Identification and immunological characterization of cuproptosis-related molecular clusters in Alzheimer's disease

Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation

Introduction

Spinal cord injury (SCI) severely affects the central nervous system. Copper homeostasis is closely related to mitochondrial regulation, and cuproptosis is a novel form of cell death associated with mitochondrial metabolism. This study aimed to explore the relationship between SCI and cuproptosis and construct prediction models.

Methods

Gene expression data of SCI patient samples from the GSE151371 dataset were analyzed. The differential expression and correlation of 13 cuproptosis-related genes (CRGs) between SCI and non-SCI samples were identified, and the ssGSEA algorithm was used for immunological infiltration analysis. Unsupervised clustering was performed based on differentially expressed CRGs, followed by weighted gene co-expression network analysis (WGCNA) and enrichment analysis. Three machine learning models (RF, LASSO, and SVM) were constructed to screen candidate genes, and a Nomogram model was used for verification. Animal experiments were carried out on an SCI rat model, including behavioral scoring, histological staining, electron microscopic observation, and qRT-PCR.

Results

Seven CRGs showed differential expression between SCI and non-SCI samples, and there were significant differences in immune cell infiltration levels. Unsupervised clustering divided 38 SCI samples into two clusters (Cluster C1 and Cluster C2). WGCNA identified key modules related to the clusters, and enrichment analysis showed involvement in pathways such as the Ribosome and HIF-1 signaling pathway. Four candidate genes (SLC31A1, DBT, DLST, LIAS) were obtained from the machine learning models, with SLC31A1 performing best (AUC = 0.958). Animal experiments confirmed a significant decrease in the behavioral scores of rats in the SCI group, pathological changes in tissue sections, and differential expression of candidate genes in the SCI rat model.

Discussion

This study revealed a close association between SCI and cuproptosis. Abnormal expression of the four candidate genes affects mitochondrial function, energy metabolism, oxidative stress, and the immune response, which is detrimental to the recovery of neurological function in SCI. However, this study has some limitations, such as unidentified SRGs, a small sample size. Future research requires more in vitro and in vivo experiments to deeply explore regulatory mechanisms and develop intervention methods.

The complexities of cell death mechanisms: a new perspective in systemic sclerosis therapy

Systemic sclerosis, also termed scleroderma, is a severe and debilitating autoimmune disease characterized by fibrosis, an aberrant immune response, and vascular dysfunction. Cell death is essential to the body's continued normal development as it removes old or damaged cells. This process is governed by several mechanisms, including programmed cell death through apoptosis, necrosis, and pyroptosis, as well as metabolic processes, such as ferroptosis and cuproptosis. This review describes the signaling pathways associated with each form of cell death, examining the linkages between these pathways, and discussing how the dysregulation of cell death processes is involved in the development of autoimmune disorders such as systemic sclerosis. Existing and promising therapeutic strategies aimed at restoring the balance of cell death in systemic sclerosis and other autoimmune disorders are also emphasized.

The up-regulation of PTBP1 expression level in patients with Insomnia by senile dementia and promote cuproptosis of nerve cell by SLC31A1

Alzheimer's disease (AD), often referred to as the modern-day scourge, stands as a significant health challenge characterized by high rates of disability and mortality, particularly among the geriatric population. Thus, the present study investigated the precise details of PTBP1 involvement in cuproptosis of nerve cell of patients with Insomnia by senile dementia (ISD). Patients with ISD, early mild cognitive impairment (EMCI) and Normal healthy volunteers were obtained. In the context of ISD, the elevated PTBP1 mRNA expressions were observed in patient samples, correlating positively with diminished cognitive function as measured by the Mini-Mental State Examination (MMSE) and increased geriatric depression scale scores. The pivotal role of PTBP1 was further underscored by its inhibitory effects in a mice model, which prevented the development of senile dementia, and its influence on neuronal cell proliferation and ROS-induced oxidative stress in vitro. Additionally, PTBP1's regulatory capacity on the cuproptosis of nerve cells and its modulation of SLC31A1 expression, through effects on ubiquitination, were revealed. The stability of PTBP1, critical for its function, was enhanced by the m6A modification mediated by METTL3, highlighting a complex regulatory network in the pathogenesis of ISD. These data confirmed that PTBP1 plays a pivotal role in promoting the oxidative response and cuproptosis in Alzheimer's disease models via the SLC31A1 pathway. The findings suggest that PTBP1 could serve as a potential biomarker for the diagnosis and prognostic evaluation of ISD and AD, paving the way for the development of novel therapeutic strategies targeting this protein.

Identification of immune characteristic biomarkers and therapeutic targets in cuproptosis for rheumatoid arthritis by integrated bioinformatics analysis and single-cell RNA sequencing analysis

Introduction

Rheumatoid arthritis (RA) is a chronic autoimmune disorder intricately liked with inflammation. Cuproptosis, an emerging type of cell death, has been implicated in the initiation and development of RA. However, the exact alterations in the expression and biological function of cuproptosis-related genes (CRGs) in RA remain poorly understood. Therefore, our study aims to elucidate the potential association between CRGs and RA, with the goal of identifying novel biomarkers for the treatment and prognosis of RA.

Methods

In this study, we identified ten differentially expressed cuproptosis-related genes (DE-CRGs) between patients with RA and controls. Through comprehensive functional enrichment and protein-protein interaction (PPI) network analysis, we explored the functional roles of the DE-CRGs. Additionally, we investigated the correlation between DE-CRGs and immune infiltration, immune factors, diagnostic efficacy, and potential therapeutic drugs.

Results

Leveraging single-cell RNA sequencing data, we conducted a detailed analysis to elucidate alterations in various cell clusters associated with RA. Our study unveiled a significant association between DE-CRGs and diverse biological functions, as well as potential drug candidates.

Discussion

These findings provide crucial insights into the involvement of DE-CRGs in the pathogenesis of RA and shed light on potential therapeutic strategies.

