Investigations into the therapeutic potential of Astragalus Mongholicus (AM, huáng qí) and Largehead Atractylodes (LA, bái zhú) reveal significant efficacy in mitigating the onset and progression of knee osteoarthritis (KOA), albeit with an elusive mechanistic understanding. This study delineates the primary bioactive constituents and their molecular targets within the AM-LA synergy by harnessing the comprehensive Traditional Chinese Medicine (TCM) network databases, including TCMSP, TCMID, and ETCM. Furthermore, an analysis of 3 gene expression datasets, sourced from the gene expression omnibus database, facilitated the identification of differential genes associated with KOA. Integrating these findings with data from 5 predominant databases yielded a refined list of KOA-associated targets, which were subsequently aligned with the gene signatures corresponding to AM and LA treatment. Through this alignment, specific molecular targets pertinent to the AM-LA therapeutic axis were elucidated. The construction of a protein-protein interaction network, leveraging the shared genetic markers between KOA pathology and AM-LA intervention, enabled the identification of pivotal molecular targets via the topological analysis facilitated by CytoNCA plugins. Subsequent GO and KEGG enrichment analyses fostered the development of a holistic herbal-ingredient-target network and a core target-signal pathway network. Molecular docking techniques were employed to validate the interaction between 5 central molecular targets and their corresponding active compounds within the AM-LA complex. Our findings suggest that the AM-LA combination modulates key biological processes, including cellular activity, reactive oxygen species modification, metabolic regulation, and the activation of systemic immunity. By either augmenting or attenuating crucial signaling pathways, such as MAPK, calcium, and PI3K/AKT pathways, the AM-LA dyad orchestrates a comprehensive regulatory effect on immune-inflammatory responses, cellular proliferation, differentiation, apoptosis, and antioxidant defenses, offering a novel therapeutic avenue for KOA management. This study, underpinned by gene expression omnibus gene chip analyses and network pharmacology, advances our understanding of the molecular underpinnings governing the inhibitory effects of AM and LA on KOA progression, laying the groundwork for future explorations into the active components and mechanistic pathways of TCM in KOA treatment.

OBJECTIVE: Most cases of hepatocellular carcinoma (HCC) arise as a consequence of cirrhosis. In this study, our objective is to construct a comprehensive diagnostic model that investigates the diagnostic markers distinguishing between cirrhosis and HCC.
METHODS: Based on multiple GEO datasets containing cirrhosis and HCC samples, we used lasso regression, random forest (RF)-recursive feature elimination (RFE) and receiver operator characteristic analysis to screen for characteristic genes. Subsequently, we integrated these genes into a multivariable logistic regression model and validated the linear prediction scores in both training and validation cohorts. The ssGSEA algorithm was used to estimate the fraction of infiltrating immune cells in the samples. Finally, molecular typing for patients with cirrhosis was performed using the CCP algorithm.
RESULTS: The study identified 137 differentially expressed genes (DEGs) and selected five significant genes (CXCL14, CAP2, FCN2, CCBE1 and UBE2C) to construct a diagnostic model. In both the training and validation cohorts, the model exhibited an area under the curve (AUC) greater than 0.9 and a kappa value of approximately 0.9. Additionally, the calibration curve demonstrated excellent concordance between observed and predicted incidence rates. Comparatively, HCC displayed overall downregulation of infiltrating immune cells compared to cirrhosis. Notably, CCBE1 showed strong correlations with the tumour immune microenvironment as well as genes associated with cell death and cellular ageing processes. Furthermore, cirrhosis subtypes with high linear predictive scores were enriched in multiple cancer-related pathways.
CONCLUSIONS: In conclusion, we successfully identified diagnostic markers distinguishing between cirrhosis and hepatocellular carcinoma and developed a novel diagnostic model for discriminating the two conditions. CCBE1 might exert a pivotal role in regulating the tumour microenvironment, cell death and senescence.

