Microarray Datasets Research Articles

Potential Mechanism and Therapeutic Targets of Polymyositis.

To investigate the variability in the expression profile of genes associated with polymyositis (PM), explore the potential molecular mechanisms underlying PM, and predict novel targets for intervention. Descriptive study. Place and Duration of the Study: Department of Rheumatology, Taizhou Municipal Hospital, Taizhou, China, from August to November 2023. Three microarray datasets (GSE3112, GSE39454, and GSE128470) were extracted from the gene expression omnibus (GEO). The analysis of this research involved identifying the differentially expressed genes (DEGs) in PM compared to normal samples. Enrichment analysis, gene-microRNA, gene-transcription factor (TF), and protein-protein interaction (PPI) network studies were conducted to identify hub genes and relevant pathways. Additionally, the drug-gene interaction database (DGIdb) was used to predict therapeutic medications. Eighty-eight DEGs were identified. The enrichment analysis results highlighted the significant involvement of downregulated DEGs in antigen processing and presentation. Based on the PPI networks, seven hub genes with high connectivity degrees were selected including a cluster of differentiation 74 (CD74), human leukocyte antigen (HLA)-DPA1, HLA-B, guanylate-binding protein 1 (GBP1), recombinant 2', 5'-oligoadenylate synthetase 1 (OAS1), HLA-C, and HLA-E. This research screened-out core genes, projected prospective therapeutic medications, discovered DEGs between PM and normal samples, and offered fresh perspectives for additional research into the possible mechanism and therapeutic targets of PM. Polymyositis, DEGs, Hub genes, Bioinformatics, Potential therapeutic agents.

CircRNA ATF6 suppresses bladder cancer cell proliferation and migration via miR-146a-5p/FLNA axis

BackgroundBladder cancer (BCa) is the most common malignancy with increasing morbidity and mortality. Circular RNA (circRNA) ATF6 level was downregulated in BCa after GSE92675 CircRNA microarray dataset was analyzed using GEO2R. However, its function and mechanism in BCa remain largely unknown. MethodsGEO2R and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were used to measure levels of circRNA ATF6, microRNA-146a-5p (miR-146a-5p), and filamin A (FLNA). CircRNA ATF6 stability was assessed by actinomycin D and RNase R assays, while circRNA ATF6 cellular localization was examined by FISH experiments in T24 cells. Cell counting kit-8 (CCK-8), colony formation, wound-healing, and transwell assays were used to study circRNA ATF6’s function in growth, motility, and invasion. By examining luciferase, starBase, RNA pull-down, and RNA immunoprecipitation (RIP) experiments, we anticipated and confirmed miR-146a-5p interactions with circRNA ATF6, as well as miR-146a-5p interactions with FLNA. On tumor-bearing mice, in vivo experiments were conducted. ResultsMiR-146a-5p expression in Bca was elevated, while circRNA ATF6 and FLNA were downregulated. CircRNA ATF6 showed better stability in BCa cells, with its expression primarily in the cytoplasm. Upregulating circRNA ATF6 lowered BCa cell viability, colony numbers, and invasion numbers, but broadened their migratory pattern. MiR-146a-5p was directly sponged up by circRNA ATF6, which also detrimentally affected miR-146a-5p levels in BCa. MiR-146a-5p reduced BCa FLNA expression by targeting FLNA. FLNA silencing abolished circRNA ATF6’s mitigating function in BCa cell proliferation, motility, and invasion. In vivo, overexpression of circRNA ATF6 significantly reduced tumor volume and weight. ConclusionCircRNA ATF6 suppresses BCa cell growth, migration and invasion through the miR-146a-5p/FLNA axis.

Analysis of machine learning based integration to identify the crosslink between inflammation and immune response in non-alcoholic fatty liver disease through bioinformatic analysis

