Gene Microarray Data Research Articles

Development and Validation of a Diagnostic Model for AKI Based on the Analysis of Ferroptosis-related Genes.

Acute kidney injury (AKI) is a common renal condition associated with various factors, including pre-renal, post-renal, and renal causes, with ischemia- reperfusion being a frequent contributor leading to tubular injury. Early identification of AKI is crucial but remains challenging. This study explored the molecular signature of AKI using gene microarray data from the GEO dataset, focusing on identifying ferroptosis-related features through three machine-learning algorithms. We also validated potential biomarkers through a hypoxia/ reoxygenation model. ROC curves, expression differences, and associations with immune cells were analyzed for the three markers to confirm their potential as AKI biomarkers, each demonstrating strong diagnostic ability. Combining these markers proved more effective. The combination of AEBP2, MDM2, and NR4A1 as diagnostic biomarkers for AKI not only enhances detection capability but also holds promise as a significant tool in clinical practice, providing patients with diagnostic and therapeutic guidance.

Taoren Honghua Decoction alleviates atherosclerosis by inducing autophagy and inhibiting the PI3K-AKT signaling pathway to regulate cholesterol efflux and inflammatory responses

BackgroundTaoren Honghua Decoction (THD) is a traditional Chinese formula known for enhancing blood circulation and demonstrating clinical efficacy in the treatment of cardiovascular and cerebrovascular diseases. However, the primary active components and the underlying mechanisms by which THD exerts its therapeutic effects on atherosclerosis (AS) remain insufficiently characterized. ObjectiveThis study aims to systematically validate the protective effects of THD on AS and elucidate its potential molecular mechanisms through an integrative approach involving network pharmacology, in vivo, and in vitro experiments. MethodsThe main active ingredients and corresponding targets of all traditional Chinese medicines in THD were collected from the TCSMP and BATMAN-TCM databases. Potential targets of AS were identified using the OMIM, DrugBank, DisGeNET, and CTD databases, and AS microarray gene data were obtained from the GEO database. A drug active ingredient-target relationship network and a PPI network were constructed using Cytoscape 3.9.2 software. The molecular functions of the core targets were annotated through GO and KEGG enrichment analyses to further elucidate the potential molecular mechanisms of THD’s anti-AS effects. The ApoE−/−mouse AS model was constructed through a high-fat diet (HFD), and RAW264.7 macrophage model was induced with ox-LDL to further validate the results of network pharmacology. ResultsNetwork pharmacology analysis revealed that the main five active ingredients of THD include quercetin, apigenin, luteolin, kaempferol, and tanshinone IIA. Subsequently, by analyzing the intersection genes of the main active ingredient targets of THD and the AS targets, a total of ten core targets were identified: TP53, PPARG, JUN, AKT1, INS, IL6, SIRT1, TNF, ESR1, and STAT3. These are considered the core targets of THD in the treatment of AS. The GO and KEGG enrichment analysis results indicate that THD may exert anti-AS effects by regulating lipid metabolism and the PI3K-AKT signaling pathway. In vivo and in vitro experiments showed that THD reduced circulating lipid levels, decreased intraplaque lipid accumulation, and increased intraplaque collagen fiber content in HFD-induced ApoE−/− mice. Additionally, THD reduced ox-LDL-induced macrophage-derived foam cell formation, inhibited the expression of inflammatory factors IL-6 and TNF-α, and promoted the expression of cholesterol efflux regulatory proteins PPARγ, ABCA1, and ABCG1. Notably, the autophagy inhibitor 3-MA reversed these effects, confirming that THD’s action involves autophagy activation, evidenced by increased LC3II/I and decreased p62 levels. ConclusionThis study demonstrates that THD exerts significant anti-AS effects through the inhibition of the PI3K/AKT signaling pathway and the activation of autophagy, thereby promoting cholesterol efflux and mitigating inflammation. By integrating network pharmacology with experimental validation, these findings provide a comprehensive understanding of THD’s mechanisms in treating AS and offer a solid theoretical basis for its potential clinical application.

Characterization of SUSD3 as a novel prognostic biomarker and therapeutic target for breast cancer.