Next-generation risk assessment relies on mechanistic data from new approach methods, including transcriptome data. Various technologies, such as high-throughput targeted sequencing methods and microarray technologies based on hybridization with complementary probes, are used to determine differentially expressed genes (DEGs). The integration of data from different technologies requires a good understanding of the differences arising from the use of various technologies.To better understand the differences between the TempO-Seq platform and Affymetrix chip technology, whole-genome data for the volatile compound dimethylamine were compared. Selected DEGs were also confirmed using RTqPCR validation. Although the overlap of DEGs between TempO-Seq and Affymetrix was no higher than 37%, a comparison of the gene regulation in terms of log2fold changes revealed a very high concordance. RTqPCR confirmed the majority of DEGs from either platform in the examined dataset. Only a few conflicts were found (11%), while 22% were not confirmed, and 3% were not detected.Despite the observed differences between the two platforms, both can be validated using RTqPCR. Here we highlight some of the differences between the two platforms and discuss their applications in toxicology.

A differentially methylated region (DMR) is a genomic region that has significantly different methylation patterns between biological conditions. Identifying DMRs between different biological conditions is critical for developing disease biomarkers. Although methods for detecting DMRs in microarray data have been introduced, developing methods with high precision, recall, and accuracy in determining the true length of DMRs remains a challenge. In this study, we propose a normalized kernel-weighted model to account for similar methylation profiles using the relative probe distance from \"nearby\" CpG sites. We also extend this model by proposing an array-adaptive version in attempt to account for the differences in probe spacing between Illumina\'s Infinium 450K and EPIC bead array respectively. We also study the asymptotic results of our proposed statistic. We compare our approach with a popular DMR detection method via simulation studies under large and small treatment effect settings. We also discuss the susceptibility of our method in detecting the true length of the DMRs under these two settings. Lastly, we demonstrate the biological usefulness of our method when combined with pathway analysis methods on oral cancer data. We have created an R package called idDMR, downloadable from GitHub repository with link: https://github.com/DanielAlhassan/idDMR, that allows for the convenient implementation of our array-adaptive DMR method.

UNASSIGNED: To investigate the molecular mechanism of pathological keratinization in the chronic phase of ocular surface (OS) diseases.
UNASSIGNED: In this study, a comprehensive gene expression analysis was performed using oligonucleotide microarrays on OS epithelial cells obtained from three patients with pathological keratinization (Stevens-Johnson syndrome [n = 1 patient], ocular cicatricial pemphigoid [n = 1 patient], and anterior staphyloma [n = 1 patient]). The controls were three patients with conjunctivochalasis. The expression in some transcripts was confirmed using quantitative real-time PCR.
UNASSIGNED: Compared to the controls, 3118 genes were significantly upregulated by a factor of 2 or more than one-half in the pathological keratinized epithelial cells (analysis of variance P < 0.05). Genes involved in keratinization, lipid metabolism, and oxidoreductase were upregulated, while genes involved in cellular response, as well as known transcription factors (TFs), were downregulated. Those genes were further analyzed with respect to TFs and retinoic acid (RA) through gene ontology analysis and known reports. The expression of TFs MYBL2, FOXM1, and SREBF2, was upregulated, and the TF ELF3 was significantly downregulated. The expression of AKR1B15, RDH12, and CRABP2 (i.e., genes related to RA, which is known to suppress keratinization) was increased more than twentyfold, whereas the expression of genes RARB and RARRES3 was decreased by 1/50. CRABP2, RARB, and RARRES3 expression changes were also confirmed by qRT-PCR.
UNASSIGNED: In pathological keratinized ocular surfaces, common transcript changes, including abnormalities in vitamin A metabolism, are involved in the mechanism of pathological keratinization.

Chromosomal microarray, including single-nucleotide polymorphism (SNP) array and array comparative genomic hybridization (aCGH), enables the detection of DNA copy-number loss and/or gain associated with unbalanced chromosomal aberrations. In addition, SNP array and aCGH with SNP component also detect copy-neutral loss of heterozygosity (CN-LOH). Here we describe the chromosomal microarray procedure from the sample preparation using extracted DNA to the scanning of the array chip.