Screening and validating genes associated with cuproptosis in systemic lupus erythematosus by expression profiling combined with machine learning

Cell death has long been a focal point in life sciences research, and recently, scientists have discovered a novel form of cell death induced by copper, termed cuproptosis. This paper aimed to identify genes associated with cuproptosis in systemic lupus erythematosus (SLE) through machine learning, combined with single-cell RNA sequencing (scRNA-seq), to screen and validate related genes. The analytical results were then experimentally verified. Two published microarray gene expression datasets (GSE65391 and GSE61635) from SLE and control peripheral blood samples were downloaded from the GEO database. The GSE65391 dataset was used as the training group, while the GSE61635 dataset served as the validation group. Differentially expressed genes from GSE65391 identified 12 differential genes. Nine diagnostic genes, considered potential biomarkers, were selected using the least absolute shrinkage and selection operator and support vector machine recursive feature elimination analysis. The receiver operating characteristic (ROC) curves for both the training and validation groups were used to calculate the area under the curve to assess discriminatory properties. CIBERSORT was used to assess the relationship between these diagnostic genes and a reference set of infiltrating immune cells. scRNA-seq data (GSE162577) from SLE patients were also obtained from the GEO database and analyzed. Experimental validation of the most important SLE biomarkers was performed. Twelve significantly different cuproptosis-related genes were identified in the GSE65391 training set. Immune cell analysis revealed 12 immune cell types and identified nine signature genes, including PDHB, glutaminase (GLS), DLAT, LIAS, MTF1, DLST, DLD, LIPT1, and FDX1. In the GSE61635 validation set, seven genes were weakly expressed, and two genes were strongly expressed in the treatment group. According to the ROC curves, PDHB and GLS demonstrated significant diagnostic value. Additionally, correlation analysis was conducted on the nine characteristic genes in relation to immune infiltration. The distribution of key genes in immune cells was determined using scRNA-seq data. Finally, the mRNA expression of the nine diagnostic genes was validated using qPCR.

Identifying Lipid Metabolism-Related Therapeutic Targets and Diagnostic Markers for Lung Adenocarcinoma by Mendelian Randomization and Machine Learning Analysis

BACKGROUND

Lipid metabolic disorders are emerging as a recognized influencing factors of lung adenocarcinoma (LUAD). This study aims to investigate the influence of lipid metabolism-related genes (LMRGs) on the diagnosis and treatment of LUAD and to identify significant biomarkers.

METHODS

DESeq2 and robust rank aggregation (RRA) analyses were employed to determine the differential expression of LMRGs from TCGA-LUAD and five GEO datasets. Mendelian randomization (MR) was conducted utilizing protein quantitative trait loci (pQTLs) in the deCODE, prot-a, and UKB-PPP Study to estimate causal relationships between plasma proteins and LUAD within the ieu-a-984, ieu-a-965, and FinnGen R10 cohorts as potential drug targets of LUAD. Subsequently, an optimal machine learning model for diagnosing LUAD was established by comparing four models: support vector machine, random forest (RF), glmBoost, and eXtreme Gradient Boosting. Finally, the diagnostic performance of five plasma proteins was validated through nomogram analysis, calibration curve assessment, decision curve analysis (DCA), independent internal and external datasets.

RESULT

A total of five biomarkers were identified from 1034 LMRGs via MR and differential expression analysis. TNFRSF21 exhibited a positive association with LUAD risk; conversely, BCHE, FABP4, LPL, and PLBD1 demonstrated negative correlations with this risk. The RF machine learning model was determined to be the optimal model for diagnosing LUAD using these five plasma proteins. Ultimately, nomogram construction, calibration curve analysis, DCA, as well as independent internal and external dataset validation confirmed that these biomarkers exhibit excellent diagnostic performance.

CONCLUSIONS

BCHE, FABP4, LPL, PLBD1, and TNFRSF21 represent potential novel reliable diagnostic markers as well as therapeutic targets for LUAD.

Cuproptosis and copper as potential mechanisms and intervention targets in Alzheimer's disease

Recently study has found a new form of copper-dependent death called cuproptosis, which differs from apoptosis, ferroptosis, and necrosis. The main process of cuproptosis is copper directly combined with lipid-acetylated proteins in the TCA cycle of mitochondrial response, leading to the aggregation of lipid-acetylated proteins and the loss of Fe-S cluster proteins, resulting in mitochondrial dysfunction, and eventually causing cell death. Previous studies demonstrated that an imbalance in copper homeostasis exacerbates the pathological progression of Alzheimer's disease (AD) through the induction of oxidative stress, inflammatory response, and the accumulation of Aβ deposition and tau protein hyperphosphorylation. However, the underlying mechanisms remains to be elucidated. More importantly, research identifies the role of cuproptosis and further elucidates the underlying molecular mechanisms in AD. This review summarized the effects of copper metabolism on AD pathology, the characteristics and mechanism of cuproptosis and we discuss the significance of cuproptosis in the pathogenesis of AD.

Identifying disulfidptosis-related biomarkers in epilepsy based on integrated bioinformatics and experimental analyses

One of the underlying mechanisms of epilepsy (EP), a brain disease characterized by recurrent seizures, is considered to be cell death. Disulfidptosis, a proposed novel cell death mechanism, is thought to play a part in the pathogenesis of epilepsy, but the exact role is unclear. The gene expression omnibus series (GSE) 33000 and GSE63808 datasets were used to search for differentially expressed disulfidptosis-related molecules (DE-DRMs). A correlation between the DE-DRMs was discovered. Individuals with epilepsy were then used to investigate molecular clusters based on the expression of DE-DRMs. Following that, the best machine learning model which is validated by GSE143272 dataset and predictor molecules were identified. The correlation between predictive molecules and clinical traits was determined. Based on the in vitro and in vivo seizures models, experimental analyses were applied to verify the DE-DRMs expressions and the correlation between them. Nine molecules were identified as DE-DRMs: glycogen synthase 1 (GYS1), solute carrier family 3 member 2 (SLC3A2), solute carrier family 7 member 11 (SLC7A11), NADH:ubiquinone oxidoreductase core subunit S1 (NDUFS1), 3-oxoacyl-ACP synthase, mitochondrial (OXSM), leucine rich pentatricopeptide repeat containing (LRPPRC), NADH:ubiquinone oxidoreductase subunit A11 (NDUFA11), NUBP iron‑sulfur cluster assembly factor, mitochondrial (NUBPL), and NCK associated protein 1 (NCKAP1). NDUFS1 interacted with NDUFA11, NUBPL, and LRPPRC, while SLC3A2 interacted with SLC7A11. The optimal machine learning model was revealed to be the random forest (RF) model. G protein guanine nucleotide-binding protein alpha subunit q (GNAQ) was linked to sodium valproate resistance. The experimental analyses suggested an upregulated SLC7A11 expression, an increased number of formed SLC3A2 and SLC7A11 complexes, and a decreased number of formed NDUFS1 and NDUFA11 complexes. This study provides previously undocumented evidence of the relationship between disulfidptosis and EP. In addition to suggesting that SLC7A11 may be a specific DRM for EP, this research demonstrates the alterations in two disulfidptosis-related protein complexes: SLC7A11-SLC3A2 and NDUFS1-NDUFA11.

