Microarray Analysis of Visceral Adipose Tissue in Obese Women Reveals Common Crossroads Among Inflammation, Metabolism, Addictive Behaviors, and Cancer: AKT3 and MAPK1 Cross Point in Obesity.

Background: Visceral adipose tissue (VAT) abnormalities are directly associated with obesity-associated disorders. The underlying mechanisms that confer increased pathological risk to VAT in obesity have not been fully described. Methods: A case-control study was conducted that included 10 women with obesity (36.80 ± 7.39 years, BMI ≥ 30 kg/m2) and 10 women of normal weight (32.70 ± 9.45 years, BMI < 24.9 kg/m2). RNA was extracted from greater omentum biopsies, and, using a DNA microarray, differential transcriptomic expression of VAT in women with obesity was evaluated taking as a reference that of women with normal weight. The differentially expressed genes (DEGs) were classified into functional biological processes and signaling pathways; moreover, the protein-protein interaction (PPI) networks were integrated for a deeper analysis of the pathways and genes involved in the central obesity-associated disorders. The expression of TNF-α, MAPK, and AKT proteins was also quantified in VAT. Results: The VAT of women with obesity had 3808 DEGs, mainly associated with the cellular process of inflammation and carbohydrates and lipid metabolism. Overexpressed genes were associated with inflammatory, metabolic, hormonal, neuroendocrine, carcinogenic, and infectious pathways. Cellular processes related to addictive behaviors were notable. MAPK and PI3K-AKT pathways were overexpressed, and Mapk1 and Akt3 genes were common crossing points among obesity-associated disorders' pathways. The increased expression of MAPK, AKT, and TNF proteins was confirmed in the VAT of women with obesity. Conclusion: VAT confers a complex and blended pathogenic transcriptomic profile in obese patients, where abnormal processes are mainly controlled by activating intracellular signaling pathways that exhibit a high degree of redundancy. Identifying shared cross points between those pathways could allow specific targeting treatments to exert a widespread effect over multiple pathogenic processes.

Identification of hub genes through integrated single-cell and microarray transcriptome analysis in osteoarthritic meniscus.

BACKGROUND: Osteoarthritis (OA) is marked by the progressive degradation of joint cartilage and subchondral bone. The precise molecular mechanisms driving meniscus deterioration in OA, especially at the single-cell level, remain poorly understood. METHOD: We analyzed two datasets from the GEO database, GSE220243 and GSE98918, focusing on meniscus tissue sequencing data from OA and non-OA patients. The standard Seurat procedure was employed to process single-cell data and identify differentially expressed genes (DEGs). Immune cell infiltration was assessed using the Microenvironment Cell Populations (MCP) counter and CIBERSORT algorithms. For the microarray data, DEGs were identified with the limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using ClusterProfiler. The overlapping DEGs from both datasets were imported into Cytoscape to generate protein-protein interaction (PPI) networks and identify hub genes. Transcription factor (TF) and miRNA interaction networks were analyzed using NetworkAnalyst, and gene-related predictive drugs were enriched through the DSigDB platform. RESULT: After quality control, 34,763 cells from the OA patients and 34,145 cells from the healthy controls were analyzed. UMAP identified and SingleR annotated 14 cell clusters. The 10 largest cell clusters were selected for further analysis. The OA group exhibited a notable increase in macrophages and a reduction in cytotoxic lymphocytes and endothelial cells in the meniscus. In GSE98918, 220 DEGs were identified, and the MCODE plug-in in Cytoscape pinpointed a key module containing 12 candidate genes. The MCC methodfiltered the top 20 DEGs in each GSE220243 cluster. Overlapping DEGs from GSE220243 and GSE98918 identified COL1A1, COL3A1, COL5A2, COL6A3, LOX, and VEGFA as significantly decreased in OA, with COL3A1, COL5A2, LOX, and VEGFA upregulated in meniscal chondrocytes. The interaction network highlighted 3 key miRNAs and 13 shared TFs. Ten gene-related predictive drug molecules were identified. CONCLUSION: This research highlights crucial genes in the OA meniscus and uncovers their differing regulatory patterns between chondrocytes and non-chondrocytes. These findings enhance our understanding of the molecular mechanisms driving OA pathogenesis and aid in identifying potential drug targets.

