Biomarker Genes Research Articles

Engineering Gizmos for Short Cancer Genetic Fragments Discrimination.

Currently, mankind is fiercely struggling with cancer. Recently, we have been winning the battle against cancer through precision medicine and accompanying diagnostic methods, and we are raising many hopes with blockbuster drugs. It would be even better if we could read the cancer nucleotide sequence, identify them in advance, and suggest treatments simultaneously. However, this may be an impossible dream because it takes a lot of time and effort to diagnose and ensure all the long gene sequences of cancer at once. Thus, victory will be even closer if a rapid and accurate diagnosis of the cancer-specific gene biomarkers that will soon be imprinted can be made. With the advent of nanotechnology, a new short cancer diagnostic toolkit has been proposed to achieve the goal. This review presents a small diagnostic device that detects genetic fragments (simply 'Gizmo') of certain cancers. The development of numerous diagnostic methods has focused on (1) directly detecting pre-selectively targeted genes using novel diagnostic systems, and (2) indirectly detecting substantial improvements in diagnostic sensitivity only through detection signal amplification without existing gene amplification steps. Our fight against cancer is not a dream, but the result of success, and it is assumed that victory will accelerate.

A comprehensive transcriptional reference for severity and progression in spinal cord injury reveals novel translational biomarker genes

Spinal cord injury (SCI) is a devastating condition that leads to motor, sensory, and autonomic dysfunction. Current therapeutic options remain limited, emphasizing the need for a comprehensive understanding of the underlying SCI-associated molecular mechanisms. This study characterized distinct SCI phases and severities at the gene and functional levels, focusing on biomarker gene identification. Our approach involved a systematic review, individual transcriptomic analysis, gene meta-analysis, and functional characterization. We compiled a total of fourteen studies with 273 samples, leading to the identification of severity- and phase-specific biomarker genes that allow the precise classification of transcriptomic profiles. We investigated the potential transferability of severity-specific biomarkers and identified a twelve-gene signature that predicted injury prognosis from human blood samples. We also report the development of MetaSCI-app - an interactive web application designed for researchers - that allows the exploration and visualization of all generated results (https://metasci-cbl.shinyapps.io/metaSCI). Overall, we present a transcriptomic reference and provide a comprehensive framework for assessing SCI considering severity and time perspectives, all integrated into a user-friendly tool.Graphical abstract

Identification of mitophagy-related biomarkers and immune infiltration in polycystic ovarian syndrome by bioinformatic study.

Polycystic ovarian syndrome (PCOS) is a common endocrine and metabolic disorder in women of reproductive age. In this study, we aimed to investigate the potential prognostic value of mitophagy-related genes in PCOS patients and to analyze their role in immune infiltration during PCOS pathogenesis and progression. Training datasets were used for differential expression genes. Gene ontology annotations and Kyoto encyclopedia of genes and genomes signaling pathway enrichment analysis were performed. The potential biomarkers of mitophagy-related and immune infiltration in PCOS were screened by protein-protein interaction network using different algorithms, and the area under the curve was calculated to analyze their diagnostic value. The test datasets were used to validate the expression of hub genes, and receiver operating characteristic curves were used to evaluate the predictive effect of hub genes. Five hub genes: CTSD, IGF2R, ATP13A2, NAPA and GRN were identified as the potential diagnostic biomarkers of immune-mitophagy-related PCOS through five algorithms. GRN and NAPA were validated to be significantly different in oocytes and granulosa cells between primary and secondary follicles, respectively. Based on the single sample Gene Set Enrichment Analysis score, the infiltration of 4 immune cell types in PCOS was associated with PCOS and mitophagy. Specifically, the hub gene GRN showed a positive correlation with monocytes and plasmacytoid dendritic cells, while hub gene NAPA was negatively correlated with gamma delta T cell. The current study identified immune-mitophagy-related hub genes for prognostic biomarkers of PCOS, which provided an innovative insight for the prevention and treatment of PCOS.

Paired Comparison of Whole Genome Sequencing and Comprehensive Targeted Sequencing of Pancreatic Cancer Tissue.