Integrated bioinformatics analysis identified cuproptosis-related hub gene Mpeg1 as potential biomarker in spinal cord injury

Spinal cord injury (SCI) is a profound ailment lacking a well-defined molecular mechanism and effective treatments. Cuproptosis, identified as a recently discovered cell death pathway, exhibits diverse roles in various cancers. Nevertheless, its involvement in SCI is yet to be elucidated. Firstly, the RNA sequencing data of 1, 3, 7 dpi SCI samples were collected from GEO database. We performed differential expression analysis on these samples with varying cuproptosis-related scores calculating by ssGSEA. Subsequently, we conducted enrichment analyses with KEGG, GO, and GSEA. Simultaneously, we executed WGCNA analysis using cuproptosis-related scores, selecting the most relevant module for enrichment analysis. Hub genes were identified at the intersection of PPI analysis results from two modules and cuproptosis-related DEGs. Additionally, relying on the immune infiltration landscape associated with cuproptosis, we carried out immune cell correlation analysis on hub genes. Finally, to corroborate our earlier findings, we utilized single-cell RNA-seq analysis and in vitro experimental validation. Based on ssGSEA, differential expression analysis and WGCNA analysis, we identified two modules that were highly relevant to cell division and immune processes, respectively. From these modules, we identified two hub genes, Cd48 and Mpeg1, which exhibited a strong positive correlation (R = 0.92) and shared similar pathways. Furthermore, we observed a positive correlation between M2 macrophages and Cd48/Mpeg1. To validate our findings, we performed external cohort validation using a single-cell RNA sequencing dataset. The results confirmed that Mpeg1 was highly expressed in microglia (macrophages in center nervous system) following spinal cord injury. Additionally, we conducted in vitro experiments to further validate the molecular functions of Mpeg1 in SCI. In summary, targeting Mpeg1, as well as cuproptosis and immune cell infiltration, holds promise as a potential strategy for reducing spinal cord tissue damage and promoting recovery after SCI. These findings provide valuable insights for future therapeutic interventions.

NDUFA11 may be the disulfidptosis-related biomarker of ischemic stroke based on integrated bioinformatics, clinical samples, and experimental analyses

Background

Programmed cell death plays an important role in neuronal injury and death after ischemic stroke (IS), leading to cellular glucose deficiency. Glucose deficiency can cause abnormal accumulation of cytotoxic disulfides, resulting in disulfidptosis. Ferroptosis, apoptosis, necroptosis, and autophagy inhibitors cannot inhibit this novel programmed cell death mechanism. Nevertheless, the potential mechanisms of disulfidptosis in IS remain unclear.

Methods

The GSE16561 dataset was used to screen for differentially expressed disulfidptosis-related biomarkers (DE-DRBs). A correlation between the DE-DRBs was detected. The optimal machine-learning (ML) model and predictor molecules were determined. The GSE58294 dataset was used to verify the accuracy of the optimal ML model. The DE-DRB expression was detected in the blood of patients with IS. Based on IS models, experimental analyses were performed to verify DE-DRB expression and the correlation between DE-DRBs.

Results

Leucine-rich pentatricopeptide repeat-containing (LRPPRC) and NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 11 (NDUFA11) were identified as DE-DRBs. The NADH: ubiquinone oxidoreductase core subunit S1 (NDUFS1) interacted with NDUFA11 and LRPPRC. The support vector machine (SVM) model was identified as the optimal ML model. The NDUFA11 expression level in the blood of patients with IS was 20.9% compared to that in normal controls. NDUFA11 expression was downregulated in the in vitro/in vivo models of IS. The number of formed complexes of NDUFS1 and NDUFA11 decreased in the in vitro/in vivo models of IS.

Conclusion

This research suggests that NDUFA11 is a specific DRB for IS and demonstrates alterations in the disulfidptosis-related protein complexes NDUFS1-NDUFA11.

Identification of biomarkers for knee osteoarthritis through clinical data and machine learning models

Knee osteoarthritis (KOA) represents a progressive degenerative disorder characterized by the gradual erosion of articular cartilage. This study aimed to develop and validate biomarker-based predictive models for KOA diagnosis using machine learning techniques. Clinical data from 2594 samples were obtained and stratified into training and validation datasets in a 7:3 ratio. Key clinical features were identified through differential analysis between KOA and control groups, combined with least absolute shrinkage and selection operator (LASSO) regression. The SHapley Additive Planning (SHAP) method was employed to rank feature importance quantitatively. Based on these rankings, predictive models were constructed using Logistic Regression (LR), Random Forest (RF), eXtreme Gradient Boosting (xGBoost), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) algorithms. Models were developed for subsets of variables, including the top 5, top 10, top 15, and all identified features. Receiver operating characteristic (ROC) curves were applied to compare diagnostic performance across models. Additionally, a risk stratification framework for KOA prediction was designed using recursive partitioning analysis (RPA). Using difference analysis and LASSO, 44 critical clinical features were identified. Among these, age, plasma prothrombin time, gender, body mass index (BMI), and prothrombin time and international normalized ratio (PTINR) emerged as the top five features, with SHAP values of 0.1990, 0.0981, 0.0471, 0.0433, and 0.0422, respectively. Machine learning analysis demonstrated that these variables provided robust diagnostic performance for KOA. In the training set, area under the curve (AUC) values for LR, RF, xGBoost, NB, SVM, and DT models were 0.947, 0.961, 0.892, 0.952, 0.885, and 0.779, respectively. Similarly, in the validation dataset, these models achieved AUC values of 0.961, 0.943, 0.789, 0.957, 0.824, and 0.76. Among them, RF consistently exhibited superior diagnostic accuracy for KOA. Additionally, RPA analysis indicated a higher prevalence of KOA among individuals aged 54 years and older. The integration of the top five clinical variables significantly enhanced the diagnostic accuracy for KOA, particularly when employing the RF model. Moreover, the RPA model offered valuable insights to assist clinicians in refining prognostic assessments and optimizing clinical decision-making processes.

