Gene Expression Omnibus Repository Research Articles

Investigation of the Shared Biomarkers in Heterotopic Ossification Between Ossification of the Ligamentum Flavum and Ankylosing Spondylitis.

Bioinformatics analysis of Gene Expression Omnibus (GEO). Ossification of the ligamentum flavum (OLF) and ankylosing spondylitis (AS) represent intricate conditions marked by the gradual progression of endochondral ossification. This investigation endeavors to unveil common biomarkers associated with heterotopic ossification and explore the potential molecular regulatory mechanisms. Microarray and RNA-sequencing datasets retrieved from the Gene Expression Omnibus (GEO) repository were harnessed to discern differentially expressed genes (DEGs) within the OLF and AS datasets. Subsequently, Weighted Gene Co-expression Network Analysis (WGCNA) was implemented to pinpoint co-expression modules linked to OLF and AS. Common genes were further subjected to an examination of functional pathway enrichment. Moreover, hub intersection genes were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by an evaluation of diagnostic performance in external OLF and AS cohorts. Lastly, an analysis of immune cell infiltration was conducted to scrutinize the correlation of immune cell presence with shared biomarkers in OLF and AS. A total of 1353 and 91 Differentially Expressed Genes (DEGs) were identified in OLF and AS, respectively. Using the Weighted Gene Co-expression Network Analysis (WGCNA), 2 modules were found to be notably significant for OLF and AS. The integrative bioinformatic analysis revealed 3 hub genes (MAB21L2, MEGF10, ISLR) as shared risk biomarkers, with MAB21L2 being the central focus. Receiver Operating Characteristic (ROC) analysis exhibited a strong diagnostic potential for these hub genes. Gene Ontology (GO) analysis indicated their involvement in the positive regulation of myoblast proliferation. Notably, MAB21L2 was singled out as the optimal common biomarker for OLF and AS. Furthermore, an analysis of immune infiltration demonstrated a correlation between MAB21L2 expression and changes in immune cells. Activated CD8 T cells were identified as shared differential immune infiltrating cells significantly linked to MAB21L2 in both OLF and AS. This study represents the first instance of identifying MAB21L2 as a prospective diagnostic marker for patients contending with OLF associated with AS. The research results indicate that the ECM-receptor interaction and the cell-cell adhesion may play a role in both disease processes. This newfound knowledge not only enhances our understanding of the pathogenesis behind spinal ligament ossification but also uncovers potential targets for therapeutic interventions.

Entropy pelican optimization algorithm (epoa) based feature selection and deep autoencoder (dae) of heart failure status prediction

Introduction: Heart Failure (HF) is a complicated condition as well as a significant public health issue. Data processing is now required for machine and statistical learning techniques while it helps to identify key features and eliminates unimportant, redundant, or noisy characteristics, hence minimizing the feature space's dimensions. A common cause of mortality in cases of heart disease is Dilated Cardiomyopathy (DCM). Methods: The feature selection in this work depends on the Entropy Pelican Optimization Algorithm (EPOA). It is a recreation of pelicans' typical hunting behaviour. This is comparable to certain characteristics that lead to better approaches for solving high-dimensional datasets. Then Deep Autoencoder (DAE) classifier has been introduced for the prediction of patients. DAE classifier is employed to compute the system's nonlinear function through data from the normal and failure state. Results: DAE was discovered to not only considerably increase accuracy but also to be beneficial when there is a limited amount of labelled data.Performance metrics like recall, precision, accuracy, f-measure, and error rate has been used for results analysis. Conclusion: Publicly available benchmark dataset has been collected from Gene Expression Omnibus (GEO) repository to evaluate and contrast the suitability of the suggested classifier with other existing methods.

