Identification Of Potential Biomarkers Research Articles

Identification of potential chemical biomarkers of hexaconazole using in vitro metabolite profiling in rat and human liver microsomes and in vivo confirmation through urinary excretion study in rats

Hexaconazole (HEX) is an azole fungicide widely used in agricultural practices across various countries and numerous studies have reported the toxic effects of HEX, such as endocrine disruption, immunotoxicity, neurotoxicity and carcinogenicity. Despite its widespread agricultural use and toxic effects, the metabolism of HEX is not completely understood, and information on urinary elimination of HEX or its metabolites is limited. Therefore, in the present study, we aimed to identify HEX metabolites in rat and human liver microsomes followed by their in vivo confirmation using a urinary excretion study in rats to identify potential candidate for exposure biomarkers for human biomonitoring studies. From the in vitro assay, a total of 12 metabolites were observed, where the single oxidation metabolites (M5 and M6) were the most abundant metabolites in both rat and human liver microsomes. The triple oxidation followed by dehydration metabolite, M8 (which could also be hexaconazole acid or hydroxy keto-hexaconazole), and the double oxidation metabolite (M9) were the major metabolites found in rat urine and were detectable in rat urine longer than the parent. These metabolites increased with decreasing concentrations of HEX in the rat urine samples. Therefore, metabolites M8, M9 and M5 could be pursued further as potential biomarkers for assessing and monitoring human exposure to HEX.

Proteomics is advancing the understanding of stallion sperm biology.

The mammalian ejaculate is very well suited to proteomics studies. As such, research concerning sperm proteomics is offering a huge amount of new information on the biology of spermatozoa. Among domestic animals, horses represent a species of special interest, in which reproductive technologies and a sizeable market of genetic material have grown exponentially in the last decade. Studies using proteomic approaches have been conducted in recent years, showing that proteomics is a potent tool to dig into the biology of the stallion spermatozoa. The aim of this review is to present an overview of the research conducted, and how these studies have improved our knowledge of stallion sperm biology. The main outcomes of the research conducted so far have been an improved knowledge of metabolism, and its importance in sperm functions, the impact of different technologies on the sperm proteome, and the identification of potential biomarkers. Moreover, proteomics of seminal plasma and phosphoproteomics are identified as areas of major interest.

Identification of potential biomarkers and pathways for asthenozoospermia by bioinformatics analysis and experiments

BackgroundAsthenozoospermia, a type of male infertility, is primarily caused by dysfunctional sperm mitochondria. Despite previous bioinformatics analysis identifying potential key lncRNAs, miRNAs, hub genes, and pathways associated with asthenospermia, there is still a need to explore additional molecular mechanisms and potential biomarkers for this condition.MethodsWe integrated data from Gene Expression Omnibus (GEO) (GSE22331, GSE34514, and GSE160749) and performed bioinformatics analysis to identify differentially expressed genes (DEGs) between normozoospermia and asthenozoospermia. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to gain insights into biological processes and signaling pathways. Weighted Gene Co-expression Network Analysis (WGCNA) identified gene modules associated with asthenozoospermia. Expression levels of key genes were assessed using datasets and experimental data. Gene Set Enrichment Analysis (GSEA) and correlation analysis identified pathways associated with the hub gene and explore the relationship between the ZNF764 and COQ9 and mitochondrial autophagy-related genes. Competitive endogenous RNA (ceRNA) networks were constructed, and in vitro experiments using exosome samples were conducted to validate this finding.ResultsCOQ9 was identified as a marker gene in asthenozoospermia, involved in autophagy, ATP-dependent chromatin remodeling, endocytosis, and cell cycle, etc. The ceRNA regulatory network (LINC00893/miR-125a-5p/COQ9) was constructed, and PCR demonstrated that LINC00893 and COQ9 were downregulated in asthenozoospermia, while miR-125a-5p and m6A methylation level of LINC00893 were upregulated in asthenozoospermia compared to normozoospermic individuals.ConclusionThe ceRNA regulatory network (LINC00893/miR-125a-5p/COQ9) likely plays a crucial role in the mechanism of asthenozoospermia. However, further functional experiments are needed to fully understand its significance.