BackgroundThe prevalence of nonalcoholic fatty liver disease (NAFLD) is a major form of chronic liver disease. This study aimed to scrutinize the diagnostic biomarkers of NAFLD and their correlation with the immune microenvironment through bioinformatic analysis. MethodsTo identify genes associated with nonalcoholic fatty liver disease (NAFLD), we obtained microarray datasets (GSE63067 and GSE89632) from the Gene Expression Omnibus (GEO) database. Machine learning techniques such as Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) were used to identify key genes. We performed gene ontology analysis to identify the driver pathways of NAFLD. External datasets (merging GSE48452, GSE66676 and GSE135251) were used to validate the identified genes and confirm protein levels by Western blotting. The CIBERSORT algorithm and immune-related techniques, such as ssGSEA, were used to assess the level of infiltration of different immune cell types and their functions. Finally, Spearman's analysis confirmed the relationship between pivotal genes and immune cells. ResultsHub genes (BBOX1, FOSB, NR4A2, RAB26 and SOCS2) were identified as potential biomarkers. This study demonstrates that these hub genes are significantly dysregulated in NAFLD, suggesting that they may be useful as diagnostic indicators and possible targets for treatment. Also covered are their possible effects on inflammation, immune cell activation, and liver damage in NAFLD. A better understanding of the intricate relationship between metabolic inefficiency, immunological response, and liver pathology in NAFLD may be gained from this work, which can lead to the development of new diagnostic tools and clinical treatments. ConclusionThe current study identified BBOX1, FOSB, NR4A2, RAB26 and SOCS2 as important diagnostic biomarkers for NAFLD. The study highlights the important function of immune cell infiltration in developing NAFLD. Their findings provide valuable molecular biological insights into the development of NAFLD and may lead to novel therapeutic strategies for treating this disease.

Detection and assessment of immune and stromal related risk genes to predict preeclampsia: A bioinformatics analysis with dataset.

This study aimed to investigate immune score and stromal score-related signatures associated with preeclampsia (PE) and identify key genes for diagnosing PE using bioinformatics analysis. Four microarray datasets, GSE75010, GSE25906, GSE44711, and GSE10588 were obtained from the Gene Expression Omnibus database. GSE75010 was utilized for differential expressed gene (DEGs) analysis. Subsequently, bioinformatic tools such as gene ontology, Kyoto Encyclopedia of Genes and Genomes, weighted gene correlation network analysis, and gene set enrichment analysis were employed to functionally characterize candidate target genes involved in the pathogenesis of PE. The least absolute shrinkage and selection operator regression approach was employed to identify crucial genes and develop a predictive model. This method also facilitated the creation of receiver operating characteristic (ROC) curves, enabling the evaluation of the model's precision. Furthermore, the model underwent external validation through the other three datasets. A total of 3286 DEGs were identified between normal and PE tissues. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed enrichments in functions related to cell chemotaxis, cytokine binding, and cytokine-cytokine receptor interaction. weighted gene correlation network analysis identified 2 color modules strongly correlated with immune and stromal scores. After intersecting DEGs with immune and stromal-related genes, 13 genes were selected and added to the least absolute shrinkage and selection operator regression. Ultimately, 7 genes were screened out to establish the risk model for discriminating preeclampsia from controls, with each gene having an area under the ROC curve >0.70. The constructed risk model demonstrated that the area under the ROC curves in internal and the other three external datasets were all greater than 0.80. A 7-gene risk signature was identified to build a potential diagnostic model and performed well in the external validation group for PE patients. These findings illustrated that immune and stromal cells played essential roles in PE during its progression.

Identification of host immune-related biomarkers in active tuberculosis: A comprehensive analysis of differentially expressed genes

Tuberculosis (TB) is a serious public health issue in India. Numerous molecular mechanisms and immunological responses play significant roles in the pathogenesis of tuberculosis. This study aimed to identify host immune-related biomarkers that are significantly differentially expressed in active TB and that play a vital role in disease progression. The methodology employed in this study included data collection, pre-processing, analysis, and interpretation of the results. Six microarray datasets were used to identify differentially expressed genes (DEGs), and only the common DEGs were used for further downstream analysis, such as hub gene identification, gene ontology, pathway enrichment analysis, and drug-gene interaction analysis. The study identified 1728 DEGs, including 906 upregulated and 822 downregulated genes. Five hub genes were identified that were: STAT1, GBP5, GBP1, FCGR1A, and BATF2. Gene ontology and pathway enrichment revealed that most of the genes were involved in interferon-gamma signaling. In addition, through drug-gene interactions, known drugs have been identified for STAT1, FCGR1A and GBP1. The findings of this study may contribute to early detection and treatment of active TB.

Comprehensive analysis of cuproptosis-related genes involved in immune infiltration and their use in the diagnosis of hepatic ischemia‒reperfusion injury: an experimental Study.

