Comprehensive analysis of cuproptosis-related genes in immune infiltration and diagnosis in ulcerative colitis

Common Regulatory Mechanisms Mediated by Cuproptosis Genes in Inflammatory Bowel Disease and Major Depressive Disorder

BACKGROUND

The prevalence of major depressive disorder (MDD) among patients with inflammatory bowel disease (IBD) is significantly higher compared to the general population, suggesting a potential link between their pathogeneses. Cuproptosis, defined as cell death caused by intracellular copper accumulation, has not been thoroughly investigated in the context of IBD and MDD. This study aims to uncover the molecular mechanisms of cuproptosis-related genes (CRGs) in both conditions and to explore novel therapeutic strategies by the modulation of CRGs.

METHODS

In this study, we identified differentially expressed CRGs between normal and disease samples. We calculated the correlation among CRGs and between CRGs and immune cell infiltrations across various tissues. Four machine learning algorithms were employed to identify key CRGs associated with IBD and MDD. Additionally, drug sensitivity, molecular docking, and molecular dynamics simulations were conducted to predict therapeutic drugs for IBD and MDD.

RESULTS

We identified DLD, DLAT, DLST, PDHB, and DBT as common DE-CRGs, and DLD, LIAS, SLC31A1, SCO2, and CDKN2A as key CRGs associated with both IBD and MDD. Consequently, DLD was recognized as a shared biomarker in both diseases. A total of 37 potential therapeutic drugs were identified for IBD and MDD. Based on the molecular docking and molecular dynamics simulation analyses, barasertib and NTP-TAE684, which target DLAT, were predicted to be the most effective compounds.

CONCLUSIONS

These findings have substantially enhanced our understanding of the similarities and differences in the regulatory mechanisms of CRGs within brain-gut axis diseases. Key biomarkers have been identified, and potential therapeutic drugs have been predicted to effectively target IBD and MDD.

Molecular heterogeneity in pediatric sepsis: identification of oxidative stress-related subtypes and diagnostic biomarkers through integrated bioinformatics analysis

BACKGROUND

Pediatric sepsis is a life-threatening condition characterized by a dysregulated immune response to infection, often involving heightened oxidative stress. Understanding the molecular heterogeneity of sepsis can provide insights into potential therapeutic targets and diagnostic biomarkers.

METHODS

Machine learning approaches were employed to identify diagnostic biomarkers. Unsupervised clustering was performed to identify distinct sepsis subtypes. We conducted an integrative analysis combining Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), differential gene expression, and functional enrichment to study oxidative stress-related subgroups in sepsis patients. Immune cell infiltration and immune-related pathway activities were analyzed using the ssGSEA algorithm. GSVA and GSEA indicated significant enrichment of oxidative stress-related pathways in sepsis patients compared to controls.

RESULTS

Differential expression analysis identified 371 upregulated and 304 downregulated genes in sepsis, with 34 genes linked to oxidative stress. LASSO and Random Forest analyses highlighted key diagnostic genes (GBA and MGST1), validated in independent datasets (GSE13904) with high diagnostic accuracy (AUC: GBA = 0.924, MGST1 = 0.857). Unsupervised clustering revealed two distinct sepsis subtypes with differential immune cell infiltration and pathway activities: Subtype 1 showed higher T cell and TFH infiltration, while Subtype 2 exhibited increased macrophage infiltration. Functional enrichment and GSEA identified key metabolic, oxidative stress, and immune pathways that were enriched in Subtype 2.

CONCLUSION

Our comprehensive bioinformatics analysis unveils significant oxidative stress-related molecular heterogeneity in sepsis, identifying potential diagnostic biomarkers and therapeutic targets. Personalized medicine approaches targeting specific oxidative stress pathways and immune responses could enhance sepsis management and patient outcomes.

Comprehensive Analysis of Immune Characteristics of Fluorosis and Cuprotosis-Related Genes in Fluorosis Targeted Drugs

This study aims to investigate the role of cuprotosis in fluorosis and identify potential targeted drugs for its treatment. The GSE70719 and GSE195920 datasets were merged using the inSilicoMerging package. DEGs between the exposure and control groups were found using R software. Overlapping genes of DEG and cuprotosis-related genes (CRGs) were obtained by Venn diagram and were enriched by GO and KEGG. Hub genes were identified using PPI networks and enriched by GSEA. ROC curves, the xCell algorithm, and consensus cluster analysis were utilized to evaluate diagnostic efficacy, examine immune cell infiltration, and identify cuproptosis subtypes, respectively. The GSE53937 dataset was used for external validation. The DSigDB database was used to predict small molecule drugs. Molecular docking was used to validate the relationship between small molecule drugs and hub genes. A total of 1522 DEGs (743 upregulated genes and 779 downregulated genes) and 33 overlapping genes of DEGs and CRGs were obtained. The 33 overlapping genes were enriched in ribosomal biogenesis and oxidative phosphorylation pathways. The hub genes DNTTIP2, GTPBP4, IMP4, MRPL12, MRPL13, MRPL2, MRPS2, MRPS22, NOP2, RSL1D1, and SURF6 were identified, demonstrating great diagnostic ability with AUC > 0.8. These hub genes were associated with immune response and inflammation. Two cuproptosis patterns were established based on 33 CRGs. Mepacrine was screened as a potential drug and demonstrated stability in docking with IMP4. In summary, the current study identified several CRGs that may serve as potential biomarkers for diagnosing fluorosis and are involved in fluoride-induced immune responses. Additionally, mepacrine was screened as a potential treatment for fluorosis by targeting CRGs.