Bioinformatics analysis of hypoxia associated genes and inflammatory cytokine profiling in COPD-PH

Pulmonary hypertension (PH) in chronic obstructive pulmonary disease (COPD) is associated with worse clinical outcomes and decreased survival rates. In absence of disease specific diagnostic/therapeutic targets and unclear pathophysiology, there is an urgent need for the identification of potential genetic/molecular markers and disease associated pathways. The present study aims to use a bioinformatics approach to identify and validate hypoxia-associated gene signatures in COPD-PH patients. Additionally, hypoxia-related inflammatory profile is also explored in these patients.Microarray dataset obtained from the Gene Expression Omnibus repository was used to identify differentially expressed genes (DEGs) in a hypoxic PH mice model. The top three hub genes identified were further validated in COPD-PH patients, with chemokine (C-X-C motif) ligand 9 (CXCL9) and CXCL12 showing significant changes in comparison to healthy controls. Furthermore, multiplexed analysis of 10 inflammatory cytokines, tumor necrosis factor alpha (TNF-α), transforming growth factor β (TGF-β), interleukin 1-beta (IL-1β), IL-4, IL-5, IL-6, IL-13, IL-17, IL-18 and IL-21 was also performed. These markers showed significant changes in COPD-PH patients as compared to controls. They also exhibited the ability to differentially diagnose COPD-PH patients in comparison to COPD. Additionally, IL-6 and IL-17 showed significant positive correlation with systolic pulmonary artery pressure (sPAP).This study is the first report to assess the levels of CXCL9 and CXCL12 in COPD-PH patients and also explores their link with the inflammatory profile of these patients. Our findings could be extended to better understand the underlying disease mechanism and possibly used for tailoring therapies exclusive for the disease.

Glucometabolic-Related Genes as Diagnostic Biomarkers and Therapeutic Targets for Alzheimer's Disease and Type 2 Diabetes Mellitus: A Bioinformatics Analysis.

Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are two widespread chronic disorders characterized by shared risk factors and molecular pathways. Glucose metabolism, pivotal for cellular homeostasis and energy supply, plays a critical role in these diseases. Its disturbance has been linked to the pathogenesis of both AD and T2DM. However, a comprehensive investigation into the specific roles of glucometabolic genes in the onset and progression of AD and T2DM has yet to be conducted. By analyzing microarray datasets from the Gene Expression Omnibus (GEO) repository, we identified differentially expressed glucometabolic genes (DEGs) in AD and T2DM cohorts. A range of bioinformatics tools were employed for functional annotation, pathway enrichment, protein interaction network construction, module analysis, ROC curve assessment, correlation matrix construction, gene set enrichment analysis, and gene-drug interaction mapping of these DEGs. Key genes were further validated using quantitative real-time polymerase chain reaction (qRT-PCR) in AD and T2DM murine models. Our investigation identified 41 glucometabolic-related DEGs, with six prominent genes (G6PD, PKM, ENO3, PFKL, PGD, and TALDO1) being common in both AD and T2DM cohorts. These genes play crucial roles in metabolic pathways including glycolysis, pentose phosphate pathway, and amino sugar metabolism. Their diagnostic potential was highlighted by area under curve (AUC) values exceeding 0.6 for AD and 0.8 for T2DM. Further analysis explored the interactions, pathway enrichments, regulatory mechanisms, and potential drug interactions of these key genes. In the AD murine model, quantitative real-time polymerase chain reaction (qRT-PCR) analysis revealed significant upregulation of G6pd, Eno3, and Taldo1. Similarly, in the T2DM murine model, elevated expression levels of G6pd, Pfkl, Eno3, and Pgd were observed. Our rigorous research sheds light on the molecular interconnections between AD and T2DM from a glucometabolic perspective, revealing new opportunities for pharmacological innovation and therapeutic approaches. This study appears to be the first to extensively investigate glucometabolic-associated DEGs and key genes in both AD and T2DM, utilizing multiple datasets. These insights are set to enhance our understanding of the complex pathophysiology underlying these widespread chronic diseases.