Hepatic ischemia reperfusion injury (HIRI) is a common injury not only during liver transplantation but also during major hepatic surgery. HIRI causes severe complications and affects the prognosis and survival of patients. Cuproptosis, a newly identified form of cell death, plays an important role in a variety of illnesses. However, its role in HIRI remains unknown. The GSE151648 dataset was mined from the Gene Expression Omnibus (GEO) database, and differences were analyzed for intersections. Based on the differentially expressed genes (DEGs), functional annotation, differentially expressed cuproptosis-related genes (DE-CRGs) identification and lasso logistic regression were conducted. Correlation analysis of DE-CRGs and immune infiltration was further conducted, and DE-CRGs were applied to construct an HIRI diagnostic model. The hierarchical clustering method was used to classify the specimens of HIRI, and functional annotation was conducted to verify the accuracy of these DE-CRGs in predicting HIRI progression. The GSE14951 microarray dataset and GSE171539 single-cell sequencing dataset were chosen as validation datasets. At the same time, the significance of DE-CRGs was verified using a mouse model of HIRI with cuproptosis inhibitors and inducers. Finally, a network of transcription-factor-DE-CRGs and miRNA-DE-CRGs was constructed to reveal the regulation mechanisms. And potential drugs for DE-CRGs were predicted using Drug Gene Interaction Database (DGIdb). Overall, 2390 DEGs and 19 DE-CRGs were identified. Through machine learning algorithms, 8 featured DE-CRGs (GNL3, ALAS1, TSC22D2, KLF5, GTF2B, DNTTIP2, SLFN11 and HNRNPU) were screened, and 2 cuproptosis-related subclusters were defined. Based on the 8 DE-CRGs obtained from the HIRI model (AUC=0.97), the nomogram model demonstrated accuracy in predicting HIRI. Eight DE-CRGs were highly expressed in HIRI samples and were negatively related to immune cell infiltration. A higher level of immune infiltration and expression of CRG group B was found in the HIRI population. Differences in cell death and immune regulation were found between the 2 groups. The diagnostic value of the 8 DE-CRGs was confirmed in the validation of two datasets. The identification of 7 DE-CRGs (SLFN11 excluded) by HIRI animal model experiments was also confirmed. Using hTFtarget, miRWalk and DGIDB database, we predicted that 17 transcription factors, 192 miRNAs and 10 drugs might interact with the DE-CRGs. This study shows that cuproptosis may occur in HIRI and is correlated with immune infiltration. Additionally, a cuproptosis-related predictive model was constructed for studying the causes of HIRI and developing targeted treatment options for HIRI.

PRIMITI: A computational approach for accurate prediction of miRNA-target mRNA interaction

Current medical research has been demonstrating the roles of miRNAs in a variety of cellular mechanisms, lending credence to the association between miRNA dysregulation and multiple diseases. Understanding the mechanisms of miRNA is critical for developing effective diagnostic and therapeutic strategies. miRNA-mRNA interactions emerge as the most important mechanism to be understood despite their experimental validation constraints. Accordingly, several computational models have been developed to predict miRNA-mRNA interactions, albeit presenting limited predictive capabilities, poor characterisation of miRNA-mRNA interactions, and low usability. To address these drawbacks, we developed PRIMITI, a PRedictive model for the Identification of novel miRNA-Target mRNA Interactions. PRIMITI is a novel machine learning model that utilises CLIP-seq and expression data to characterise functional target sites in 3’-untranslated regions (3’-UTRs) and predict miRNA-target mRNA repression activity. The model was trained using a reliable negative sample selection approach and the robust extreme gradient boosting (XGBoost) model, which was coupled with newly introduced features, including sequence and genetic variation information. PRIMITI achieved an area under the receiver operating characteristic (ROC) curve (AUC) up to 0.96 for a prediction of functional miRNA-target site binding and 0.96 for a prediction of miRNA-target mRNA repression activity on cross-validation and an independent blind test. Additionally, the model outperformed state-of-the-art methods in recovering miRNA-target repressions in an unseen microarray dataset and in a collection of validated miRNA-mRNA interactions, highlighting its utility for preliminary screening. PRIMITI is available on a reliable, scalable, and user-friendly web server at https://biosig.lab.uq.edu.au/primiti.

