BACKGROUND: Diabetic retinopathy (DR) is a leading cause of vision loss, with complex mechanisms. The study aimed to comprehensively explore vitreous humor of diabetic and non-diabetic individuals, paving the way for identifying the potential molecular mechanisms underlying DR.
METHODS: Vitreous samples from type 2 diabetic and non-diabetic subjects, collected post-mortem, were analyzed using liquid chromatography-mass spectrometry. Pathway enrichment and gene ontology analyses were conducted to identify dysregulated pathways and characterize protein functions.
RESULTS: Pathway analysis revealed dysregulation in multiple metabolic and signaling pathways associated with diabetes, including glycerolipid metabolism, histidine metabolism, and Wnt signaling. Gene ontology analysis identified proteins involved in inflammation, immune response dysregulation, and calcium signaling. Notably, proteins such as Inositol 1,4,5-trisphosphate receptor type 2 (ITPR2), Calcium homeostasis endoplasmic reticulum protein (CHERP), and Coronin-1A (CORO1A) were markedly upregulated in diabetic vitreous, implicating aberrant calcium signaling, inflammatory responses, and cytoskeletal reorganization in DR.
CONCLUSIONS: Our study provides valuable insights into the intricate mechanisms underlying DR and highlights the significance of inflammation, immune dysregulation, and metabolic disturbances in disease progression. Identification of specific proteins as potential biomarkers underscores the multifactorial nature of DR. Future research in this area is vital for advancing therapeutic interventions and translating findings into clinical practice.

Microarray experiments, a mainstay in gene expression analysis for nearly two decades, pose challenges due to their complexity. To address this, we introduce DExplore, a user-friendly web application enabling researchers to detect differentially expressed genes using data from NCBI\'s GEO. Developed with R, Shiny, and Bioconductor, DExplore integrates WebGestalt for functional enrichment analysis. It also provides visualization plots for enhanced result interpretation. With a Docker image for local execution, DExplore accommodates unpublished data. To illustrate its utility, we showcase two case studies on cancer cells treated with chemotherapeutic drugs. DExplore streamlines microarray data analysis, empowering molecular biologists to focus on genes of biological significance.

One of the most common oral carcinomas is oral squamous cell carcinoma (OSCC), bringing a heavy burden to global health. Although progresses have been made in the intervention of OSCC, 5 years survival of patients suffering from OSCC is poor like before regarding to the high invasiveness of OSCC, which causes metastasis and recurrence of the tumor. The relationship between pyroptosis and OSCC remains to be further investigated as pyroptosis in carcinomas has gained much attention. Herein, the key pyroptosis-related genes were identified according to The Cancer Genome Atlas (TCGA) dataset. Additionally, a prognostic model was constructed based upon three key genes (CTLA4, CD5, and IL12RB2) through least absolute shrinkage and selection operator (LASSO) analyses, as well as univariate and multivariate COX regression in OSCC. It was discovered that the high expression of these three genes was associated with the low-risk group. We also identified LAIR2 as a hub gene, whose expression negatively correlated with the risk score and the different immune cell infiltration. Finally, we proved that these three genes were independent prognostic factors linked to overall survival (OS), and reliable consequences could be predicted by this model. Our study revealed the relationship between pyroptosis and OSCC, providing insights into new treatment targets for preventing and treating OSCC.

During SARS-CoV-2 infection, the virus transforms the infected host cell into factories that produce new viral particles. As infection progresses, the infected cells undergo numerous changes in various pathways. One of these changes is the occurrence of a cytokine storm, which leads to severe symptoms. In this study, we examined the transcriptomic changes caused by COVID-19 by analyzing RNA-seq data obtained from COVID-19-positive patients as well as COVID-19-negative donors. RNA-seq data were collected for the purpose of identification of potential biomarkers associated with a different course of the disease. We analyzed the first datasets, consisting of 96 samples to validate our methods. The objective of this publication is to report the pilot results. To explore potential biomarkers related to disease severity, we conducted a differential expression analysis of human transcriptome, focusing on COVID-19 positivity and symptom severity. Given the large number of potential biomarkers we identified, we further performed pathway enrichment analysis with terms from Kyoto Encyclopedia of Genes and Genomics (KEGG) to obtain a more profound understanding of altered pathways. Our results indicate that pathways related to immune processes, response to infection, and multiple signaling pathways were affected. These findings align with several previous studies that also reported the influence of SARS-CoV-2 infection on these pathways.

