Search Tool For The Retrieval Of Interacting Genes Research Articles

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.

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

AbstractBackgroundMetaplastic 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.MethodsDifferentially 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, and quantitative PCR and western blot, were used to examine KLK5 messenger RNA and 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.ResultsKLK5 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 transition was 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.ConclusionOur 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.

Identification of LOX as a candidate prognostic biomarker in Glioblastoma multiforme

BackgroundGlioblastoma multiforme (GBM) is the most malignant type of glioma. GBM tumors grow rapidly, have a high degree of malignancy, and are characterized by a fast disease progression. Unfortunately, there is a lack of effective treatments. An effective strategy for the treatment of GBM would be to identify key biomarkers correlating with the occurrence and progression of GBM and developing these biomarkers into therapeutic targets. Method and ResultsIn this study, using integrated bioinformatics analysis, we identified differentially expressed genes (DEGs), including 130 genes that were upregulated in GBM compared to normal brain tissue, and 128 genes that were downregulated in GBM. Based on Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis, these genes were associated with regulation of tumor cell adhesion, differentiation, morphology in GBM and were mainly enriched in Complement and coagulation cascades pathway. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to construct a Protein-Protein Interaction network. Ten hub genes were identified, including FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2, all of which were significantly upregulated in GBM, these results were confirmed by oncomine database exploration. Alteration analysis of hub genes found that patients with alteration in at least one of the hub genes showed shorter median survival times (p = 0.013) and shorter median disease-free survival times (p = 2.488E-3) than patients without alterations in any of the hub genes. Multiple tests for survival analysis showed that among individual hub genes only expression of LOX was correlated with patient survival (P < 0.05).GDS4467 data set was used to analyze the expression of LOX in gliomas with different degrees of malignancy, and it was found that the expression level of LOX was positively correlated with the malignant degree of gliomas.By analyzing GDS 4535 data set showed that the expression level of LOX was positively correlated with the differentiation degree of GBM cells ConclusionThis research suggests that FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2 are key genes in GBM. However, only LOX is correlated with patient survival and promotes glioblastoma cell differentiation and tumor recurrence. LOX may be a candidate prognostic biomarker and potential therapeutic target for GBM.

Screening and identification of potential biomarkers for pancreatic cancer: An integrated bioinformatics analysis

Pancreatic cancer is one of the highly invasive and the seventh most common cause of death among cancers worldwide. To identify essential genes and the involved mechanisms in pancreatic cancer, we used bioinformatics analysis to identify potential biomarkers for pancreatic cancer management. Gene expression profiles of pancreatic cancer patients and normal tissues were screened and downloaded from The Cancer Genome Atlas (TCGA) bioinformatics database. The Differentially expressed genes (DEGs) were identified among gene expression signatures of normal and pancreatic cancer, using R software. Then, enrichment analysis of the DEGs, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, was performed by an interactive and collaborative HTML5 gene list enrichment analysis tool (enrichr) and ToppGene. The protein-protein interaction (PPI) network was also constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and ToppGenet web based tool followed by identifying hub genes of the top 100 DEGs in pancreatic cancer using Cytoscape software. Over 2000 DEGs with variable log2 fold (LFC) were identified among 34,706 genes. Principal component analysis showed that the top 20 DEGs, including H1–4, H1–5, H4C3, H4C2, RN7SL2, RN7SL3, RN7SL4P, RN7SKP80, SCARNA12, SCARNA10, SCARNA5, SCARNA7, SCARNA6, SCARNA21, SCARNA9, SCARNA13, SNORA73B, SNORA53, SNORA54 might distinguish pancreatic cancer from normal tissue. GO analysis showed that the top DEGs have more enriched in the negative regulation of gene silencing, negative regulation of chromatin organization, negative regulation of chromatin silencing, nucleosome positioning, regulation of chromatin silencing, and nucleosomal DNA binding. KEGG analysis identified an association between pancreatic cancer and systemic lupus erythematosus, alcoholism, neutrophil extracellular trap formation, and viral carcinogenesis. In PPI network analysis, we found that the different types of histone-encoding genes are involved as hub genes in the carcinogenesis of pancreatic cancer. In conclusion, our bioinformatics analysis identified genes that were significantly related to the prognosis of pancreatic cancer patients. These genes and pathways could serve as new potential prognostic markers and be used to develop treatments for pancreatic cancer patients.