A Bioinformatics-Based Approach to Discover Novel Biomarkers in Hepatocellular Carcinoma

Background: Liver hepatocellular carcinoma (LIHC) is a common cancer with a poor prognosis and high recurrence rate. We aimed to identify potential biomarkers for LIHC by investigating the involvement of hub genes, microRNAs (miRNAs), transcription factors (TFs), and protein kinases (PKs) in its occurrence. Methods: we conducted a bioinformatics analysis using microarray datasets, the TCGA-LIHC dataset, and text mining to identify differentially expressed genes (DEGs) associated with LIHC. They then performed functional enrichment analysis and gene-disease association analysis. The protein-protein interaction network of the genes was established, and hub genes were identified. The expression levels and survival analysis of these hub genes were evaluated, and their association with miRNAs, TFs, and PKs was assessed. Results: The analysis identified 122 common genes involved in LIHC pathogenesis. Ten hub genes were filtered out, including CDK1, CCNB1, CCNB2, CCNA2, ASPM, NCAPG, BIRC5, RRM2, KIF20A, and CENPF. The expression level of all hub genes was confirmed, and high expression levels of all hub genes were correlated with poor overall survival of LIHC patients. Conclusion: Identifying potential biomarkers for LIHC can aid in the design of targeted treatments and improve the survival of LIHC patients. The findings of this study provide a basis for further research in the field of LIHC and contribute to the understanding of its molecular pathogenesis.

Evaluation of Genes and Molecular Pathways Involved in Pathogenesis of Sickle Cell Anemia: A Bioinformatics Analysis and Future Perspective

Background: Sickle cell disease (SCD) is one of the hematological disorders characterized by a defect in the structure and function of globin chains. Hereditary factors play an important role in the pathogenesis of SCD. We aimed to investigate the genes and pathways related to the pathogenesis of SCD. Methods: Microarray dataset was downloaded from the Gene Expression Omnibus (GEO) database. LIMMA package of R-software was used to detect UP and Down regulations between SCD and control subjects. Enrichment analysis and Protein-protein interaction (PPI) networks were performed using GeneCodis4 software and GeneMANIA database, respectively. PrognoScan database was used to evaluate the relationship between the hub genes and patients' survival. Results: Overall, 447 DEGs were identified in SCD patients compared to control subjects. Out of 447 DEGs, 345 genes were up-regulated and 102 genes were down-regulated. Effective hub genes in SCD pathogenesis include SLC4A1, DTL, EPB42, SNCA, and TOP2A. In addition, hub genes had a high diagnostic value. Conclusion: Evaluation of hub genes in SCD can be used as a diagnostic panel to detect high-risk patients. In addition, by identifying the UP and Down stream pathways, treatment strategies in the monitoring and treatment of patients can be designed.

Deciphering the molecular landscape of rheumatoid arthritis offers new insights into the stratified treatment for the condition.

For Rheumatoid Arthritis (RA), a long-term chronic illness, it is essential to identify and describe patient subtypes with comparable goal status and molecular biomarkers. This study aims to develop and validate a new subtyping scheme that integrates genome-scale transcriptomic profiles of RA peripheral blood genes, providing a fresh perspective for stratified treatments. We utilized independent microarray datasets of RA peripheral blood mononuclear cells (PBMCs). Up-regulated differentially expressed genes (DEGs) were subjected to functional enrichment analysis. Unsupervised cluster analysis was then employed to identify RA peripheral blood gene expression-driven subtypes. We defined three distinct clustering subtypes based on the identified 404 up-regulated DEGs. Subtype A, named NE-driving, was enriched in pathways related to neutrophil activation and responses to bacteria. Subtype B, termed interferon-driving (IFN-driving), exhibited abundant B cells and showed increased expression of transcripts involved in IFN signaling and defense responses to viruses. In Subtype C, an enrichment of CD8+ T-cells was found, ultimately defining it as CD8+ T-cells-driving. The RA subtyping scheme was validated using the XGBoost machine learning algorithm. We also evaluated the therapeutic outcomes of biological disease-modifying anti-rheumatic drugs. The findings provide valuable insights for deep stratification, enabling the design of molecular diagnosis and serving as a reference for stratified therapy in RA patients in the future.