A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involved in the pathogenesis of COPD. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of disulfidptosis in the development of COPD could provide a opportunity for primary prediction, targeted prevention, and personalized treatment of the disease.

METHODS

We analyzed the expression profiles of DRGs and immune cell infiltration in COPD patients by using the GSE38974 dataset. According to the DRGs, molecular clusters and related immune cell infiltration levels were explored in individuals with COPD. Next, co-expression modules and cluster-specific differentially expressed genes were identified by the Weighted Gene Co-expression Network Analysis (WGCNA). Comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB), we constructed the ptimal machine learning model.

RESULTS

DE-DRGs, differential immune cells and two clusters were identified. Notable difference in DRGs, immune cell populations, biological processes, and pathway behaviors were noted among the two clusters. Besides, significant differences in DRGs, immune cells, biological functions, and pathway activities were observed between the two clusters.A nomogram was created to aid in the practical application of clinical procedures. The SVM model achieved the best results in differentiating COPD patients across various clusters. Following that, we identified the top five genes as predictor genes via SVM model. These five genes related to the model were strongly linked to traits of the individuals with COPD.

CONCLUSION

Our study demonstrated the relationship between disulfidptosis and COPD and established an optimal machine-learning model to evaluate the subtypes and traits of COPD. DRGs serve as a target for future predictive diagnostics, targeted prevention, and individualized therapy in COPD, facilitating the transition from reactive medical services to PPPM in the management of the disease.

NMF typing and machine learning algorithm-based exploration of preeclampsia-related mechanisms on ferroptosis signature genes

BACKGROUND

Globally, pre-eclampsia (PE) poses a major threat to the health and survival of pregnant women and fetuses, contributing significantly to morbidity and mortality. Recent studies suggest a pathological link between PE and ferroptosis. We aim to utilize non-negative matrix factorization (NMF) clustering and machine learning algorithms to pinpoint disease-specific genes related to the process of ferroptosis in PE and investigate likely underlying biochemistry mechanisms.

METHODS

The acquisition of four microarray datasets from the Gene Expression Omnibus (GEO) repository, the integration of these datasets, and the elimination of batch effects formed the core procedure. Genes related to ferroptosis in PE (DE-FRG) were identified. NMF clustering was performed on DE-FRG for unsupervised analysis, generating a heatmap for clustering validation via principal component analysis. Immunocyte infiltration differences between different subtypes were compared to elucidate the impact of ferroptosis on immune infiltration in the placental tissue of PE patients. The application of weighted gene co-expression network analysis (WGCNA) revealed important module genes linked to sample subtypes and disease status. The screening of PE feature genes involved employing SVM, RF, GLM, and XGB machine learning algorithms, and their predictive performance was validated using various analyses and an external dataset. The iRegulon tool was utilized to predict upstream transcription factors associated with ferroptosis feature genes, from which differentially expressed transcription factors were screened to construct a "Transcription Factor-FRG-ferroptosis" regulatory network. Finally, in vitro (cultured cells) and in vivo (rat) models were utilized to evaluate the regulatory mechanisms of ferroptosis in normal and PE placental tissues.

RESULTS

Differential analysis of the four merged GEO datasets identified 41 DE-FRGs. NMF clustering based on DE-FRGs revealed two PE subtypes. Immunocyte infiltration analysis indicated significant differences in immune levels between these subtypes. Further WGCNA analysis identified module genes associated with PE and these two subtypes. Subsequently, we developed an integrated machine learning model incorporating five FRGs and validated its predictive efficacy using various analyses and an external validation dataset. Finally, based on the transcription factor ARID3A and ferroptosis feature genes EPHB3 and PAPPA2, we constructed a "Transcription Factor-FRG-ferroptosis" regulatory network, with in vitro and in vivo experiments confirming that ARID3A promotes the progression of PE and ferroptosis by activating the expression of EPHB3 and PAPPA2.

CONCLUSION

This analytical journey illuminated a critical regulatory nexus in PE, underscoring the central influence of ARID3A on PE through ferroptosis-mediated pathways.

The study on cuproptosis in Alzheimer's disease based on the cuproptosis key gene FDX1

Background

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by memory and cognitive impairments. Previous studies have shown neuronal death in the brains of AD patients, but the role of cuproptosis and its associated genes in AD neurons remains unclear.

Methods

Intersection analysis was conducted using the AD transcriptome dataset GSE63060, neuron dataset GSE147528, and reported cuproptosis-related genes to identify the cuproptosis key gene FDX1 highly expressed in AD. Subsequently, cell experiments were performed by treating SH-SY5Y cells with Aβ25-35 to establish AD cell model. The real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) and western blotting (WB) assays were employed to detect the expression levels of FDX1, DLAT, and DLST. Cell proliferation was analyzed by counting Kit-8 (CCK8), mitochondrial ROS levels were analyzed using flow cytometry. shRNA was used to downregulate FDX1 expression, followed by repetition of the aforementioned experiments. Clinical experiments utilized qPCR to detect FDX1 mRNA levels in peripheral venous blood of patients, and analyzed FDX1 expression differences in different APOE genotypes of AD patients. Finally, a protein-protein interaction (PPI) network of FDX1 was constructed based on the GeneMANIA database, immune infiltration analysis was conducted using R language, and transcription factors prediction for FDX1 was performed based on the ENCODE database.

Results

The cuproptosis key gene FDX1 showed significantly higher expression in peripheral blood and neuron models of AD compared to non-AD individuals, with significantly higher expression in APOE ε4/ε4 genotype than other APOE genotype of AD patients. Knockdown of FDX1 expression reduced the lipidation levels of DLAT and DLST in neurons, alleviated ROS accumulation in mitochondria, improved cell viability, and mitigated cuproptosis. Immune infiltration analysis results indicated a high enrichment of peripheral blood γδ-T lymphocytes in AD, and FDX1 was significantly associated with the infiltration of four immune cells and may be regulated by three transcription factors.