Identification and Immunological Characterization of Cuproptosis Related Genes in Preeclampsia Using Bioinformatics Analysis and Machine Learning

Preeclampsia (PE) is a pregnancy-specific disorder characterized by an unclearly understood pathogenesis and poses a great threat to maternal and fetal safety. Cuproptosis, a novel form of cellular death, has been implicated in the advancement of various diseases. However, the role of cuproptosis and immune-related genes in PE is unclear. The current study aims to elucidate the gene expression matrix and immune infiltration patterns of cuproptosis-related genes (CRGs) in the context of PE. The GSE98224 dataset was obtained from the Gene Expression Omnibus (GEO) database and utilized as the internal training set. Based on the GSE98224 dataset, we explored the differentially expressed cuproptosis related genes (DECRGs) and immunological composition. We identified 10 DECRGs conducted Gene Ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and a protein-protein interaction (PPI) network. Furthermore, patients with PE were categorized into two distinct clusters, and an investigation was conducted to examine the status of immune cell infiltration. Additionally, the application of Weighted Gene Co-expression Network Analysis (WGCNA) was utilized to differentiate modules consisting of co-expressed genes and conduct clustering analysis. The intersecting genes were obtained by intersecting differently expressed genes in PE and PE clusters. The most precise forecasting model was chosen by evaluating the effectiveness of four machine learning models. The ResNet model was established to score the hub genes. The prediction accuracy was assessed by receiver operating characteristic (ROC) curves and an external dataset. We successfully identified five key DECREGs and two pathological clusters in PE, each with distinct immune profiles and biological characteristics. Subsequently, the RF model was deemed the most optimal model for the identification of PE with a large area under the curve (AUC = 0.733). The five genes that ranked highest in the RF machine learning model were considered to be predictor genes. The calibration curve demonstrated a high level of accuracy in aligning the predicted outcomes with the actual outcomes. We validate the ResNet model using the ROC curve with the area under the curve (AUC = 0.82). Cuproptosis and immune infiltration may play an important role in the pathogenesis of PE. The present study elucidated that GSTA4, KCNK5, APLNR, IKZF2, and CAP2 may be potential markers of cuproptosis-associated PE and are considered to play a significant role in the initiation and development of cuproptosis-induced PE.

Impacts of Copper Deficiency on Oxidative Stress and Immune Function in Mouse Spleen

INTRODUCTION

Copper is an essential trace element crucial for enzyme synthesis and metabolism. Adequate copper levels are beneficial for maintaining the normal immune function of the spleen. Copper deficiency disrupts the metabolic processes within the spleen and impairs its immune function. This research examines the impact of copper deficiency on the spleen and the potential recovery following copper supplementation.

METHODS

Weaned mice underwent a 4-week copper-deficient diet, succeeded by 1-week of copper repletion via intraperitoneal copper sulfate injection. Histological examination was used to assess pathological changes in the spleen. Biochemical assays were performed to measure oxidative stress levels in the spleen. ELISA, qPCR, and Western blot were employed to examine alterations in inflammatory markers, immune indicators, and oxidative regulatory factors across various levels.

RESULTS

Copper deficiency caused histological damage to the spleen, altered the expression of oxidative stress regulatory pathways (Nrf2, Keap1, and HO-1), and affected the expression of key inflammatory enzymes (iNOS, COX2) and transcription factor NF-κB, leading to oxidative damage. This was reflected by decreased levels of SOD, GSH, and T-AOC, along with increased levels of CAT and MDA. The levels of inflammatory cytokines IL-1β, TNF-α, IL-6, and IL-8 were notably increased. Copper supplementation significantly improved these changes.

CONCLUSIONS

Copper deficiency leads to spleen tissue damage in mice, affecting the Nrf2 regulatory pathway and inducing oxidative damage. Subsequent copper supplementation with copper sulfate effectively ameliorates the damage caused by copper deficiency.