Overexpression of COL11A1 confers tamoxifen resistance in breast cancer

Breast cancer is the most commonly diagnosed malignancy and benefits from endocrine agents such as tamoxifen. However, the development of drug resistance in cancerous cells often leads to recurrence, thus limiting the therapeutic benefit. Identification of potential biomarkers that can predict response to tamoxifen and recognize patients who will clinically benefit from this therapy is urgently needed. In this study, we report that high collagen type XI alpha 1 (COL11A1) expression was associated with poor therapeutic response and prognosis in breast cancer patients treated with tamoxifen. To confirm the role of COL11A1 in the development of tamoxifen resistance, we established MCF-7/COL11A1 and T47D/COL11A1 cell lines, which stably expressed COL11A1. Compared with parental MCF-7 and T47D, MCF-7/COL11A1 and T47D/COL11A1 cells were more resistant to 4-OHT-induced growth inhibition. Moreover, the level of COL11A1 expression was upregulated in tamoxifen-resistant MCF-7/TamR and T47D/TamR cell lines, and depletion of COL11A1 markedly sensitized the cells to 4-OHT in vitro and in vivo. Interestingly, the level of estrogen receptor α (ERα) expression was elevated, probably due to the increased COL11A1 in TamR cells. In addition, knockdown of COL11A1 decreased the expression of ERα and its downstream target genes. Overall, our findings suggest that overexpressed COL11A1 contributes to tamoxifen resistance, and targeting COL11A1 holds great promise for reversing endocrine resistance.

Identification of potential biomarkers for pancreatic ductal adenocarcinoma: a bioinformatics analysis.

PDA is an aggressive cancer with a 5-year survival rate, which is very low. There is no effective prognosis or therapy for PDA because of the lack of target biomarkers. The objective of this article is to identify the target biomarkers for PDA using a bioinformatics approach. In this work, we have analysed the three microarray datasets from the NCBI GEO database. We used the Geo2R tool to analyse the microarray data with the Benjamini and Hochberg false discovery rate method, and the significance level cut-off was set to 0.05. We have identified 659 DEGs from the datasets. There are a total of 15 hub genes that were selected from the PPI network constructed using the STRING application. Furthermore, these 15 genes were evaluated on PDA patients using TCGA and GTEx databases in (GEPIA). The online tool DAVID was used to analyse the functional annotation information for the DEGs. The functional pathway enrichment was performed on the GO and KEGG. The hub genes were mainly enriched for cell division, chromosome segregation, protein binding and microtubule binding. Further, the gene alteration study was performed using the cBioportal tool and screened out six hub genes (ASPM, CENPF, BIRC5, TTK, DLGAP5, and TOP2A) with a high alteration rate in PDA samples. Furthermore, Kaplan-Meier survival analysis was performed on the six hub genes and identified poor-survival outcomes that may be involved in tumorigenesis and PDA development. So, this study concludes that, these six hub genes may be potential prognostic biomarkers for PDA.

Identification of potential biomarkers of leprosy: A study based on GEO datasets.

Leprosy has a high rate of cripplehood and lacks available early effective diagnosis methods for prevention and treatment, thus novel effective molecule markers are urgently required. In this study, we conducted bioinformatics analysis with leprosy and normal samples acquired from the GEO database(GSE84893, GSE74481, GSE17763, GSE16844 and GSE443). Through WGCNA analysis, 85 hub genes were screened(GS > 0.7 and MM > 0.8). Through DEG analysis, 82 up-regulated and 3 down-regulated genes were screened(|Log2FC| > 3 and FDR < 0.05). Then 49 intersection genes were considered as crucial and subjected to GO annotation, KEGG pathway and PPI analysis to determine the biological significance in the pathogenesis of leprosy. Finally, we identified a gene-pathway network, suggesting ITK, CD48, IL2RG, CCR5, FGR, JAK3, STAT1, LCK, PTPRC, CXCR4 can be used as biomarkers and these genes are active in 6 immune system pathways, including Chemokine signaling pathway, Th1 and Th2 cell differentiation, Th17 cell differentiation, T cell receptor signaling pathway, Natural killer cell mediated cytotoxicity and Leukocyte transendothelial migration. We identified 10 crucial gene markers and related important pathways that acted as essential components in the etiology of leprosy. Our study provides potential targets for diagnostic biomarkers and therapy of leprosy.