As an increasing number of drugs are approved for targeted cancer therapy, comprehensive genomic profiling of cancer patients is frequently conducted to identify potentially relevant genetic variants. Different sequencing technologies are used for this purpose; targeted gene panels cover a selected set of biomarker genes and hotspot regions, while whole genome sequencing (WGS) delivers genome-wide data. This comparison study aimed at evaluating whether one method performs superiorly to the other regarding the detection of targetable variants. To evaluate the performance of the two sequencing technologies, we compared the results of targeted sequencing using the Ion Torrent Oncomine Comprehensive Assay Plus (OCA-Plus) panel to Illumina WGS reports in 11 patients diagnosed with pancreatic cancer (PC). All pathogenic and likely pathogenic variants, including those relevant for targeted therapy, reported by WGS and OCA-Plus, were included in the final comparison. Both techniques identified common driver mutations implicated in PC with high concordance (81%) across all variants. For variants relevant to targeted therapy, a 100% concordance between the technologies was observed. A comparable number of variants were reported by WGS and OCA-Plus, and all genetic variants relevant for targeted therapy were identified by both technologies. Thus, WGS does not provide substantial additional information in this patient group.

Chronic Exposure to Lead Causes Neurotoxicity by Generating Oxidative Stress and Inducing DNA Damages in Zebrafish Brain: Involvement of Nrf2-Keap1 Regulation and DNA Repair Pathways.

Toxicity of lead (Pb) causes several health problems in human beings. The present study reveals the potential effects of Pb on adult zebrafish (Danio rerio) brain at an environmentally relevant concentration. Pb generated reactive oxygen species-mediated oxidative stress, as evidenced by alterations of GSH, MDA levels, and CAT activity. The Nrf2-Keap1 pathway counteracted this stress as a part of cytoprotection. The gene expression and immunolocalization studies confirmed the augmentation of Nrf2 in the brain. Activation of the Nrf2-Keap1 pathway influenced downstream nqo1 and ho1 gene expressions. The alterations in histopathology and mRNA expressions of biomarker genes like hsp70 and ache revealed the toxic insults of Pb in the brain. DNA damage assay verified the genotoxic potential of Pb. The expression pattern of the candidate genes of two critical repair pathways (base excision and mismatch repair) was studied to assess the DNA damage responses. The damages in DNA caused by 15days of Pb exposure were sufficient to trigger the expression of BER (ogg1, apex1, polβ, and creb1) and MMR (msh2, msh6, and mlh1) genes to protect cells. Chronic exposure for 30days suppressed both the machinery, predisposing mutations. The overexpression of crucial tumor suppressor genes p53 and brca2 indicated their protective role against cancer progression. Understanding the molecular mechanisms underlying Pb-induced neurotoxicity and the DNA damage response may help to improve our current knowledge for the prevention of Pb poisoning.

EGFR and JAK2 were significant master gene biomarkers in invasive and noninvasive pituitary adenoma

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Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning

The constantly emerging evidence indicates a close association between coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD). However, the exact mechanisms underlying their mutual relationship remain undefined. This study aims to explore the common signature genes, potential mechanisms, diagnostic markers, and therapeutic targets for CAD and NAFLD. We downloaded CAD and NAFLD datasets from the Gene Expression Omnibus (GEO) database and analyzed the differentially expressed genes (DEGs) by limma. Protein–protein interaction (PPI) network was constructed with common DEGs (co-DEGs), and hub genes were screened by Maximal Clique Centrality (MCC) algorithm. Candidate biomarkers were selected from intersection of three machine learning algorithms. Expression levels, nomogram, the areas under the receiver operating characteristic curve (AUC) of candidate biomarkers were performed. CIBERSORT algorithm was used to assess the immune cell infiltration, and Spearman’s correlations tests were used for calculating the correlation of biomarker genes. A total of 554 overlapping DEGs associated with CAD and NAFLD were obtained by analysis of GSE113079 and GSE89632 datasets. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that the co-DEGs were significantly enriched in immune effector process, inflammation response and lipid metabolism. The PPI network generated a 1245-edge network, and top 50 genes were selected using the MCC algorithm. The candidate biomarkers were screened from intersection of machine learning in GSE89632, including CEBPA, CXCL2, JUN and FOXO1. The ROC results showed that these four biomarker genes have good diagnostic value for patients with both CAD and NAFLD. Then we explored the immune landscape, immune infiltration and the correlation between biomarker gene expression in CAD and NAFLD samples. In this study, we predict that CEBPA, CXCL2, JUN and FOXO1 can be used to diagnose CAD and NAFLD. Our study provided new insights for potential biomarkers, molecular mechanism and therapeutic targets for both diseases.