Analyzing Pooled Microarray Gene Expression Data to Uncover Common Pathways in Periodontitis and Oral Squamous Cell Carcinoma from the Gene Expression Omnibus

ABSTRACT Periodontitis and oral squamous cell carcinoma (OSCC) are prevalent oral diseases with distinct etiologies, yet they share certain molecular and biological characteristics. Gene expression datasets from the gene expression omnibus (GEO) repository (GSE30784 for OSCC and GSE10334 for periodontitis) were analyzed. Data preprocessing and differential gene expression analysis using GEO2R identified common differentially expressed genes (DEGs), and FunRich software facilitated the construction of a protein-protein interaction (PPI) network on the STRING database. Cytoscape, coupled with the CytoHubba plugin, identified Cluster of Differentiation 19 (CD19) and Von Willebrand Factor (VWF) as the top hub genes, with Complement C3 (C3) also highly ranked. Functional enrichment analysis highlighted pathways such as the B-cell receptor signaling pathway, complement and coagulation cascades, and hematopoietic cell lineage. Additionally, miRNet analysis identified key miRNAs, including hsa-mir-26a-5p, hsa-mir-129-2-3p, and hsa-mir-27a-3p, associated with these pathways. These findings suggested an association of molecular mechanisms between periodontitis and OSCC, with identified hub genes and miRNAs potentially serving as therapeutic targets.

Humoral immune transcriptome signature in myelomeningocele patients

IntroductionMyelomeningocele (MMC) results from incomplete closure of the neural tube, and has a complex multifactorial etiology, including an inflammatory microenvironment. ObjectiveWe evaluated the contribution of humoral immune response for development of inflammatory milieu. MethodsUsing public repository Gene Expression Omnibus (GEO), we retrieve dataset transcriptome from the amniotic fluid of ten fetuses with myelomeningocele and ten healthy control fetuses to found differential gene expression associated with disturbances and inflammatory signatures in MMC. The identified DEGs were submitted to enrichment, network, and matrix correlation analyses. ResultsOur initial analysis revealed 90 DEGs in MMC, mainly associated with signaling pathways of inflammation, including the immune modules, humoral immune response and IFN-type I signatures. Protein-protein analysis (PPI) revealed an association with three protein networks; positive regulation of B cell proliferation constituted the largest network. Matrix correlation analyses showed that MMC alters the co-expression of genes related to inflammatory processes that promote microenvironment inflammation. ConclusionThese results revealed an altered humoral immune response in MMC patients, contributing to an inflammatory profile and providing opportunities for identifying potential biomarkers in myelomeningocele disease.

Drug Repurposing and Personalized Treatment Strategies for Bipolar Disorder Using Transcriptomic.

The present study combined transcriptomic data and computational techniques based on gene expression signatures to identify novel bioactive compounds or FDA-approved drugs for the management of Bipolar Disorder (BD). Five transcriptomic datasets, comprising a total of 165 blood samples from BD case-control, were selected from the Gene Expression Omnibus repository (GEO). The number of subjects varied from 6 to 60, with a mean age ranging from 35 to 48, with a gender variation between them. Most of the patients were on pharmacological treatment. Master Regulator Analysis (MRA) and Gene Set Enrichment Analysis (GSEA) were performed to identify statistically significant genes between BD and HC and their association with the mood states of BD. Additionally, existing molecules with the potential to reverse the transcriptomic profiles of disease-altered regulons in BD were identified using the LINCS and cMap databases. MRA identified 59 potential MRs candidates modulating the regulatory units enriched with genes altered in BD, while the GSEA identified 134 enriched genes, and a total of 982 regulons had their activation state determined. Both analyses showed genes exclusively associated with mania, depression, or euthymia, and some genes were common between the three mood states. We identified bioactive compounds and licensed drug candidates, including antihypertensives and antineoplastics, as promising candidates for treating BD. Nevertheless, experimental validation is essential to authenticate these findings in subsequent studies. Although preliminary, our data provides some insights regarding the biological patterns of BD into distinct mood states and potential therapeutic targets. The combined transcriptomic and bioinformatics strategy offers a route to advance drug discovery and personalized medicine by tapping into gene expression information.

Genome-wide transcriptome profiling and development of age prediction models in the human brain.