Detection of chromosome 5q interstitial deletion of 5q14.3-q31.1 by chromosome microarray analysis in a second-trimester fetus with multiple congenital anomalies and a literature review of chromosome 5q interstitial deletion syndrome.

OBJECTIVE: We present application of chromosome microarray analysis (CMA) in the detection of chromosome 5q interstitial deletion of 5q14.3-q31.1 in a second-trimester fetus with multiple congenital anomalies on fetal ultrasound. CASE REPORT: A 30-year-old, gravida 2, para 1, woman was found to have multiple anomalies in the fetus at 14 weeks of gestation by prenatal ultrasound screening. The fetal anomalies included echogenic bowel, a left neck cyst, hypoplastic left heart, single umbilical artery and bilateral clubfeet. The pregnancy was subsequently terminated, and a 64-g malformed fetus was delivered. CMA by array comparative genomic hybridization (aCGH) analysis on the DNA extracted from umbilical cord revealed the result of arr 5q14.3q31.1 (83,557,042-130,841,093) × 1.0 [GRCh37] with a 47.3-Mb 5q14.3-q31.1 deletion encompassing 95 OMIM genes including NR2F1, MEF2C, APC, KCNN2 and FBN2. Quantitative fluorescent polymerase chain reaction (QF-PCR) analysis on the DNA extracted from parental bloods and umbilical cord using the informative markers of D5S2496 (5q21.3) and D5S818 (5q23.2) showed that the fetus inherited only one maternal allele, indicating a paternal origin of the interstitial 5q deletion in the fetus. CONCLUSION: CMA is useful for genetic investigation of unknown congenital anomalies detected by fetal ultrasound.

Porphyromonas gingivalis triggers microglia activation and neurodegenerative processes through NOX4.

Periodontitis and infections with periodontal bacteria have been highlighted as risk factors for dementia. In recent years, attention has been drawn to the role of microglia cells in neurodegenerative diseases. However, there is limited knowledge of the influence of periodontal bacteria on microglia cells. The aim of the present study was to investigate the interactions between the periodontal bacteria Porphyromonas gingivalis and microglia cells and to unravel whether these interactions could contribute to the pathology of Alzheimer's disease. We found, through microarray analysis, that stimulation of microglia cells with P. gingivalis resulted in the upregulation of several Alzheimer's disease-associated genes, including NOX4. We also showed that P. gingivalis lipopolysaccharides (LPS) mediated reactive oxygen species (ROS) production and interleukin 6 (IL-6) and interleukin 8 (IL-8) induction via NOX4 in microglia. The viability of neurons was shown to be reduced by conditioned media from microglia cells stimulated with P. gingivalis LPS and the reduction was NOX4 dependent. The levels of total and phosphorylated tau in neurons were increased by conditioned media from microglia cells stimulated with P. gingivalis or LPS. This increase was NOX4-dependent. In summary, our findings provide us with a potential mechanistic explanation of how the periodontal pathogen P. gingivalis could trigger or exacerbate AD pathogenesis.

Deciphering disease through glycan codes: leveraging lectin microarrays for clinical insights.

Glycosylation, a crucial posttranslational modification, plays a significant role in numerous physiological and pathological processes. Lectin microarrays, which leverage the high specificity of lectins for sugar binding, are ideally suited for profiling the glycan spectra of diverse and complex biological samples. In this review, we explore the evolution of lectin detection technologies, as well as the applications and challenges of lectin microarrays in analyzing the glycome profiles of various clinical samples, including serum, saliva, tissues, sperm, and urine. This review not only emphasizes significant advancements in the high-throughput analysis of polysaccharides but also provides insight into the potential of lectin microarrays for diagnosing and managing diseases such as tumors, autoimmune diseases, and chronic inflammation. We aim to provide a clear, concise, and comprehensive overview of the use of lectin microarrays in clinical settings, thereby assisting researchers in conducting clinical studies in glycobiology.

Comprehensive microarray analysis of severe preeclampsia placenta to identify differentially expressed genes, biological pathways, hub genes, and their related non-coding RNAs.