Expression of Anoikis-Related Genes and Potential Biomarkers in Colon Cancer Based on RNA-seq and scRNA-seq.

Colon cancer (CC) is a malignant tumor in the colon. Despite some progress in the early detection and treatment of CC in recent years, some patients still experience recurrence and metastasis. Therefore, it is urgent to better predict the prognosis of CC patients and identify new biomarkers. Recent studies have shown that anoikis-related genes (ARGs) play a significant role in the progression of many tumors. Hence, it is essential to confirm the role of ARGs in the development and treatment of CC by integrating scRNA-seq and transcriptome data. This study integrated transcriptome and single-cell sequencing (scRNA-seq) data from CC samples to evaluate patient stratification, prognosis, and ARG expression in different cell types. Specifically, differential expression of ARGs was identified through consensus clustering to classify CC subtypes. Subsequently, a CC risk model composed of CDKN2A, NOX4, INHBB, CRYAB, TWIST1, CD36, SERPINE1, and MMP3 was constructed using prognosis-related ARGs. Finally, using scRNA-seq data of CC, the expression landscape of prognostic genes in different cell types and the relationship between important immune cells and other cells were explored. Through the above analysis, two CC subtypes were identified, showing significant differences in prognosis and clinical factors. Subsequently, a risk model comprising aforementioned genes successfully categorized all CC samples into two risk groups, which also exhibited significant differences in prognosis, clinical factors, involved pathways, immune landscape, and drug sensitivity. Multiple pathways (cell adhesion molecules (CAMs), and extracellular matrix (ECM) receptor interaction) and immune cells/immune functions (B cell naive, dendritic cell activate, plasma cells, and T cells CD4 memory activated) related to CC were identified. Furthermore, it was found that prognostic genes were highly expressed in various immune cells, and B cells exhibited more and stronger interaction pathways with other cells. The results of this study may provide references for personalized treatment and potential biomarker identification in CC.

Genetic-driven biomarkers for liver fibrosis through bioinformatic approach

BackgroundLiver fibrosis is a widespread chronic liver ailment linked to substantial mortality and limited therapeutic options. An in-depth comprehension of the genetic underpinnings of liver fibrogenesis is crucial for the development of effective management and treatment approaches.ResultsUsing bioinformatics tools and the DisGeNET database, we pinpointed 105 genes significantly linked to liver fibrosis. Subsequently, we conducted functional assessments, incorporating gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and the STRING database, to construct protein–protein interaction networks (PPI) for these 105 liver fibrosis-associated genes. These analyses were executed via the WebGestalt 2019 online platform. We employed Cytoscape plugins, MCODE, and CytoHubba, to identify potential biomarker genes from these functional networks. Noteworthy hub genes encompassed TGF-β1, MMP2, CTNNB1, FGF2, IL6, LOX, CTGF, SMAD3, ALB, and VEGFA. TGF-β1 and MMP-2 exhibited substantial promise as liver fibrosis biomarkers, as denoted by their high systemic scores determined through the MCC algorithm in the CytoHubba methodology.ConclusionsIn summary, this study presents a robust genetic biomarker strategy that may prove invaluable in the identification of potential liver fibrosis biomarkers.