Breast cancer (BC) remains a significant global health challenge, contributing substantially to cancer-related deaths worldwide. Its prevalence and associated death rates remain alarmingly high, highlighting the persistent public health burden. The objective of this study was to systematically examine the involvement of SUSD3 (Sushi Domain-Containing 3) in BC, highlighting its crucial role in the pathogenesis and progression of this disease. BC-related gene microarray data, along with corresponding clinicopathological information, were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Leveraging TIMER and HPA databases, we conducted comparative analyses to evaluate SUSD3 expression in BC. We then analyzed the association between SUSD3 and clinical traits, as well as the prognostic value of SUSD3. SUSD3-related differential expression genes (DEGs) were sent for analysis utilizing GO, KEGG, and GSEA. We utilized SUSD3 mRNA expression to assess immune cells' scores in BC tissues calculated by single-sample enrichment analyses based on "CIBERSORT" R package. Drug sensitivity analysis was used to screen potential drugs sensitive to SUSD3. R software was used for statistical analyses and graphical representation of the data. Our findings confirmed a significant upregulation of SUSD3 expression in BC, which correlated with a favorable prognosis. Clinical correlation analysis further emphasized the strong association between SUSD3 expression and key clinical parameters like estrogen receptor (ER) status, progesterone receptor (PR) status, stage, and T classification in breast cancer. Univariate and multivariate Cox regression analyses showed that SUSD3 could be used as an independent prognostic factor for BC. Differentially expressed genes (DEGs) co-expressed with SUSD3 were significantly associated with various biological processes, such as the cell cycle, DNA replication, p53 signaling pathway, cancer-related pathways, and Wnt signaling pathway, as indicated by gene set enrichment analysis (GSEA). Furthermore, our analysis demonstrated that SUSD3 generally exhibited negative associations with immune modulators. Drug sensitivity analysis revealed positive correlations between SUSD3 and the efficacy of Fulvestrant, Raloxifene, and Fluphenazine. The research emphasizes the significance of SUSD3 as a potential marker for BC, providing insights into the underlying molecular mechanisms implicated in tumorigenesis. SUSD3 holds promise in helping the classification of breast cancer pathological groups, predicting prognosis, and facilitating targeted therapy.

Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis.

This article investigates the effectiveness of feature extraction and selection techniques in enhancing the performance of classifier accuracy in Type II Diabetes Mellitus (DM) detection using microarray gene data. To address the inherent high dimensionality of the data, three feature extraction (FE) methods are used, namely Short-Time Fourier Transform (STFT), Ridge Regression (RR), and Pearson's Correlation Coefficient (PCC). To further refine the data, meta-heuristic algorithms like Bald Eagle Search Optimization (BESO) and Red Deer Optimization (RDO) are utilized for feature selection. The performance of seven classification techniques, Non-Linear Regression-NLR, Linear Regression-LR, Gaussian Mixture Models-GMMs, Expectation Maximization-EM, Logistic Regression-LoR, Softmax Discriminant Classifier-SDC, and Support Vector Machine with Radial Basis Function kernel-SVM-RBF, are evaluated with and without feature selection. The analysis reveals that the combination of PCC with SVM-RBF achieved a promising accuracy of 92.85% even without feature selection. Notably, employing BESO with PCC and SVM-RBF maintained this high accuracy. However, the highest overall accuracy of 97.14% was achieved when RDO was used for feature selection alongside PCC and SVM-RBF. These findings highlight the potential of feature extraction and selection techniques, particularly RDO with PCC, in improving the accuracy of DM detection using microarray gene data.

