Search Tool For The Retrieval Of Interacting Genes Research Articles

The co-expression of Crohn’s disease and colon cancer network was analyzed by bioinformatics-CXCL1 tumour microenvironment and prognosis-related gene CXCL1

PurposeThis study aimed to investigate the molecular links and mechanisms between Crohn’s disease (CD) and colorectal cancer (CRC).MethodsThis study used the Gene Expression Omnibus (GEO) database to identify Differentially expressed genes (DEGs) in CD (GSE112366) and CRC (GSE110224), analyzed by 'edgeR' and 'limma'. The Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes explored DEG functions, and the Search Tool for the Retrieval of Interacting Genes (STRING) informed the protein–protein interaction network construction visualized in Cytoscape (version 3.7.2). Cyto-Hubba identified key genes, whose biomarker potential for CD and CRC was evaluated.ResultsThe study discovered 61 DEGs, with 44 up- and 17 down-regulated, linked to immune responses and signaling pathways. CXCL1, highly expressed in colon cancer, correlated with better prognosis and lower staging. It also showed associations with immune infiltration and checkpoint molecules, suggesting a role in cancer progression and retreat.ConclusionCXCL1 may play a role in the development of colorectal cancer from inflammatory bowel disease.

HLTF/SERPINE1 Axis Plays a Crucial Pro-Oncogenic Role in the Progression from Cervical Precancerous Lesions to Cervical Carcinoma in vitro

Objectives: Cervical carcinoma (CC) is prevalent among women worldwide with increasing risk. Finding effective methods for treating CC is of utmost importance. The aim of this study was to investigate the effect of SERPINE1 on the progression of cervical precancerous lesions to CC. Design: This study used transcriptome sequencing and in vitro cell line. Participants/Materials: Cervical precancerous lesions and CC samples and human cervical epithelial immortalized cell line H8, human CC cell lines HeLa, and CaSki were involved in this study. Setting and Methods: Next-generation sequencing was applied to identify 100 differentially expressed genes from cervical precancerous lesions and CC samples. With the application of the Search Tool for the Retrieval of Interacting Genes (STRING) database, we carried out the protein-protein interaction network analysis, thus screening out serine protease inhibitor clade E member 1 (SERPINE1) with significant upregulation in CC cells. The helicase-like transcription factor (HLTF) was predicted as the upstream transcription factor using Human Transcription Factor Database (HumanTFDB). The chromatin immunoprecipitation (ChIP) experiment was conducted to validate the interaction between SERPINE1 and HLTF. The immunohistochemistry was employed to determine the expression of SERPINE1 and HLTF in CC tissues. Following the upregulation or downregulation of SERPINE1 and HLTF, the real-time quantitative reverse transcription polymerase chain reaction was carried out to assess mRNA expression levels of SERPINE1 and HLTF in cells. Cell viability, migration, and invasion were evaluated using MTT assay, cell scratch assay, and Transwell assay, respectively. Western blot analysis was conducted to assess changes in the expression levels of matrix metalloproteinases and proteins related to epithelial-mesenchymal transition (EMT). Results: The ChIP experiment confirmed the interaction between HLTF and SERPINE1. HLTF and SERPINE1 were upregulated in CC tissues and cells, and silencing SERPINE1 inhibited the EMT process and viability, migration, and invasion of CC cells. However, overexpression of SERPINE1 in CC cells showed the opposite trend. Rescue experiments demonstrated that silencing HLTF repressed CC cell viability, migration, and invasion, which could be restored by overexpressing SERPINE1. Limitations: The effect of the HLTF/SERPINE1 axis on CC malignant progression has not been confirmed by in vivo experiments. Conclusion: HLTF transcriptionally activates SERPINE1, promoting the progression from cervical precancerous lesions to CC.

Unveiling the role of RARs in stomach adenocarcinoma: clinical implications and prognostic biomarkers.