Aging-related transcriptome changes in various regions of the healthy human brain have been explored in previous works, however, a study to develop prediction models for age based on the expression levels of specific panels of transcripts is lacking. Moreover, studies that have assessed sexually dimorphic gene activities in the aging brain have reported discrepant results, suggesting that additional studies would be advantageous. The prefrontal cortex (PFC) region was previously shown to have a particularly large number of significant transcriptome alterations during healthy aging in a study that compared different regions in the human brain. We harmonized neuropathologically normal PFC transcriptome datasets obtained from the Gene Expression Omnibus (GEO) repository, ranging in age from 21 to 105 years, and found a large number of differentially regulated transcripts in the old and elderly, compared to young samples overall, and compared female and male-specific expression alterations. We assessed the genes that were associated with age by employing ontology, pathway, and network analyses. Furthermore, we applied various established (least absolute shrinkage and selection operator (Lasso) and Elastic Net (EN)) and recent (eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)) machine learning algorithms to develop accurate prediction models for chronological age and validated them. Studies to further validate these models in other large populations and molecular studies to elucidate the potential mechanisms by which the transcripts identified may be related to aging phenotypes would be advantageous.

The role of microRNAs in understanding sex-based differences in Alzheimer’s disease

BackgroundThe incidence of Alzheimer's disease (AD)—the most frequent cause of dementia—is expected to increase as life expectancies rise across the globe. While sex-based differences in AD have previously been described, there remain uncertainties regarding any association between sex and disease-associated molecular mechanisms. Studying sex-specific expression profiles of regulatory factors such as microRNAs (miRNAs) could contribute to more accurate disease diagnosis and treatment.MethodsA systematic review identified six studies of microRNA expression in AD patients that incorporated information regarding the biological sex of samples in the Gene Expression Omnibus repository. A differential microRNA expression analysis was performed, considering disease status and patient sex. Subsequently, results were integrated within a meta-analysis methodology, with a functional enrichment of meta-analysis results establishing an association between altered miRNA expression and relevant Gene Ontology terms.ResultsMeta-analyses of miRNA expression profiles in blood samples revealed the alteration of sixteen miRNAs in female and 22 miRNAs in male AD patients. We discovered nine miRNAs commonly overexpressed in both sexes, suggesting a shared miRNA dysregulation profile. Functional enrichment results based on miRNA profiles revealed sex-based differences in biological processes; most affected processes related to ubiquitination, regulation of different kinase activities, and apoptotic processes in males, but RNA splicing and translation in females. Meta-analyses of miRNA expression profiles in brain samples revealed the alteration of six miRNAs in female and four miRNAs in male AD patients. We observed a single underexpressed miRNA in female and male AD patients (hsa-miR-767-5p); however, the functional enrichment analysis for brain samples did not reveal any specifically affected biological process.ConclusionsSex-specific meta-analyses supported the detection of differentially expressed miRNAs in female and male AD patients, highlighting the relevance of sex-based information in biomedical data. Further studies on miRNA regulation in AD patients should meet the criteria for comparability and standardization of information.

Identification of BANF1 as a novel prognostic biomarker in gastric cancer and validation via in-vitro and in-vivo experiments.

Gastric cancer (GC) is a widespread malignancy characterized by a notably high incidence rate and an unfavorable prognosis. We conducted a meticulous analysis of GC high-throughput sequencing data downloaded from the Gene Expression Omnibus (GEO) repository to pinpoint distinctive genes associated with GC. Our investigation successfully identified three signature genes implicated in GC, with a specific focus on the barrier to autointegration factor 1 (BANF1), which exhibits elevated expression across various cancer types, including GC. Bioinformatic analysis has highlighted BANF1 as a prognostic indicator for patients with GC, with direct implications for immune cell infiltration. To gain a more comprehensive understanding of the significance of BANF1 in GC, we performed a series of in vitro experiments to confirm its high expression in GC tissues and cellular components. Intriguingly, the induction of BANF1 knockdown resulted in a marked attenuation of proliferation, migratory capacity, and invasive potential in GC cells. Moreover, our in vivo experiments using nude mouse models revealed a notable impediment in tumor growth following BANF1 knockdown. These insights underscore the feasibility of BANF1 as a novel therapeutic target for GC.