Uncovering the anti-breast cancer activity potential of east Kalimantan propolis by In vitro and bioinformatics analysis

Numerous side effects of breast cancer drugs have prompted researchers to explore more into new therapeutic approaches derived from natural substances. In this context, our study focused on uncovering the potential of East Kalimantan propolis from Trigona apicalis for breast cancer treatment including the underlying mechanisms through bioinformatics approached. We conducted integrated in vitro and bioinformatics analysis of network pharmacology, molecular docking, molecular dynamics and MM-GBSA analysis. Initially, in vitro cytotoxic assay demonstrated the anti-breast cancer activity potential of ethanol extract of East Kalimantan propolis, particularly its ethyl acetate fraction, which exhibited similar activity to doxorubicin, as indicated by their IC50 value. This study revealed eight propolis compounds, consisting of flavonoids and phenolic acids, in East Kalimantan propolis. By integrating microarray datasets (GSE29431, GSE36295, and GSE42568) analysis with potential targets derived from propolis compounds, 39 shared target genes were identified. Subsequently, GO and KEGG pathway, protein–protein interaction (PPI) network, core hub genes and gene expression analysis revealed three major targets, namely, PTGS2, CXCL2, and MMP9. Among them, only MMP9 was highly expressed in breast cancer than normal. Moreover, molecular docking revealed the six of propolis compounds which exhibited pronounced binding affinity towards MMP-9, better than marimastat as control drug. Dynamic simulation confirmed the stability of chrysin and quercetin as best compounds. Additionally, MM-GBSA analysis revealed a relative binding energy for chrysin (−25.6403 kcal/mol) that was comparable to marimastat (−27.3827 kcal/mol). In conclusion, this study reveals how East Kalimantan Propolis affect breast cancer and emphasizes MMP9 as a key target for future therapeutics.

In silico analysis unveiling potential biomarkers in gallbladder carcinogenesis

Gallbladder cancer (GBC) is a rare but very aggressive most common digestive tract cancer with a high mortality rate due to delayed diagnosis at the advanced stage. Moreover, GBC progression shows asymptomatic characteristics making it impossible to detect at an early stage. In these circumstances, conventional therapy like surgery, chemotherapy, and radiotherapy becomes refractive. However, few studies reported some molecular markers like KRAS (Kirsten Rat Sarcoma) mutation, upregulation of HER2/neu, EGFR (Epidermal Growth Factor Receptor), and microRNAs in GBC. However, the absence of some specific early diagnostic and prognostic markers is the biggest hurdle for the therapy of GBC to date. The present study has been designed to identify some specific molecular markers for precise diagnosis, and prognosis, for successful treatment of the GBC. By In Silico a network-centric analysis of two microarray datasets; (GSE202479) and (GSE13222) from the Gene Expression Omnibus (GEO) database, shows 50 differentially expressed genes (DEGs) associated with GBC. Further network analysis revealed that 12 genes are highly interconnected based on the highest MCODE (Molecular Complex Detection) value, among all three genes; TRIP13 (Thyroid Receptor Interacting Protein), NEK2 (Never in Mitosis gene-A related Kinase 2), and TPX2 (Targeting Protein for Xklp2) having highest network interaction with transcription factors and miRNA suggesting critically associated with GBC. Further survival analysis data corroborate the association of these genes; TRIP13, NEK2, and TPX2 with GBC. Thus, TRIP13, NEK2, and TPX2 genes are significantly correlated with a greater risk of mortality, transforming them from mere biomarkers of the GBC for early detections and may emerge as prognostic markers for treatment.

Dysregulated tryptophan metabolism and AhR pathway contributed to CXCL10 upregulation in stable non-segmental vitiligo

BackgroundTryptophan metabolism dysregulation has been observed in vitiligo. However, drawing a mechanistic linkage between this metabolic disturbance and vitiligo pathogenesis remains challenging. ObjectiveAim to reveal the characterization of tryptophan metabolism in vitiligo and investigate the role of tryptophan metabolites in vitiligo pathophysiology. MethodsLC-MS/MS, dual-luciferase reporter assay, ELISA, qRT-PCR, small interfering RNA, western blotting, and immunohistochemistry were employed. ResultsKynurenine pathway activation and KYAT enzyme-associated deviation to kynurenic acid (KYNA) in the plasma of stable non-segmental vitiligo were determined. Using a public microarray dataset, we next validated the activation of kynurenine pathway was related with inflammatory-related genes expression in skin of vitiligo patients. Furthermore, we found that KYNA induced CXCL10 upregulation in keratinocytes via AhR activation. Moreover, the total activity of AhR agonist was increased while the AhR concentration per se was decreased in the plasma of vitiligo patients. Finally, higher KYAT, CXCL10, CYP1A1 and lower AhR expression in vitiligo lesional skin were observed by immunohistochemistry staining. ConclusionThis study depicts the metabolic and genetic characterizations of tryptophan metabolism in vitiligo and proposes that KYNA, a tryptophan-derived AhR ligand, can enhance CXCL10 expression in keratinocytes.

