-protein interaction (PPI) networks were constructed based on the STRING database to highlight key genes. A diagnostic model was subsequently developed utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm and Support Vector Machine (SVM) method, with its discriminative capacity assessed via Receiver Operating Characteristic (ROC) curves. A total of twenty-eight immune cell types were analyzed, revealing significant differences in eight types between the AR and control groups. Through WGCNA, three disease-related modules comprising 4278 candidate genes were identified. Differential expression analysis identified 326 significant DEGs, of which 257 overlapped with WGCNA-selected genes. These genes exhibited significant enrichment in immune-related pathways, including “cytokine-cytokine receptor interaction” and “chemokine signaling pathway.” Gene Set Enrichment Analysis (GSEA) further uncovered 12 KEGG pathways significantly associated with disease risk scores. Drug screening identified 24 small molecule drugs related to key genes. A diagnostic model incorporating five genes (RFC4, LYN, IL3, TNFRSF1B, and RBBP7) was constructed, demonstrating diagnostic efficiencies of 0.843 and 0.739 in the training and validation sets, respectively. An AR mouse model was successfully established, and the expression levels of relevant genes were validated through RT-qPCR experiments. The five-gene diagnostic model established in this study exhibits strong predictive ability in distinguishing AR patients from healthy controls, with potential clinical applications in diagnosing AR and advancing novel diagnostic and therapeutic strategies.

This study was designed to identify immune-related biomarkers associated with allergic rhinitis (AR) and construct a robust a diagnostic model. Two datasets (GSE5010 and GSE50223) were downloaded from the NCBI GEO database, containing 38 and 84 blood CD4 + T cell samples, respectively. To eliminate batch effects, the surrogate variable analysis (sva) R package (version 3.38.0) was employed, enabling the integration of data for subsequent analysis. Immune cell infiltration profiles were assessed using the Gene Set Variation Analysis (GSVA) R package (version 1.36.3). A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. Protein-protein interaction (PPI) networks were constructed based on the STRING database to highlight key genes. A diagnostic model was subsequently developed utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm and Support Vector Machine (SVM) method, with its discriminative capacity assessed via Receiver Operating Characteristic (ROC) curves. A total of twenty-eight immune cell types were analyzed, revealing significant differences in eight types between the AR and control groups. Through WGCNA, three disease-related modules comprising 4278 candidate genes were identified. Differential expression analysis identified 326 significant DEGs, of which 257 overlapped with WGCNA-selected genes. These genes exhibited significant enrichment in immune-related pathways, including “cytokine-cytokine receptor interaction” and “chemokine signaling pathway.” Gene Set Enrichment Analysis (GSEA) further uncovered 12 KEGG pathways significantly associated with disease risk scores. Drug screening identified 24 small molecule drugs related to key genes. A diagnostic model incorporating five genes (RFC4, LYN, IL3, TNFRSF1B, and RBBP7) was constructed, demonstrating diagnostic efficiencies of 0.843 and 0.739 in the training and validation sets, respectively. An AR mouse model was successfully established, and the expression levels of relevant genes were validated through RT-qPCR experiments. The five-gene diagnostic model established in this study exhibits strong predictive ability in distinguishing AR patients from healthy controls, with potential clinical applications in diagnosing AR and advancing novel diagnostic and therapeutic strategies.

This study aimed to analyze metabolite abundances and proteome differences between Binglangjiang buffalo milk (BBM) and Dehong buffalo milk (DBM). Untargeted ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), label-free quantitative proteomics approaches, and bioinformatics analyses including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) were performed.