DNA methylation profiling of labial salivary gland tissues revealed hypomethylation of B-cell-related genes in primary Sjögren's syndrome.

The objective of this epigenetic study was to investigate the cellular proportions based on DNA methylation signatures and pathways of differentially methylated genes in labial salivary gland (LSG) tissues of individuals with Sjögren's syndrome (SS). Two methylation array datasets from the Gene Expression Omnibus repository (GSE166373 and GSE110007) were utilized, consisting of 159 LSG tissues from 77 SS cases and 82 non-SS controls. The raw data underwent analysis using the Chip Analysis Methylation Pipeline (ChAMP) in R statistical tool, which identified differential methylation probes and regions. The EpiDISH and minfi packages in R were employed to identify proportions of epithelial cells, fibroblasts, and immune cells, as well as immune cell subsets. The results showed that proportions of immune cells were increased, while proportions of epithelial cells and fibroblasts were significantly decreased in the LSG of individuals with SS compared to non-SS controls. Specifically, proportions of B-cells and CD8 T-cells were increased, while CD4 T-cells, Treg, monocytes, and neutrophils were decreased in the LSG of individuals with SS. Pathway analysis indicated that genes involved in immune responses to Epstein-Barr virus infection were significantly hypomethylated in SS, and gene set enrichment analysis highlighted the hypomethylation of genes involved in the somatic recombination of immune receptors in SS. Additionally, Disease Ontology analysis showed enriched pathways related to multiple myeloma, arthritis, and the human immunodeficiency virus. The study also revealed significant hypomethylation of the WAS gene on chromosome X in LSG tissues of individuals with SS. Overall, the findings suggest an increased proportion of B-cells and genes related to B-cell function, as well as hypomethylation of genes involved in immune responses and immune receptor recombination, in LSG tissues of individuals with SS compared to non-SS controls.

Identification of m6A-Related Biomarkers in Systemic Lupus Erythematosus: A Bioinformation-Based Analysis.

Systemic Lupus Erythematosus (SLE), a prototypical autoimmune disorder, presents a challenge due to the absence of reliable biomarkers for discerning organ-specific damage within SLE. A growing body of evidence underscores the pivotal involvement of N6-methyladenosine (m6A) in the etiology of autoimmune conditions. The datasets, which primarily encompassed the expression profiles of m6A regulatory genes, were retrieved from the Gene Expression Omnibus (GEO) repository. The optimal model, selected from either Random Forest (RF) or Support Vector Machine (SVM), was employed for the development of a predictive nomogram model. To identify pivotal genes associated with SLE, a comprehensive screening process was conducted utilizing LASSO, SVM-RFE, and RF techniques. Within the realm of SLE susceptibility, Weighted Gene Co-expression Network Analysis (WGCNA) was harnessed to delineate relevant modules and hub genes. Additionally, MeRIP-qPCR assays were performed to elucidate key genes correlated with m6A targets. Furthermore, a Mendelian randomization study was conducted based on genome-wide association studies to assess the causative influence of MMP9 on ischemic stroke (IS), which is not only a severe cerebrovascular event but also a common complication of SLE. Twelve m6A regulatory genes was identified, demonstrating statistical significance (p < 0.05) and utilized for constructing a nomogram model using the RF algorithm. EPSTI1, USP18, HP, and MMP9, as the hub genes, were identified. MMP9 uniquely correlates with m6A modification and was causally linked to an increased risk of IS, as indicated by our inverse variance weighting analysis showing an odds ratio of 1.0134 (95% CI=1.0004-1.0266, p = 0.0440). Our study identified twelve m6A regulators, shedding light on the molecular mechanisms underlying SLE risk genes. Importantly, our analysis established a causal relationship between MMP9, a key m6A-related gene, and ischemic stroke, a common complication of SLE, thereby providing critical insights for presymptomatic diagnostic approaches.