Improving prediction of blood cancer using leukemia microarray gene data and Chi2 features with weighted convolutional neural network

Blood cancer has emerged as a growing concern over the past decade, necessitating early diagnosis for timely and effective treatment. The present diagnostic method, which involves a battery of tests and medical experts, is costly and time-consuming. For this reason, it is crucial to establish an automated diagnostic system for accurate predictions. A particular field of focus in medical research is the use of machine learning and leukemia microarray gene data for blood cancer diagnosis. Even with a great deal of research, more improvements are needed to reach the appropriate levels of accuracy and efficacy. This work presents a supervised machine-learning algorithm for blood cancer prediction. This work makes use of the 22,283-gene leukemia microarray gene data. Chi-squared (Chi2) feature selection methods and the synthetic minority oversampling technique (SMOTE)-Tomek resampling is used to overcome issues with imbalanced and high-dimensional datasets. To balance the dataset for each target class, SMOTE-Tomek creates synthetic data, and Chi2 chooses the most important features to train the learning models from 22,283 genes. A novel weighted convolutional neural network (CNN) model is proposed for classification, utilizing the support of three separate CNN models. To determine the importance of the proposed approach, extensive experiments are carried out on the datasets, including a performance comparison with the most advanced techniques. Weighted CNN demonstrates superior performance over other models when coupled with SMOTE-Tomek and Chi2 techniques, achieving a remarkable 99.9% accuracy. Results from k-fold cross-validation further affirm the supremacy of the proposed model.

Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a fatal disease of unknown etiology with a poor prognosis, characterized by a lack of effective diagnostic and therapeutic interventions. The role of immunity in the pathogenesis of IPF is significant, yet remains inadequately understood. This study aimed to identify potential key genes in IPF and their relationship with immune cells by integrated bioinformatics analysis and verify by in vivo and in vitro experiments. Gene microarray data were obtained from the Gene Expression Omnibus (GEO) for differential expression analysis. The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. Then their diagnostic significance and potential co-regulators were elucidated. We further analyzed the correlation between key genes and immune infiltrating cells via single-sample gene set enrichment analysis (ssGSEA). Subsequently, a single-cell RNA sequencing data (scRNA-seq) was used to explore which cell types expressed key genes in IPF samples. Finally, a series of in vivo and in vitro experiments were conducted to validate the expression of candidate genes by western blot (WB), quantitative real-time PCR (qRT-PCR), and immunohistochemistry (IHC) analysis. A total of 647 DEGs of IPF were identified based on two datasets, including 225 downregulated genes and 422 upregulated genes. They are closely related to biological functions such as cell migration, structural organization, immune cell chemotaxis, and extracellular matrix. CFH and FHL2 were identified as key genes with diagnostic accuracy for IPF by three machine learning algorithms. Analysis using ssGSEA revealed a significant association of both CFH and FHL2 with diverse immune cells, such as B cells and NK cells. Further scRNA-seq analysis indicated CFH and FHL2 were specifically upregulated in human IPF tissues, which was confirmed by in vitro and in vivo experiments. In this study, CFH and FHL2 have been identified as novel potential biomarkers for IPF, with potential diagnostic utility in future clinical applications. Subsequent investigations into the functions of these genes in IPF and their interactions with immune cells may enhance comprehension of the disease's pathogenesis and facilitate the identification of therapeutic targets.

A framework for performance enhancement of classifiers in detection of prostate cancer from microarray gene

Prostate cancer is a major world health problem for men. This shows how important early detection and accurate diagnosis are for better treatment and patient outcomes. This study compares different ways to find Prostate Cancer (PCa) and label tumors as normal or abnormal, with the goal of speeding up current work in microarray gene data analysis. The study looks at how well several feature extraction methods work with three feature selection strategies: Harmonic Search (HS), Firefly Algorithm (FA), and Elephant Herding Optimization (EHO). The techniques tested are Expectation Maximization (EM), Nonlinear Regression (NLR), K-means, Principal Component Analysis (PCA), and Discrete Cosine Transform (DCT). Eight classifiers are used for the task of classification. These are Random Forest, Decision Tree, Adaboost, XGBoost, and Support Vector Machine (SVM) with linear, polynomial, and radial basis function kernels. This study looks at how well these classifiers work with and without feature selection methods. It finds that the SVM with radial basis function kernel, using DCT for feature extraction and EHO for feature selection, does the best of all of them, with an accuracy of 94.8 % and an error rate of 5.15 %.