Novel potential urinary biomarkers for effective diagnosis and prognostic evaluation of high-grade bladder cancer.

High-grade bladder cancer (HGBC) has a higher malignant potential, recurrence and progression rate compared to low-grade phenotype. Its early symptoms are often vague, making non-invasive diagnosis using urinary biomarkers a promising approach. The gene expression data from urine samples of patients with HGBC was extracted from the GSE68020 dataset. The clinical information and gene expression data in tumor tissues of HGBC patients were obtained from The Cancer Genome Atlas (TCGA) database. Multivariate Cox analysis was used to predict the optimal risk model. The protein-protein interaction (PPI) analysis was performed via the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape. Overall survival (OS) was evaluated in the Gene Expression Profiling Interactive Analysis (GEPIA) online platform. Competing endogenous RNA (ceRNA) network was also visualized using Cytoscape. The expression levels of specific genes were assessed through quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). Moreover, co-expressed genes and potential biological functions related to specific genes were explored based on the Cancer Cell Line Encyclopedia (CCLE) database. A total of 560 differentially expressed genes (DEGs) were identified when comparing the urine sediment samples from HGBC patients with the benign ones. Using these urinary DEGs and the clinical information of HGBC patients, we developed an optimal risk model consisting of eight genes to predict the patient outcome. By integrating the node degree values in the PPI network with the expression changes in both urine and tissue samples, eighteen hub genes were selected out. Among them, DKC1 and SNRPG had the most prominent comprehensive values, and EFTUD2, LOR and EBNA1BP2 were relevant to a worse OS in bladder cancer patients. The ceRNA network of hub genes indicated that DKC1 may be directly regulated by miR-150 in HGBC. The upregulation of both SNRPG and DKC1 were detected in HGBC cells, which were also observed in various tumor tissues and malignant cell lines, displaying high correlations with other hub genes. Our study may provide theoretical basis for the development of effective non-invasive detection and treatment strategies, and further research is necessary to explore the clinical applications of these findings.

Identification of Key Biomarkers in Hepatocellular Carcinoma Induced by Non-alcoholic steatohepatitis or Metabolic Syndrome via Integrated Bioinformatics Analysis.

The burden of hepatocellular carcinoma (HCC) is steadily growing because obesity, type 2 diabetes, and nonalcoholic fatty liver disease (NAFLD) are replacing viral- and alcohol-related liver disease as major pathogenic promoters. The current study attempted to identify the key genes and pathways in the non-alcoholic steatohepatitis (NASH) induced development of HCC using integrated bioinformatics analyses. Two gene expression profiling datasets, GSE102079 and GSE164760 were downloaded. Differentially expressed genes (DEGs) from HCC and healthy control samples were screened. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource. Then protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Expression and survival analysis of hub genes, methylation and genetic mutation analysis were explored withGEPIA2, UALCAN, GSCA, and TIMER2.0 databases. We identified 158 overlapping genes from the 2 datasets. Up-regulated genes were mainly related to the proliferation, adhesion and metastasis of tumors, while down-regulated genes were mainly related to oxidative stress and energy metabolism. CDKN2A, SPP1, CYP2C9 and CYP4A11 were associated with prognostic performance and were considered the potential crucial genes, which SPP1, CYP2C9 and CYP4A11 were identified as the DNA methylation-driven genes. In different mutation statuses of HCC, gene expression of CDKN2A, SPP1, CYP2C9 and CYP4A11 showed significant differences. CDKN2A and SPP1 were identified as risk genes, while CYP2C9 and CYP4A11 were identified as protective genes, which may affect the transformation of NASH into HCC.

Characteristics of immune cell infiltration in inflamed mucosa of ulcerative colitis patients, hub gene candidates and key pathways in intestinal macrophage expression