Cuproptosis in cancer therapy: mechanisms, therapeutic application and future prospects

Cuproptosis is a regulated form of cell death induced by the accumulation of metal ions and is closely linked to aspects of cellular drug resistance, cellular metabolism, and signalling pathways. Due to its crucial role in regulating physiological and pathological processes, cuproptosis has gained increasing significance as a potential target for anticancer drug development. In this review, we introduce the definition of cuproptosis and provide a comprehensive discussion of the mechanisms of cuproptosis. In addition, the methods for the detection of cuproptosis are summarized, and recent advances in cuproptosis in cancer therapy are reviewed, mainly in terms of elesclomol (ES)-mediated cuproptosis and disulfiram (DSF)-mediated cuproptosis, which provided practical value for applications. Finally, the current challenges and future development of cuproptosis-mediated cancer therapy are discussed. In summary, this review highlights recent progress on cuproptosis in cancer therapy, offering novel ideas and strategies for future research and applications.

The diagnostic value investigation of programmed cell death genes in heart failure

BACKGROUND

We aimed to identify the potential diagnostic markers and associated molecular mechanisms based on programmed cell death (PCD)-related genes in patients with heart failure (HF).

METHODS

Three HF gene expression data were extracted from the GEO database, including GSE57345 (training data), GSE141910 and GSE76701 (validation data), followed by differentially PCD related genes (DPCDs) was shown between HF and control samples. Enrichment and protein-protein interaction (PPI) network analyses were performed based on the DPCDs. Subsequently, a diagnostic model was constructed and validated after exploring the diagnostic markers using machine learning. A nomogram was used to determine the clinical diagnostic value. Diagnostic marker-based immune, transcription network, and gene set enrichment (GSE) analyses were performed. Finally, the drug-target network was investigated.

RESULTS

Twenty DPCDs were revealed between the two groups. These genes, such as Serpin Family E Member 1 (SERPINE1), are mainly enriched in pathways such as the regulation of the inflammatory response. A PPI network was constructed using 14 DPCDs. Eight diagnostic markers, such as SERPINE1, CD38 molecule (CD38), and S100 calcium-binding protein A9 (S100A9), were explored using machine learning algorithms, followed by diagnostic model construction. A nomogram and immune-associated analysis was used to validate the diagnostic value of these genes and the model. Moreover, the transcription regulation network and drug-target interactions were further investigated. Finally, qRT-PCR confirmed that the expression levels of eight signature genes (CD14, CD38, CTSK, LAPTM5, S100A9, SERPINE1, SLC11A1, and STAT3) were significantly elevated in the observation group, consistent with the results of bioinformatics analysis.

CONCLUSIONS

This study constructed a valuable diagnostic model for HF using the eight identified DPCDs as diagnostic markers.

Utilizing an integrated bioinformatics and machine learning approach to uncover biomarkers linking ulcerative colitis to purine metabolism-related genes

Background

Ulcerative colitis (UC) is an increasing incidence of inflammatory disorder in the colon mucosa. One of the current research focuses is the alteration of metabolic networks in UC. One of the important aspects of this metabolic shift is the expression of purine metabolism genes (PMGs) vital for nucleic acid synthesis. Nevertheless, the precise function of PMGs in the pathophysiology of UC is not yet fully known.

Methods

To this end, this study used state-of-the-art bioinformatics tools and approaches to discover and confirm the PMGs involved in UC. All the 114 candidate PMGs were compared for their expression levels. GSEA and GSVA were applied to define the functional and pathway implications of these PMGs. Lasso regression and SVM-RFE approaches were used for the identification of hub genes and to assess the diagnostic potential of eight PMGs in UC classification. The relationship between these critical PMGs and clinical features was also systematically evaluated as well. The expression levels of these eight PMGs were validated using datasets GSE206285 and GSE179285.

Results

Using bioinformatics and machine learning, this work seeks to establish the involvement of PMGs in UC. From the LASSO and SVM models, 114 DE PMGs were selected and investigated to build a stable predictive model. Based on these studies, the following genes: IMPDH1, GUK1, POLE3, ADCY3, ADCY4, PDE6B, PNPT1 and PDE4D were suggested as potential biomarkers of UC. Gene ontology enrichment analysis revealed that these genes are implicated in the biological processes of particular relevance to immune and inflammatory responses. The study also provided a lot of information on the interaction between immune cells and PMGs indicating that these genes may control some immune-related pathways in UC. Moreover, drug-gene interaction analysis presents potential therapeutic opportunities for potential drug targets which were further confirmed through molecular docking. Mendelian randomization analysis revealed that ADCY4 and PDAZN are involved in PMG-related processes, thus opening new possibilities for treatment.

Conclusions

This work reveals eight PMGs closely related to UC and provides new perspectives on possible markers of this inflammatory disease. These findings not only increase the understanding of the pathogenesis of UC but also offer potential for improving the surveillance of disease and its progression.