Alzheimer’s disease: an integrative bioinformatics and machine learning analysis reveals glutamine metabolism-associated gene biomarkers

BackgroundAlzheimer’s disease (AD), a hallmark of age-related cognitive decline, is defined by its unique neuropathology. Metabolic dysregulation, particularly involving glutamine (Gln) metabolism, has emerged as a critical but underexplored aspect of AD pathophysiology, representing a significant gap in our current understanding of the disease.MethodsTo investigate the involvement of GlnMgs in AD, we conducted a comprehensive bioinformatic analysis. We began by identifying differentially expressed GlnMgs from a curated list of 34 candidate genes. Subsequently, we employed GSEA and GSVA to assess the biological significance of these GlnMgs. Advanced techniques such as Lasso regression and SVM-RFE were utilized to identify key hub genes and evaluate the diagnostic potential of 14 central GlnMgs in AD. Additionally, we examined their correlations with clinical parameters and validated their expression across multiple independent AD cohorts (GSE5281, GSE37263, GSE106241, GSE132903, GSE63060).ResultsOur rigorous analysis identified 14 GlnMgs—GLS2, GLS, GLUD2, GLUL, GOT1, HAL, AADAT, PFAS, ASNSD1, PPAT, NIT2, ALDH5A1, ASRGL1, and ATCAY—as potential contributors to AD pathogenesis. These genes were implicated in vital biological processes, including lipid transport and the metabolism of purine-containing compounds, in response to nutrient availability. Notably, these GlnMgs demonstrated significant diagnostic potential, highlighting their utility as both diagnostic and prognostic biomarkers for AD.ConclusionsOur study uncovers 14 GlnMgs with potential links to AD, expanding our understanding of the disease’s molecular underpinnings and offering promising avenues for biomarker development. These findings not only enhance the molecular landscape of AD but also pave the way for future diagnostic and therapeutic innovations, potentially reshaping AD diagnostics and patient care.

Effects of endometrial embryokines on the preimplantation bovine embryo to create a gene expression signature consistent with a high competence phenotype†.

Optimal embryonic development depends upon cell-signaling molecules released by the maternal reproductive tract called embryokines. Identity of specific embryokines that enhance competence of the embryo for sustained survival is largely lacking. The current objective was to evaluate effects of three putative embryokines in cattle on embryonic development to the blastocyst stage. The molecules tested were vascular endothelial growth factor A (VEGFA), C-X-C motif chemokine ligand 12 (CXCL12), and interleukin-6 (IL6). Molecules were added from day 4 to 7.5 of culture at 50ng/mL (VEGFA and CXCL12) or 100ng/mL (IL6). Endpoints were development to the blastocyst stage, and transcript abundance for 94 specific genes involved in lineage commitment, epigenetic regulation, and other functions. Among the genes examined were eight whose transcript abundance have been related to embryo competence for survival after embryo transfer. None of the molecules increased the proportion of putative zygotes or cleaved embryos becoming blastocyst at day 7.5 of development. An embryo competence index based on a Bayesian multiple regression formula to weigh transcript abundance of the eight biomarker genes was not affected by treatment with VEGFA but was increased by both CXCL12 and IL6. Transcript abundance of five genes were modified by VEGFA, 19 by CXCL12 and 19 by IL6. A total of 11 genes were modified in a similar manner by CXCL12 and IL6. Most differentially-expressed genes for CXCL12 and IL6 were downregulated, suggesting that the embryokines may promote a less energetically-demanding metabolic state than would be the case in their absence.

Metagenomics and Metagenome-Assembled Genomes: Analysis of Cupei from Sichuan Baoning Vinegar, One of the Four Traditional Renowned Vinegars in China

The microbial community in vinegar has primarily been investigated by analyzing short reads to determine operational taxonomic units, but it is also crucial to identify metagenome-assembled genomes (MAGs). In this study, the microbial diversity and functionality in Sichuan Baoning vinegar were examined through deep metagenomic sequencing and metagenomic binning. Results revealed that the most prevalent phylum was Firmicutes, followed by Proteobacteria and unclassified Bacteria. The most abundant bacterial species was Acetilactobacillus jinshanensis, while Saccharomyces cerevisiae was the most prevalent fungal species. The predominant viral species were Hopescreekvirus LfeInf, Myoviridae sp., and Siphoviridae sp. A total of 1395 MAGs were reconstructed, with 660 of them annotated. The majority of MAGs resolved at the species level were attributed to Firmicutes (n = 308), with Acetilactobacillus jinshanensis being the most abundant. According to the average nucleotide identity values, 223 out of the 660 MAGs might represent novel species. The recovered MAGs exhibited biomarker genes indicative of the genetic potential to encode several important secondary metabolites. This study helps to uncover the microbial composition and functional potential of microbial genomes in Sichuan Baoning vinegar.