INTRODUCTION: Preeclampsia (PE) is a serious pregnancy-related complication caused by high blood pressure in pregnant women. The severe form has more devastating effects. According to the growing evidence, the placenta is a crucial component in the pathogenesis of PE, and eliminating it will alleviate symptoms. METHODS: GEO's severe preeclampsia placenta microarray datasets; GSE147776, GSE66273, GSE102897, and GSE10588, were chosen to identify differentially expressed genes (DEGs) in different biological pathways. The analysis of hub genes and related non-coding RNAs was done as well. RESULTS: A total of 347 DEGs with adj p-value <0.05 and ǀlog2FoldChangeǀ> 0.5 were discovered between severe PEs and healthy pregnancies, including 204 over-expressed genes and 143 under-expressed genes. The MCC method identified ISG15, IFI44L, MX2, OAS2, MX1, FN1, LDHA, ITGB3, TKT, HK2 genes as the top ten hub genes. Interactions between hub genes and noncoding RNAs were also conducted. The most enriched pathways were as follows; HIF-1 signaling pathway; Pathways in cancer; Alanine, aspartate and glutamate metabolism; Arginine biosynthesis; Human papillomavirus infection; Glycolysis/Gluconeogenesis; Central carbon metabolism in cancer; Valine, leucine and isoleucine degradation; Cysteine and methionine metabolism; and Galactose metabolism. DISCUSSION: This is a secondary data analysis conducted on severe preeclampsia placenta to identify differentially expressed genes, biological pathways, hub-genes, and related noncoding RNAs. Functional studies are crucial to understanding the precise role of these genes in the pathogenesis of PE. Also, accepting a gene as a diagnostic or prognostic marker for early diagnosis and management of PE requires multiple lines of evidence.

Identification of biomarkers in multiple myeloma: A comprehensive study combining microarray analysis and Mendelian randomization.

Despite remarkable advancements in the treatment of multiple myeloma (MM), relapse remains a challenge. However, the mechanisms underlying this disease remain unclear. This study aimed to identify potential biomarkers that could open new avenues for MM treatment. Microarray data and clinical characteristics of patients with MM were obtained from the Gene Expression Omnibus database. Differential expression analysis and protein-protein interaction (PPI) network construction were used to identify hub genes associated with MM. Predictive performance was further assessed using receiver operating characteristic curves and nomogram construction. Functional enrichment analysis was conducted to investigate possible mechanisms. Mendelian randomization (MR) was used to evaluate the causal relationship between the crucial gene and MM risk. Topological analysis of the PPI network revealed five hub genes associated with MM, with myeloperoxidase (MPO) being the key gene owing to its highest degree and area under the curve values. MPO showed significant differences between patients with MM and controls across all datasets. Functional enrichment analysis revealed a strong association between MPO and immune-related pathways in MM. MR analysis confirmed a causal relationship between MPO and the risk of MM. By integrating microarray analysis and MR, we successfully identified and validated MPO as a promising biomarker for MM that is potentially implicated in MM pathogenesis and progression through immune-related pathways.

Identification of Autophagy-Related Genes Involved in Intervertebral Disc Degeneration by Microarray Data Analysis.

BACKGROUND: Nucleus pulposus cells survive in a hypoxic, acidic, nutrient-poor, and hypotonic microenvironment. Consequently, they maintain low proliferation and undergo autophagy to protect themselves from cellular stress. Therefore, we aimed to identify autophagy-related biomarkers involved in intervertebral disc degeneration pathogenesis. METHODS: Autophagy-related differentially expressed genes were derived from the intersection between the public GSE147383 microarray data set to identify differentially expressed genes and online databases to identify autophagy-related genes. Furthermore, we assessed their biological functions with gene annotation and enrichment analysis in the Metscape portal. Then, the STRING database and Cytoscape software allowed inferring a protein-protein interaction (PPI) network and identifying hub genes. In addition, to predict transcription factors that may regulate the hub genes, we used the GeneMANIA website. Finally, the competing endogenous RNA prediction tools and Cytoscape were also used to construct an mRNA-miRNA-lncRNA network. RESULTS: A total of 123 autophagy-related differentially expressed genes were identified, they were mainly involved in phosphoinositide 3-kinase-Akt signaling, autophagy animal, and apoptosis pathways. Nine were identified as hub genes (PTEN, MYC, CTNNB1, JUN, BECN1, ERBB2, FOXO3, ATM, and FN1) and 36 transcription factors were associated with them. Finally, an autophagy-associated competing endogenous RNA network was constructed based on the 9 hub genes. CONCLUSIONS: Nine hub genes were identified and a network of competing endogenous RNA associated with autophagy was established. They can be used as autophagy-related biomarkers of intervertebral disc degeneration and for further exploration.