Retinoic acid receptors (RARs) family are known to play a significant role in the occurrence and development of tumors. However, the relationship between RARs and stomach adenocarcinoma (STAD) has not yet been clearly identified. The aim of this study is to evaluate the expression profile and clinical value of the RARs family in STAD. The expression level, clinical characteristics, prognostic value, immunity-related evaluations, genetic alteration and methylation site of RARs in STAD were explored using a series of online databases including gene expression profiling interactive analysis (GEPIA), tumor immune estimation resource (TIMER), University of Alabama at Birmingham cancer data (UALCAN), Human Protein Atlas (HPA), Kaplan-Meier plotter, gene set cancer analysis (GSCA), cBioPortal, MethSurv, GeneMANIA, LinkedOmics, Metascape, Search tool for the retrieval of interacting genes (STRING), tumor immune single-cell hub (TISCH) and cancer cell line encyclopedia (CCLE). We discovered dramatically increased expression of RARA and decreased expression of RARB in STAD tissues, and many clinical variables were closely related to RARs. Notably, higher expressions of RARA and RARB as well as lower expression of RARG correlated with worse overall survival (OS) for STAD patients. The clinical value of prognostic model indicated that RARs were identified to be potential prognostic biomarkers for STAD patients. Moreover, RARB was closely related to immune cell infiltration, which had effect on the role of RARB in STAD prognosis. And the genetic alteration of RARB was significantly associated with the longer disease-free survival (DFS) of STAD patients. Additionally, some CpG sites of the RARs family were related with the prognosis of STAD patients. Functional enrichment analyses indicated that several pathways in STAD might be pivotal pathways regulated by RARs. At the single-cell level, there was some extent of infiltration of tumor microenvironment-related cells in the RARs expression in STAD. Our results evaluated the expression profile and clinical values of RARs in patients with STAD, which provided a basis for future in-depth exploration of the specific mechanisms of each member of RARs in STAD.

Comprehensive analysis and identification of subtypes and hub genes of high immune response in lung adenocarcinoma

BackgroundThe advent of immunotherapy targeting immune checkpoints has conferred significant clinical advantages to patients with lung adenocarcinoma (LUAD); However, only a limited subset of patients exhibit responsiveness to this treatment. Consequently, there is an imperative need to stratify LUAD patients based on their response to immunotherapy and enhance the therapeutic efficacy of these treatments.MethodsThe differentially co-expressed genes associated with CD8 + T cells were identified through weighted gene co-expression network analysis (WGCNA) and the Search Tool for the Retrieval of Interacting Genes (STRING) database. These gene signatures facilitated consensus clustering for TCGA-LUAD and GEO cohorts, categorizing them into distinct immune subtypes (C1, C2, C3, and C4). The Tumor Immune Dysfunction and Exclusion (TIDE) model and Immunophenoscore (IPS) analysis were employed to assess the immunotherapy response of these subtypes. Additionally, the impact of inhibitors targeting five hub genes on the interaction between CD8 + T cells and LUAD cells was evaluated using CCK8 and EDU assays. To ascertain the effects of these inhibitors on immune checkpoint genes and the cytotoxicity mediated by CD8 + T cells, flow cytometry, qPCR, and ELISA methods were utilized.ResultsAmong the identified immune subtypes, subtypes C1 and C3 were characterized by an abundance of immune components and enhanced immunogenicity. Notably, both C1 and C3 exhibited higher T cell dysfunction scores and elevated expression of immune checkpoint genes. Multi-cohort analysis of Lung Adenocarcinoma (LUAD) suggested that these subtypes might elicit superior responses to immunotherapy and chemotherapy. In vitro experiments involved co-culturing LUAD cells with CD8 + T cells and implementing the inhibition of five pivotal genes to assess their function. The inhibition of these genes mitigated the immunosuppression on CD8 + T cells, reduced the levels of PD1 and PD-L1, and promoted the secretion of IFN-γ and IL-2.ConclusionsCollectively, this study delineated LUAD into four distinct subtypes and identified five hub genes correlated with CD8 + T cell activity. It lays the groundwork for refining personalized therapy and immunotherapy strategies for patients with LUAD.

Whole-Exome Sequencing Detecting a Recurrent Pathogenic Mutation, HFE p.His63Asp (H63D) in COVID-19 Patients and Its Effect on Mortality.