Comprehensive assessment of cellular senescence in intestinal immunity and biologic therapy response in ulcerative colitis

Ulcerative Colitis (UC) is an inflammatory disorder characterized by chronic intestinal inflammation and immune dysregulation. Despite a clear association between cellular senescence and chronic inflammation and immune dysregulation, the mechanisms underlying cellular senescence in UC remain unclear. We screened differentially expressed genes (DEGs) associated with cellular senescence in multiple UC datasets, performed immune infiltration analysis, and constructed clinical diagnostic models. Additionally, we investigated the relationship between key genes related to cellular senescence and disease remission in UC patients undergoing biologic therapy, validating their expression in a single-cell dataset. We identified six DEGs associated with cellular senescence (TWIST1, IGFBP5, MME, IFNG, ME1, FOS). Immune infiltration results indicated strong correlations of four of these genes with immune cells and pathways. Through WGCNA, GO, and KEGG analyses, we found that gene modules strongly associated with the expression of hub genes in cellular senescence were enriched in inflammation-related pathways. In the single-cell dataset, the expression of these six key genes exhibited similarities with Immune infiltration results. Additionally, we constructed a nomogram using these six genes for diagnosing UC, demonstrating good diagnostic capability and clinical efficacy. Kaplan-Meier survival analysis revealed a significant association between changes in the expression levels of these cell genes and disease remission in UC patients undergoing biologic therapy. This study utilizes bioinformatic analysis and machine learning to identify and analyze features associated with cellular senescence in multiple UC datasets. It provides insights into the role of cellular senescence in the premature onset of intestinal aging in UC and offers new perspectives for exploring novel therapeutic targets.

HSP90β shapes the fate of Th17 cells with the help of glycolysis-controlled methylation modification

BACKGROUND AND PURPOSE

Ulcerative colitis (UC) is a refractory inflammatory disease associated with immune dysregulation. Elevated levels of heat shock protein (HSP) 90 in the β but not α subtype were positively associated with disease status in UC patients. This study validated the possibility that pharmacological inhibition or reduction of HSP90β would alleviate colitis, induced by dextran sulfate sodium, in mice and elucidated its mechanisms.

EXPERIMENTAL APPROACH

Histopathological and biochemical analysis assessed disease severity, and bioinformatics and correlation analysis explained the association between the many immune cells and HSP90β. Flow cytometry was used to analyse the homeostasis and transdifferentiation of Th17 and Treg cells. In vitro inhibition and adoptive transfer assays were used to investigate functions of the phenotypically transformed Th17 cells. Metabolomic analysis, DNA methylation detection and chromatin immunoprecipitation were used to explore these mechanisms.

KEY RESULTS

The selective pharmacological inhibitor (HSP90βi) and shHSP90β significantly mitigated UC in mice and promoted transformation of Th17 to Treg cell phenotype, via Foxp3 transcription. The phenotypically-transformed Th17 cells by HSP90βi or shHSP90β were able to inhibit lymphocyte proliferation and colitis in mice. HSP90βi and shHSP90β selectively weakened glycolysis by stopping the direct association of HSP90β and GLUT1, the key glucose transporter, to accelerate ubiquitination degradation of GLUT1, and enhance the methylation of Foxp3 CNS2 region. Then, the mediator path was identified as the "lactate-STAT5-TET2" cascade.

CONCLUSION AND IMPLICATIONS

HSP90β shapes the fate of Th17 cells via glycolysis-controlled methylation modification to affect UC progression, which provides a new therapeutic target for UC.

Evaluating the significance of ECSCR in the diagnosis of ulcerative colitis and drug efficacy assessment

Background

The main challenge in diagnosing and treating ulcerative colitis (UC) has prompted this study to discover useful biomarkers and understand the underlying molecular mechanisms.

Methods

In this study, transcriptomic data from intestinal mucosal biopsies underwent Robust Rank Aggregation (RRA) analysis to identify differential genes. These genes intersected with UC key genes from Weighted Gene Co-expression Network Analysis (WGCNA). Machine learning identified UC signature genes, aiding predictive model development. Validation involved external data for diagnostic, progression, and drug efficacy assessment, along with ELISA testing of clinical serum samples.

Results

RRA integrative analysis identified 251 up-regulated and 211 down-regulated DEGs intersecting with key UC genes in WGCNA, yielding 212 key DEGs. Subsequently, five UC signature biomarkers were identified by machine learning based on the key DEGs-THY1, SLC6A14, ECSCR, FAP, and GPR109B. A logistic regression model incorporating these five genes was constructed. The AUC values for the model set and internal validation data were 0.995 and 0.959, respectively. Mechanistically, activation of the IL-17 signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway in UC was indicated by KEGG and GSVA analyses, which were positively correlated with the signature biomarkers. Additionally, the expression of the signature biomarkers was strongly correlated with various UC types and drug efficacy in different datasets. Notably, ECSCR was found to be upregulated in UC serum and exhibited a positive correlation with neutrophil levels in UC patients.

Conclusions

THY1, SLC6A14, ECSCR, FAP, and GPR109B can serve as potential biomarkers of UC and are closely related to signaling pathways associated with UC progression. The discovery of these markers provides valuable information for understanding the molecular mechanisms of UC.