Gene Expression Profiling Reveals Fundamental Sex-Specific Differences in SIRT3-Mediated Redox and Metabolic Signaling in Mouse Embryonic Fibroblasts.

Sirt-3 is an important regulator of mitochondrial function and cellular energy homeostasis, whose function is associated with aging and various pathologies such as Alzheimer's disease, Parkinson's disease, cardiovascular diseases, and cancers. Many of these conditions show differences in incidence, onset, and progression between the sexes. In search of hormone-independent, sex-specific roles of Sirt-3, we performed mRNA sequencing in male and female Sirt-3 WT and KO mouse embryonic fibroblasts (MEFs). The aim of this study was to investigate the sex-specific cellular responses to the loss of Sirt-3. By comparing WT and KO MEF of both sexes, the differences in global gene expression patterns as well as in metabolic and stress responses associated with the loss of Sirt-3 have been elucidated. Significant differences in the activities of basal metabolic pathways were found both between genotypes and between sexes. In-depth pathway analysis of metabolic pathways revealed several important sex-specific phenomena. Male cells mount an adaptive Hif-1a response, shifting their metabolism toward glycolysis and energy production from fatty acids. Furthermore, the loss of Sirt-3 in male MEFs leads to mitochondrial and endoplasmic reticulum stress. Since Sirt-3 knock-out is permanent, male cells are forced to function in a state of persistent oxidative and metabolic stress. Female MEFs are able to at least partially compensate for the loss of Sirt-3 by a higher expression of antioxidant enzymes. The activation of neither Hif-1a, mitochondrial stress response, nor oxidative stress response was observed in female cells lacking Sirt-3. These findings emphasize the sex-specific role of Sirt-3, which should be considered in future research.

Investigating the potential of Juglans regia phytoconstituents for the treatment of cervical cancer utilizing network biology and molecular docking approach.

The fourth most frequent type of cancer in women and the leading cause of mortality for females worldwide is cervical cancer. Traditionally, medicinal plants have been utilized to treat various illnesses and ailments. The molecular docking method is used in the current study to look into the phytoconstituents of Juglans regia's possible anticancer effects on cervical cancer target proteins. This work uses the microarray dataset analysis of GSE63678 from the NCBI Gene Expression Omnibus database to find differentially expressed genes. Furthermore, protein-protein interactions of differentially expressed genes were constructed using network biology techniques. The top five hub genes (IGF1, FGF2, ESR1, MYL9, and MYH11) are then determined by computing topological parameters with Cytohubba. In addition, molecular docking research was performed on Juglans regia phytocompounds that were extracted from the IMPPAT database versus hub genes that had been identified. Utilizing molecular dynamics, simulation confirmed that prioritized docked complexes with low binding energies were stable.

Prenatal chromosomal microarray analysis and karyotyping in fetuses with isolated choroid plexus cyst: A retrospective case-control study.

OBJECTIVES: To evaluate the the diagnostic yield of chromosomal microarray analysis (CMA) in fetuses with isolated CPC (iCPC). METHODS: A total of 315 fetuses with iCPC (iCPC group) and 364 fetuses without abnormal ultrasound findings (control group) were recruited between July 2014 to March 2018. RESULTS: The overall diagnostic yield of chromosomal abnormalities by CMA and karyotyping in iCPC group was up to 4.1 %, higher than 1.4 % in the control group, p < 0.05. The detection rate of pathogenic or likely pathogenic copy number variants (CNVs) with clinical significance by CMA in iCPC group (1.3 %) was higher than in control group (0 %), p < 0.05. According to the type of chromosome abnormalities, the missed diagnosis rate of non-invasive prenatal testing (NIPT) was 1.6 % in our study. CONCLUSIONS: The presence of iCPC on ultrasound examination suggests a potential indication for genetic counseling. Karyotyping and chromosomal microarray analysis may be considered for fetuses with iCPC. It is important to be aware of the limitations of non-invasive prenatal testing, as there is a possibility of residual risk.