Conclusion

The cuproptosis key gene FDX1 is highly expressed in AD and may promote cuproptosis in AD neurons by regulating the lipidation levels of DLAT and DLST, thereby participating in the onset and development of AD. This provides a potential target for the diagnosis and treatment of AD.

Identification of cuproptosis-related genes in septic shock based on bioinformatic analysis

BACKGROUND

Septic shock is a life-threatening condition characterized by a failure of organ systems and a high mortality rate. Cuproptosis is a new form of cell death that is triggered by copper overload. However, the relationship between cuproptosis-related genes and septic shock remains unclear.

METHODS

The GSE26440 dataset from the GEO database was used to screen differentially expressed genes (DEGs) between control and septic shock samples. Additionally, hub genes related to the progression of septic shock and cuproptosis were screened by Venn analysis. RT-qPCR was utilized to validate the expression of hub genes in peripheral blood lymphocytes from septic shock patients and healthy controls. Next, functional analysis and immune cells infiltration were performed.

RESULTS

SLC31A1 and MTF1 levels were obviously elevated and LIAS and LIPT1 levels were downregulated in septic shock samples, compared to normal controls. The diagnostic values of the four genes were confirmed with receiver operating characteristic (ROC) curves. Additionally, SLC31A1 and MTF1 showed a positive correlation with natural killer cells and LIAS and LIPT1 exhibited a positive correlation with CD8+ T cells. Furthermore, compared to low-level groups, MAPK signaling was activated in the high-SLC31A1 level group, VEGF signaling was activated in the high-MTF1 level group and lipoic acid metabolism was activated in high-LIAS and high-LIPT1 level groups.

CONCLUSION

This study demonstrates that SLC31A1, MTF1, LIAS, and LIPT1 are dysregulated in septic shock samples, and these genes exhibit potential diagnostic efficacy in septic shock, suggesting that these genes may be potential biomarkers for the diagnosis of septic shock.

Neuroinflammation in Age-Related Neurodegenerative Diseases: Role of Mitochondrial Oxidative Stress

A shared hallmark of age-related neurodegenerative diseases is the chronic activation of innate immune cells, which actively contributes to the neurodegenerative process. In Alzheimer's disease, this inflammatory milieu exacerbates both amyloid and tau pathology. A similar abnormal inflammatory response has been reported in Parkinson's disease, with elevated levels of cytokines and other inflammatory intermediates derived from activated glial cells, which promote the progressive loss of nigral dopaminergic neurons. Understanding the causes that support this aberrant inflammatory response has become a topic of growing interest and research in neurodegeneration, with high translational potential. It has been postulated that the phenotypic shift of immune cells towards a proinflammatory state combined with the presence of immunogenic cell death fuels a vicious cycle in which mitochondrial dysfunction plays a central role. Mitochondria and mitochondria-generated reactive oxygen species are downstream effectors of different inflammatory signaling pathways, including inflammasomes. Dysfunctional mitochondria are also recognized as important producers of damage-associated molecular patterns, which can amplify the immune response. Here, we review the major findings highlighting the role of mitochondria as a checkpoint of neuroinflammation and immunogenic cell deaths in neurodegenerative diseases. The knowledge of these processes may help to find new druggable targets to modulate the inflammatory response.

Copper homeostasis and cuproptosis in central nervous system diseases

Copper (Cu), an indispensable micronutrient for the sustenance of living organisms, contributes significantly to a vast array of fundamental metabolic processes. The human body maintains a relatively low concentration of copper, which is mostly found in the bones, liver, and brain. Despite its low concentration, Cu plays a crucial role as an indispensable element in the progression and pathogenesis of central nervous system (CNS) diseases. Extensive studies have been conducted in recent years on copper homeostasis and copper-induced cell death in CNS disorders, including glioma, Alzheimer's disease, Amyotrophic lateral sclerosis, Huntington's disease, and stroke. Cuproptosis, a novel copper-induced cell death pathway distinct from apoptosis, necrosis, pyroptosis, and ferroptosis, has been identified as potentially intricately linked to the pathogenic mechanisms underlying various CNS diseases. Therefore, a systematic review of copper homeostasis and cuproptosis and their relationship with CNS disorders could deepen our understanding of the pathogenesis of these diseases. In addition, it may provide new insights and strategies for the treatment of CNS disorders.

Identification of cuproptosis-related genes in Alzheimer's disease based on bioinformatic analysis

OBJECTIVE

To explore the role of cuproptosis in Alzheimer's disease (AD).

METHODS

An AD-related microarray dataset was downloaded from the Gene Expression Omnibus (GEO) database (GSE140830). Weighted gene co-expression network analysis was used to identify AD-related modular genes. The Venn analysis was performed to obtain module genes associated with apoptosis and cuproptosis. Besides, we conducted an enrichment analysis of overlapped genes and constructed the protein-protein interaction (PPI) network, followed by screening hub genes and those significantly associated with AD were used to construct models of apoptosis and cuproptosis, respectively. Further, receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA), and subgroup analysis were used to compare the AD prediction performance of two models. Finally, the accuracy and reliability of AD prediction models were verified by GSE26927.

RESULTS

We obtained 42 module genes related to apoptosis and 9 module genes related to cuproptosis. The enrichment analysis results revealed MAPK signaling pathway as the common signaling pathway of apoptosis- and cuproptosis-related genes. Next, the hub genes associated with apoptosis (TRADD, FADD, BIRC2, and CASP2) and cuproptosis (MAP2K1, SLC31A1, and PDHB) in AD were identified, which were used to construct apoptosis and cuproptosis models to distinguish AD patients from the control group (P < 0.05). The ROC, DCA, and subgroup analysis results showed that apoptosis-related models and cuproptosis-related models had comparable ability in predicting AD. GSE26927 further confirmed that the two models have comparable predictive effects for AD.

CONCLUSIONS

The cuproptosis model had a certain performance in predicting AD. Three hub genes (MAP2K1, SLC31A1, and PDHB) closely related to cuproptosis in AD might serve as biomarkers for AD diagnosis and treatment.

