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Frontiers in Immunology 2022Glypican 2 (GPC2), a member of glypican (GPC) family genes, produces proteoglycan with a glycosylphosphatidylinositol anchor. It has shown its ascending significance in...
BACKGROUND
Glypican 2 (GPC2), a member of glypican (GPC) family genes, produces proteoglycan with a glycosylphosphatidylinositol anchor. It has shown its ascending significance in multiple cancers such as neuroblastoma, malignant brain tumor, and small-cell lung cancer. However, no systematic pan-cancer analysis has been conducted to explore its function in diagnosis, prognosis, and immunological prediction.
METHODS
By comprehensive use of datasets from The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), Genotype-Tissue Expression Project (GTEx), cBioPortal, Human Protein Atlas (HPA), UALCAN, StarBase, and Comparative Toxicogenomics Database (CTD), we adopted bioinformatics methods to excavate the potential carcinogenesis of GPC2, including dissecting the correlation between GPC2 and prognosis, gene mutation, immune cell infiltration, and DNA methylation of different tumors, and constructed the competing endogenous RNA (ceRNA) networks of GPC2 as well as explored the interaction of GPC2 with chemicals and genes.
RESULTS
The results indicated that GPC2 was highly expressed in most cancers, except in pancreatic adenocarcinoma, which presented at a quite low level. Furthermore, GPC2 showed the early diagnostic value in 16 kinds of tumors and was positively or negatively associated with the prognosis of different tumors. It also verified that GPC2 was a gene associated with most immune-infiltrating cells in pan-cancer, especially in thymoma. Moreover, the correlation with GPC2 expression varied depending on the type of immune-related genes. Additionally, GPC2 gene expression has a correlation with DNA methylation in 20 types of cancers.
CONCLUSION
Through pan-cancer analysis, we discovered and verified that GPC2 might be useful in cancer detection for the first time. The expression level of GPC2 in a variety of tumors is significantly different from that of normal tissues. In addition, the performance of GPC2 in tumorigenesis and tumor immunity also confirms our conjecture. At the same time, it has high specificity and sensitivity in the detection of cancers. Therefore, GPC2 can be used as an auxiliary indicator for early tumor diagnosis and a prognostic marker for many types of tumors.
Topics: Adenocarcinoma; Biomarkers, Tumor; Glypicans; Humans; Pancreatic Neoplasms; Prognosis
PubMed: 35345673
DOI: 10.3389/fimmu.2022.857308 -
Frontiers in Pharmacology 2021Rheumatoid arthritis is a chronic autoimmune disease characterized by persistent hyperplasia of the synovial membrane and progressive erosion of articular cartilage....
Rheumatoid arthritis is a chronic autoimmune disease characterized by persistent hyperplasia of the synovial membrane and progressive erosion of articular cartilage. Disequilibrium between the proliferation and death of RA fibroblast-like synoviocytes (RA-FLSs) is the critical factor in progression of RA. Naringin has been reported to exert anti-inflammatory and antioxidant effect in acute and chronic animal models of RA. However, the therapeutic effect and underlying mechanisms of naringin in human RA-FLS remain unclear. Based on network pharmacology, the corresponding targets of naringin were identified using SwissTargetPrediction database, STITCH database, and Comparative Toxicogenomics Database. Deferentially expressed genes (DEGs) in RA were obtained from the GEO database. The protein-protein interaction (PPI) networks of intersected targets were constructed using the STRING database and visualized using Cytoscape. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, and the pathways directly related to pathogenesis of RA were integrated manually. Further, studies were carried out based on network pharmacology. 99 target genes were intersected between targets of naringin and DEGs. The PPI network and topological analysis indicated that IL-6, MAPK8, MMP-9, TNF, and MAPK1 shared the highest centrality among all. GO analysis and KEGG analysis indicated that target genes were mostly enriched in (hsa05200) pathways in cancer, (hsa05161) hepatitis B, (hsa04380) osteoclast differentiation, (hsa04151) PI3K-Akt signaling pathway, and (hsa05142) Chagas disease (American trypanosomiasis). studies revealed that naringin exposure was found to promote apoptosis of RA-FLS, increased the activation of caspase-3, and increased the ratio of Bax/Bcl-2 in a dose-dependent manner. Furthermore, treatment of naringin attenuated the production of inflammatory cytokines and matrix metalloproteinases (MMPs) in TNF-ɑ-induced RA-FLS. Moreover, treatment of naringin inhibited the phosphorylation of Akt and ERK in RA-FLS. Network pharmacology provides a predicative strategy to investigate the therapeutic effects and mechanisms of herbs and compounds. Naringin inhibits inflammation and MMPs production and promotes apoptosis in RA-FLS PI3K/Akt and MAPK/ERK signaling pathways.
PubMed: 34054546
DOI: 10.3389/fphar.2021.672054 -
International Journal of Molecular... Jul 2022Pollution is defined as the presence in or introduction of a substance into the environment that has harmful or poisonous effects [...].
Pollution is defined as the presence in or introduction of a substance into the environment that has harmful or poisonous effects [...].