Multiple primary dermatofibrosarcoma protuberans tumors in a single patient with chromosomal microarray analysis: A case report and review.

Dermatofibrosarcoma protuberans (DFSP) is a cutaneous sarcoma with a high propensity for local invasion and recurrence. Although it is a rare event, the occurrence of multiple tumors in a single patient raises a diagnostic dilemma, as metastatic disease should be differentiated from multiple primary malignant events. In more than 90% of DFSP, a pathogenic t(17;22) translocation leads to the expression of COL1A1::PDGFB fusion transcripts. Karyotype analysis, fluorescence in situ hybridization, and RT-PCR can be useful ancillary studies in detecting this characteristic rearrangement, and sequencing of the fusion transcript can be used to support a clonal origin in metastatic and multifocal disease. However, previous reports have demonstrated variable sensitivity of these assays, in part due to the high sequence variability of the COL1A1::PDGFB fusion. Here, we report a patient who developed two distinct DFSP tumors over the course of 7 years. Chromosomal microarray analysis identified distinctive genomic alterations in the two tumors, supporting the occurrence of multiple primary malignant events.

Chromosomal Microarray Analysis in Fetuses With Ultrasonographic Soft Markers: A Meta-Analysis of the Current Evidence.

BACKGROUND: Ultrasonographic soft markers are normal variants, rather than fetal abnormalities, and guidelines recommend a detailed survey of fetal anatomy to determine the necessity of antenatal karyotyping. Anecdotal reports have described cases with ultrasonographic soft markers in which chromosomal microarray analysis (CMA) revealed pathogenic copy number variants (CNVs) despite normal results on conventional karyotyping, but CMA for ultrasonographic soft markers remains a matter of debate. In this systematic review, we evaluated the clinical significance of CMA for pregnancies with isolated ultrasonographic soft markers and a normal fetal karyotype. METHODS: An electronic search was conducted by an experienced librarian through the MEDLINE, Embase, and Cochrane CENTRAL databases. We reviewed 3,338 articles (3,325 identified by database searching and 13 by a hand search) about isolated ultrasonographic soft markers, and seven ultrasonographic markers (choroid plexus cysts, echogenic bowel, echogenic intracardiac focus, hypoplastic nasal bone, short femur [SF], single umbilical artery, and urinary tract dilatation) were included for this study. RESULTS: Seven eligible articles were included in the final review. Pathogenic or likely pathogenic CNVs were found in fetuses with isolated ultrasonographic soft markers and a normal karyotype. The overall prevalence of pathogenic or likely pathogenic CNVs was 2.0% (41 of 2,048). The diagnostic yield of CMA was highest in fetuses with isolated SF (9 of 225, 3.9%). CONCLUSION: CMA could aid in risk assessment and pregnancy counseling in pregnancies where the fetus has isolated ultrasonographic soft markers along with a normal karyotype.

Hybrid whale algorithm with evolutionary strategies and filtering for high-dimensional optimization: Application to microarray cancer data.

The standard whale algorithm is prone to suboptimal results and inefficiencies in high-dimensional search spaces. Therefore, examining the whale optimization algorithm components is critical. The computer-generated initial populations often exhibit an uneven distribution in the solution space, leading to low diversity. We propose a fusion of this algorithm with a discrete recombinant evolutionary strategy to enhance initialization diversity. We conduct simulation experiments and compare the proposed algorithm with the original WOA on thirteen benchmark test functions. Simulation experiments on unimodal or multimodal benchmarks verified the better performance of the proposed RESHWOA, such as accuracy, minimum mean, and low standard deviation rate. Furthermore, we performed two data reduction techniques, Bhattacharya distance and signal-to-noise ratio. Support Vector Machine (SVM) excels in dealing with high-dimensional datasets and numerical features. When users optimize the parameters, they can significantly improve the SVM's performance, even though it already works well with its default settings. We applied RESHWOA and WOA methods on six microarray cancer datasets to optimize the SVM parameters. The exhaustive examination and detailed results demonstrate that the new structure has addressed WOA's main shortcomings. We conclude that the proposed RESHWOA performed significantly better than the WOA.

Serum antibody screening using glycan arrays.