Deciphering cuproptosis-related signatures in pediatric allergic asthma using integrated scRNA-seq and bulk RNA-seq analysis

OBJECTIVE

Allergic asthma (AA) is common in children. Excess copper is observed in AA patients. It is currently unclear whether copper imbalance can cause cuproptosis in pediatric AA.

METHODS

The datasets about pediatric AA (GSE40732 and GSE40888) were obtained from Gene Expression Omnibus (GEO) database. The expression of cuproptosis-related genes (CRGs) and immune cell infiltration in pediatric AA samples were analyzed. Single-cell RNA sequencing (scRNA-seq) data (GSE193816) were used to evaluate the expression patterns of CRGs in AA. The identification of differentially expressed genes within clusters was conducted using weighted gene co-expression network analysis. Subsequently, disease progression and cuproptosis-related models were screened using random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and general linear model (GLM) algorithms.

RESULTS

Four CRGs were notably increased in pediatric AA samples. CD4+ T cells, macrophages and mast cells exhibited a lower cuproptosis score in AA samples, indicating that these immune cells may be closely associated with cuproptosis in AA development. Co-expression network of CRGs in AA was constructed. AA samples were divided into two cuprotosis clusters. Following construction of four machine-learning models, SVM model exhibited the highest efficacy of prediction in the testing set (AUC = 0.952). SVM model containing five important variables can be used for prediction of AA.

CONCLUSION

This work provided a machine learning model containing five important variables, which may have good diagnostic efficiency for pediatric AA.

Identification of diagnostic markers and molecular clusters of cuproptosis-related genes in alcohol-related liver disease based on machine learning and experimental validation

BACKGROUND AND AIMS

Alcohol-related liver disease (ALD) is a worldwide burden. Cuproptosis has been shown to play a key role in the development of several diseases. However, the role and mechanisms of cuproptosis in ALD remain unclear.

METHODS

The RNA-sequencing data of ALD liver samples were downloaded from the Gene Expression Omnibus (GEO) database. Bioinformatical analyses were performed using the R data package. We then identified key genes through multiple machine learning methods. Immunoinfiltration analyses were used to identify different immune cells in ALD patients and controls. The expression levels of key genes were further verified.

RESULTS

We identified three key cuproptosis-related genes (CRGs) (DPYD, SLC31A1, and DBT) through an in-depth analysis of two GEO datasets, including 28 ALD samples and eight control samples. The area under the curve (AUC) value of these three genes combined in determining ALD was 1.0. In the external datasets, the three key genes had AUC values as high as 1.0 and 0.917, respectively. Nomogram, decision curve, and calibration curve analyses also confirmed these genes' ability to predict the diagnosis. These three key genes were found to be involved in multiple pathways associated with ALD progression. We confirmed the mRNA expression of these three key genes in mouse ALD liver samples. Regarding immune cell infiltration, the numbers of B cells, CD8 (+) T cells, NK cells, T-helper cells, and Th1 cells were significantly lower in ALD patient samples than in control liver samples. Single sample gene set enrichment analysis (ssGSEA) was then used to estimate the immune microenvironment of different CRG clusters and CRG-related gene clusters. In addition, we calculated CRG scores through principal component analysis (PCA) and selected Sankey plots to represent the correlation between CRG clusters, gene clusters, and CRG scores. Finally, the three key genes were confirmed in mouse ALD liver samples and liver cells treated with ethanol.

CONCLUSIONS

We first established a prognostic model for ALD based on 3 CRGs and robust prediction efficacy was confirmed. Our investigation contributes to a comprehensive understanding of the role of cuproptosis in ALD, presenting promising avenues for the exploration of therapeutic strategies.

Identification of cuproptosis-related genes related to the progression of ankylosing spondylitis by integrated bioinformatics analysis

Ankylosing spondylitis (AS) is an autoimmune disease, and the relationship between copper death and AS is not clear. The aim of this study was to analyze and identify potential cuprosis-related genes associated with the onset of AS by bioinformatics methods. We obtained the AS gene expression profile GSE25101 from the Gene Expression Omnibus (GEO) database, which consists of blood samples from 16 active AS patients and 16 sex-and age-matched controls. After analyzing the data, we utilized the WGCNA method to identify genes that exhibited significant differential expression. In order to assess the prognostic and predictive power of these genes, we constructed receiver operating characteristic (ROC) curves. To further validate our predictions, we employed nomograms, calibration curves, decision curve analysis, and external datasets. Lastly, we conducted an analysis on immune infiltration and explored the correlation between key genes and immune response. Three genes, namely INPP5E, CYB5R1, and HGD, have been identified through analysis to be associated with AS. The diagnosis of patients using these genes has been found to possess a high level of accuracy. The area under the ROC curve is reported to be 0.816 for INPP5E, 0.879 for CYB5R1, and also 0.879 for HGD. Furthermore, the nomogram demonstrates an excellent predictive power, and it has been calibrated using a Calibration curve. Its clinical usefulness and net benefit have been thoroughly analyzed and estimated through the use of a DCA curve. Moreover, INPP5E, CYB5R1, and HGD are found to be associated with various types of immune cells. In conclusion, the systematic analysis of cuprosis-related genes may aid in the identification of mechanisms related to copper-induced cell death in AS and offer valuable biomarkers for the diagnosis and treatment of AS.

Identification of copper death-associated molecular clusters and immunological profiles for lumbar disc herniation based on the machine learning

Lumbar disc herniation (LDH) is a common clinical spinal disorder, yet its etiology remains unclear. We aimed to explore the role of cuproptosis-related genes (CRGs) and identify potential diagnostic biomarkers. Our analysis involved interrogating the GSE124272 and GSE150408 datasets for differential gene expression profiles associated with CRGs and immune characteristics. Molecular clustering was performed on LDH samples, followed by expression and immune infiltration analyses. Using the WGCNA algorithm, specific genes within CRG clusters were identified. After selecting the most predictive genes from the optimal model, four machine learning models were constructed and validated. This study identified nine CRGs associated with copper-regulated cell death. Two copper-containing molecular clusters linked to death were detected in LDH samples. Elevated expression and immune infiltration levels were found in LDH patients, particularly in CRG cluster C2. Utilizing XGB, five genes were identified for constructing a diagnostic model, achieving an area under the curve values of 0.715. In conclusion, this research provides valuable insights into the association between LDH and copper-regulated cell death, alongside proposing a promising predictive model.