Modified Gegen Qinlian Decoction modulated the gut microbiome and bile acid metabolism and restored the function of goblet cells in a mouse model of ulcerative colitis

Objective

Modified Gegen Qinlian Decoction (MGQD) has been shown to effectively relieve ulcerative colitis (UC) without a known pharmacological mechanism. In this study, the anti-colitis efficaciousness of MGQD and its underlying mechanisms in UC were evaluated.

Methods

Mice with colitis were administered MGQD for 7 days. Following the evaluation of clinical symptoms, gut microbiota in the feces of UC mice was examined using 16S rRNA sequencing and bile acids (BAs) were examined using LC/MS. Gut microbiota consumption and fecal microbiota transplantation (FMT) were used to explore the involvement of gut microbiota in the anti-UC action of MGQD.

Results

MGQD relieved colitis as shown by weight loss protection, a lower disease activity index (DAI), restoration of intestinal length reduction, and lower histopathologic scores. MGQD also restored crypt stem cell proliferation and function of colonic goblet cells, and promoted MUC2 protein secretion. Interestingly, investigations using gut bacterial depletion and FMT showed that MGQD attenuated colonic damage in a gut-dependent way. The modulation of the gut microbiota by MGQD might be attributed to a decrease in Odoribacter and an increase in norank_f_Muribaculaceae. In addition, MGQD modulated the metabolism of BAs while restoring the structure of the gut microbiota.

Conclusion

MGQD significantly alleviated colitis in mice, which may be associated with the modulation of gut microbiota and BA metabolism and restoration of function of goblet cells. However, factors other than the gut microbiota may also be involved in the amelioration of UC by MGQD.

The Molecular Mechanisms of Cuproptosis and Small-Molecule Drug Design in Diabetes Mellitus

In the field of human health research, the homeostasis of copper (Cu) is receiving increased attention due to its connection to pathological conditions, including diabetes mellitus (DM). Recent studies have demonstrated that proteins associated with Cu homeostasis, such as ATOX1, FDX1, ATP7A, ATPB, SLC31A1, p53, and UPS, also contribute to DM. Cuproptosis, characterized by Cu homeostasis dysregulation and Cu overload, has been found to cause the oligomerization of lipoylated proteins in mitochondria, loss of iron-sulfur protein, depletion of glutathione, production of reactive oxygen species, and cell death. Further research into how cuproptosis affects DM is essential to uncover its mechanism of action and identify effective interventions. In this article, we review the molecular mechanism of Cu homeostasis and the role of cuproptosis in the pathogenesis of DM. The study of small-molecule drugs that affect these proteins offers the possibility of moving from symptomatic treatment to treating the underlying causes of DM.

Identification and experimental validation of cuproptosis regulatory program in a sepsis immune microenvironment through a combination of single-cell and bulk RNA sequencing

Background

In spite of its high mortality rate and poor prognosis, the pathogenesis of sepsis is still incompletely understood. This study established a cuproptosis-based risk model to diagnose and predict the risk of sepsis. In addition, the cuproptosis-related genes were identified for targeted therapy.

Methods

Single-cell sequencing analyses were used to characterize the cuproptosis activity score (CuAS) and intercellular communications in sepsis. Differential cuproptosis-related genes (CRGs) were identified in conjunction with single-cell and bulk RNA sequencing. LASSO and Cox regression analyses were employed to develop a risk model. Three external cohorts were conducted to assess the model's accuracy. Differences in immune infiltration, immune cell subtypes, pathway enrichment, and the expression of immunomodulators were further evaluated in distinct groups. Finally, various in-vitro experiments, such as flow cytometry, Western blot, and ELISA, were used to explore the role of LST1 in sepsis.

Results

ScRNA-seq analysis demonstrated that CuAS was highly enriched in monocytes and was closely related to the poor prognosis of sepsis patients. Patients with higher CuAS exhibited prominent strength and numbers of cell-cell interactions. A total of five CRGs were identified based on the LASSO and Cox regression analyses, and a CRG-based risk model was established. The lower riskScore cohort exhibited enhanced immune cell infiltration, elevated immune scores, and increased expression of immune modulators, indicating the activation of an antibacterial response. Ultimately, in-vitro experiments demonstrated that LST1, a key gene in the risk model, was enhanced in the macrophage in response to LPS, which was closely related to the decrease of macrophage survival rate, the enhancement of apoptosis and oxidative stress injury, and the imbalance of the M1/M2 phenotype.

Conclusions

This study constructed a cuproptosis-related risk model to accurately predict the prognosis of sepsis. We further characterized the cuproptosis-related gene LST1 to provide a theoretical framework for sepsis therapy.