TimeFlies: an snRNA-seq aging clock for the fruit fly head sheds light on sex-biased aging.

Although multiple high-performing epigenetic aging clocks exist, few are based directly on gene expression. Such transcriptomic aging clocks allow us to extract age-associated genes directly. However, most existing transcriptomic clocks model a subset of genes and are limited in their ability to predict novel biomarkers. With the growing popularity of single-cell sequencing, there is a need for robust single-cell transcriptomic aging clocks. Moreover, clocks have yet to be applied to investigate the elusive phenomenon of sex differences in aging. We introduce TimeFlies, a pan-cell-type scRNA-seq aging clock for the Drosophila melanogaster head. TimeFlies uses deep learning to classify the donor age of cells based on genome-wide gene expression profiles. Using explainability methods, we identified key marker genes contributing to the classification, with lncRNAs showing up as highly enriched among predicted biomarkers. The top biomarker gene across cell types is lncRNA: roX1 , a regulator of X chromosome dosage compensation, a pathway previously identified as a top biomarker of aging in the mouse brain. We validated this finding experimentally, showing a decrease in survival probability in the absence of roX1 in vivo . Furthermore, we trained sex-specific TimeFlies clocks and noted significant differences in model predictions and explanations between male and female clocks, suggesting that different pathways drive aging in males and females.

Comparison of Anticancer Effects between Platinum Levetiracetam and Platinum Azidothymidine through the Expression of Biomarker Genes on Cancer Cell Lines

Background: The utilization of biomarkers is a way to assess the efficacy of recently created anticancer drugs. MiRNAs, telomerase, and Bcl-2 are extensively utilized as biomarkers for this purpose. This study aims to evaluate the comparison of the newly synthesized platinum compounds such as Platinum Azidothymidine (Pt-AZT) with Platinum Levetiracetam (Pt-Lev) on HepG2 cancer cell lines via the biomarkers. Methods: In this study, cells were divided into four groups: Group A (HDF cells) were the normal negative control group, group B were HepG2 untreated cancer cells, and groups C and D were treated cancer cells with Pt-AZT and Pt-Lev, respectively. After evaluating the LC50 for the drugs by MTT test, the relative gene expression of the biomarkers was determined by qPCR. Results: The results showed a significant decrease for antiapoptotic genes including miRNA-21 (5.1±0.014), telomerase (0.56±0.48), Bcl-2 (0.41±0.276) in group D, whereas in group C was more than group D for miRNA-21 (6.0±0.141), telomerase (3.49± 0.231), Bcl-2 (4.93±0.276) also there was a significant increase in miRNA-122 (33.97± 0.04) in group D, whereas in group C was (28.36±0.007) and so lower than group D. Most of the investigated groups showed a significant difference (p&lt;0.05). In addition, there were widespread apoptotic regions in Pt-Lev treatment compared to Pt-AZT. Conclusion: The advantages of using Pt-Lev were more powerful anticancer effects on biomarkers through inhibition of antiapoptotic and stimulation proapoptotic factors and also lower side effects and lower drug resistance than Pt-AZT; therefore, it can be considered a more effective anti-cancer therapy.

Proximity-activated guide RNA of CRISPR-Cas12a for programmable diagnostic detection and gene regulation.