Background: The ulcerative colitis (UC) associated immune cell network was revealed by analyzing the immune cell composition of inflamed and uninflamed mucosal biopsy samples from the same patients with UC. For differentially expressed macrophages, the differentially expressed genes (DEGs), functional enrichment analyses, and protein–protein interaction (PPI) network was analyzed to understand further the role of macrophages in the occurrence and development of disease. Methods: GSE 179285 dataset and GSE123141 dataset were downloaded from the GEO database. We used the CIBERSORT algorithm to map immune cell infiltration between paired inflamed and uninflamed intestinal mucosal biopsies. GO and KEGG functional enrichment analyses were used to analyze DEGs in intestinal macrophage populations. The PPI network was constructed by the Search Tool for the Retrieval of Interacting Genes (STRING) database. Immunohistochemistry and qPCR were used to verify hub genes in the mouse model of UC. Results: The inflamed tissues had a higher infiltration of M1 macrophages and M0 macrophages. M2 macrophages, naive B cells, gamma delta T cells (γδT cells), and resting mast cells were more abundant in uninflamed tissues. Functional enrichment analyses showed that the 141 DEGs are involved in multiple processes in the inflammatory response. The three genes (LCN2, CXCL1, SAA1) had the highest degrees of connectivity and may be hub genes. LCN2 was significantly overexpressed in colitis tissues compared with normal control tissues in the mouse model of UC. Conclusion: Changes in macrophages were notable in the inflamed mucosa of UC patients. 141 DEGs were entered into GO and KEGG functional enrichment analyses; three hub gene candidates were selected. Highlights: There were significant differences in the distribution of macrophages in the inflamed and uninflamed mucosa of UC patients. Functional enrichment analyses of DEGs in UC-associated intestinal macrophages were displayed. Three genes (LCN2, CXCL1, SAA1) were identified as hub gene candidates.

High-Throughput Sequencing Data Reveal an Antiangiogenic Role of HNF4A-Mediated CACNA1A/VEGFA Axis in Proliferative Diabetic Retinopathy.

Proliferative diabetic retinopathy (PDR) is characterized by retinal new vessel formation, pointing to the importance of the antiangiogenic treatment in PDR. Hepatocyte nuclear factor 4A (HNF4A) has been highlighted to inhibit vascular endothelial growth factor (VEGF)-stimulated in vitro angiogenesis. Therefore, this study aims to elucidate the potential antiangiogenic mechanisms of HNF4A in PDR. PDR-related high-throughput sequencing datasets (GSE94019, GSE102485, and GSE191210) were obtained from the Gene Expression Omnibus (GEO) database, followed by the screening of differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the candidate DEGs was constructed based on gene set enrichment analysis (GSEA) data and Search Tool for the Retrieval of Interacting Genes (STRING) data. In addition, the key genes and pathways related to angiogenesis were screened by functional enrichment analysis. Furthermore, human retinal microvascular cells were used for further in vitro validation. Four key genes (CACNA1A, CACNA1E, PDE1B, and CHRM3) related to PDR were identified in the grey module. CACNA1A affected angiogenesis in PDR by regulating vascular endothelial growth factor A (VEGFA) expression. Furthermore, HNF4A participated in angiogenesis in PDR by activating CACNA1A. In vitro experiments further identified that inhibition of HNF4A reduced CACNA1A expression and increased VEGFA expression, thus promoting angiogenesis in PDR. In conclusion, the obtained findings suggest that antiangiogenic HNF4A activates the CACNA1A/VEGFA axis in PDR. Our work provides new insights into the angiogenic mechanism of PDR and offers potential targets for translational applications.

Decreased zinc-fingers and homeoboxes family expression was associated with unfavorable outcomes and immune infiltration in lung adenocarcinoma.

The zinc-fingers and homeoboxes (ZHX) family is a group of nuclear homodimeric transcriptional repressors that play an essential role in developing and progressing diverse malignancies. However, the association of ZHX family expression with prognosis and immune infiltration in lung adenocarcinoma (LUAD) is still unclear. The current study aimed to investigate the relationship between ZHX family expression and clinical outcomes and immune infiltration in LUAD patients. ZHXs family expression was determined by using the Oncomine database and Cancer Cell Line Encyclopedia (CCLE). The impact of ZHXs family expression on prognosis was analyzed by using the Kaplan-Meier-plotter online database. The Search Tool for the Retrieval of Interacting Genes (STRING) database was utilized to construct the interaction network based on the selected differentially expressed genes associated with ZHXs. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to perform the enrichment of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The functional state of the ZHXs family in diverse types of malignancies was determined by CancerSEA. The Tumor Immune Estimation Resource (TIMER) database was used to evaluate the association of the ZHXs family with immune cell infiltrates. ZHXs family expression was validated by the Gene Expression Omnibus (GEO) database and real-time polymerase chain reaction (RT-PCR) in 10 paired tumors and normal tissues. ZHX1-3 expression level significantly decreased in LUAD compared with normal tissues. Attenuated ZHXs expression was significantly associated with unfavorable overall survival in LUAD patients. ZHX family members were positively associated with immune infiltration of monocytes, tumor-associated macrophages (TAMs), M1 and M2 macrophages in LUAD. ZHX family expression was also significantly related to a variety of immune marker sets in LUAD. GEO analysis and RT-PCR validated the significant decrease of ZHXs expression level in LUAD. The current study revealed that ZHX family expression was significantly correlated with unfavorable outcomes and immune infiltration in LUAD. The findings herein provide a promising basis for further study into the potential biological function of the ZHX family in LUAD and lay a foundation for developing therapeutic targets for LUAD patients.