Neutrophil Extracellular Trap-Related Genes and Immune Infiltration in Diabetic Foot Ulcers by Bioinformatics Analysis and Experimental Verification

The study aims to investigate the correlation between diabetic foot ulcer (DFU) and neutrophil extracellular traps (NETs), as well as to identify potential biomarkers. Gene microarray data from the Gene Expression Omnibus (GEO) was employed in this research. The GSE134431 and GSE80178 datasets were obtained for bioinformatics analysis to identify differentially expressed genes, which were then cross-referenced with a curated database of genes associated with neutrophil extracellular traps (NETs). Next, the specificity of these potential genes for the disease was determined via Receiver Operating Characteristic (ROC) analysis. Subsequently, the protein-protein interaction (PPI) networks and the Least Absolute Shrinkage and Selection Operator (Lasso) regression were used to identify potential key genes. Additionally, the MCPcounter algorithm was utilized to evaluate the immune infiltration and analyze the relationship between core genes and immune cell infiltration. Furthermore, the levels of expression of these candidate genes were verified by RT-qPCR. Four core genes (CXCL8, S100A12, CXCL12, S100A9) were identified through ROC and Lasso regression analyses. Moreover, individuals suffering from diabetic foot disease exhibited decreased expression levels of T cells, CD8 T cells, cytotoxic lymphocytes, NK cells, fibroblasts, and myeloid dendritic cells; In contrast, monocytes and neutrophils exhibited elevated expression levels. The expression levels of CXCL12 and CXCL8 were positively linked to a wide range of immune and endothelial cells, whereas S100A12 and S100A9 showed distinct correlation patterns with specific immune cell types. qPCR analysis showed increased expression of CXCL8 and CXCL12 as the levels of glucose increased in vitro experimental hyperglycemia. In conclusion, the intricate interplay among genes and immune cell types suggests that DFU and NETs hold potential as valuable biomarkers for the diagnosis and treatment of these conditions, thereby enhancing their clinical meanings.

In Silico Identification of Effective Genes for Acute Leukemia Classification Using a Spline Regression-based Framework

Background: Microarray technology enables the examination of gene expression in thousands of genes and can be highly effective in identifying various types of cancers, including leukemia. However, many genes in microarray data are redundant and lack useful information for cancer diagnosis. The main objective of this study is to identify relevant and effective genes in classification of leukemia microarray data using a spline regression-based method, taking into account the correlation between genes.
 Materials and Methods: In this analytical study, leukemia microarray data are used to identify relevant genes in classification of leukemia into Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL) using a spline regression-based gene selection method, called SRS3FS based on ℓ2,p-norm (0 < p ≤ 1). Subsequently, the support vector machine (SVM) algorithm is employed to classify leukemia data into AML and ALL.
 Results: In this study, the classification results of SVM algorithm for 5, 10, 15, and 20 genes reveal that the SRS3FS method, employing ℓ2,1/4-norm, ℓ2,1/2-norm and ℓ2,3/4-norm, exhibited the highest accuracy of 97.06% when identifying 10 genes for distinguishing between AML and ALL. Moreover, the leukemia data was classified into AML and ALL with an accuracy of 100%, using a gene identified by the SRS3FS method based on ℓ2,3/4-norm and ℓ2,1-norm. The gene labeled as number 3252, annotated as GLUTATHIONE S-TRANSFERASE, MICROSOMAL, is recognized as the most important gene.
 Conclusion: The experimental results on leukemia microarray data demonstrate that the spline regression-based gene selection method can effectively identify relevant genes in classification and prediction of leukemia.

Analysis and experimental validation of IL-17 pathway and key genes as central roles associated with inflammation in hepatic ischemia-reperfusion injury.