In recent years, various coronaviruses have caused severe respiratory illnesses worldwide. For example the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infections of COVID-19 outbreak in 2019 in Wuhan, China. Genome-wide association studies (GWAS) have significantly expanded our comprehension of how specific genetic variations are linked to diseases. Research has demonstrated the existence of genetic factors influencing susceptibility to coronaviruses. The objective of this study was to examine the association of certain loci with the COVID-19 in Saudi population. In the present study we have examined the link between the COVID-19 disease and certain genetic variants in hospitalized COVID-19 patients (n = 16) in Tabuk and Bisha, Kingdom Saudi Arabia. We used the genome Analysis Toolkit (GATK) and Comprehensive variant annotation was performed different databases and tools such as Search Tool for the Retrieval of Interacting Genes (STRING), PanelApp and PolyPhen-2. The study showed that the genetic variants associated with genes such as Homeostatic Iron Regulator (HFE) (found in 7 patients, representing 44%), complement factor H (CFH) (6 patients, 38%), cadherin 23 (CDH23) (4 patients, 25%), cytotoxic T-lymphocyte associated protein 4 (CTLA-4) (3 patients, 19%), Transforming Growth Factor Beta 1 (TGFB1) (3 patients, 19%), CREB-binding protein (CREBBP) (2 patients, 13%), E1A Binding Protein P300 (EP300) (2 patients, 13%), hemoglobin subunit beta (HBB) (2 patients, 13%), interferon regulatory factor 7 (IRF7) (2 patients, 13%), and unc-119 lipid binding chaperone (UNC119) (2 patients, 13%) might be associated with susceptibility to coronavirus. We also identified mutations in the COVID-19 patient that are pathogenic or likely pathogenic. A recurrent pathogenic mutation, HFE p.His63Asp (H63D), was identified in 7 patients, suggesting its potential contribution to disease severity. Additionally, a likely pathogenic variant, HBB p.Glu7Val (E7V), was present in 2 patients, highlighting its potential role in disease susceptibility. Our results shed light on the key genetic mechanisms of COVID-19 pathogenesis and help to identify and stratify the individuals or populations that are at risk to corona virus infection. The identification of susceptible individuals or populations assist in prevention and/or in treatment programs.

Identification and validation of ferroptosis-related prognostic gene signature in patients with cervical cancer.

Ferroptosis is an iron-dependent cell death, which is distinct from the other types of regulated cell death. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in cervical cancer (CC) remains unclear. This study aims to explore the ferroptosis-related prognostic genes (FRPGs) expression profiles and their prognostic values in CC. The ferroptosis-related genes (FRGs) were obtained from The Cancer Genome Atlas (TCGA) and FerrDb databases. Core FRGs were determined by the Search Tool for the Retrieval of Interacting Genes (STRING) website. FRPGs were identified using univariate and multivariate Cox regressions, and the ferroptosis-related prognostic model was constructed. FRPGs were verified in clinical specimens. The relationship between FRPGs and tumor infiltrating immune cells were assessed through the CIBERSORT algorithm and the LM22 signature matrix. Bioinformatics functions of FRPGs were explored with the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Thirty-three significantly up-regulated and 28 down-regulated FRGs were screened from databases [P<0.05; false discovery rate (FDR) <0.05; and |log2 fold change (FC)| ≥2]. Twenty-four genes were found closely interacting with each other and regarded as hub genes (degree ≥3). Solute carrier family 2 member 1 (SLC2A1), carbonic anhydrases IX (CA9), and dual oxidase 1 (DUOX1) were identified as independent prognostic signatures for overall survival (OS) in a Cox regression. Time-dependent receiver operating characteristic (ROC) curves showed the predictive ability of the ferroptosis-related prognostic model, especially for 1-year OS [area under the curve (AUC) =0.76]. Consistent with the public data, our experiments demonstrated that the mRNA levels of SLC2A1 and DUOX1, and the protein levels of SLC2A1, DUOX1, and CA9 were significantly higher in the tumor tissues. Further analysis showed that there was a significant difference in the proportion of tumor infiltrating immune cells between the low- and high-risk group based on our prognostic model. The function enrichment of FRPGs was explored by applying Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. In this study, the features of FRPGs in CC were pictured. The results implicated that targeting ferroptosis may be a new reliable biomarker and an alternative therapy for CC.

Bioinformatics analysis of the association between obesity and gastric cancer.

Obesity and gastric cancer (GC) are prevalent diseases worldwide. In particular, the number of patients with obesity is increasing annually, while the incidence and mortality rates of GC are ranked high. Consequently, these conditions seriously affect the quality of life of individuals. While evidence suggests a strong association between these two conditions, the underlying mechanisms of this comorbidity remain unclear. We obtained the gene expression profiles of GSE94752 and GSE54129 from the Gene Expression Omnibus database. To investigate the associated biological processes, pathway enrichment analyses were conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes for the shared differentially expressed genes in obesity and GC. A protein-protein interaction (PPI) network was subsequently established based on the Search Tool for the Retrieval of Interacting Genes (STRING) database, followed by the screening of the core modules and central genes in this network using Cytoscape plug-in MCODE. Furthermore, we scrutinized the co-expression network and the interplay network of transcription factors (TFs), miRNAs, and mRNAs linked to these central genes. Finally, we conducted further analyses using different datasets to validate the significance of the hub genes. A total of 246 shared differentially expressed genes (209 upregulated and 37 downregulated) were selected for ensuing analyses. Functional analysis emphasized the pivotal role of inflammation and immune-associated pathways in these two diseases. Using the Cytoscape plug-in CytoHubba, nine hub genes were identified, namely, CXCR4, CXCL8, CXCL10, IL6, TNF, CCL4, CXCL2, CD4, and CCL2. IL6 and CCL4 were confirmed as the final hub genes through validation using different datasets. The TF-miRNA-mRNA regulatory network showed that the TFs primarily associated with the hub genes included RELA and NFKB1, while the predominantly associated miRNAs included has-miR-195-5p and has-miR-106a-5p. Using bioinformatics methods, we identified two hub genes from the Gene Expression Omnibus datasets for obesity and GC. In addition, we constructed a network of hub genes, TFs, and miRNAs, and identified the major related TFs and miRNAs. These factors may be involved in the common molecular mechanisms of obesity and GC.