Humans and other animals produce a diverse collection of antibodies, many of which bind to carbohydrate chains, referred to as glycans. These anti-glycan antibodies are a critical part of our immune systems' defenses. Whether induced by vaccination or natural exposure to a pathogen, anti-glycan antibodies can provide protection against infections and cancers. Alternatively, when an immune response goes awry, antibodies that recognize self-glycans can mediate autoimmune diseases. In any case, serum anti-glycan antibodies provide a rich source of information about a patient's overall health, vaccination history, and disease status. Glycan microarrays provide a high-throughput platform to rapidly interrogate serum anti-glycan antibodies and identify new biomarkers for a variety of conditions. In addition, glycan microarrays enable detailed analysis of the immune system's response to vaccines and other treatments. Herein we review applications of glycan microarray technology for serum anti-glycan antibody profiling.

Machine Learning Methods for Gene Selection in Uveal Melanoma.

Uveal melanoma (UM) is the most common primary intraocular malignancy with a limited five-year survival for metastatic patients. Limited therapeutic treatments are currently available for metastatic disease, even if the genomics of this tumor has been deeply studied using next-generation sequencing (NGS) and functional experiments. The profound knowledge of the molecular features that characterize this tumor has not led to the development of efficacious therapies, and the survival of metastatic patients has not changed for decades. Several bioinformatics methods have been applied to mine NGS tumor data in order to unveil tumor biology and detect possible molecular targets for new therapies. Each application can be single domain based while others are more focused on data integration from multiple genomics domains (as gene expression and methylation data). Examples of single domain approaches include differentially expressed gene (DEG) analysis on gene expression data with statistical methods such as SAM (significance analysis of microarray) or gene prioritization with complex algorithms such as deep learning. Data fusion or integration methods merge multiple domains of information to define new clusters of patients or to detect relevant genes, according to multiple NGS data. In this work, we compare different strategies to detect relevant genes for metastatic disease prediction in the TCGA uveal melanoma (UVM) dataset. Detected targets are validated with multi-gene score analysis on a larger UM microarray dataset.

Identification of new subtypes and potential genetic signatures in triple-negative breast cancer using weighted gene co-expression network analysis.

OBJECTIVE: Triple-negative breast cancer (TNBC) is a heterogeneous disease with aggressive behavior and poor prognosis. Here, we used gene expression profiling to define new subtypes of TNBC, which may improve prevention and treatment through personalized medicine. MATERIALS AND METHODS: Gene expression profiles from the public datasets GSE76250, GSE61724, GSE61723, and GES76275 were subjected to co-expression analysis to identify differentially expressed genes (DEGs) between TNBC and non-TNBC tissues. Consistency clustering was used to define TNBC subtypes, whose correlation with gene modules was analyzed. Enrichment analysis was used to identify module genes' biological functions and pathways. Single-sample gene set enrichment analysis was used to assess immune cell infiltration in the different TNBC subtypes, and the ChAMP package was used to examine methylation sites in TNBC. RESULTS: A total of 4,958 DEGs in TNBC were identified, which showed the same expression differences across all datasets as in the dataset GSE76250 and clustered into 9 co-expression modules. TNBC samples clustered into two subtypes based on nine hub genes from the modules. Class I showed the most significant correlation with module 1, whose genes were related mainly to interleukin-1 response, while class II showed the most significant correlation with module 6, whose genes were related mainly to the transforming growth factor-ß pathway. Class I was significantly enriched in cell cycle and DNA replication, and tumors of this subtype showed lower immune cell infiltration than class II tumors. Tumor infiltration by Th2 cells correlated positively with the expression of MCM10 and negatively with the expression of PREX2. A greater methylation of CIDEC, DLC1, EDNRB, EGR2 and SRPK1 correlated with better prognosis. CONCLUSIONS: Class I TNBC, for which a useful biomarker is MCM10, may be associated with a worse prognosis than class II TNBC, for which PREX2 may serve as a biomarker.

Gene Expression Profiling of Post Mortem Midbrain of Parkinson's Disease Patients and Healthy Controls.