NCBI GEO: archive for gene expression and epigenomics data sets: 23-year update.

The Gene Expression Omnibus (GEO) is an international public repository that archives gene expression and epigenomics data sets generated by next-generation sequencing and microarray technologies. Data are typically submitted to GEO by researchers in compliance with widespread journal and funder mandates to make generated data publicly accessible. The resource handles raw data files, processed data filesand descriptive metadata for over 200000 studies and 6.5 million samples, all of which are indexed, searchable and downloadable. Additionally, GEO offers web-based tools that facilitate analysis and visualization of differential gene expression. This article presents the current status and recent advancements in GEO, including the generation of consistently computed gene expression count matrices for thousands of RNA-seq studies, and new interactive graphical plots in GEO2R that help users identify differentially expressed genes and assess data set quality. The GEO repository is built and maintained by the National Center for Biotechnology Information (NCBI), a division of the National Library of Medicine (NLM), and is publicly accessible at https://www.ncbi.nlm.nih.gov/geo/.

UBE2C promotes malignancy of cutaneous squamous cell carcinoma.

Our study aimed to study the involvement of ubiquitin-conjugating enzyme E2C (UBE2C) in cutaneous squamous cell carcinoma (cSCC). As the second most common malignancy with a rising incidence, understanding the molecular mechanisms driving cSCC is crucial for improved diagnosis and treatment. We combined multiple datasets of cSCC in Gene Expression Omnibus (GEO) repository to investigate its expression and diagnostic value. We collected patient specimens and performed immunohistochemistry to examine its expression in patients and its correlation with tumor histological grade. Moreover, we compared UBE2C expression between cSCC cells and primary human epidermal keratinocytes. Subsequently, we explored the effects of UBE2C inhibition on tumor cell proliferation, migration and apoptosis through CCK8, wound healing, Transwell, and flow cytometry assay. The integrated analysis revealed an upregulation of UBE2C level in cSCC. Immunohistochemistry demonstrated high UBE2C expression was associated with poorer tumor histological grade. Cell experiments further supported the crucial role of UBE2C in promoting the malignant behavior of cSCC cells. Our findings indicate UBE2C is up-regulated in cSCC and contributes to its malignant behavior. These results suggest UBE2C has the potential to serve as both a cSCC biomarker and a therapeutic target.

Landscape and the immune patterns of cuproptosis in oral squamous cell carcinoma.

Oral squamous cell carcinoma is an increasingly prevalent cancer type characterized by high incidence and mortality rates. Its early detection is challenging, primarily because of the absence of early molecular markers. Cuproptosis is a novel regulatory mechanism of cell death with implications in various cancers. In this study, we aimed to study cuproptosis-related genes in oral squamous cell carcinoma to identify their prognostic value. By analyzing genomic, bulk RNA-seq, and single-cell RNA-seq data, we investigated 13 cuproptosis-related genes in The Cancer Genome Atlas-Oral Squamous Cell Carcinoma dataset and Gene Expression Omnibus repository (GSE172577). ATP7A, ATP7B, and DLST were the most frequently mutated genes, with nine of our studied genes associated with overall survival. Single-cell analysis was conducted to identify cuproptosis-related tumor cells in oral squamous cell carcinoma, which revealed two distinct patterns based on the expression of cuproptosis-related genes. These patterns exhibit differences in genetic alterations and tumor immune microenvironment. Finally, we developed a cuproptosis index using a random forest algorithm based on cuproptosis pattern-related genes in which higher levels were linked to poorer prognosis. Our findings provide valuable insights into the mechanisms underlying oral squamous cell carcinoma-associated cuproptosis.