SnRNA-Seq analysis reveals ten hub genes associated with alveolar epithelial cell injury during pulmonary acute respiratory distress syndrome

BackgroundAlveolar epithelial cell injury is a key factor in the occurrence and development of pulmonary acute respiratory distress syndrome (ARDSp). Yet the gene expression profile of alveolar epithelial cells of patients with ARDSp remains unclear. MethodsWe analyzed single nuclear RNA sequencing (snRNA-Seq) data from autopsy lung tissues of both ARDSp patients and healthy donors. Sequence data for type 2 alveolar epithelizal cells (AT2) were extracted by the Seurat package. Differentially expressed genes (DEGs) in AT2 were identified by the criteria |log2FC| ≥ 0.25 and P < 0.05 with DESeq2. A protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software to identify hub genes. We then constructed an ARDSp rat model through induction by lipopolysaccharide (LPS) airway instillation. Left lung RNA was extracted and sequenced via Illumina Hiseq platforms. Analysis of the rat RNA sequencing data was then used to verify hub genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the identified hub genes. ResultsIn AT2, a total of 289 genes were identified as differentially expressed between those from ARDSp patients and healthy donors, and these included 190 upregulated and 99 downregulated genes. Ten hub genes were further identified (RPS27A, ACTG1, CAV1, HSP90AA1, HSPA5, CCND1, ITGA3, B2M, NEDD4L, and SEMA5A). There was a similar expression trend of HSPA5 between rat RNA and snRNA sequencing data. DiscussionARDSp altered the gene expression profile of AT2. The identified hub genes were enriched in biological processes mainly involved in cell growth and transformation. Relatedly, ferroptosis and autophagy are possibly involved in AT2 injury during ARDSp. These novel insights into ARDSp may aid the discovery of potential targets for the diagnosis and treatment of ARDSp.

Identification of hub genes affecting gestational diabetes mellitus based on GEO database

ABSTRACT This research aimed to obtain gestational diabetes mellitus (GDM) related hub genes, providing new targets for clinical diagnosis and treatment of GDM. The microarray data of GSE9984 and GSE103552 were obtained from the Gene Expression Omnibus (GEO). The dataset GSE9984 contained placental gene expression profiles of 8 GDM patients and four healthy specimens. The dataset GSE103552 contained 20 specimens from GDM patients and 17 normal specimens. The differentially expressed genes (DEGs) were identified by GEO2R online analysis. DAVID database was applied to conduct functional enrichment analysis of the DEGs. The Search Tool for the Retrieval of Interacting Genes (STRING) database was adopted to acquire protein–protein interaction (PPI) networks. A total of 195 up-regulated and 371 down-regulated DEGs were selected in the GSE9984, and total of 191 up-regulated and 229 down-regulated DEGs were selected in the GSE103552. In the two datasets, 24 common differential genes were obtained and named co-DEGs. The Gene Ontology (GO) annotation analysis indicated the DEGs participated in multi-multicellular organism process, endocrine hormone secretion, long-chain fatty acid biosynthetic process, cell division, unsaturated fatty acid biosynthetic process, cell adhesion and cell recognition. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis suggested that GSE9984 and GSE103552 were related to vitamin digestion and absorption, tryptophan metabolism, steroid hormone biosynthesis, Ras signaling pathway, protein digestion and absorption, PPAR signaling pathway, PI3K-Akt signaling pathway, p53 signaling pathway. PPI was constructed in string database, and six hub genes were selected, including CCNB1, APOA2, AHSG and IGFBP1. Four critical genes were identified to be considered as therapeutic potential biomarkers of GDM, including CCNB1, APOA2, AHSG and IGFBP1.