The flexibility and programmability of CRISPR-Cas technology have made it one of the most popular tools for biomarker diagnostics and gene regulation. Especially, the CRISPR-Cas12 system has shown exceptional clinical diagnosis and gene editing capabilities. Here, we discovered that although the top loop of the 5' handle of guide RNA can undergo central splitting, deactivating CRISPR-Cas12a, the segments can dramatically restore CRISPR function through nucleic acid self-assembly or interactions with small molecules and aptamers. This discovery forms the basis of an engineered Cas12a system with a programmable proximity-activated guide RNA (PARC-Cas12a) that links targets of interest to dsDNA. Leveraging the efficient trans- and cis-cleavage of Cas12, our findings further inspired a detection platform design for RNAs or non-nucleic acid biomarkers, enabling highly sensitive and multiplexed analysis. We further demonstrated the feasibility of RNA-controllable gene knockout/knockdown in Escherichia coli. Notably, we successfully validated the gene regulatory capabilities of the PARC-Cas12a system within mammalian cell systems by utilizing the classical theophylline molecule-aptamer system. Our results introduce a programmable toolbox for precise diagnostics and cell regulation, allowing the development of versatile diagnostic tools, complex synthetic biological circuits, and cellular biosensors.

Interpreting Lung Cancer Health Disparity at Transcriptome Level.

Lung cancer is a leading cause of cancer-related mortality, with disparities in incidence and outcomes observed across different racial and sex groups. Understanding the genetic factors of these disparities is critical for developing targeted treatment therapies. This study aims to identify both patient-specific and cohort-specific biomarker genes that contribute to lung cancer health disparities among African American males (AAMs), European American males (EAMs), African American females (AAFs), and European American females (EAFs). The real-world data is highly imbalanced with respect to race, and the lung cancer dataset is no exception. So, classification with race labels will generate highly biased results toward the larger cohort. We developed a computational framework by designing the classification problems with disease conditions instead of races and leveraging the local interpretability of explainable AI, SHAP (SHapley Additive exPlanations). This study used three disease conditions of lung cancer, including Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LUSC), and Healthy samples (HEALTHY) to design four classification tasks: one 3-class problem (LUAD-LUSC-HEALTHY) and three 2-class problems (LUAD-LUSC, LUAD-HEALTHY, and LUSC-HEALTHY). This multiple-classification approach allows a LUAD patient to be interrogated via three classification problems, namely LUAD-LUSC-HEALTHY, LUAD-LUSC, and LUAD-HEALTHY, thus providing a robust approach of retrieving disparity information for individual patients through the local interpretation of SHAP. The proposed method successfully discovered the sets of genes and pathways related to health disparities in lung cancer between two cohorts, including AAMs vs. EAMs, AAFs vs. EAFs, AAMs vs. AAFs, and EAMs vs. EAFs. The discovered list of genes and pathways provide a short list for biological scientists to conduct wet lab experiment.

Factors influencing endometrial receptivity in women with recurrent implantation failure

BackgroundEmbryo implantation involves two key elements: a good quality embryo and receptive endometrium. Endometrial receptivity abnormalities are known as one of the possible causes of recurrent implantation failure (RIF), especially when the embryo is euploid. This study was aimed to evaluate the impact of age and other clinical factors on endometrial receptivity in women with RIF.Methods68 women with RIF (defined as at least three unsuccessful transfers of good quality embryo of at least 1BB category of blastocysts) and 49 controls (women undergoing IVF treatment because of idiopathic infertility or male factor) were included to the study. After preparation of the endometrium by the hormone replacement therapy endometrial biopsies were taken from each patient and sequenced with beREADY test TAC targeting 67 biomarker genes for endometrial receptivity. Depending on the test result patients were classified into one of four different groups: pre-receptive (n = 16), early-receptive (n = 54), receptive (n = 44) and late-receptive (n = 3).ResultsIn women with RIF pre-receptive endometrium has been detected substantially more often than in controls − 13 (19,1%) vs. 3 (6,1%) patients (p = 0,043). Early-receptive endometrium was diagnosed in the majority of patients with idiopathic infertility − 12 (66.7%) vs. 6 (33.3%) women (p = 0.042) and with polycystic ovary syndrome (PCOS) − 12 (70,6%) vs. 3 (17.7%) women (p = 0,0447). We found significant association between abnormal endometrial receptivity and patient’s age and duration of infertility. Young women were diagnosed significantly more often as normal or late-receptive, whereas older women with longer history of infertility as early-receptive and pre-receptive.ConclusionsIn patients with RIF in comparison to other women undergoing IVF procedures, patient’s age and infertility duration are the most important factors related to endometrial receptivity abnormalities, indicating that older women with a longer history of infertility may benefit the most from endometrial receptivity testing.Trial registrationNot applicable.