Topics: Biomarkers; Risk Assessment; Toxicogenetics
PubMed: 35955413
DOI: 10.3390/ijms23158280 -
Mitochondrion Jan 2022Although mitochondrial dysfunction is the known cause of primary mitochondrial disease, mitochondrial dysfunction is often difficult to measure and prove, especially... (Review)
Review
Although mitochondrial dysfunction is the known cause of primary mitochondrial disease, mitochondrial dysfunction is often difficult to measure and prove, especially when biopsies of affected tissue are not available. In order to identify blood biomarkers of mitochondrial dysfunction, we reviewed studies that measured blood biomarkers in genetically, clinically or biochemically confirmed primary mitochondrial disease patients. In this way, we were certain that there was an underlying mitochondrial dysfunction which could validate the biomarker. We found biomarkers of three classes: 1) functional markers measured in blood cells, 2) biochemical markers of serum/plasma and 3) DNA markers. While none of the reviewed single biomarkers may perfectly reveal all underlying mitochondrial dysfunction, combining biomarkers that cover different aspects of mitochondrial impairment probably is a good strategy. This biomarker panel may assist in the diagnosis of primary mitochondrial disease patients. As mitochondrial dysfunction may also play a significant role in the pathophysiology of multifactorial disorders such as Alzheimer's disease and glaucoma, the panel may serve to assess mitochondrial dysfunction in complex multifactorial diseases as well and enable selection of patients who could benefit from therapies targeting mitochondria.
Topics: Biomarkers; Humans; Mitochondrial Diseases
PubMed: 34740866
DOI: 10.1016/j.mito.2021.10.008 -
Frontiers in Immunology 2022The coexistence of neuromyelitis optica spectrum disorder (NMOSD) with other autoimmune diseases has been well recognized. However, the causal association between these...
OBJECTIVES
The coexistence of neuromyelitis optica spectrum disorder (NMOSD) with other autoimmune diseases has been well recognized. However, the causal association between these two conditions has not been fully studied. The etiology and therapies of NMOSD coexisting with autoimmune diseases also need to be elucidated.
METHODS
We performed two-sample Mendelian randomization (MR) analysis to examine the causality. Genome-wide association (GWAS) summary data from NMOSD, autoimmune thyroid disease (AITD), systemic lupus erythematosus (SLE), and Sjogren's syndrome (SS) were used to identify genetic instruments. Causal single-nucleotide polymorphisms (SNPs) were annotated and searched for cis-expression quantitative trait loci (cis-eQTL) data. Pathway enrichment analysis was performed to identify the mechanism of NMOSD coexisting with AITD, SLE, and SS. Potential therapeutic chemicals were searched using the Comparative Toxicogenomics Database.
RESULTS
The MR analysis found that AITD, SLE, and SS were causally associated with NMOSD susceptibility, but not vice versa. Gene Ontology (GO) enrichment analysis revealed that MHC class I-related biological processes and the interferon-gamma-mediated signaling pathway may be involved in the pathogenesis of NMOSD coexisting with AITD, SLE, and SS. A total of 30 chemicals were found which could inhibit the biological function of cis-eQTL genes.
CONCLUSIONS
Our findings could help better understand the etiology of NMOSD and provide potential therapeutic targets for patients with coexisting conditions.
Topics: Humans; Autoimmune Diseases; Genome-Wide Association Study; Interferon-gamma; Lupus Erythematosus, Systemic; Neuromyelitis Optica; Sjogren's Syndrome
PubMed: 36248893
DOI: 10.3389/fimmu.2022.959469 -
Life (Basel, Switzerland) Mar 2024We are exposed to a mixture of environmental man-made and natural xenobiotics. We experience a wide spectrum of environmental exposure in our lifetime, including the... (Review)
Review
We are exposed to a mixture of environmental man-made and natural xenobiotics. We experience a wide spectrum of environmental exposure in our lifetime, including the effects of xenobiotics on gametogenesis and gametes that undergo fertilization as the starting point of individual development and, moreover, in utero exposure, which can itself cause the first somatic or germline mutation necessary for breast cancer (BC) initiation. Most xenobiotics are metabolized or/and bioaccumulate and biomagnify in our tissues and cells, including breast tissues, so the xenobiotic metabolism plays an important role in BC initiation and progression. Many considerations necessitate a more valuable explanation regarding the molecular mechanisms of action of xenobiotics which act as genotoxic and epigenetic carcinogens. Thus, exposomics and the exposome concept are based on the diversity and range of exposures to physical factors, synthetic chemicals, dietary components, and psychosocial stressors, as well as their associated biologic processes and molecular pathways. Existing evidence for BC risk (BCR) suggests that food-borne chemical carcinogens, air pollution, ionizing radiation, and socioeconomic status are closely related to breast carcinogenesis. The aim of this review was to depict the dynamics and kinetics of several xenobiotics involved in BC development, emphasizing the role of new omics fields related to BC exposomics, such as environmental toxicogenomics, epigenomics and interactomics, metagenomics, nutrigenomics, nutriproteomics, and nutrimiRomics. We are mainly focused on food and nutrition, as well as endocrine-disrupting chemicals (EDCs), involved in BC development. Overall, cell and tissue accumulation and xenobiotic metabolism or biotransformation can lead to modifications in breast tissue composition and breast cell morphology, DNA damage and genomic instability, epimutations, RNA-mediated and extracellular vesicle effects, aberrant blood methylation, stimulation of epithelial-mesenchymal transition (EMT), disruption of cell-cell junctions, reorganization of the actin cytoskeleton, metabolic reprogramming, and overexpression of mesenchymal genes. Moreover, the metabolism of xenobiotics into BC cells impacts almost all known carcinogenic pathways. Conversely, in our food, there are many bioactive compounds with anti-cancer potential, exerting pro-apoptotic roles, inhibiting cell cycle progression and proliferation, migration, invasion, DNA damage, and cell stress conditions. We can conclude that exposomics has a high potential to demonstrate how environmental exposure to xenobiotics acts as a double-edged sword, promoting or suppressing tumorigenesis in BC.