Hepatic ischemia-reperfusion injury (HIRI) elicits an immune-inflammatory response that may result in hepatocyte necrosis and apoptosis, ultimately culminating in postoperative hepatic dysfunction and hepatic failure. The precise mechanisms governing the pathophysiology of HIRI remain incompletely understood, necessitating further investigation into key molecules and pathways implicated in disease progression to guide drug discovery and potential therapeutic interventions. Gene microarray data was downloaded from the GEO expression profile database. Integrated bioinformatic analyses were performed to identify HIRI signature genes, which were subsequently validated for expression levels and diagnostic efficacy. Finally, the gene expression was verified in an experimental HIRI model and the effect of anti-IL17A antibody intervention in three time points (including pre-ischemic, post-ischemic, and at 1h of reperfusion) on HIRI and the expression of these genes was investigated. Bioinformatic analyses of the screened characterized genes revealed that inflammation, immune response, and cell death modulation were significantly associated with HIRI pathophysiology. CCL2, BTG2, GADD45A, FOS, CXCL10, TNFRSF12A, and IL-17 pathway were identified as key components involved in the HIRI. Serum and liver IL-17A expression were significantly upregulated during the initial phase of HIRI. Pretreatment with anti-IL-17A antibody effectively alleviated the damage of liver tissue, suppressed inflammatory factors, and serum transaminase levels, and downregulated the mRNA expression of CCL2, GADD45A, FOS, CXCL10, and TNFRSF12A. Injection of anti-IL17A antibody after ischemia and at 1h of reperfusion failed to demonstrate anti-inflammatory and attenuating HIRI benefits relative to earlier intervention. Our study reveals that the IL-17 pathway and related genes may be involved in the proinflammatory mechanism of HIRI, which may provide a new perspective and theoretical basis for the prevention and treatment of HIRI.

Exploring hub genes and crucial pathways linked to oxidative stress in bipolar disorder depressive episodes through bioinformatics analysis.

Bipolar disorder (BD) is a complex and serious psychiatric condition primarily characterized by bipolar depression, with the underlying genetic determinants yet to be elucidated. There is a substantial body of literature linking psychiatric disorders, including BD, to oxidative stress (OS). Consequently, this study aims to assess the relationship between BD and OS by identifying key hub genes implicated in OS pathways. We acquired gene microarray data from GSE5392 through the Gene Expression Omnibus (GEO). Our approach encompassed differential expression analysis, weighted gene co-expression network analysis (WGCNA), and Protein-Protein Interaction (PPI) Network analysis to pinpoint hub genes associated with BD. Subsequently, we utilized Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) to identify hub genes relevant to OS. To evaluate the diagnostic accuracy of these hub genes, we performed receiver operating characteristic curve (ROC) analysis on both GSE5388 and GSE5389 datasets. Furthermore, we conducted a study involving ten BD patients and ten healthy controls (HCs) who met the special criteria, assessing the expression levels of these hub genes in their peripheral blood mononuclear cells (PBMCs). We identified 411 down-regulated genes and 69 up-regulated genes for further scrutiny. Through WGCNA, we obtained 22 co-expression modules, with the sienna3 module displaying the strongest association with BD. By integrating differential analysis with genes linked to OS, we identified 44 common genes. Subsequent PPI Network and WGCNA analyses confirmed three hub genes as potential biomarkers for BD. Functional enrichment pathway analysis revealed their involvement in neuronal signal transduction, oxidative phosphorylation, and metabolic obstacle pathways. Using the Cytoscape plugin "ClueGo assay," we determined that a majority of these targets regulate neuronal synaptic plasticity. ROC curve analysis underscored the excellent diagnostic value of these three hub genes. Quantitative reverse transcription-PCR (RT-qPCR) results indicated significant changes in the expression of these hub genes in the PBMCs of BD patients compared to HCs. We identified three hub genes (TAC1, MAP2K1, and MAP2K4) in BD associated with OS, potentially influencing the diagnosis and treatment of BD. Based on the GEO database, our study provides novel insights into the relationship between BD and OS, offering promising therapeutic targets.