A potential prognostic prediction model for metastatic osteosarcoma based on bioinformatics analysis

Osteosarcoma (OS) is a malignant primary bone tumor with a high incidence. This study aims to construct a prognostic prediction model by screening the prognostic mRNA of metastatic OS. Data on four eligible expression profiles from the National Center for Biotechnology Information Gene Expression Omnibus repository were obtained based on inclusion criteria and defined as the training set or the validation set. The differentially expressed genres (DEGs) between meta- static and non-metastatic OS samples in the training set were first identified, and DEGs related to prognosis were screened by univariate Cox regression analysis. In total, 107 DEGs related to the prognosis of metastatic OS were identified. Then, 46 DEGs were isolated as the optimized prognostic gene signature, and a metastatic-OS discriminating classifier was constructed, which had a high accuracy in distinguishing metastatic from non-metastatic OS samples. Furthermore, four optimized prognostic gene signatures (ALOX5AP, COL21A1, HLA-DQB1, and LDHB) were further screened, and the prognostic prediction model for metastatic OS was constructed. This model possesses a relatively satisfying prediction ability both in the training set and validation set. The prognostic prediction model that was constructed based on the four prognostic mRNA signatures has a high predictive ability for the prognosis of metastatic OS.

Identification and verification of promising diagnostic genes in bisphenol A-associated breast cancer development via in silico analysis

Lifestyle patterns and exposure to toxic chemicals or environmental pollutants are the strongest risk factors for the chances of developing breast cancer, the leading and most lethal form of cancer in women. Bisphenol A (BPA), found in various consumer products, is known to deregulate multiple cellular signaling pathways, but its effect on cancer initiation and development in breast tissue has not yet been fully elucidated. Therefore, the identification of hub drivers is necessary to understand the molecular mechanisms underlying BPA-related malignancy and may help determine novel diagnosis and treatment strategies. This work aims at elucidating the molecular actors and mechanisms of action involved in BPA-induced breast cancer development using a bioinformatics analysis approach. A microarray dataset suitable for the study purposes was obtained from the publicly available Gene Expression Omnibus (GEO) repository, followed by DEG (differentially expressed genes) extraction, enrichment, and protein-protein interaction analyses to identify the hub genes. Expressional patterns, prognostic potentials, and immune infiltration levels of identified targets were tested and validated in silico using GEPIA2 and KM-plotter tools. According to PPI network results, CCNA2 and CCNB1 were identified as critical hub genes. Validation analyses clearly indicated that the identified genes are extremely critical in BPA-associated breast cancer processes. Findings from this study revealed that CCNA2 and CCNB1, two cell cycle signaling-related hub genes that are overexpressed as a consequence of BPA exposure, are strongly associated with breast cancer.

Identification of the Immune Landscapes and Follicular Helper T Cell-Related Genes for the Diagnosis of Age-Related Macular Degeneration.

Age-related macular degeneration (AMD) is a progressive ocular ailment causing age-associated vision deterioration, characterized by dysregulated immune cell activity. Notably, follicular helper T (Tfh) cells have emerged as pivotal contributors to AMD pathogenesis. Nonetheless, investigations into Tfh-associated gene biomarkers for this disorder remain limited. Utilizing gene expression data pertinent to AMD procured from the Gene Expression Omnibus (GEO) repository, we employed the "DESeq2" R software package to standardize and preprocess expression levels. Concurrently, CIBERSORT analysis was utilized to compute the infiltration proportions of 22 distinct immune cell types. Subsequent to weighted gene correlation network analysis (WGCNA), coupled with differential expression scrutiny, we pinpointed genes intricately linked with Tfh cells. These potential genes underwent further screening using the MCODE function within Cytoscape software. Ultimately, a judicious selection of pivotal genes from these identified clusters was executed through the LASSO algorithm. Subsequently, a diagnostic nomogram was devised based on these selected genes. Evident Tfh cell disparities between AMD and control cohorts were observed. Our amalgamated analysis, amalgamating differential expression data with co-expression patterns, unveiled six genes closely associated with Tfh cells in AMD. Subsequent employment of the LASSO algo-rithm facilitated identification of the most pertinent genes conducive to predictive modeling. From these, GABRB3, MFF, and PROX1 were elected as prospective diagnostic biomarkers for AMD. This investigation discerned three novel biomarker genes, linked to inflammatory mechanisms and pivotal in diagnosing AMD. Further exploration of these genes holds potential to foster novel therapeutic modalities and augment comprehension of AMD's disease trajectory.