PubMed: 38541726
DOI: 10.3390/life14030402 -
Advances in Clinical and Experimental... Aug 2023The majority of Americans, accounting for 51% of the population, take 2 or more drugs daily. Unfortunately, nearly 100,000 people die annually as a result of adverse...
The majority of Americans, accounting for 51% of the population, take 2 or more drugs daily. Unfortunately, nearly 100,000 people die annually as a result of adverse drug reactions (ADRs), making it the 4th most common cause of mortality in the USA. Drug-drug interactions (DDls) and their impact on patients represent critical challenges for the healthcare system. To reduce the incidence of ADRs, this study focuses on identifying DDls using a machine-learning approach. Drug-related information was obtained from various free databases, including DrugBank, BioGRID and Comparative Toxicogenomics Database. Eight similarity matrices between drugs were created as covariates in the model in order to assess their infiuence on DDls. Three distinct machine learning algorithms were considered, namely, logistic regression (LR), extreme Gradient Boosting (XGBoost) and neural network (NN). Our study examined 22 notable drugs and their interactions with 841 other drugs from DrugBank. The accuracy of the machine learning approaches ranged from 68% to 78%, while the F1 scores ranged from 78% to 83%. Our study indicates that enzyme and target similarity are the most significant parameters in identifying DDls. Finally, our data-driven approach reveals that machine learning methods can accurately predict DDls and provide additional insights in a timely and cost-effective manner.
Topics: Humans; Drug Interactions; Drug-Related Side Effects and Adverse Reactions; Algorithms; Databases, Factual; Machine Learning
PubMed: 37589227
DOI: 10.17219/acem/169852 -
Biomedicine & Pharmacotherapy =... Jul 2023More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards... (Review)
Review
More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.
Topics: Humans; Artificial Intelligence; Precision Medicine; Toxicogenetics; Algorithms; Technology
PubMed: 37121152
DOI: 10.1016/j.biopha.2023.114784 -
Genes Jan 2021Thyroid cancer is not just a common type of cancer, it is the most frequently diagnosed endocrine malignancy worldwide [...].
Thyroid cancer is not just a common type of cancer, it is the most frequently diagnosed endocrine malignancy worldwide [...].
Topics: Biomarkers, Tumor; Disease Management; Genetic Association Studies; Genetic Predisposition to Disease; Genetic Variation; Humans; Thyroid Neoplasms
PubMed: 33498289
DOI: 10.3390/genes12020126 -
F1000Research 2021Nanotoxicology is a relatively new field of research concerning the study and application of nanomaterials to evaluate the potential for harmful effects in parallel with...
Nanotoxicology is a relatively new field of research concerning the study and application of nanomaterials to evaluate the potential for harmful effects in parallel with the development of applications. Nanotoxicology as a field spans materials synthesis and characterisation, assessment of fate and behaviour, exposure science, toxicology / ecotoxicology, molecular biology and toxicogenomics, epidemiology, safe and sustainable by design approaches, and chemoinformatics and nanoinformatics, thus requiring scientists to work collaboratively, often outside their core expertise area. This interdisciplinarity can lead to challenges in terms of interpretation and reporting, and calls for a platform for sharing of best-practice in nanotoxicology research. The F1000Research Nanotoxicology collection, introduced via this editorial, will provide a place to share accumulated best practice, via original research reports including no-effects studies, protocols and methods papers, software reports and living systematic reviews, which can be updated as new knowledge emerges or as the domain of applicability of the method, model or software is expanded. This editorial introduces the Nanotoxicology Collection in . The aim of the collection is to provide an open access platform for nanotoxicology researchers, to support an improved culture of data sharing and documentation of evolving protocols, biological and computational models, software tools and datasets, that can be applied and built upon to develop predictive models and move towards nanotoxicology and nanoinformatics. Submissions will be assessed for fit to the collection and subjected to the F1000Research open peer review process.
Topics: Nanostructures; Research Design; Software
PubMed: 34853679
DOI: 10.12688/f1000research.75113.1