Immune patterns of cuproptosis in ischemic heart failure: A transcriptome analysis

Cuproptosis is a recently discovered programmed cell death pattern that affects the tricarboxylic acid (TCA) cycle by disrupting the lipoylation of pyruvate dehydrogenase (PDH) complex components. However, the role of cuproptosis in the progression of ischemic heart failure (IHF) has not been investigated. In this study, we investigated the expression of 10 cuproptosis-related genes in samples from both healthy individuals and those with IHF. Utilizing these differential gene expressions, we developed a risk prediction model that effectively distinguished healthy and IHF samples. Furthermore, we conducted a comprehensive evaluation of the association between cuproptosis and the immune microenvironment in IHF, encompassing infiltrated immunocytes, immune reaction gene-sets and human leukocyte antigen (HLA) genes. Moreover, we identified two different cuproptosis-mediated expression patterns in IHF and explored the immune characteristics associated with each pattern. In conclusion, this study elucidates the significant influence of cuproptosis on the immune microenvironment in ischemic heart failure (IHF), providing valuable insights for future mechanistic research exploring the association between cuproptosis and IHF.

Artificial intelligence in cardiology: a bibliometric study

OBJECTIVES

To perform a comprehensive bibliometric analysis of global publications on the applications of artificial intelligence (AI) in cardiology.

METHODS

Documents related to AI in cardiology published between 2002 and 2022 were retrieved from Web of Science Core Collection. R package "bibliometrix", VOSviewers and Microsoft Excel were applied to perform the bibliometric analysis.

RESULTS

A total of 4332 articles were included. United States topped the list of countries publishing articles, followed by China and United Kingdom. The Harvard University was the institution that contributed the most to this field, followed by University of California System and University of London. Disease risk prediction, diagnosis, treatment, disease detection, and prognosis assessment were the research hotspots for AI in cardiology.

CONCLUSIONS

Enhancing cooperation between different countries and institutions is a critical step in leading to breakthroughs in the application of AI in cardiology. It is foreseeable that the application of machine learning and deep learning in various areas of cardiology will be a research priority in the coming years.

Novel molecular hepatocellular carcinoma subtypes and RiskScore utilizing apoptosis-related genes

Hepatocellular carcinoma (HCC) is the third leading cause of global cancer-related deaths. Despite immunotherapy offering hope for patients with HCC, only some respond to it. However, it remains unclear how to pre-screen eligible patients. Our study aimed to address this issue. In this study, we identified 13 prognostic genes through univariate Cox regression analysis of 87 apoptosis-related genes. Subsequently, these 13 genes were analyzed using ConsensusClusterPlus, and patients were categorized into three molecular types: C1, C2, and C3. A prognostic model and RiskScore were constructed using Lasso regression analysis of 132 significant genes identified between C1 and C3. We utilized quantitative polymerase chain reaction to confirm the model's transcript level in Huh7 and THLE2 cell lines. Both molecular subtypes and RiskScores effectively predicted patients benefiting from immunotherapy. Cox regression analysis revealed RiskScore as the most significant prognosis factor, suggesting its clinical application potential and providing a foundation for future experimental research.

Alleviating effect of Nexrutine on mucosal inflammation in mice with ulcerative colitis: Involvement of the RELA suppression

BACKGROUND

Nexrutine is an herbal extract derived from Phellodendron amurense, known for its anti-inflammatory, antidiarrheal, and hemostatic properties. However, its effect on ulcerative colitis (UC) remains unclear.

METHODS

A mouse model of UC was induced by 3% dextran sulfate sodium, while human colonic epithelial cells NCM-460 were exposed to lipopolysaccharide. Both models were treated with Nexrutine at 300 or 600 mg/kg, with Mesalazine applied as a positive control regimen. The disease activity index (DAI) of mice was calculated, and the pathological injury scores were assessed through hematoxylin and eosin staining. The viability of NCM-460 cells was determined using the CCK-8 method. Inflammatory cytokines were detected using ELISA kits. Expression of mucin 3 (MUC3), Claudin-1, and tight junction protein (ZO-1) was detected to analyze mucosal barrier integrity. Target genes of Nexrutine were predicted using bioinformatics tools. Expression of RELA proto-oncogene (RELA) was analyzed using qPCR and western blot assays.

RESULTS

The Nexrutine treatments significantly alleviated DAI of mice, mitigated pathological changes in their colon tissues, decreased the production of pro-inflammatory cytokines, enhanced the barrier integrity-related proteins, and increased NCM-460 cell viability in vitro. RELA, identified as a target gene of Nexrutine, showed elevated levels in UC models but was substantially suppressed by Nexrutine treatment. Adenovirus-mediated RELA upregulation in mice or the overexpression plasmid of RELA in cells counteracted the effects of Nexrutine treatments, exacerbating UC-related symptoms.

CONCLUSION

This study demonstrates that Nexrutine alleviates inflammatory mucosal barrier damage in UC by suppressing RELA transcription.