Identification of Potential Biomarkers for Major Depressive Disorder: Based on Integrated Bioinformatics and Clinical Validation

Identification of Potential Biomarkers for Major Depressive Disorder: Based on Integrated Bioinformatics and Clinical Validation

Assessment of the Developmental Neurotoxicity of Environmental Chemicals: Investigating the Mechanisms &amp; Identifying Potential Biomarkers

Background: Developmental neurotoxicity resulting from exposure to environmental chemicals is a significant public health concern. Understanding the mechanisms underlying such neurotoxicity and identifying potential biomarkers are essential for early detection and effective risk assessment. Objective: This study aimed to assess the developmental neurotoxicity of selected environmental chemicals and investigate the underlying mechanisms. Additionally, the study sought to identify potential biomarkers for early detection and monitoring of developmental neurotoxicity. Methods: In vitro neurodevelopmental models were established, including neuronal cell cultures and organoid systems, to evaluate the neurotoxic effects of environmental chemicals. Key endpoints, such as neuronal viability, morphology, neurite outgrowth, synaptogenesis, and synaptic activity, were assessed using appropriate assays and imaging techniques. Mechanistic investigations involved exploring oxidative stress, inflammation, disruption of neurotransmitter systems, and interference with neurodevelopmental processes through gene expression analysis, protein profiling, and signaling pathway investigations. Results: Exposure of the in vitro models to selected environmental chemicals resulted in significant neurotoxic effects, including impaired neuronal viability, disrupted morphological development, and altered synaptic activity. Mechanistic investigations revealed the involvement of oxidative stress and disruption of neurotransmitter systems in the observed neurotoxicity. Furthermore, several potential biomarkers, including gene expression changes and epigenetic modifications, showed significant correlations with developmental neurotoxicity. Conclusion: This study demonstrates the developmental neurotoxicity of environmental chemicals in in vitro neurodevelopmental models. The findings highlight the role of oxidative stress and neurotransmitter disruption as key mechanisms underlying neurotoxic effects. Moreover, the identification of potential biomarkers provides promising avenues for early detection and monitoring of developmental neurotoxicity. These results contribute to a better understanding of the risks associated with environmental chemical exposure during neurodevelopment and have implications for risk assessment and regulatory guidelines.

Proteomic Insights into Osteoporosis: Unraveling Diagnostic Markers of and Therapeutic Targets for the Metabolic Bone Disease.

Osteoporosis (OP), a prevalent skeletal disorder characterized by compromised bone strength and increased susceptibility to fractures, poses a significant public health concern. This review aims to provide a comprehensive analysis of the current state of research in the field, focusing on the application of proteomic techniques to elucidate diagnostic markers and therapeutic targets for OP. The integration of cutting-edge proteomic technologies has enabled the identification and quantification of proteins associated with bone metabolism, leading to a deeper understanding of the molecular mechanisms underlying OP. In this review, we systematically examine recent advancements in proteomic studies related to OP, emphasizing the identification of potential biomarkers for OP diagnosis and the discovery of novel therapeutic targets. Additionally, we discuss the challenges and future directions in the field, highlighting the potential impact of proteomic research in transforming the landscape of OP diagnosis and treatment.

Identification of clinical potential diagnostic blood-based biomarkers associated with prostate cancer by LC-MS/MS approach

Identification of clinical potential diagnostic blood-based biomarkers associated with prostate cancer by LC-MS/MS approach

Molecular Targets and Mechanisms of Hedyotis diffusa Willd. for Esophageal Adenocarcinoma Treatment Based on Network Pharmacology and Weighted Gene Co-expression Network Analysis.

Hedyotis diffusa Willd. (HDW) is a common anticancer herbal medicine in China, and its therapeutic effectiveness has been demonstrated in a range of cancer patients. There is no consensus about the therapeutic targets and molecular mechanisms of HDW, which contains many active ingredients. To clarify the mechanism of HDW for esophageal adenocarcinoma (EAC), we utilized network pharmacology and weighted gene co-expression network analysis methods (WGCNA). The gene modules that were linked with the clinical features of EAC were obtained through the WGCNA method. Then, the potential target genes were retrieved through the network pharmacology method in order to determine the targets of the active components. After enrichment analysis, a variety of signaling pathways with significant ratios of target genes were found, including regulation of trans-synaptic signaling, neuroactive ligand-receptor interaction and modulation of chemical synaptic transmission. By means of protein-protein interaction (PPI) network analysis, we have successfully identified the hub genes, which were AR, CNR1, GRIK1, MAPK10, MAPT, PGR and PIK3R1. Our study employed molecular docking simulations to evaluate the binding affinity of the active components with the hub gene. The identified active anticancer constituents in HDW are scopoletol, quercetin, ferulic acid, coumarin, and trans-4-methoxycinnamyl alcohol. Our findings shed light on the molecular underpinnings of HDW in the treatment of EAC and hold great promise for the identification of potential HDW compounds and biomarkers for EAC therapy.