Early-stage biomarkers identification by integrated genomic analysis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC), a type of liver cancer, ranks as the third-leading cause of death due to the lack of definite biomarkers for early-stage detection. HCC progression occurs by the dysregulation of several genes. Though several studies focus on biomarkers for HCC diagnosis, stage-specific marker identification remains elusive. Hence, the present study aims to identify early-stage biomarkers for the detection of HCC through integrated in silico analysis. The differential gene expression was performed using GEO2R for the datasets (GSE14520, GSE63898, GSE121248, GSE124535, GSE94660, and GSE6764) retrieved from Gene Expression Omnibus (GEO) of patients with cirrhotic liver or HCC. The common differentially expressed gene enrichment analysis was performed using Funrich for Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomics (KEGG) gene mapping. The Protein-Protein Interaction (PPI) network was performed using the Search Tool for the Retrieval of Interacting Genes (STRING). The hub genes were identified using the CytoHubba plug-in of Cytoscape software. The identified genes were verified for their prognostic value using the Kaplan-Meier plotter and Immunohistochemistry micrographs obtained from the Human Protein Atlas database. An overall of 243 common differentially expressed genes (DEGs) were identified containing 171 upregulated and 72 downregulated genes. With the help of PPI network construction, ten hub genes were identified as CDK1, AURKA, CCNB1, CCNB2, CENPF, CDC20, TOP2A, BUB1, RRM2, and HMMR, which are dysregulated owing to HCC proliferation, tumorigenesis and poor prognosis in patients. These hub genes are suitable waypoints for the diagnosis and targeted therapy against early-stage HCC.

Identification of Potential Breast Cancer Stem Cell Biomarkers in the Secretome Using a Network Interaction Approach Analysis.

Breast cancer stem cells (BCSCs) play a role in the high rates of resistance, recurrence, and metastasis. The precise biomarkers of BCSCs can assist effectively in identifying cancer, assessing prognosis, diagnosing, and monitoring therapy. The aim of this study was to give a complete analysis for predicting specific biomarkers of BCSCs. We aggregated profile datasets in this work to shed light on the underlying critical genes and pathways of BCSCs. We obtained two expression profiling by array datasets (GSE7513 and GSE7515) from the Gene Expression Omnibus (GEO) database to identify biomarkers in BCSCs. Enrichr was used to do functional analysis, including gene ontology (GO) and reactome pathway. Furthermore, the protein-protein interaction (PPI) of these differential expression genes (DEGs) was visualized using Cytoscape with the search tool for the retrieval of interacting genes (STRING). The hub genes in the PPI network were chosen for further investigation. We identified 65 up-regulated and 190 down- regulated DEGs and the GO enrichment analysis revealed that these DEGs were enriched in biological process related to tumorigenesis and stemness, including alter the extracellular matrix's physicochemical properties, cytoskeletal reorganisation, adhesion, motility, migration, growth, and survival. The Reactome analysis indicated that these DEGs were also involved in modulating function of ECM, regulation cancer metabolism and angiogenesis, tumor growth, proliferation, and metastasis. Our bioinformatic study revealed that FYN, INADL, OCLN, F11R, and TOP2A were potential biomarker panel of BCSCs from secretome.

Yiqi Kaimi prescription regulates protein phosphorylation to promote intestinal motility in slow transit constipation