BACKGROUND: The aim of this study was to investigate the common gene signatures and potential molecular mechanisms of bladder urothelial carcinoma (BLCA) and metabolic syndrome (MS).
METHODS: Transcriptome data for BLCA and MS were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was utilized to identify co-expression networks associated with BLCA and MS, and five hub genes were further screened and validated using logistic least absolute shrinkage and selection operator (LASSO) regression models and receiver operating characteristic (ROC) curve, and external dataset for validation. The relationship between the hub genes and the clinicopathological characteristics and prognosis of BLCA patients was explored in the GEO and The Cancer Genome Atlas (TCGA)-BLCA cohorts, respectively. Differences in the immune microenvironment of BLCA and MS were analyzed using the database CIBERSORT and the R package \"ssGSEA\", and the correlation between hub genes and tumor microenvironment, immune score and targeted drugs was analyzed with the help of the TCGA-BLCA cohort. Finally, BLCA single-cell RNA (scRNA) data were used to analyze the expression levels of the hub genes in various cell types of BLCA and molecular mechanisms.
RESULTS: Five hub genes were screened by WGCNA and LASSO regression analysis, namely AP2-associated protein kinase 1 (AAK1), ATP-binding cassette subfamily F member 2 (ABCF2), Mitochondrial ribosomal protein L42 (MRPL42), La-related protein 3 (SSB) and TATA-box binding protein-associated factor 10 (TAF10). Analyzed in the GEO and TCGA-BLCA cohorts, we found that the hub genes (TAF10 and ABCF2) were closely associated with the clinicopathological characteristics and prognosis of BLCA patients. In CIBERSORT, we discovered that the hub genes are closely linked to the immune microenvironment, immune score, and especially with dendritic cells (DCs). In the single-cell RNA sequencing (scRNA-seq) analysis of BLCA, we identified that SSB was significantly differentially expressed in BLCA and normal bladder tissues and that it plays an important role in the development of BLCA.
CONCLUSIONS: The interaction of BLCA with MS may be associated with several cancer pathways being activated and identified TAF10 and ABCF2 as potential biomarkers and therapeutic targets for patients with BLCA and MS.

OBJECTIVE: We aimed to identify the associated single nucleotide polymorphisms (SNPs) with gastric cancer (GC) risk by genome-wide association study (GWAS) and to explore the pathway enrichment of implicated genes and gene-sets with expression patterns.
METHODS: The study population was comprised of 1,253 GC cases and 4,827 controls from National Cancer Center and an urban community of the Korean Genome Epidemiology Study and their genotyping was performed. SNPs were annotated, and mapped to genes to prioritize by three mapping approaches by functional mapping and annotation (FUMA). The gene-based analysis and gene-set analysis were conducted with full GWAS summary data using MAGMA. Gene-set pathway enrichment test with those prioritized genes were performed.
RESULTS: In GWAS, rs2303771, a nonsynonymous variant of KLHDC4 gene was top SNP associated significantly with GC (odds ratio, 2.59; p=1.32×10-83). In post-GWAS, 71 genes were prioritized. In gene-based GWAS, seven genes were under significant p < 3.80×10-6 (0.05/13,114); DEFB108B had the lowest p=5.94×10-15, followed by FAM86C1 (p=1.74×10-14), PSCA (p=1.81×10-14), and KLHDC4 (p=5.00×10-10). In gene prioritizing, KLDHC4 was the only gene mapped with all three gene-mapping approaches. In pathway enrichment test with prioritized genes, FOLR2, PSCA, LY6K, LYPD2, and LY6E showed strong enrichment related to cellular component of membrane; a post-translation modification by synthesis of glycosylphosphatidylinositol (GPI)-anchored proteins pathway.
CONCLUSIONS: While 37 SNPs were significantly associated with the risk of GC, genes involved in signaling pathways related to purine metabolism and GPI-anchored protein in cell membrane are pinpointed to be playing important role in GC.

Zika virus (ZIKV) is an arbovirus of the Flaviviridae genus that has rapidly disseminated from across the Pacific to the Americas. Robust evidence has indicated a crucial role of ZIKV in congenital virus syndrome, including neonatal microcephaly. Moreover, emerging evidence suggests an association between ZIKV infection and the development of an extensive spectrum of central nervous system inflammatory demyelinating diseases (CNS IDD), such as multiple sclerosis-like clinical phenotypes. However, the underlying mechanisms of host-pathogen neuro-immune interactions remain to be elucidated. This study aimed to identify common transcriptional signatures between multiple sclerosis (MS) and ZIKV infection to generate molecular interaction networks, thereby leading to the identification of deregulated processes and pathways, which could give an insight of these underlying molecular mechanisms. Our investigation included publicly available transcriptomic data from MS patients in either relapse or remission (RR-MS) and datasets of subjects acutely infected by ZIKV for both immune peripheral cells and central nervous system cells. The protein-protein interaction (PPI) analysis showed upregulated AP-1 transcription factors (JUN and FOS) among the top hub and bottleneck genes in RR-MS and ZIKV data. Gene enrichment analysis retrieved a remarkable presence of ontologies and pathways linked to oxidative stress responses, immune cell function, inflammation, interleukin signaling, cell division, and transcriptional regulation commonly enriched in both scenarios. Considering the recent findings concerning AP-1 function in immunological tolerance breakdown, regulation of inflammation, and its function as an oxidative stress sensor, we postulate that the ZIKV trigger may contribute as a boost for the activation of such AP-1-regulated mechanisms that could favor the development of MS-like phenotypes following ZIKV infection in a genetically susceptible individual.