Identification of potential tissue-specific biomarkers involved in pig fat deposition through integrated bioinformatics analysis and machine learning

Backfat thickness (BT) and intramuscular fat (IMF) content are closely appertained to meat production and quality in pig production. Deposition in subcutaneous adipose (SA) and IMF concerns different genes and regulatory mechanisms. And larger studies with rigorous design should be carried to explore the molecular regulation of fat deposition in different tissues. The purpose of this study is to gain a better understanding of the molecular mechanisms underlying differences in fat deposition among different tissues and identify tissue-specific genes involved in regulating fat deposition. The SA-associated datasets (GSE122349 and GSE145956) and IMF-associated datasets (GSE165613 and GSE207279) were downloaded from the Gene Expression Omnibus (GEO) as the BT and IMF group, respectively. Subsequently, the Robust Rank Aggregation (RRA) algorithm identified 27 down- and 29 up-regulated differentially expressed genes (DEGs) in the BT group. Based on bioinformatics and three machine learning algorithms, four SA deposition-related potential biomarkers, namely ACLY, FASN, ME1, and ARVCF were selected. FASN was evaluated as the most valuable biomarker for the SA mechanism. The 18 down- and 34 up-regulated DEGs in the IMF group were identified, and ACTA2 and HMGCL were screened as the IMF deposition-related candidate core genes, especially the ACTA2 may play the critical role in IMF deposition regulation. Moreover, based on the constructed ceRNA network, we postulated that the role of predicted ceRNA interaction network of XIST, NEAT1/miR-15a-5p, miR-16-5p, miR-424-5p, miR-497-5p/FASN were vital in the SA metabolism, XIST, NEAT1/miR-27a/b-3p, 181a/c-5p/ACTA2 might contribute to the regulation to IMF metabolism, which all gave suggestions in molecular mechanism for regulation of fat deposition. These findings may facilitate advancements in porcine quality at the genetic and molecular levels and assist with human obesity-associated diseases.

Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics.

This study aims to decipher crucial biomarkers regulated by p73 for the early detection of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling techniques. The transcriptome profile of HCT116 cell line p53 p73 and p53 p73 knockdown was performed to identify differentially expressed genes (DEGs). This was corroborated with three CRC tissue expression datasets available in Gene Expression Omnibus. Further analysis involved KEGG and Gene ontology to elucidate the functional roles of DEGs. The protein-protein interaction (PPI) network was constructed using Cytoscape to identify hub genes. Kaplan-Meier (KM) plots along with GEPIA and UALCAN database analysis provided the insights into the prognostic and diagnostic significance of these hub genes. Machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1289 upregulated and 1897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. Comprehensive analysis using gene ontology and KEGG revealed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan-Meier, GEPIA, and UALCAN analyses uncovered the clinicopathological relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. The study represents a pioneer endeavor amalgamating transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-associated genes. These genes are found relevant regarding the patients' prognosis and diagnosis. The unveiled biomarkers exhibit robustness in TNM-stage prediction, thereby laying the foundation for future clinical applications and therapeutic interventions in CRC management.

Diagnosing Parkinson’s disease and monitoring its progression: Biomarkers from combined GC-TOF MS and LC-MS/MS untargeted metabolomics