Parkinson's disease (PD) stands as the most prevalent degenerative movement disorder, marked by the degeneration of dopaminergic neurons in the substantia nigra of the midbrain. In this study, we conducted a transcriptome analysis utilizing post mortem mRNA extracted from the substantia nigra of both PD patients and healthy control (CTRL) individuals. Specifically, we acquired eight samples from individuals with PD and six samples from CTRL individuals, with no discernible pathology detected in the latter group. RNA sequencing was conducted using the TapeStation 4200 system from Agilent Technologies. A total of 16,148 transcripts were identified, with 92 mRNAs displaying differential expression between the PD and control groups. Specifically, 33 mRNAs were significantly up-regulated, while 59 mRNAs were down-regulated in PD compared to the controls. The identification of statistically significant signaling pathways, with an adjusted p-value threshold of 0.05, unveiled noteworthy insights. Specifically, the enriched categories included cardiac muscle contraction (involving genes such as ATPase Na+/K+ transporting subunit beta 2 (ATP1B2), solute carrier family 8 member A1 (SLC8A1), and cytochrome c oxidase subunit II (COX2)), GABAergic synapse (involving GABA type A receptor-associated protein-like 1 (GABARAPL1), G protein subunit beta 5 (GNB5), and solute carrier family 38 member 2 (SLC38A2), autophagy (involving GABARAPL1 and tumor protein p53-inducible nuclear protein 2 (TP53INP2)), and Fc gamma receptor (FcγR) mediated phagocytosis (involving amphiphysin (AMPH)). These findings uncover new pathophysiological dimensions underlying PD, implicating genes associated with heart muscle contraction. This knowledge enhances diagnostic accuracy and contributes to the advancement of targeted therapies.

Microarray Analysis to Determine Gene Expression Changes in Zebrafish Embryos.

Chemical exposure in humans begins from the zygote stage and continues throughout the development of the embryo and the fetus. Zebrafish are one of the most powerful model organisms used in many research areas, including genetics, environmental toxicology, development, DNA damage and repair, cancer, and other diseases. Among the advantages that facilitate the use of zebrafish as a model for studies are features such as high homology with the human genome, small size, and high reproductive potential in short periods. The use of zebrafish embryos in research has increased rapidly due to their advantageous properties, including extrauterine development and the transparent feature of the embryos. However, there are thousands of genes that can be encountered in research, and in this case, the workforce is too much. This workload has been alleviated with the developed technologies. Microarray is one of these technologies. An important parameter in this assay is the RIN value. The RIN value ranges from 1 to 10, indicating mRNA degradation, and therefore helps to decide whether to continue the study. In this chapter, microarray analysis, which is one of the main techniques used in the determination of gene expression in zebrafish embryos, is described.

An improved binary particle swarm optimization algorithm for clinical cancer biomarker identification in microarray data.

BACKGROUND AND OBJECTIVE: The limited number of samples and high-dimensional features in microarray data make selecting a small number of features for disease diagnosis a challenging problem. Traditional feature selection methods based on evolutionary algorithms are difficult to search for the optimal set of features in a limited time when dealing with the high-dimensional feature selection problem. New solutions are proposed to solve the above problems. METHODS: In this paper, we propose a hybrid feature selection method (C-IFBPFE) for biomarker identification in microarray data, which combines clustering and improved binary particle swarm optimization while incorporating an embedded feature elimination strategy. Firstly, an adaptive redundant feature judgment method based on correlation clustering is proposed for feature screening to reduce the search space in the subsequent stage. Secondly, we propose an improved flipping probability-based binary particle swarm optimization (IFBPSO), better applicable to the binary particle swarm optimization problem. Finally, we also design a new feature elimination (FE) strategy embedded in the binary particle swarm optimization algorithm. This strategy gradually removes poorer features during iterations to reduce the number of features and improve accuracy. RESULTS: We compared C-IFBPFE with other published hybrid feature selection methods on eight public datasets and analyzed the impact of each improvement. The proposed method outperforms other current state-of-the-art feature selection methods in terms of accuracy, number of features, sensitivity, and specificity. The ablation study of this method validates the efficacy of each component, especially the proposed feature elimination strategy significantly improves the performance of the algorithm. CONCLUSIONS: The hybrid feature selection method proposed in this paper helps address the issue of high-dimensional microarray data with few samples. It can select a small subset of features and achieve high classification accuracy on microarray datasets. Additionally, independent validation of the selected features shows that those chosen by C-IFBPFE have strong correlations with disease phenotypes and can identify important biomarkers from data related to biomedical problems.