Establishment of a prognostic risk model for prostate cancer based on Gleason grading and cuprotosis related genes

INTRODUCTION

Prostate cancer (PCa) is common in aging males, diagnosed via the Gleason grading system. The study explores the unexamined prognostic value of cuprotosis, a distinct cell death type, alongside Gleason grades in PCa.

METHODS

We explored Cuprotosis-related genes (CRGs) in prostate cancer (PCa), using NMF on TCGA-PRAD data for patient classification and WGCNA to link genes with Gleason scores and prognosis. A risk model was crafted via LASSO Cox regression. STX3 knockdown in PC-3 cells, analyzed for effects on cell behaviors and tumor growth in mice, highlighted its potential therapeutic impact.

RESULTS

We identified five genes crucial for a prognostic risk model, with higher risk scores indicating worse prognosis. Survival analysis and ROC curves confirmed the model's predictive accuracy in TCGA-PRAD and GSE70769 datasets. STX3 was a key adverse prognostic factor, with its knockdown significantly reducing mRNA and protein levels, impairing PC-3 cell functions. In vivo, STX3 knockdown in PC-3 cells led to significantly smaller tumors in nude mice, underscoring its potential therapeutic value.

CONCLUSION

Our prognostic model, using five genes linked to Gleason scores, effectively predicts prostate cancer outcomes, offering a novel treatment strategy angle.

Mechanisms of cuproptosis and its relevance to distinct diseases

Copper is a trace element required by the organism, but once the level of copper exceeds the threshold, it becomes toxic and even causes death. The underlying mechanisms of copper-induced death are inconclusive, with different studies showing different opinions on the mechanism of copper-induced death. Multiple investigations have shown that copper induces oxidative stress, endoplasmic reticulum stress, nucleolar stress, and proteasome inhibition, all of which can result in cell death. The latest research elucidates a copper-dependent death and denominates it as cuproptosis. Cuproptosis takes place through the combination of copper and lipoylated proteins of the tricarboxylic acid cycle, triggering agglomeration of lipoylated proteins and loss of iron-sulfur cluster proteins, leading to proteotoxic stress and ultimately death. Given the toxicity and necessity of copper, abnormal levels of copper lead to diseases such as neurological diseases and cancer. The development of cancer has a high demand for copper, neurological diseases involve the change of copper contents and the binding of copper to proteins. There is a close relationship between these two kinds of diseases and copper. Here, we summarize the mechanisms of copper-related death, and the association between copper and diseases, to better figure out the influence of copper in cell death and diseases, thus advancing the clinical remedy of these diseases.

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.

Identification of HK3 as a promising immunomodulatory and prognostic target in sepsis-induced acute lung injury

BACKGROUND

Sepsis is a life-threatening global disease with a significant impact on human health. Acute lung injury (ALI) has been identified as one of the primary causes of mortality in septic patients. This study aimed to identify candidate genes involved in sepsis-induced ALI through a comprehensive approach combining bioinformatics analysis and experimental validation.

METHODS

The datasets GSE65682 and GSE32707 obtained from the Gene Expression Omnibus database were merged to screen for sepsis-induced ALI related differentially expressed genes (DEGs). Functional enrichment and immune infiltration analyses were conducted on DGEs, with the construction of protein-protein interaction (PPI) networks to identify hub genes. In vitro and in vivo models of sepsis-induced ALI were used to study the expression and function of hexokinase 3 (HK3) using various techniques including Western blot, real-time PCR, immunohistochemistry, immunofluorescence, Cell Counting Kit-8, Enzyme-linked immunosorbent assay, and flow cytometry.

RESULTS

The results of bioinformatics analysis have identified HK3, MMP9, and S100A8 as hub genes with diagnostic and prognostic significance for sepsis-induced ALI. The HK3 has profound effects on sepsis-induced ALI and exhibits a correlation with immune regulation. Experimental results showed increased HK3 expression in lung tissue of septic mice, particularly in bronchial and alveolar epithelial cells. In vitro studies demonstrated upregulation of HK3 in lipopolysaccharide (LPS)-stimulated lung epithelial cells, with cytoplasmic localization around the nucleus. Interestingly, following the knockdown of HK3 expression, lung epithelial cells exhibited a significant decrease in proliferation activity and glycolytic flux, accompanied by an increase in cellular inflammatory response, oxidative stress, and cell apoptosis.

CONCLUSIONS

It was observed for the first time that HK3 plays a crucial role in the progression of sepsis-induced ALI and may be a valuable target for immunomodulation and therapy.Bioinformatics analysis identified HK3, MMP9, and S100A8 as hub genes with diagnostic and prognostic relevance in sepsis-induced ALI. Experimental findings showed increased HK3 expression in the lung tissue of septic mice, particularly in bronchial and alveolar epithelial cells. In vitro experiments demonstrated increased HK3 levels in lung epithelial cells stimulated with LPS, with cytoplasmic localization near the nucleus. Knockdown of HK3 expression resulted in decreased proliferation activity and glycolytic flux, increased inflammatory response, oxidative stress, and cell apoptosis in lung epithelial cells.

Identification of cuproptosis-realated key genes and pathways in Parkinson's disease via bioinformatics analysis

INTRODUCTION

Parkinson's disease (PD) is the second most common worldwide age-related neurodegenerative disorder without effective treatments. Cuproptosis is a newly proposed conception of cell death extensively studied in oncological diseases. Currently, whether cuproptosis contributes to PD remains largely unclear.

METHODS

The dataset GSE22491 was studied as the training dataset, and GSE100054 was the validation dataset. According to the expression levels of cuproptosis-related genes (CRGs) and differentially expressed genes (DEGs) between PD patients and normal samples, we obtained the differentially expressed CRGs. The protein-protein interaction (PPI) network was achieved through the Search Tool for the Retrieval of Interacting Genes. Meanwhile, the disease-associated module genes were screened from the weighted gene co-expression network analysis (WGCNA). Afterward, the intersection genes of WGCNA and PPI were obtained and enriched using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, the key genes were identified from the datasets. The receiver operating characteristic curves were plotted and a PPI network was constructed, and the PD-related miRNAs and key genes-related miRNAs were intersected and enriched. Finally, the 2 hub genes were verified via qRT-PCR in the cell model of the PD and the control group.