Expression characteristics of lipid metabolism-related genes and correlative immune infiltration landscape in acute myocardial infarction

Lipid metabolism is an important part of the heart's energy supply. The expression pattern and molecular mechanism of lipid metabolism-related genes (LMRGs) in acute myocardial infarction (AMI) are still unclear, and the link between lipid metabolism and immunity is far from being elucidated. In this study, 23 Common differentially expressed LMRGs were discovered in the AMI-related mRNA microarray datasets GSE61144 and GSE60993. These genes were mainly related to “leukotriene production involved in inflammatory response”, “lipoxygenase pathway”, “metabolic pathways”, and “regulation of lipolysis in adipocytes” pathways. 12 LMRGs (ACSL1, ADCY4, ALOX5, ALOX5AP, CCL5, CEBPB, CEBPD, CREB5, GAB2, PISD, RARRES3, and ZNF467) were significantly differentially expressed in the validation dataset GSE62646 with their AUC > 0.7 except for ALOX5AP (AUC = 0.699). Immune infiltration analysis and Pearson correlation analysis explored the immune characteristics of AMI, as well as the relationship between these identified LMRGs and immune response. Lastly, the up-regulation of ACSL1, ALOX5AP, CEBPB, and GAB2 was confirmed in the mouse AMI model. Taken together, LMRGs ACSL1, ALOX5AP, CEBPB, and GAB2 are significantly upregulated in AMI patients' blood, peripheral blood of AMI mice, myocardial tissue of AMI mice, and therefore might be new potential biomarkers for AMI.

In silico identification of differentially expressed microRNAs in thyroid cancer

BackgroundAbnormal expression of microRNAs is one of the crucial features contributing to the thyroid cancer (TC) progression. However, a comprehensive identification of the dysregulated microRNA profile and the associated molecular pathways that underlie the TC pathogenesis has not been completely provided. In the current study, bioinformatic analysis tools and microarray datasets were used to evaluate the biological roles of differentially expressed microRNAs and their targets in TC. MethodsGEO2R was used to identify differentially expressed microRNAs in TC samples. The mRNA targets of these microRNAs were predicted using different databases. DAVID and Reactome databases were used to perform gene ontology and pathway enrichment analyses of target genes. Then, the protein-protein interaction networks were constructed among them through the STRING database. MCODE was applied to screen hub genes. The prognostic values of hub genes were examined in TCGA THCA dataset using GEPIA2 platform. The relationship between hub genes and the ERBB2 protein was revealed using GeneMANIA. ResultsWe found a significant decrease in five microRNAs and a significant increase in five others in TC samples. Target genes of upregulated and downregulated microRNAs in TC were associated with ERBB2 signaling and ion exchanger pathways, respectively. CUX2 and DCUN1D4, the targets of upregulated microRNAs, were downregulated, whereas AP1S1, the target of downregulated microRNAs, were overexpressed in TCGA THCA samples. EZR and CUL5 were mediators for the interaction of ERBB2 with CUX2 or DCUN1D4, respectively. ConclusionWe suggest that CUX2/DCUN1D4 and AP1S1 may act as tumor suppressor and oncogene in TC onset and progression, respectively.

Human gastric cancer progression and stabilization of ATG2B through RNF5 binding facilitated by autophagy-associated CircDHX8

The role of circDHX8 in the interplay between autophagy and gastric cancer (GC) progression remains unclear. In this study, we investigated the mechanism underlying the role of hsa_circ_003899 (circDHX8) in the malignancy of GC. Differential expression of circRNAs between GC and normal tissues was determined using circle-seq and microarray datasets (GSE83521). These circRNAs were validated using qPCR and Sanger sequencing. The function of circDHX8 was investigated through interference with circDHX8 expression experiments using in vitro and in vivo functional assays. Western blotting, immunofluorescence, and transmission electron microscopy were used to establish whether circDHX8 promoted autophagy in GC cells. To elucidate the mechanism underlying the circDHX8-mediated regulation of autophagy, we performed bioinformatics analysis, RNA pull-down, mass spectrometry (MS), RNA immunoprecipitation (RIP), and other western Blot related experiments. Hsa_circ_0003899 (circDHX8) was identified as upregulated and shown to enhance the malignant progression in GC cells by promoting cellular autophagy. Mechanistically, circDHX8 increased ATG2B protein levels by preventing ubiquitin-mediated degradation, thereby facilitating cell proliferation and invasion in GC. Additionally, circDHX8 directly interacts with the E3 ubiquitin-protein ligase RNF5, inhibiting the RNF5-mediated degradation of ATG2B. Concurrently, ATG2B, an acetylated protein, is subjected to SIRT1-mediated deacetylation, enhancing its binding to RNF5. Consequently, we established a novel mechanism for the role of circDHX8 in the malignant progression of GC.