Bioinformatics analysis identifies coagulation factor II receptor as a potential biomarker in stomach adenocarcinoma

Coagulation factor 2 thrombin receptor (F2R), a member of the G protein-coupled receptor family, plays an important role in regulating blood clotting through protein hydrolytic cleavage mediated receptor activation. However, the underlying biological mechanisms by which F2R affects the development of gastric adenocarcinoma are not fully understood. This study aimed to systematically analyze the role of F2R in gastric adenocarcinoma. Stomach adenocarcinoma (STAD)-related gene microarray data and corresponding clinicopathological information were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differential expression genes (DEGs) associated with F2R were analyzed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), and protein–protein interaction (PPI) networks. F2R mRNA expression data were utilized to estimate stromal cell and immune cell scores in gastric cancer tissue samples, including stromal score, immune score, and ESTIMATE score, derived from single-sample enrichment studies. Analysis of TCGA and GEO databases revealed significantly higher F2R expression in STAD tissues compared to normal tissues. Patients with high F2R expression had shorter survival times than those with low F2R expression. F2R expression was significantly correlated with tumor (T) stage, node (N) stage, histological grade and pathological stage. Enrichment analysis of F2R-related genes showed that GO terms were mainly related to circulation-mediated human immune response, immunoglobulin, cell recognition and phagocytosis. KEGG analysis indicated associations to extracellular matrix (ECM) receptor interactions, neuroactive ligand-receptor interactions, the phosphoinositide-3-kinase-protein kinase B/Akt (PI3K-AKT) signaling pathway, the Wnt signaling pathway and the transforming growth factor-beta (TGF-β) signaling pathway. GSEA revealed connections to DNA replication, the Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway, the mitogen-activated protein kinase (MAPK) signaling pathway and oxidative phosphorylation. Drug sensitivity analysis demonstrated positive correlations between F2R and several drugs, including BEZ235, CGP-60474, Dasatinib, HG-6-64-1, Aazopanib, Rapamycin, Sunitinib and TGX221, while negative correlation with CP724714, FH535, GSK1904529A, JNK-9L, LY317615, pyrimidine, rTRAIL and Vinorelbine. Knocking down F2R in GC cell lines resulted in slowed proliferation, migration, and invasion. All statistical analyses were performed using R software (version 4.2.1) and GraphPad Prism 9.0. p < 0.05 was considered statistically significant. In conclusion, this study underscores the significance of F2R as a potential biomarker in gastric adenocarcinoma, shedding light on its molecular mechanisms in tumorigenesis. F2R holds promise for aiding in the diagnosis, prognosis, and targeted therapy of STAD.

Novel HGDBO: A Hybrid Genetic and Dung Beetle Optimization Algorithm for Microarray Gene Selection and Efficient Cancer Classification

Introduction: Ovarian cancer ranks as the seventh most frequently diagnosed cancer and stands as the eighth leading cause of cancer-related mortality among women globally. Early detection significantly improves survival rates and outcomes, highlighting the need for enhanced screening methods and increased awareness to facilitate early diagnosis and treatment. Microarray gene data, characterized by its high dimensionality, includes the expression levels of thousands of genes across numerous samples, posing both opportunities and challenges in the analysis of gene functions and disease mechanisms. Method: This paper presents a novel hybrid gene feature selection method called HGDBO, which combines the Dung Beetle Optimization (DBO) algorithm with the Genetic Algorithm (GA) to increase the effectiveness of microarray data analysis. The proposed HGDBO method utilizes the exploratory capabilities of DBO and the exploitative strengths of GA to identify the most relevant genes for disease classification. Experimental results on multiple microarray datasets demonstrate that the hybrid approach offers superior classification performance, stability, and computational efficiency compared to traditional and state-of-the-art methods. To classify ovarian cancer, Naïve-Bayes (NB) and Random-Forest (RF) classification algorithms were employed. Results and Discussion: The proposed Random Forest model outperforms the Naive Bayes model across all metrics, achieving better accuracy (0.96 vs. 0.91), precision (0.95 vs. 0.91), recall (0.97 vs. 0.90), F-1 score (0.95 vs. 0.91), and specificity (0.97 vs. 0.86). Conclusion: These results underscore the effectiveness of the HGDBO method and the Random Forest classifier in enhancing the analysis and classification of ovarian cancer using microarray gene data.

Identification of key genes associated with acute myocardial infarction using WGCNA and two-sample mendelian randomization study.