Identification of hypertension gene expression biomarkers based on the DeepGCFS algorithm.

Hypertension is a critical risk factor and cause of mortality in cardiovascular diseases, and it remains a global public health issue. Therefore, understanding its mechanisms is essential for treating and preventing hypertension. Gene expression data is an important source for obtaining hypertension biomarkers. However, this data has a small sample size and high feature dimensionality, posing challenges to biomarker identification. We propose a novel deep graph clustering feature selection (DeepGCFS) algorithm to identify hypertension gene biomarkers with more biological significance. This algorithm utilizes a graph network to represent the interaction information between genes, builds a GNN model, designs a loss function based on link prediction and self-supervised learning ideas for training, and allows each gene node to obtain a feature vector representing global information. The algorithm then uses hybrid clustering methods for gene module detection. Finally, it combines integrated feature selection methods to determine the gene biomarkers. The experiment revealed that all the ten identified hypertension biomarkers were significantly differentiated, and it was found that the classification performance of AUC can reach 97.50%, which is better than other literature methods. Six genes (PTGS2, TBXA2R, ZNF101, KCNJ2, MSRA, and CMTM5) have been reported to be associated with hypertension. By using GSE113439 as the validation dataset, the AUC value of classification performance was to be 95.45%, and seven of the genes (LYSMD3, TBXA2R, KLC3, GPR171, PTGS2, MSRA, and CMTM5) were to be significantly different. In addition, this algorithm's performance of gene feature vector clustering was better than other comparative methods. Therefore, the proposed algorithm has significant advantages in selecting potential hypertension biomarkers.

Exploring the Kidney-Brain Crosstalk: Biomarkers for Early Detection of Kidney Injury-Related Alzheimer's Disease.

The phenomenon of "kidney-brain crosstalk" has stimulated scholarly inquiry into the correlations between kidney injury (KI) and Alzheimer's disease (AD). Nonetheless, the precise interactions and shared mechanisms between KI and AD have yet to be fully investigated. The primary goal of this study was to investigate the link between KI and AD, with a specific focus on identifying diagnostic biomarkers for KI-related AD. The first step of the present study was to use Mendelian randomization (MR) analysis to investigate the link between KI and AD, followed by verification of in vivo and in vitro experiments. Subsequently, bioinformatics and machine learning techniques were used to identify biomarkers for KI-associated ferroptosis-related genes (FRGs) in AD, which were validated in following experiments. Moreover, the relationship between hub biomarkers and immune infiltration was assessed using CIBERSORT, and the potential drugs or small molecules associated with the core biomarkers were identified via the DGIdb database. MR analysis showed that KI may be a risk factor for AD. Experiments showed that the combination of D-galactose and aluminum chloride was found to induce both KI and AD, with ferroptosis emerging as a bridge to facilitate crosstalk between KI and AD. Besides, we identified EGFR and RELA have significant diagnostic value. These biomarkers are associated with NK_cells_resting and B_cells_memory and could be targeted for intervention in KI-related AD by treating gefitinib and plumbagin. Our study elucidates that ferroptosis may be an important pathway for kidney-brain crosstalk. Notably, gefitinib and plumbagin may be therapeutic candidates for intervening in KI-associated AD by targeting EGFR and RELA.

Persistent pre-exhausted CD8+ T cells shape the tumor immune microenvironment in anaplastic thyroid cancer.