Uncovering periodontitis-associated markers through the aggregation of transcriptomics information from diverse sources.

Periodontitis, a common chronic inflammatory disease, significantly impacted oral health. To provide novel biological indicators for the diagnosis and treatment of periodontitis, we analyzed public microarray datasets to identify biomarkers associated with periodontitis. The Gene Expression Omnibus (GEO) datasets GSE16134 and GSE106090 were downloaded. We performed differential analysis and robust rank aggregation (RRA) to obtain a list of differential genes. To obtain the core modules and core genes related to periodontitis, we evaluated differential genes through enrichment analysis, correlation analysis, protein-protein interaction (PPI) network and competing endogenous RNA (ceRNA) network analysis. Potential biomarkers for periodontitis were identified through comparative analysis of dual networks (PPI network and ceRNA network). PPI network analysis was performed in STRING. The ceRNA network consisted of RRA differentially expressed messenger RNAs (RRA_DEmRNAs) and RRA differentially expressed long non-coding RNAs (RRA_DElncRNAs), which regulated each other's expression by sharing microRNA (miRNA) target sites. RRA_DEmRNAs were significantly enriched in inflammation-related biological processes, osteoblast differentiation, inflammatory response pathways and immunomodulatory pathways. Comparing the core ceRNA module and the core PPI module, C1QA, CENPK, CENPU and BST2 were found to be the common genes of the two core modules, and C1QA was highly correlated with inflammatory functionality. C1QA and BST2 were significantly enriched in immune-regulatory pathways. Meanwhile, LINC01133 played a significant role in regulating the expression of the core genes during the pathogenesis of periodontitis. The identified biomarkers C1QA, CENPK, CENPU, BST2 and LINC01133 provided valuable insight into periodontitis pathology.

In silico approach to investigate the potential HKT gene responsive to salt stress in rice

Rice is frequently subjected to various environmental stresses, resulting in significant production losses, with drought and salinity are the leading causes of plant damage globally. This study aims to characterize and understand the function of rice high-affinity potassium transporters (HKTs) genes in response to salinity stress. Initially, the genome-wide analysis was undertaken to reveal the evolutionarily conserved function of the OsHKT in higher plants. To investigate the transcription level of OsHKT during the vegetative and reproductive stages, two microarray datasets (GSE19024 and GSE3053) were analyzed, and salt-treated samples were subsequently evaluated using real-time PCR. Differentially expressed genes (DEGs) were identified from microarray datasets (GSE41650 and GSE14403), followed by constructing a DEG network that highlighted interaction partners of the OsHKTs. Genome mining of rice revealed 9 HKT genes, namely OsHKT1;1–1;5 and OsHKT2;1–2;4. These genes exhibited a well-conserved domain structure called TrkH. Comprehensive phylogenetic and motif analyses clustered genes encoding HKT proteins into seven monophyletic groups, and the motifs were relatively conserved. Ka/Ks ratios indicated a high degree of purifying selection during evolutionary time. Gene ontology findings suggested the involvement of OsHKT in stress response. Besides, several CRE motifs in the promoter regions of OsHKT have demonstrated their potential roles in abiotic stress responses. Furthermore, we analyzed the top 250 significant DEGs from the two datasets (p-value < 0.05; fold two change ≥ 1 or ≤ − 1) to evaluate the relationship among the DEGs and HKTs. Three co-expressed OsHKT genes were discovered to be upregulated in seedlings under salinity treatment, including OsP5CS2, OsHAK1, and OsNHX2, whereas OsP5CS1 and OsHAK27 were downregulated. The transcripts of OsHKT were found to be differentially expressed in a tissue-specific manner. Analysis of microarray datasets validated by real-time PCR shows that OsHKT1;5 had a higher expression level, followed by OsHKT1;1, OsHKT1;3, and OsHKT2;1 after salinity treatment. In addition, several micro-RNA targets in rice HKT genes regulate their expression in response to stress. This study paves the way for future investigation on genes and miRNA-target interaction in plants under environmental stresses, offering potential strategies to enhance stress tolerance in crops via targeted ion transport modification.