Acute myocardial infarction (AMI) is a severe condition with high morbidity and mortality rates. This study aimed to identify hub genes potentially associated with AMI and assess their clinical utility in predicting AMI occurrence. Gene microarray data were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted on samples from patients with AMI and control samples to identify modules significantly associated with AMI. GO and KEGG analyses were applied to investigate the potential functions of these hub genes. Lastly, the mendelian randomization (MR) method was applied to analyze the causal relationship between the hub gene TNF and AMI. 285 differentially expressed genes (DEGs) were identified through WCGNA and were clustered into 6 modules. The yellow module appeared most relevant to AMI. Further exploration through GO and KEGG pathway enrichment showed that key hub genes in the yellow module were linked to positive regulation of cytokine production, cytokine receptor binding, NF-kappa B signaling pathway, IL-17 signaling pathway, and TNF signaling pathway. The top 10 genes identified through Cytoscape software analysis were IL1B, TNF, TLR4, TLR2, FCGR3B, MMP9, CXCL8, TLR8, ICAM1, and JUK. Utilizing inverse variance weighting (IVW) analysis, we discovered a significant association between TNF and AMI risk, with an OR of 0.946 (95% CI = 0.911-0.984, p = 0.005). The result of this study indicated that TNF, TLR2, TLR4, IL1B and FCGR3B may be potential biodiagnostic markers for AMI. TNF can inhibit inflammatory and oxidative stress responses in AMI, exerting a protective role in the heart.

Hybrid Ant Lion Mutated Ant Colony Optimizer Technique With Particle Swarm Optimization for Leukemia Prediction Using Microarray Gene Data

Hybrid Ant Lion Mutated Ant Colony Optimizer Technique With Particle Swarm Optimization for Leukemia Prediction Using Microarray Gene Data

CEP55, serving as a diagnostic marker gene for osteosarcoma, triggers the JAK2-STAT3-MMPs axis.

Osteosarcoma (OS) stands as the prevailing form of primary bone cancer in clinical practice. Lack of effective treatment options and an overall poor prognosis are caused by the disease's exceptionally rare occurrence and unclear rationale. This study's goal is to determine diagnostic marker genes involved in the progression of OS and investigate related pathways and mechanisms with the purpose of offering effective methods for OS diagnostics and therapy. The Gene Expression Omnibus database provided the gene microarray data. Core genes were identified through differential expression analysis and WGCNA. Three techniques for machine learning, random forest, least absolute shrinkage and selection operator regression, and support vector machine recursive feature elimination, were used to further screen the core genes and obtain diagnostic marker genes for OS. The specificity and sensitivity of the diagnostic marker genes for OS diagnosis were evaluated using receiver operating characteristic curves. Western blotting analysis was used for preliminary validation of the diagnostic marker genes and their related pathways. Two diagnostic marker genes were identified through screening, including CEP55 and VWF. Receiver operating characteristic curves have been utilized to assess the diagnostic and therapeutic effects of CEP55 and VWF on OS. Western blotting analysis preliminarily validated the overexpression of CEP55 in OS and its capacity to control MMP2 and MMP9 levels by activating the JAK2/STAT3 signaling pathway. At the first time, this research shows that CEP55 and VWF are more powerful diagnostic and predictive indicators for OS. CEP55 holds the capacity to activate the JAK2/STAT3 signaling pathway and modulate MMP2 and MMP9 levels, thereby positioning it as a promising target in OS treatment.

Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data.

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier's performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier's performance emphasizes the role of feature selection methods.

Identification of Competing Endogenous RNA Networks and Potential Therapeutic Drugs for Gastroesophageal Reflux Disease