The function and immune role of cuproptosis associated hub gene in Barrett's esophagus and esophageal adenocarcinoma

Barrett's esophagus (BE) is a precancerous lesion of esophageal adenocarcinoma (EAC), with approximately 3-5% of patients developing EAC. Cuproptosis is a kind of programmed cell death phenomenon discovered in recent years, which is related to the occurrence and development of many diseases. However, its role in BE and EAC is not fully understood. We used single sample Gene Set Enrichment Analysis (ssGSEA) for differential analysis of BE in the database, followed by enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) and GSEA, Protein-Protein Interaction (PPI), Weighted Gene Co-expression Network Analysis (WGCNA), Receiver Operating Characteristic Curve (ROC) and finally Quantitative Real Time Polymerase Chain Reaction (qRT-PCR) and immunohistochemistry (IHC) of clinical tissues. Two hub genes can be obtained by intersection of the results obtained from the cuproptosis signal analysis based on BE. The ROC curves of these two genes predicted EAC, and the Area Under the Curve (AUC) values could reach 0.950 and 0.946, respectively. The mRNA and protein levels of Centrosome associated protein E (CENPE) and Shc SH2 domain binding protein 1 (SHCBP1) were significantly increased in clinical EAC tissues. When they were grouped by protein expression levels, high expression of CENPE or SHCBP1 had a poor prognosis. The CENPE and SHCBP1 associated with cuproptosis may be a factor promoting the development of BE into EAC which associated with the regulation of NK cells and T cells.

Research hotspots and trends of artificial intelligence in rheumatoid arthritis: A bibliometric and visualized study

Artificial intelligence (AI) applications on rheumatoid arthritis (RA) are becoming increasingly popular. In this bibliometric study, we aimed to analyze the characteristics of publications relevant to the research of AI in RA, thereby developing a thorough overview of this research topic. Web of Science was used to retrieve publications on the application of AI in RA from 2003 to 2022. Bibliometric analysis and visualization were performed using Microsoft Excel (2019), R software (4.2.2) and VOSviewer (1.6.18). The overall distribution of yearly outputs, leading countries, top institutions and authors, active journals, co-cited references and keywords were analyzed. A total of 859 relevant articles were identified in the Web of Science with an increasing trend. USA and China were the leading countries in this field, accounting for 71.59% of publications in total. Harvard University was the most influential institution. Arthritis Research & Therapy was the most active journal. Primary topics in this field focused on estimating the risk of developing RA, diagnosing RA using sensor, clinical, imaging and omics data, identifying the phenotype of RA patients using electronic health records, predicting treatment response, tracking the progression of the disease and predicting prognosis and developing new drugs. Machine learning and deep learning algorithms were the recent research hotspots and trends in this field. AI has potential applications in various fields of RA, including the risk assessment, screening, early diagnosis, monitoring, prognosis determination, achieving optimal therapeutic outcomes and new drug development for RA patients. Incorporating machine learning and deep learning algorithms into real-world clinical practice will be a future research hotspot and trend for AI in RA. Extensive collaboration to improve model maturity and robustness will be a critical step in the advancement of AI in healthcare.

Screening of miR-15a-5p as a potential biomarker for intervertebral disc degeneration through RNA-sequencing

Low back pain (LBP) is a prevalent clinical condition that imposes substantial economic burdens on society. Intervertebral disc degeneration (IVDD) is recognized as a major contributing factor to LBP. Recent studies have highlighted the pivotal role of microRNAs (miRNAs) in regulating the onset and progression of IVDD. Understanding the involvement of miRNAs in IVDD will expand our knowledge of the underlying mechanisms and potentially identify novel therapeutic targets for managing LBP. However, the pathological process of IVDD and the miRNA-mediated pathomechanism in IVDD remain unclear. Herein, we comprehensively analyzed and divided the pathological process of IVDD into three stages based on the analysis by Risbud and colleagues. Results showed that IVDD was especially associated with cell death, oxidative stress, inflammatory and immune response, and extracellular matrix (ECM) metabolism. Subsequently, we obtained human normal and degenerative nucleus pulposus tissues, which were visually confirmed through histological staining techniques such as HE and TUNEL staining. RNA sequencing was then performed on these tissue samples. Additionally, miRNA (GSE116726) and mRNA (GSE56081/GSE70362/GSE23130/GSE34095) datasets were collected from the GEO database. Our analysis revealed that miR-15a-5p was significantly upregulated IVDD, as validated by both RNA sequencing and qRT-PCR experiments. To further refine our findings, bioinformatics analysis was conducted, merging the targets of miR-15a-5p and multiple mRNA datasets, ultimately identifying the overlapping IVDD-associated mRNAs. Notably, many cuproptosis-related genes (CRGs), ferroptosis-related genes, oxidative stress-related genes, and immunity-related genes were potential targets of miR-15a-5p. The miR-15a-5p-mRNA network was constructed using Cytoscape software. Additionally, PPI, functional, and pathway enrichment analyses of the CRGs were also performed. We found that MTF1, one of the CRGs, was highly expressed in IVDD and primarily localized in the nucleus of nucleus pulposus cells. These findings suggest that miR-15a-5p is a potential biomarker in IVDD, and targeting the miR-15a-5p-mRNA signaling pathway may be a promising strategy for treating IVDD diseases.