Construction of Lung Adenocarcinoma Prognosis Model and Drug Sensitivity Analysis Based on Cuproptosis Related Genes

Lung cancer is one of the most common malignant tumors in the world, and the current lung cancer screening and treatment strategies are constantly improving, but its 5-year survival rate is still very low, which seriously endangers human health. Therefore, it is critical to explore new biomarkers to provide personalized treatment and improve the prognosis. Cuproptosis is a newly discovered type of cell death, which is due to the accumulation of excess copper ions in the cell, eventually leading to cell death, which has been suggested by studies to be closely related to the occurrence and development of lung adenocarcinoma (LUAD). Based on The Cancer Genome Atlas (TCGA) database, this study explored the association between cuproptosis-related genes (CRGs) and LUAD prognosis, established a prognostic risk model, and analyzed the interaction between CRGs and LUAD immune cell infiltration. The RNA-seq data of LUAD tissue and paracancerous or normal lung tissue were downloaded from the TCGA database; the RNA-seq data of normal lung tissue was downloaded from the Genotype-tissue Expression (GTEx) database, and the data of 462 lung adenocarcinoma cases were downloaded from the Gene Expression Omnibus repository (GEO) as verification. T the risk score model to assess prognosis was constructed by univariate Cox and Lasso-Cox regression analysis, and the predictive ability of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve. Immune-related and drug susceptibility analysis was further performed on high- and low-risk groups. A total of 1656 CRGs and 1356 differentially expressed CRGs were obtained, and 13 CRGs were screened out based on univariate Cox and Lasso-Cox regression analysis to construct a prognostic risk model, and the area under the curves (AUCs) of ROC curves 1-, 3- and 5- year were 0.749, 0.740 and 0.689, respectively. Further study of immune-related functions and immune checkpoint differential analysis between high- and low-risk groups was done. High-risk groups were more sensitive to drugs such as Savolitinib, Palbociclib, and Cytarabine and were more likely to benefit from immunotherapy. The risk model constructed based on 13 CRGs has good prognostic value, which can assist LUAD patients in individualized treatment, and provides an important theoretical basis for the treatment and prognosis of LUAD.

Transcriptome analysis for the screening of hub genes and potential drugs in hypersensitivity pneumonitis

Hypersensitivity pneumonitis (HP), an immune-mediated subtype of interstitial lung diseases (ILDs), is mediated by repetitive inhalation of certain antigens. Transcriptomic signatures, which provide insight into the genetic alterations associated with a disease, somehow remain obscure for HP. In this study, unique transcriptome fingerprints associated with the pathogenesis of HP were investigated using systems biology approach. Furthermore, the metabolic signatures identified in our earlier study, were correlated with the differentially expressed genes (DEGs) to explore the integrated pathways. Two datasets (GSE150910 and GSE19314) procured from the Gene Expression Omnibus repository were used to identify the DEGs of HP patients. The enriched biological processes, altered pathways and protein-protein interaction (PPI) network associated with the DEGs were determined. Based on topological algorithms, 12 hub genes were screened; among which chemokine (C-X-C motif) ligand 13 (CXCL13), CXCL9 and ribosomal protein large 36A (RPL36A) were identified as the top-ranked hub genes. The expression levels of these three candidate genes were experimentally validated in an HP patient cohort using quantitative reverse transcription polymerase chain reaction (qRT-PCR). The findings indicated a trend similar to that of the bioinformatics observations. We integrated the metabolic signatures identified earlier with these hub genes and found glycolysis and phosphatidylinositol-3-kinase (PI3K/AKT) pathways to be impaired in these patients. Finally, the drug-gene interaction database DGIdb 4.0 indicated rituximab to be a promising drug for the management of HP. The present study is the first of its kind to combine bioinformatics and experimental data to identify and validate the genetic signatures and downstream pathways associated with HP. Our findings open up a new window to probe into the gene-metabolite crosstalk involved in the pathogenesis of HP and highlight promising therapeutic strategies.