Background: The diagnosis and cure rate of gastroesophageal reflux disease (GERD) is low, and its pathogenesis remains unclear. Aims: In this study, we aimed to construct the competing endogenous RNA (ceRNA) of GERD by bioinformatics and explore the potential therapeutic drugs for GERD. Methods: The differentially expressed genes (DEGs) were got by limma package and visualized by ggplot2 and pheatmap package in R version 4.1.3 from the GSE63401 gene microarray data. The pro-tein-protein interaction (PPI) network was constructed by the STRING database. The hub genes and subnetwork modules were obtained by Cytoscape software. The microRNA and lncRNA were pre-dicted by the miRTarBase and miRNet databases to construct the ceRNA network. Potential drugs tar-geting DEGs were screened by the CMAP database. Results: In this study, a total of 571 GEDs were obtained, including 462 up-regulated genes and 109 down-regulated genes, which mainly participated in the leukocyte migration process and the cytokine-cytokine receptor interaction pathway. Seventeen miRNAs and three key lncRNAs involved in regulat-ing hub genes were screened through miRTarBase and miRNet databases. Finally, nine ceRNA net-works were obtained, including TTN-AS1/NEAT1/XIST - miR-139-5p - CXCR4 axis, TTN-AS1/NEAT1/XIST - miR-185-5p - IL1B axis and TTN-AS1/NEAT1/XIST - miR-495-3p - LYN axis. In addition, five potential drugs (Pimavanserin, BMS-182874, CL-218872, Losartan, and Laropiprant) for GERD were obtained through the CMAP database. Conclusion: We constructed nine GERD-related ceRNA networks and screened five potential thera-peutic drugs for GERD, providing a theoretical basis for further research on the pathogenesis of GERD.

An in-Depth Exploration of the Genetic Interaction Network Between Ferroptosis and Acute Pancreatitis.

Ferroptosis plays an important role in a variety of disease processes and is equally important in pancreatic diseases. However, the role of ferroptosis-related genes (FRGs) in acute pancreatitis (AP) remains unknown, and their specific potential mechanisms still need to be explored extensively. AP-related gene microarray data were obtained from the GEO database, while FRGs were obtained from the ferroptosis database (FerrDb). Differentially expressed genes (DEGs) were screened by the "limma" package, and GSEA was performed. The corresponding ferroptosis-related differentially expressed genes (FRDEGs) were screened, and GO and KEGG pathway analyses were performed. A PPI network was constructed to identify hub FRDEGs by CytoHubba, MCODE and CTD scores. Transcription factors and miRNAs predicted using the NetworkAnalyst database were used to establish the regulatory network. Immune cell infiltration analysis was performed by the R package "ssGSEA" algorithm. The hub genes were validated by transcriptome sequencing of AP model mice and immunohistochemistry in rats and mice. A total of 82 FRDEGs were screened, and these genes were mainly associated with ferroptosis, hypoxic response, autophagy, mitophagy and immune inflammation. However, we also found that these genes are also jointly involved in other cell death modalities, such as apoptosis and necroptosis. Further analysis obtained 7 hub genes from 82 genes, and single-sample gene set enrichment analysis (ssGSEA) showed that the hub genes are closely associated with the infiltration of specific immune cells and the activation of immune pathways. This study reveals the complex functions and important roles of ferroptosis-related genes in AP and provides gene targets for further studies of AP.

Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance.

Diabetes is a life-threatening, non-communicable disease. Diabetes mellitus is a prevalent chronic disease with a significant global impact. The timely detection of diabetes in patients is necessary for an effective treatment. The primary objective of this study is to propose a novel approach for identifying type II diabetes mellitus using microarray gene data. Specifically, our research focuses on the performance enhancement of methods for detecting diabetes. Four different Dimensionality Reduction techniques, Detrend Fluctuation Analysis (DFA), the Chi-square probability density function (Chi2pdf), the Firefly algorithm, and Cuckoo Search, are used to reduce high dimensional data. Metaheuristic algorithms like Particle Swarm Optimization (PSO) and Harmonic Search (HS) are used for feature selection. Seven classifiers, Non-Linear Regression (NLR), Linear Regression (LR), Logistics Regression (LoR), Gaussian Mixture Model (GMM), Bayesian Linear Discriminant Classifier (BLDC), Softmax Discriminant Classifier (SDC), and Support Vector Machine-Radial Basis Function (SVM-RBF), are utilized to classify the diabetic and non-diabetic classes. The classifiers' performances are analyzed through parameters such as accuracy, recall, precision, F1 score, error rate, Matthews Correlation Coefficient (MCC), Jaccard metric, and kappa. The SVM (RBF) classifier with the Chi2pdf Dimensionality Reduction technique with a PSO feature selection method attained a high accuracy of 91% with a Kappa of 0.7961, outperforming all of the other classifiers.