Ethnopharmacological relevanceThe clinical efficacy of the Yiqi Kaimi prescription has been confirmed in slow transit constipation. However, the effects and biological mechanism of Yiqi Kaimi prescription are still unclear. Aims of the studyTo identify the effects of Yiqi Kaimi prescription on intestinal motility; To reveal the potential key targets and pathways of Yiqi Kaimi prescription for the treatment of slow transit constipation. Materials and methodsThe effects of Yiqi Kaimi prescription on slow transit constipation were investigated in a mouse model. The terminal ink propulsion experiment and fecal indocyanine green imaging was used to measure the intestinal transit time. Protein phosphorylation changes in colon tissues treated with Yiqi Kaimi prescription were detected using a Phospho Explorer antibody microarray. Bioinformatic analyses were performed using the Database for Annotation Visualization and Integrated Discovery (DAVID) and the Search Tool for the Retrieval of Interacting Genes (STRING). Western blot analysis and immunohistochemistry confirmed the observed changes in phosphorylation. Results: Yiqi Kaimi prescription significantly increased the intestinal transit rate (P < 0.05 vs. model) and reduced the time to first discharge of feces containing fecal indocyanine green imaging in mice (P < 0.05 vs. model). The administration of Yiqi Kaimi prescription induced phosphorylation changes in 41 proteins, with 9 upregulated proteins and 32 downregulated proteins. Functional classification of the phosphorylated proteins with DAVID revealed that the critical biological processes included tyrosine protein kinases, positive regulation of calcium-mediated signaling and response to muscle stretch. The phosphorylation of the spleen tyrosine kinase (SYK) at Tyr348 increased 2.19-fold, which was the most significant change. The phosphorylation level of the transcription factor p65 (RELA) at Thr505 was decreased 0.57-fold. SYK was a hub protein in the protein-protein interaction network and SYK and RELA formed the core of the secondary subnetwork. The key protein phosphorylation after treatment with Yiqi Kaimi prescription were verified by Western blot analysis and immunohistochemistry. ConclusionYiqi Kaimi prescription significantly enhanced intestinal motility. This effect was attributed to alterations in the phosphorylation levels of various target proteins. The observed changes in protein phosphorylation, including SYK and RELA, may serve as crucial factors in the treatment of slow transit constipation.

Inhibition of RAC attenuates Adriamycin-induced podocyte injury

Minimal Change Disease (MCD), which is associated with podocyte injury, is the leading cause of nephrotic syndrome in children. A considerable number of patients experience relapses and require prolonged use of prednisone and immunosuppressants. Multi-drug resistance and frequent relapses can lead to disease progression to focal and segmental glomerulosclerosis (FSGS).To identify potential targets for therapy of podocyte injury, we examined microarray data of mRNAs in glomerular samples from both MCD patients and healthy donors, obtained from the GEO database. Differentially expressed genes (DEGs) were used to construct the protein-protein interactions (PPI) network through the application of the search tool for the retrieval of interacting genes (STRING) tool. The most connected genes in the network were ranked using cytoHubba. 16 hub genes were selected and validated by qRT-PCR. RAC2 was identified as a potential therapeutic target for further investigation. By downregulating RAC2, Adriamycin (ADR)-induced human podocytes (HPCs) injury was attenuated. EHT-1864, a small molecule inhibitor that targets the RAC (RAC1, RAC2, RAC3) family, proved to be more effective than RAC2 silencing in reducing HPCs injury. In conclusion, our research suggests that EHT-1864 may be a promising new molecular drug candidate for patients with MCD and FSGS.

Identification of a Group of Therapeutic Targets and Prognostic Biomarker for Triple Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease. Mining differentially expressed genes of TNBC is helpful to explore new therapeutic targets. This study aimed to investigate diagnostic biomarker genes in TNBC compared to normal tissue. Additionally, we explored the functions and prognostic value of these key genes as well as potential targeted drugs that could affect these genes. Differential gene expression analysis was conducted using the R software with data from the Gene Expression Omnibus (GEO) database. Then, the identified differentially expressed genes (DEGs) were used to construct a protein-protein interaction (PPI) network using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software. The mRNA expression levels of key genes were analyzed using the UALCAN database with data from The Cancer Genome Atlas (TCGA). Enrichment and survival analyses were performed using R software. In addition, potential compounds showing sensitivity to key genes were identified by gene set cancer analysis (GSCA). Compared with normal tissues, a total of 203 DEGs were upregulated in TNBC. These DEGs participated in various biological processes including nuclear division, microtubule binding, cell cycle, and the p53 signaling pathway. Through the PPI network analysis, ten key genes were identified, among which four genes showed significant correlation with poor progression-free interval (PFI) in patients with TNBC. Moreover, the four survival-related genes were found to act as sensitive therapeutic targets. The identified four key genes were considered new biomarkers for diagnosis and prognosis and also potential therapeutic targets for TNBC.