Macrophages in ulcerative colitis: A perspective from bibliometric and visual analysis

Objectives

Despite the many reported studies on macrophages in ulcerative colitis (UC), the overall research trends in this field are unclear. This study evaluates the research trends and hotspots regarding macrophages in UC using bibliometric analysis.

Methods

A systematic search was conducted in the Web of Science database to identify publications related to macrophages in UC from 2012 to 2021. R package 'bibliometrix', VOSviewers, CiteSpace and Microsoft Excel were utilised for the bibliometric analysis.

Results

1074 articles published between 2012 and 2021 were analysed. The number of publications on macrophages in UC showed a consistently increasing trend, with USA and China as the leading contributors to this field. Notably, Georgia State University and Nanjing University contributed significantly to this field. Among the authors, Wang Y had the highest productivity, while Wu X received the most citations. The journal Gut was identified as the most authoritative journal in this field. Co-citation analysis revealed that the exploration of the mechanisms of macrophages in UC through in vivo and in vitro experiments was the primary focus of research. Moreover, the emerging research hotspots included keywords such as 'macrophage polarization', 'gut microbiota' and 'NLRP3 inflammasome'.

Conclusions

Research on macrophages in UC holds significant value and practical implications. Additionally, China demonstrated prolific output in this field, while the USA had the most influential contributions. Currently, research hotspots are centred around the modulation of gut microbiota to regulate macrophage polarization and macrophage pyroptosis as potential strategies for mitigating UC.

Current Status and Future Trends of Cold Atmospheric Plasma as an Oncotherapy

Cold atmospheric plasma (CAP), a redox modulation tool, is capable of inhibiting a wide spectrum of cancers and has thus been proposed as an emerging onco-therapy. However, with incremental successes consecutively reported on the anticancer efficacy of CAP, no consensus has been made on the types of tumours sensitive to CAP due to the different intrinsic characteristics of the cells and the heterogeneous design of CAP devices and their parameter configurations. These factors have substantially hindered the clinical use of CAP as an oncotherapy. It is thus imperative to clarify the tumour types responsive to CAP, the experimental models available for CAP-associated investigations, CAP administration strategies and the mechanisms by which CAP exerts its anticancer effects with the aim of identifying important yet less studied areas to accelerate the process of translating CAP into clinical use and fostering the field of plasma oncology.

Identification of cuproptosis-related biomarkers in dilated cardiomyopathy and potential therapeutic prediction of herbal medicines

Background: Dilated cardiomyopathy (DCM) is one of the significant causes of heart failure, and the mechanisms of metabolic ventricular remodelling due to disturbances in energy metabolism are still poorly understood in cardiac pathology. Understanding the biological mechanisms of cuproptosis in DCM is critical for drug development. Methods: The DCM datasets were downloaded from Gene Expression Omnibus, their relationships with cuproptosis-related genes (CRGs) and immune signatures were analyzed. LASSO, RF, and SVM-RFE machine learning algorithms were used to identify signature genes and the eXtreme Gradient Boosting (XGBoost) model was used to assess diagnostic efficacy. Molecular clusters of CRGs were identified, and immune Infiltration analysis was performed. The WGCNA algorithm was used to identify specific genes in different clusters. In addition, AUCell was used to analyse the cuproptosis scores of different cell types in the scRNA-seq dataset. Finally, herbal medicines were predicted from an online database, and molecular docking and molecular dynamics simulations were used to support the confirmation of the potential of the selected compounds. Results: We identified dysregulated cuproptosis genes and activated immune responses between DCM and healthy controls. Two signature genes (FDX1, SLC31A1) were identified and performed well in an external validation dataset (AUC = 0.846). Two molecular clusters associated with cuproptosis were further defined in DCM, and immune infiltration analysis showed B-cell naive, Eosinophils, NK cells activated and T-cell CD4 memory resting is significant immune heterogeneity in the two clusters. AUCell analysis showed that cardiomyocytes had a high cuproposis score. In addition, 19 and 3 herbal species were predicted based on FDX1 and SLC31A1. Based on the molecular docking model, the natural compounds Rutin with FDX1 (-9.3 kcal/mol) and Polydatin with SLC31A1 (-5.5 kcal/mol) has high stability and molecular dynamics simulation studies further validated this structural stability. Conclusion: Our study systematically illustrates the complex relationship between cuproptosis and the pathological features of DCM and identifies two signature genes (FDX1 and SLC31A1) and two natural compounds (Rutin and Polydatin). This may enhance our diagnosis of the disease and facilitate the development of clinical treatment strategies for DCM.