Parkinson's disease (PD) is a prevalent neurodegenerative disorder with a poorly understood etiology. An accurate diagnosis of idiopathic PD remains challenging as misdiagnosis is common in routine clinical practice. Moreover, current therapeutics focus on symptomatic management rather than curing or slowing down disease progression. Therefore, identification of potential PD biomarkers and providing a better understanding of the underlying disease pathophysiology are urgent. Herein, hydrophilic interaction liquid chromatography–mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-TOF MS) based metabolomics approaches were used to profile the serum metabolome of 50 patients with different stages of idiopathic PD (early, mid and advanced) and 45 age-matched controls. Levels of 57 metabolites including cysteine-S-sulfate and N-acetyl tryptophan were significantly higher in patients with PD compared to controls, with lower amounts of additional 51 metabolites including vanillic acid, and N-acetylaspartic acid. Xanthines, including caffeine and its downstream metabolites, were lowered in patients with PD relative to controls indicating a potential role caffeine and its metabolites against neuronal damage. Seven metabolites, namely cysteine-S-sulfate, 1-methylxanthine, vanillic acid, N-acetylaspartic acid, 3-N-acetyl tryptophan, 5-methoxytryptophol, and 13-HODE yielded a ROC curve with a high classification accuracy (AUC 0.977). Comparison between different PD stages showed that cysteine-S-sulfate levels were significantly increasing with the advancement of PD stages while LPI 20:4 was significantly decreasing with disease progression. Our findings provide new biomarker candidates to assist in the diagnosis of PD and monitor its progression. Unusual metabolites like cysteine-S-sulfate might point to therapeutic targets that could enhance the development of novel PD treatments, such as NMDA antagonists.

Identification of SOCS3 and PTGS2 as new biomarkers for the diagnosis of gout by cross-species comprehensive analysis

BackgroundGout is the most common inflammatory arthritis in adults. Gout is an arthritic disease caused by the deposition of monosodium urate crystal (MSU) in the joints, which can lead to acute inflammation and damage adjacent tissue. Hyperuricemia is the main risk factor for MSU crystal deposition and gout. With the increasing burden of gout disease, the identification of potential biomarkers and novel targets for diagnosis is urgently needed. MethodsFor the analysis of this subject paper, we downloaded the human gout data set GSE160170 and the gout mouse model data set GSE190138 from the GEO database. To obtain the differentially expressed genes (DEGs), we intersected the two data sets. Using the cytohubba algorithm, we identified the key genes and enriched them through GO and KEGG. The gene expression trends of three subgroups (normal control group, intermittent gout group and acute gout attack group) were analyzed by Series Test of Cluster (STC) analysis, and the key genes were screened out, and the diagnostic effect was verified by ROC curve. The expression of key genes in dorsal root nerve and spinal cord of gout mice was analyzed. Finally, the clinical samples of normal control group, hyperuricemia group, intermittent gout group and acute gout attack group were collected, and the expression of key genes at protein level was verified by ELISA. ResultWe obtained 59 co-upregulated and 28 co-downregulated genes by comparing the DEGs between gout mouse model data set and human gout data set. 7 hub DEGs(IL1B, IL10, NLRP3, SOCS3, PTGS2) were screened out via Cytohubba algorithm. The results of both GO and KEGG enrichment analyses indicate that 7 hub genes play a significant role in regulating the inflammatory response, cytokine production in immune response, and the TNF signaling pathway. The most representative hub genes SOCS3 and PTGS2 were screened out by Series Test of Cluster, and ROC analysis results showed the AUC values were both up to 1.000. In addition, we found that PTGS2 expression was significantly elevated in the dorsal root ganglia and spinal cord in monosodium urate(MSU)-induced gout mouse model. The ELISA results revealed that the expression of SOCS3 and PTGS2 was notably higher in the acute gout attack and intermittent gout groups compared to the normal control group. This difference was statistically significant, indicating a clear distinction between the groups. ConclusionThrough cross-species comprehensive analysis and experimental verification, SOCS3 and PTGS2 were proved to be new biomarkers for diagnosing gout and predicting disease progression.

Polycystic ovary syndrome (PCOS): progress towardsabetter understanding and treatment ofthesyndrome.