Morphological changes and hub genes screening in rats with anorectal malformations during hindgut development

BackgroundAnorectal malformations (ARMs) are among the most common congenital digestive tract malformations worldwide. Although there have been extensive efforts for elucidating their pathogenesis, the specific molecular mechanisms underlying ARMs remain unclear. MethodsWe generated ARM Wistar rat models using ethylenethiourea. Nine pregnant rats were allocated to the ARM and control groups, respectively. Hindgut tissue was isolated from embryos collected on gestational days 14, 15, and 16, representing the key timepoints of anorectal development. High-throughput sequencing was used to identify differentially expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the obtained genes. Module analysis was performed via Search Tool for the Retrieval of Interacting Genes (STRING), and a protein-protein interaction (PPI) network was constructed using Cytoscape software. Gene Set Enrichment Analysis (GSEA) was conducted to verify the enrichment of development/apoptosis/epithelium/morphogenesis/Wnt pathway-related core gene sets and identify ARM hub genes. Reverse transcription-quantitative polymerase chain reaction assay was employed to verify mRNA levels of hub genes. Results313 GSEA core genes form ten core gene sets were selected. Based on the 281 differentially expressed circRNAs (|Fc| > 2, FDR < 0.05), 42 PPI core genes were obtained. According to the integration of GSEA analysis and PPI network, three hub genes (Cul1, Gli3, and Osr2) highly associated with ETU-induced ARMs were identified. The qRT-PCR validation data were consistent with the sequencing results. ConclusionsThese results provide a theoretical basis for the further identification of potential diagnostic and therapeutic targets in ARMs.

HIF‑1 and macrophage activation signalling pathways are potential biomarkers of invasive aspergillosis.

Invasive aspergillosis (IA) is a severe disease, the pathogenesis of which remains unclear. The present study aimed to determine the molecular mechanism of IA and to identify potential biomarkers using bioinformatics analysis. The GSE78000 dataset, which includes data from patients with IA and healthy individuals, was downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) between the IA and control groups were identified with the 'affy' package in R software. The Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) databases were then used to analyse the function and pathway enrichment of DEGs. The protein-protein interaction network was analysed with the Search Tool for the Retrieval of Interacting Genes (STRING) website. In addition, DEGs were confirmed using reverse transcription-quantitative PCR and western blotting in samples with IA (n=6) and control samples (n=6) collected from the Department of Respiratory and Critical Care Medicine of the First Affiliated Hospital of Henan University of Science and Technology (Luoyang, China). The present study identified 735 DEGs, including 312 upregulated and 423 downregulated genes. Through GO and KEGG analyses of the DEGs, macrophage activation and hypoxia-inducible factor 1 (HIF-1) signalling pathways were revealed to be significantly upregulated and downregulated, respectively, in patients with IA compared with that of the healthy individuals. Subsequently, correlation analysis of macrophage activation and HIF-1 signalling pathways was revealed using correlation as a distance metric for hierarchical clustering correlation analysis. However, there was no protein-protein interaction between the macrophage activity regulation and HIF-1 signalling pathways based on STRING analysis. In summary, the present study identified candidate genes and associated molecules that may be associated to IA and revealed potential biomarkers and therapeutic targets for IA.

25-hydroxycholesterol aggravates oxygen-glucose deprivation/reoxygenation-induced pyroptosis through promoting activation of NLRP3 inflammasome in H9C2 cardiomyocytes.

25-hydroxycholesterol (25-HC) plays a role in the regulation of cell survival and immunity. However, the effect of 25-HC on myocardial ischemia/reperfusion (MI/R) injury remains unknown. Our present study aimed to investigate whether 25-HC aggravated MI/R injury through NLRP3 inflammasome-mediated pyroptosis. The overlapping differentially expressed genes (DEGs) in MI/R were identified from the GSE775, GSE45818, GSE58486, and GSE46395 datasets in Gene Expression Omnibus (GEO) database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the database of Annotation, Visualization and Integration Discovery (DAVID). The protein-protein interaction (PPI) network of the overlapping DEGs was established using the Search Tool for the Retrieval of Interacting Genes (STRING) database. These bioinformatics analyses indicated that cholesterol 25-hydroxylase (CH25H) was one of the crucial genes in MI/R injury. The oxygen-glucose deprivation/reoxygenation (OGD/R) cell model was established to simulate MI/R injury. Western blot and RT-qPCR analysis demonstrated that CH25H was significantly upregulated in OGD/R-stimulated H9C2 cardiomyocytes. Moreover, knockdown of CH25H inhibited the OGD/R-induced pyroptosis and nod-like receptor protein 3 (NLRP3) inflammasome activation, as demonstrated by cell counting kit-8 (CCK8), lactate dehydrogenase (LDH), RT-qPCR, and western blotting assays. Conversely, 25-HC, which is synthesized by CH25H, promoted activation of NLRP3 inflammasome in OGD/R-stimulated H9C2 cardiomyocytes. In addition, the NLRP3 inhibitor BAY11-7082 attenuated 25-HC-induced H9C2 cell injury and pyroptosis under OGD/R condition. In conclusion, 25-HC could aggravate OGD/R-induced pyroptosis through promoting activation of NLRP3 inflammasome in H9C2 cells.