BACKGROUND: Extracellular vesicle-derived (EV)-miRNAs have potential to serve as biomarkers for the diagnosis of various diseases. miRNA microarrays are widely used to quantify circulating EV-miRNA levels, and the preprocessing of miRNA microarray data is critical for analytical accuracy and reliability. Thus, although microarray data have been used in various studies, the effects of preprocessing have not been studied for Toray\'s 3D-Gene chip, a widely used measurement method. We aimed to evaluate batch effect, missing value imputation accuracy, and the influence of preprocessing on measured values in 18 different preprocessing pipelines for EV-miRNA microarray data from two cohorts with amyotrophic lateral sclerosis using 3D-Gene technology.
RESULTS: Eighteen different pipelines with different types and orders of missing value completion and normalization were used to preprocess the 3D-Gene microarray EV-miRNA data. Notable results were suppressed in the batch effects in all pipelines using the batch effect correction method ComBat. Furthermore, pipelines utilizing missForest for missing value imputation showed high agreement with measured values. In contrast, imputation using constant values for missing data exhibited low agreement.
CONCLUSIONS: This study highlights the importance of selecting the appropriate preprocessing strategy for EV-miRNA microarray data when using 3D-Gene technology. These findings emphasize the importance of validating preprocessing approaches, particularly in the context of batch effect correction and missing value imputation, for reliably analyzing data in biomarker discovery and disease research.

BACKGROUND: The issue of male fertility is becoming increasingly common due to genetic differences inherited over generations. Gene expression and evaluation of non-coding RNA (ncRNA), crucial for sperm development, are significant factors. This gene expression can affect sperm motility and, consequently, fertility. Understanding the intricate protein interactions that play essential roles in sperm differentiation and development is vital. This knowledge could lead to more effective treatments and interventions for male infertility.
METHODS: Our research aim to identify new and key genes and ncRNA involved in non-obstructive azoospermia (NOA), improving genetic diagnosis and offering more accurate estimates for successful sperm extraction based on an individual\'s genotype.
RESULTS: We analyzed the transcript of three NOA patients who tested negative for genetic sperm issues, employing comprehensive genome-wide analysis of approximately 50,000 transcript sequences using microarray technology. This compared gene expression profiles between NOA sperm and normal sperm. We found significant gene expression differences: 150 genes were up-regulated, and 78 genes were down-regulated, along with 24 ncRNAs up-regulated and 13 ncRNAs down-regulated compared to normal conditions. By cross-referencing our results with a single-cell genomics database, we identified overexpressed biological process terms in differentially expressed genes, such as \"protein localization to endosomes\" and \"xenobiotic transport.\" Overrepresented molecular function terms in up-regulated genes included \"voltage-gated calcium channel activity,\" \"growth hormone-releasing hormone receptor activity,\" and \"sialic acid transmembrane transporter activity.\" Analysis revealed nine hub genes associated with NOA sperm: RPL34, CYB5B, GOL6A6, LSM1, ARL4A, DHX57, STARD9, HSP90B1, and VPS36.
CONCLUSIONS: These genes and their interacting proteins may play a role in the pathophysiology of germ cell abnormalities and infertility.

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The transcriptome profile is a representative phenotype-based descriptor of compounds, widely acknowledged for its ability to effectively capture compound effects. However, the presence of batch differences is inevitable. Despite the existence of sophisticated statistical methods, many of them presume a substantial sample size. How should we design a transcriptome analysis to obtain robust compound profiles, particularly in the context of small datasets frequently encountered in practical scenarios? This study addresses this question by investigating the normalization procedures for transcriptome profiles, focusing on the baseline distribution employed in deriving biological responses as profiles. Firstly, we investigated two large GeneChip datasets, comparing the impact of different normalization procedures. Through an evaluation of the similarity between response profiles of biological replicates within each dataset and the similarity between response profiles of the same compound across datasets, we revealed that the baseline distribution defined by all samples within each batch under batch-corrected condition is a good choice for large datasets. Subsequently, we conducted a simulation to explore the influence of the number of control samples on the robustness of response profiles across datasets. The results offer insights into determining the suitable quantity of control samples for diminutive datasets. It is crucial to acknowledge that these conclusions stem from constrained datasets. Nevertheless, we believe that this study enhances our understanding of how to effectively leverage transcriptome profiles of compounds and promotes the accumulation of essential knowledge for the practical application of such profiles.