Prognostic Values of Core Genes in Pilocytic Astrocytom

Pilocytic astrocytoma (PA) is the most common primary brain tumor in children and adolescents. Treatment strategy largely depends on its key genes and molecular mutations. This study aimed to identify potential biomarkers of PA closely related to its prognosis. The gene expression profiles (series numbers GSE50161, GSE66354, and GSE86574) of PA and normal brain tissues were downloaded from the Gene Expression Omnibus database. The Gene Expression Omnibus2R was used to identify differentially expressed genes. The overlapping differentially expressed genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. A protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. The Gene Expression Profiling Interactive Analysis 2 (GEPIA2) tool analyzed the impact of hub genes on PA prognosis based on the Kaplan-Meier curves. Compared with normal brain tissues (n= 36), a total of 37 upregulated and 144 downregulated genes were identified in PA (n= 40). In the protein-protein interaction network construction and GEPIA2 survival analysis, 2 of the top 10 hub genes were significantly associated with decreased overall survival of PA patients, namely Gamma-aminobutyric acid A receptor alpha 2 (hazard ratio  =  2.8, P  <  0.01) and regulating synaptic membrane exocytosis protein 1) (hazard ratio=  3.2, P < 0.01). This bioinformatics analysis reveals that low expression of Gamma-aminobutyric acid A receptor alpha 2 and regulating synaptic membrane exocytosis protein 1 is associated with a favorable prognosis for PA patients. These 2 hub genes could be novel biomarkers for prognosis assessment, furthermore a key element for treatment decisions in the future.

Bioinformatics analysis illustrates the functions of miR-377-5p in cervical cancer

ABSTRACT Cervical cancer (CC) is a frequent disease in women whose development is related with miRNA disorder. MiR-377-5p plays a negative role in the development of some tumors, while few studies have revealed its role in CC. In this study, the functions of miR-377-5p in CC were investigated by bioinformatics. Briefly, the expression and survival curve of miR-377-5p in CC was analyzed with the Cancer Genome Atlas (TCGA) database, and the abundance of miR-377-5p in clinical samples and CC cell lines were measured by qRT-PCR. Moreover, the MicroRNA Data Integration Portal (miRDIP) database was used to predict targets of miR-377-5p, and the Database for Annotation Visualization and Integrated Discovery (David) was used for enrichment analysis of the functions of the miR-377-5p. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to screen the hub targets of miR-377-5p. Moreover, the Gene Expression Profiling Interactive Analysis (GEPIA) database was used to analyze the abundance of the genes in CC. Results showed that decreased miR-377-5p was found in the CC tissues and cell lines, and low miR-377-5p was connected with poor prognosis of patients. Besides, the targets of miR-377-5p were enriched in the PI3K/AKT, MAPK and RAS signaling pathways. Moreover, CDC42, FLT1, TPM3 and CAV1 were screened as hub nodes in the targets of miR-377-5p, and increased CDC42, FLT1, TPM3 and CAV1 also indicated the poor survival rates of the patients in the long term. In conclusion, this study suggests that miR-377-5p downregulation is a biomarker event for CC progression.

Serelaxin Alleviates Fibrosis in Thyroid-Associated Ophthalmopathy via the Notch Pathway.

Fibrosis is the late stage of thyroid-associated ophthalmopathy (TAO), resulting in serious complications. Effective therapeutic drugs are still lacking. We aimed to explore the mechanism of TAO fibrosis and to find a targeted drug. High-throughput RNA sequencing was performed on orbital connective tissues from twelve patients with TAO and six healthy controls. Protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and we identified the hub gene by Cytoscape software. Additionally, the RNA sequencing results were validated by quantitative real-time polymerase chain reaction (qRT-PCR). Bioinformatic prediction identified the functions of differentially expressed genes (DEGs). Further orbital connective tissue and serum samples of the TAO and control groups were collected for subsequent experiments. Histologic staining, Western blotting (WB), qRT-PCR, enzyme-linked immunosorbent assays (ELISAs), gene overexpression through lentiviral infection or silencing gene by short interfering RNA (siRNA) were performed. We found that the relaxin signaling pathway is an important regulatory pathway in TAO fibrosis pathogenesis. Serelaxin exerts antifibrotic and anti-inflammatory effects in TAO. Furthermore, the downstream Notch pathway was activated by serelaxin and was essential to the antifibrotic effect of serelaxin in TAO. The antifibrotic effect of serelaxin is dependent on RXFP1.