Ferroptosis-Related Genes in IgA Nephropathy: Screening for Potential Targets of the Mechanism.

Aims: Exploring key genes and potential molecular pathways of ferroptosis in immunoglobulin A nephropathy (IgAN). Methods: The IgAN datasets and ferroptosis-related genes (FRGs) were obtained in the Gene Expression Omnibus (GEO) and FerrDb database. Differentially expressed genes (DEGs) were identified using R software and intersected with FRGs to obtain differentially expressed FRGs (DE-FRGs). After that, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis (PEA) and Gene Ontology (GO) functional annotation were performed on DE-FRGs. In the Search Tool for the Retrieval of Interacting Genes (STRING) website, we construct a protein-protein interaction (PPI) network. The PPI network was further investigated with screening hub genes with Cytoscape software. The core genes were then subjected to gene set enrichment analysis (GSEA). Finally, the samples were analyzed for immune infiltration in R, and the correlation between hub genes and immune cells was analyzed. Results: A total of 347 DEGs were identified. CD44, CDO1, CYBB, IL1B, RRM2, AKR1C1, activated transcription factor-3 (ATF3), CDKN1A, GDF15, JUN, MGST1, MIOX, MT1G, NR4A1, PDK4, TNFAIP3, and ZFP36 were determined as DE-FRGs. JUN, IL1B, and ATF3 were then screened as hub genes. GSEA and immune infiltration analysis revealed that the hub genes were closely associated with immune inflammatory responses such as NOD-like receptor signaling, IL-17 signaling, and TNF signaling. Conclusions: Our results show that JUN and ATF3 are possibly critical genes in the process of IgAN ferroptosis and may be related with immune cell infiltration.

RNA-seq unravels distinct expression profiles of keloids and Dupuytren's disease

Keloid scars and Dupuytren's disease are two common, chronic, and incurable fibroproliferative disorders that, among other shared clinical features, may induce joint contractures. We employed bulk RNA sequencing to discern potential shared gene expression patterns and underlying pathological pathways between these two conditions. Our aim was to uncover potential molecular targets that could pave the way for novel therapeutic strategies. Differentially expressed genes (DEGs) were functionally annotated using Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways with the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The protein-protein-interaction (PPI) networks were constructed by using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. The Molecular Complex Detection (MCODE) plugin was used for downstream analysis of the PPI networks. A total of 1922 DEGs were identified within Dupuytren's and keloid samples, yet no overlapping gene expression profiles were detected. Significantly enriched GO terms were related to skin development and tendon formation in keloid scars and Dupuytren's disease, respectively. The PPI network analysis revealed 10 genes and the module analysis provided six protein networks, which might play an integral part in disease development. These genes, including CDH1, ERBB2, CASP3 and RPS27A, may serve as new targets for future research to develop biomarkers and/or therapeutic agents.

Bioinformatics analysis of human kallikrein 5 (KLK5) expression in metaplastic triple-negative breast cancer.

Metaplastic breast carcinoma (MBC) is a rare breast cancer subtype; most cases are triple-negative breast cancers (TNBCs) and are poorly responsive to conventional systemic therapy. Few potential diagnostic and prognostic markers for distinguishing between metaplastic TNBC and nonmetaplastic TNBC have been discovered. We performed bioinformatic analysis to explore the underlying mechanism by which metaplastic TNBC differs from nonmetaplastic TNBC and provides potential pathogenic genes of metaplastic TNBC. Differentially expressed genes (DEGs) in metaplastic tumors and nonmetaplastic tumors from TNBC patients were screened using GSE165407. The GSE76275 data set and The Cancer Genome Atlas (TCGA) database were used to screen DEGs in TNBC and non-TNBC. Metascape and DAVID were used for the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Ontology (GO) analysis of DEGs. Online databases, including UALCAN, GEPIA, HPA, Breast Cancer Gene-Expression Miner, andquantitativePCRand western blot, were used to examine KLK5messenger RNAand protein expression in breast cancer. Analysis of KLK5‑associated genes was performed with TCGA data, and the LinkedOmics database was used to detect the genes co-expressed with KLK5. STRING (Search Tool for the Retrieval of Interacting Genes) and Cytoscape were used to screen for hub genes. Kaplan‑Meier plotter was used for survival analysis. KLK5 was identified among the DEGs in nonmetaplastic TNBC and metaplastic TNBC. The KLK5 gene was overexpressed in nonmetaplastic TNBC but downregulated in metaplastic TNBC. KEGG and GO analyses revealed that epithelial-to-mesenchymal transitionwas a pathogenic mechanism in metaplastic TNBC and an important pathway by which KLK5 and its associated genes DSG1 and DSG3 influence metaplastic TNBC progression. Prognosis analysis showed that only low expression of KLK5 in metaplastic TNBC had clinical significance. Our research indicated that KLK5 may be a pivotal molecule with a key role in the mechanism of tumorigenesis in metaplastic TNBC.