An integrated bioinformatic analysis of microarray datasets to identify biomarkers and miRNA-based regulatory networks in leishmaniasis

Micro RNAs (miRNAs, miRs) and relevant networks might exert crucial functions during differential host cell infection by the different Leishmania species. Thus, a bioinformatic analysis of microarray datasets was developed to identify pivotal shared biomarkers and miRNA-based regulatory networks for Leishmaniasis. A transcriptomic analysis by employing a comprehensive set of gene expression profiling microarrays was conducted to identify the key genes and miRNAs relevant for Leishmania spp. infections. Accordingly, the gene expression profiles of healthy human controls were compared with those of individuals infected with Leishmania mexicana, L. major, L. donovani, and L. braziliensis. The enrichment analysis for datasets was conducted by utilizing EnrichR database, and Protein–Protein Interaction (PPI) network to identify the hub genes. The prognostic value of hub genes was assessed by using receiver operating characteristic (ROC) curves. Finally, the miRNAs that interact with the hub genes were identified using miRTarBase, miRWalk, TargetScan, and miRNet. Differentially expressed genes were identified between the groups compared in this study. These genes were significantly enriched in inflammatory responses, cytokine-mediated signaling pathways and granulocyte and neutrophil chemotaxis responses. The identification of hub genes of recruited datasets suggested that TNF, SOCS3, JUN, TNFAIP3, and CXCL9 may serve as potential infection biomarkers and could deserve value as prognostic biomarkers for leishmaniasis. Additionally, inferred data from miRWalk revealed a significant degree of interaction of a number of miRNAs (hsa-miR-8085, hsa-miR-4673, hsa-miR-4743-3p, hsa-miR-892c-3p, hsa-miR-4644, hsa-miR-671-5p, hsa-miR-7106-5p, hsa-miR-4267, hsa-miR-5196-5p, and hsa-miR-4252) with the majority of the hub genes, suggesting such miRNAs play a crucial role afterwards parasite infection. The hub genes and hub miRNAs identified in this study could be potentially suggested as therapeutic targets or biomarkers for the management of leishmaniasis.

Sinomenine treats rheumatoid arthritis by inhibiting MMP9 and inflammatory cytokines expression: bioinformatics analysis and experimental validation

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease marked by inflammatory cell infiltration and joint damage. The Chinese government has approved the prescription medication sinomenine (SIN), an effective anti-inflammation drug, for treating RA. This study evaluated the possible anti-inflammatory actions of SIN in RA based on bioinformatics analysis and experiments. Six microarray datasets were acquired from the gene expression omnibus (GEO) database. We used R software to identify differentially expressed genes (DEGs) and perform function evaluations. The CIBERSORT was used to calculate the abundance of 22 infiltrating immune cells. The weighted gene co-expression network analysis (WGCNA) was used to discover genes associated with M1 macrophages. Four public datasets were used to predict the genes of SIN. Following that, function enrichment analysis for hub genes was performed. The cytoHubba and least absolute shrinkage and selection operator (LASSO) were employed to select hub genes, and their diagnostic effectiveness was predicted using the receiver operator characteristic (ROC) curve. Molecular docking was undertaken to confirm the affinity between the SIN and hub gene. Furthermore, the therapeutic efficacy of SIN was validated in LPS-induced RAW264.7 cells line using Western blot and Enzyme-linked immunosorbent assay (ELISA). The matrix metalloproteinase 9 (MMP9) was identified as the hub M1 macrophages-related biomarker in RA using bioinformatic analysis and molecular docking. Our study indicated that MMP9 took part in IL-17 and TNF signaling pathways. Furthermore, we found that SIN suppresses the MMP9 protein overexpression and pro-inflammatory cytokines, including tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) in the LPS-induced RAW264.7 cell line. In conclusion, our work sheds new light on the pathophysiology of RA and identifies MMP9 as a possible RA key gene. In conclusion, the above findings demonstrate that SIN, from an emerging research perspective, might be a potential cost-effective anti-inflammatory medication for treating RA.