Polycystic ovary syndrome (PCOS) is the most common endocrine and metabolic disorder in women of reproductive age. It has a strong hereditary component estimated at 60 to 70% in daughters. It has been suggested that environmental factors during the fetal period may be involved in the development of the syndrome in adulthood. However, the underlying mechanisms of its transmission remain unknown, thus limiting the development of effective therapeutic strategies.This article highlights how an altered fetal environment (prenatal exposure to high levels of anti-Müllerian hormone) can contribute to the onset of PCOS in adulthood and lead to the transgenerational transmission of neuroendocrine and metabolic traits through alterations in the DNA methylation process.The originality of the translational findings summarized here involves the identification of potential biomarkers for early diagnosis of the syndrome, in addition to the validation of a promising therapeutic avenue in a preclinical model of PCOS, which can improve the management of patients suffering from the syndrome.

Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets.

Lupus nephritis (LN) is a complication of SLE characterised by immune dysfunction and oxidative stress (OS). Limited options exist for LN. We aimed to identify LN-related OS, highlighting the need for non-invasive diagnostic and therapeutic approaches. LN-differentially expressed genes (DEGs) were extracted from Gene Expression Omnibus datasets (GSE32591, GSE112943 and GSE104948) and Molecular Signatures Database for OS-associated DEGs (OSEGs). Functional enrichment analysis was performed for OSEGs related to LN. Weighted gene co-expression network analysis identified hub genes related to OS-LN. These hub OSEGs were refined as biomarker candidates via least absolute shrinkage and selection operator. The predictive value was validated using receiver operating characteristic (ROC) curves and nomogram for LN prognosis. We evaluated LN immune cell infiltration using single-sample gene set enrichment analysis and CIBERSORT. Additionally, gene set enrichment analysis explored the functional enrichment of hub OSEGs in LN. The study identified four hub genes, namely STAT1, PRODH, TXN2 and SETX, associated with OS related to LN. These genes were validated for their diagnostic potential, and their involvement in LN pathogenesis was elucidated through ROC and nomogram. Additionally, alterations in immune cell composition in LN correlated with hub OSEG expression were observed. Immunohistochemical analysis reveals that the hub gene is most correlated with activated B cells and CD8 T cells. Finally, we uncovered that the enriched pathways of OSEGs were mainly involved in the PI3K-Akt pathway and the Janus kinase-signal transducer and activator of transcription pathway. These findings contribute to advancing our understanding of the complex interplay between OS, immune dysregulation and molecular pathways in LN, laying a foundation for the identification of potential diagnostic biomarkers and therapeutic targets.

Identification of potential biomarkers of gout through weighted gene correlation network analysis.

Although hyperuricemia is not always associated with acute gouty arthritis, uric acid is a significant risk factor for gout. Therefore, we investigated the specific mechanism of uric acid activity. Using the gout-associated transcriptome dataset GSE160170, we conducted differential expression analysis to identify differentially expressed genes (DEGs). Moreover, we discovered highly linked gene modules using weighted gene coexpression network analysis (WGCNA) and evaluated their intersection. Subsequently, we screened for relevant biomarkers using the cytoHubba and Mcode algorithms in the STRING database, investigated their connection to immune cells and constructed a competitive endogenous RNA (ceRNA) network to identify upstream miRNAs and lncRNAs. We also collected PBMCs from acute gouty arthritis patients and healthy individuals and constructed a THP-1 cell gout inflammatory model, RT-qPCR and western blotting (WB) were used to detect the expression of C-X-C motif ligand 8 (CXCL8), C-X-C motif ligand 2 (CXCL2), and C-X-C motif ligand 1 (CXCL1). Finally, we predicted relevant drug targets through hub genes, hoping to find better treatments. According to differential expression analysis, there were 76 upregulated and 28 downregulated mRNAs in GSE160170. Additionally, WGCNA showed that the turquoise module was most strongly correlated with primary gout; 86 hub genes were eventually obtained upon intersection. IL1β, IL6, CXCL8, CXCL1, and CXCL2 are the principal hub genes of the protein-protein interaction (PPI) network. Using RT-qPCR and WB, we found that there were significant differences in the expression levels of CXCL8, CXCL1, and CXCL2 between the gouty group and the healthy group, and we also predicted 10 chemicals related to these proteins. In this study, we screened and validated essential genes using a variety of bioinformatics tools to generate novel ideas for the diagnosis and treatment of gout.