Increased antibiotic resistance in Helicobacter pylori (H. pylori), a major human pathogen, constitutes a substantial threat to human health. Understanding the pathophysiology and development of antibiotic resistance can aid our battle with the infections caused by H. pylori. The aim of this study is to discover the high-impact key regulatory mechanisms and genes involved in antimicrobial drug resistance (AMR). In this study, we constructed a functional gene interaction network by integrating multiple sources of data related to antibiotic resistant genes (number-77) from H. pylori. The gene interaction network was assortative, with a hierarchical, scale-free topology enriched in a variety of gene ontology (GO) categories and KEGG pathways. Using an iterative clustering methodology, we identified a number of communities in the AMR gene network that comprised nine genes (sodB, groEL, gyrA, recA, polA, tuf, infB, rpsJ, and gyrB) that were present at the deepest level and hence were key regulators of AMR. Further, an antibiotic-resistant gene network-based centrality analysis revealed superoxide dismutase (sodB) as a bottleneck node in the network. Our findings suggested that sodB is critically enriched in the cellular response to oxidative stress, removal of superoxide radicals, cellular oxidant detoxification processes, cellular component biogenesis, response to reactive oxygen species, urea metabolic process, nitrogen cycle metabolic process and reactive oxygen species metabolic process. We demonstrated how the sodB, which are involved in the response to reactive oxygen species, urea metabolic process, nitrogen cycle metabolic process, reactive oxygen species metabolic process, regulated by Fur gene/proteins, claim a major authority over regulation and signal propagation in the AMR.

Chronic stress contributes to the risk of developing depression; the habenula, a nucleus in epithalamus, is associated with many neuropsychiatric disorders. Using genome-wide gene expression analysis, we analyzed the transcriptome of the habenula in rats exposed to chronic restraint stress for 14 days. We identified 379 differentially expressed genes (DEGs) that were affected by chronic stress. These genes were enriched in neuroactive ligand-receptor interaction, the cAMP (cyclic adenosine monophosphate) signaling pathway, circadian entrainment, and synaptic signaling from the Kyoto Encyclopedia of Genes and Genomes pathway analysis and responded to corticosteroids, positive regulation of lipid transport, anterograde trans-synaptic signaling, and chemical synapse transmission from the Gene Ontology analysis. Based on protein-protein interaction network analysis of the DEGs, we identified neuroactive ligand-receptor interactions, circadian entrainment, and cholinergic synapse-related subclusters. Additionally, cell type and habenular regional expression of DEGs, evaluated using a recently published single-cell RNA sequencing study (GSE137478), strongly suggest that DEGs related to neuroactive ligand-receptor interaction and trans-synaptic signaling are highly enriched in medial habenular neurons. Taken together, our findings provide a valuable set of molecular targets that may play important roles in mediating the habenular response to stress and the onset of chronic stress-induced depressive behaviors.

The molecular process of cellular senescence, which is known to contribute to aging, has been implicated in several diseases of the central nervous system (CNS). The purpose of this study was to generate an unbiased survey of cellular senescence gene expression with whole brain tissues using a standardized, curated set of 88 genes associated with cellular senescence. We performed a comparative analysis of aged brains with two CNS disease models; the 5xFAD mouse model of Alzheimer\'s disease, and cuprizone-induced CNS demyelination. Each experimental group could be distinguished from the others by expression of unique subsets of cellular senescence genes, with minimal overlap between each group. Gene ontology analyses identified unique processes within cellular senescence among each group. To examine how these changes translate to the human condition, we interrogated gene expression data from publicly available databases of human aging and AD cases which also corroborated our finding that cellular senescence gene expression changes in AD differ significantly from healthy aging, although the changes in human did not always correlate with the murine models. These data provide important insight on the common and unique global changes in expression of cellular senescence genes in the CNS accompanying aging, injury or disease. Future studies may define, using more refined cellular assays, the specific cellular phenotype differences, and how disparate drivers of unique disease pathologies all seemingly culminate in a common activation of cellular senescence.