Intrinsic disorder in PRAME and its role in uveal melanoma

IntroductionThe PReferentially expressed Antigen in MElanoma (PRAME) protein has been shown to be an independent biomarker for increased risk of metastasis in Class 1 uveal melanomas (UM). Intrinsically disordered proteins and regions of proteins (IDPs/IDPRs) are proteins that do not have a well-defined three-dimensional structure and have been linked to neoplastic development. Our study aimed to evaluate the presence of intrinsic disorder in PRAME and the role these structureless regions have in PRAME( +) Class 1 UM.MethodsA bioinformatics study to characterize PRAME’s propensity for the intrinsic disorder. We first used the AlphaFold tool to qualitatively assess the protein structure of PRAME. Then we used the Compositional Profiler and a set of per-residue intrinsic disorder predictors to quantify the intrinsic disorder. The Database of Disordered Protein Prediction (D2P2) platform, IUPred, FuzDrop, fIDPnn, AUCpred, SPOT-Disorder2, and metapredict V2 allowed us to evaluate the potential functional disorder of PRAME. Additionally, we used the Search Tool for the Retrieval of Interacting Genes (STRING) to analyze PRAME's potential interactions with other proteins.ResultsOur structural analysis showed that PRAME contains intrinsically disordered protein regions (IDPRs), which are structureless and flexible. We found that PRAME is significantly enriched with serine (p-value < 0.05), a disorder-promoting amino acid. PRAME was found to have an average disorder score of 16.49% (i.e., moderately disordered) across six per-residue intrinsic disorder predictors. Our IUPred analysis revealed the presence of disorder-to-order transition (DOT) regions in PRAME near the C-terminus of the protein (residues 475–509). The D2P2 platform predicted a region from approximately 140 and 175 to be highly concentrated with post-translational modifications (PTMs). FuzDrop predicted the PTM hot spot of PRAME to be a droplet-promoting region and an aggregation hotspot. Finally, our analysis using the STRING tool revealed that PRAME has significantly more interactions with other proteins than expected for randomly selected proteins of the same size, with the ability to interact with 84 different partners (STRING analysis result: p-value < 1.0 × 10–16; model confidence: 0.400).ConclusionOur study revealed that PRAME has IDPRs that are possibly linked to its functionality in the context of Class 1 UM. The regions of functionality (i.e., DOT regions, PTM sites, droplet-promoting regions, and aggregation hotspots) are localized to regions of high levels of disorder. PRAME has a complex protein–protein interaction (PPI) network that may be secondary to the structureless features of the polypeptide. Our findings contribute to our understanding of UM and suggest that IDPRs and DOT regions in PRAME may be targeted in developing new therapies for this aggressive cancer.BBcyEW8tS6_KkPjyEwduzxVideo

Identification of distinct gene co-expression modules and specific hub genes in skin lesions of atopic dermatitis and psoriasis by WGCNA.

Atopic dermatitis (AD) and psoriasis are among the most common chronic inflammatory skin diseases. Although AD and psoriasis are distinguished using clinical criteria, the lesions of these two diseases are sometimes highly similar, making diagnosis difficult. In addition, the mechanisms underlying these two diseases are not fully clear. Here, we aimed to identify potential genes and regulatory mechanisms in AD and psoriasis patients to aid in the diagnosis and treatment of AD and psoriasis. The GSE121212 dataset was obtained from the NCBI Gene Expression Omnibus database and weighted gene co-expression network analysis (WGCNA) was applied. The functions of genes in modules of interest were determined using Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes analysis with the ggplot2 package of r. The hub genes were obtained using the Search Tool for the Retrieval of Interacting Genes database and then visualized using cytoscape. The MEgreen and MEbrown modules were identified to associate with AD and psoriasis, respectively, and the biological functions and pathways of genes in clinically significant modules were detected and analyzed. Hub genes in these two modules and details on potential protein interactions were also revealed. The genes and modules identified by WGCNA might contribute to our understanding of the molecular mechanisms of AD and psoriasis and aid in their diagnosis and treatment.