Anaplastic thyroid cancer (ATC) is an aggressive form of cancer with poor prognosis, heavily influenced by its tumor immune microenvironment (TIME). Understanding the cellular and gene expression dynamics within the TIME is crucial for developing targeted therapies. This study analyzes the immune microenvironment of ATC and papillary thyroid cancer (PTC) using single-cell RNA sequencing (scRNA-seq). We performed a comprehensive scRNA-seq analysis on ATC and PTC samples, incorporating cell type annotation, marker gene identification and clustering based on gene expression. A specific focus was on the prevalence and biomarkers of pre-exhausted CD8+ T cells in ATC, utilizing the single-cell tumor immune atlas for immune cell characterization. The scRNA-seq analysis identified distinct immune cell populations and differentially expressed genes in ATC and PTC samples. Notably, pre-exhausted CD8+ T cells were found to be prevalent in ATC datasets. Additional immunofluorescence staining and coculture experiments with the ATC cell line identified GNLY, a member of the saposin-like protein family, as a potential biomarker for pre-exhausted CD8+ T cells in ATC. This study provides valuable insights into the immune landscape of ATC, emphasizing the prevalence of pre-exhausted CD8+ T cells and identifying GNLY as a potential biomarker. Understanding the TIME composition and the role of specific immune cells in cancer progression can inform the development of targeted immunotherapies for ATC. Future research should explore the functional implications of GNLY and other identified biomarkers in modulating the immune response in thyroid cancer. A computational pipeline was constructed to identify ATC-specific immune cell populations and differentially expressed genes via multiple independent ATC and PTC single-cell transcriptomes.A total of 221 uniquely differentially expressed genes associated with the adaptive immune response across two ATC datasets were identified.Markedly prevalent pre-exhausted CD8+ T cells in ATC datasets compared with PTC datasets were identified.One hundred fifteen potential biomarker genes of pre-exhausted CD8+ T cells were identified, with GNLY experimentally validated as the top candidate.

Plastic additives affect estrogenic pathways and lipid metabolism in precision - cut - liver slices in Atlantic cod (Gadus morhua).

The overall aim of the present study was to determine if exposure to three high volume plastic additives, including diethylhexyl phthalate (DEHP), bisphenol A (BPA) and benzotriazoles (BT), have the potential to promote adverse effects in Atlantic cod (G. morhua). Ex vivo precision cut - liver slices (PCLS) from six male juvenile Atlantic cod were exposed to four concentrations of mono-(2-ethylhexyl)-phthalate (MEHP, the main metabolite of DEHP), BPA and BT both singly and in mixtures ranging from 0.1 to 100μM (MEHP), 0.022-22μM (BPA) and 0.042-42μM (BT). Histology and transmission electron microscopy (TEM) were used to assess pathological changes and ultrastructure of the exposed liver tissue. Vitellogenin (Vtg) produced by the hepatic tissue was analyzed using ELISA, and the transcription levels of selected biomarker genes (vtg1, esr1, cyp1a, scdb, aclya, fabp1a, acox1, hnf4a and cebp) were measured using Quantitative real-time polymerase chain reaction (Q-PCR). An estrogenic effect was observed with a significant upregulation of the vtg1 and esr1 genes and increase in Vtg protein synthesis following exposure to BPA and a mixture of the selected compounds. The hnf4a showed a significant downregulation following mixture exposure, where the BPA was suspected to be the main driver for this response although not inducing a significant downregulation in the single component exposure. There was no significant difference between the mixture exposure and the individual compound exposures, nevertheless a tendency of an antagonistic mixture effect for the biomarkers of estrogenic effect (vtg1, esr1 and Vtg), and possibly synergistic or additive effect on the lipid metabolism related gene hnf4a, warrants further investigation.

Scientific knowledge about gene expression in ruminants under heat stress - A scientometric review.

Heat stress can alter the expression of genes in the individual's molecular response. The identification of these genes makes it possible to better understand the molecular response, identifying biomarker genes and indirect response pathways that can help with genetic improvement studies, animal welfare, separating more thermotolerant varieties and mitigating the effects of heat stress. The aim of this scientometric review was to characterize the state of the art of scientific research into gene expression in ruminants under heat stress, to define the most studied species, biology systems and genes, as well as the related biological pathways and processes. The articles for the dataset were compiled in the Web of Science database, refined individually and analyzed using the CiteSpace, RStudio, Excel and GraphPad Prism programs and the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. The publications formed a data set containing 271 articles and an H-index of 37. The number of publications increased from 2011. The countries with the highest frequency of publications are India, the United States, China and Brazil, the ruminant species are cattle, buffaloes, sheep and goats, all zootechnical interest, and biology systems was reproduction, blood and lactation, due to the economic importance of the quality and quantity of production, to the ease of collecting and possibility of studies in vitro. Cattle have been extensively studied in comparison to other ruminants. The HSP70 gene has been the most studied, followed by the HSP family, HSF, BAX, TLR and BCL-2, these genes can be molecular markers of heat stress. The main pathways and biological processes of genes were in cattle the cancer pathway; in goats the Mixed, incl. myd88-dependent toll-like receptor signaling pathway, and lipopolys; in sheep the oxidoreductase; and in buffalo it was the BCL-2 family. The molecular responses are still recent and have not been established.