RESULTS

525 DEGs in the dataset GSE22491 were identified, including 128 upregulated genes and 397 downregulated genes. Based on the PPI network, 41 genes were obtained. Additionally, the dataset was integrated into 34 modules by WGCNA. 36 intersection genes found from WGCNA and PPI were significantly abundant in 7 pathways. The expression levels of the genes were validated, and 2 key genes were obtained, namely peptidase inhibitor 3 (PI3) and neuroserpin family I member 1 (SERPINI1). PD-related miRNAs and key genes-related miRNAs were intersected into 29 miRNAs including hsa-miR-30c-2-3p. At last, the qRT-PCR results of 2 hub genes showed that the expressions of mRNA were up-regulated in PD.

CONCLUSION

Taken together, this study demonstrates the coordination of cuproptosis in PD. The key genes and miRNAs offer novel perspectives in the pathogenesis and molecular targeting treatment for PD.

Lysosome-related biomarkers in preeclampsia and cancers: Machine learning and bioinformatics analysis

BACKGROUND

Lysosomes serve as regulatory hubs, and play a pivotal role in human diseases. However, the precise functions and mechanisms of action of lysosome-related genes remain unclear in preeclampsia and cancers. This study aimed to identify lysosome-related biomarkers in preeclampsia, and further explore the biomarkers shared between preeclampsia and cancers.

MATERIALS AND METHODS

We obtained GSE60438 and GSE75010 datasets from the Gene Expression Omnibus database, pre-procesed them and merged them into a training cohort. The limma package in R was used to identify the differentially expressed mRNAs between the preeclampsia and normal control groups. Differentially expressed lysosome-related genes were identified by intersecting the differentially expressed mRNAs and lysosome-related genes obtained from Gene Ontology and GSEA databases. Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed using the DAVID database. The CIBERSORT method was used to analyze immune cell infiltration. Weighted gene co-expression analyses and three machine learning algorithm were used to identify lysosome-related diagnostic biomarkers. Lysosome-related diagnostic biomarkers were further validated in the testing cohort GSE25906. Nomogram diagnostic models for preeclampsia were constructed. In addition, pan-cancer analysis of lysosome-related diagnostic biomarkers were identified by was performed using the TIMER, Sangebox and TISIDB databases. Finally, the Drug-Gene Interaction, TheMarker and DSigDB Databases were used for drug-gene interactions analysis.

RESULTS

A total of 11 differentially expressed lysosome-related genes were identified between the preeclampsia and control groups. Three molecular clusters connected to lysosome were identified, and enrichment analysis demonstrated their strong relevance to the development and progression of preeclampsia. Immune infiltration analysis revealed significant immunity heterogeneity among different clusters. GBA, OCRL, TLR7 and HEXB were identified as lysosome-related diagnostic biomarkers with high AUC values, and validated in the testing cohort GSE25906. Nomogram, calibration curve, and decision curve analysis confirmed the accuracy of predicting the occurrence of preeclampsia based on OCRL and HEXB. Pan-cancer analysis showed that GBA, OCRL, TLR7 and HEXB were associated with the prognosis of patients with various tumors and tumor immune cell infiltration. Twelve drugs were identified as potential drugs for the treatment of preeclampsia and cancers.

CONCLUSION

This study identified GBA, OCRL, TLR7 and HEXB as potential lysosome-related diagnostic biomarkers shared between preeclampsia and cancers.

A cuproptosis score model and prognostic score model can evaluate clinical characteristics and immune microenvironment in NSCLC

BACKGROUND

Cuproptosis-related genes (CRGs) are associated with lung adenocarcinoma. However, the links between CRGs and non-small-cell lung cancer (NSCLC) are not clear. In this study, we aimed to develop two cuproptosis models and investigate their correlation with NSCLC in terms of clinical features and tumor microenvironment.

METHODS

CRG expression profiles and clinical data from NSCLC and normal tissues was obtained from GEO (GSE42127) and TCGA datasets. Molecular clusters were classified into three patterns based on CRGs and cuproptosis cluster-related specific differentially expressed genes (CRDEGs). Then, two clinical models were established. First, a prognostic score model based on CRDEGs was established using univariate/multivariate Cox analysis. Then, through principal component analysis, a cuproptosis score model was established based on prognosis-related genes acquired via univariate analysis of CRDEGs. NSCLC patients were divided into high/low risk groups.

RESULTS

Eighteen CRGs were acquired, all upregulated in tumor tissues, 15 of which significantly (P < 0.05). Among the three CRG clusters, cluster B had the best prognosis. In the CRDEG clusters, cluster C had the best survival. In the prognostic score model, the high-risk group had worse prognosis, higher tumor mutation load, and lower immune infiltration while in the cuproptosis score model, a high score represented better survival, lower tumor mutation load, and high-level immune infiltration.

CONCLUSIONS

The cuproptosis score model and prognostic score model may be associated with NSCLC prognosis and immune microenvironment. These novel findings on the progression and immune landscape of NSCLC may facilitate the provision of more personalized immunotherapy interventions for NSCLC patients.

Copper Metabolism and Cuproptosis: Molecular Mechanisms and Therapeutic Perspectives in Neurodegenerative Diseases

Copper is an essential trace element, and plays a vital role in numerous physiological processes within the human body. During normal metabolism, the human body maintains copper homeostasis. Copper deficiency or excess can adversely affect cellular function. Therefore, copper homeostasis is stringently regulated. Recent studies suggest that copper can trigger a specific form of cell death, namely, cuproptosis, which is triggered by excessive levels of intracellular copper. Cuproptosis induces the aggregation of mitochondrial lipoylated proteins, and the loss of iron-sulfur cluster proteins. In neurodegenerative diseases, the pathogenesis and progression of neurological disorders are linked to copper homeostasis. This review summarizes the advances in copper homeostasis and cuproptosis in the nervous system and neurodegenerative diseases. This offers research perspectives that provide new insights into the targeted treatment of neurodegenerative diseases based on cuproptosis.

Identification of Key Genes and Immunological Features Associated with Copper Metabolism in Parkinson's Disease by Bioinformatics Analysis

To explore diagnostic genes associated with cuproptosis in Parkinson's disease (PD) and to characterize immune cell infiltration by comprehensive bioinformatics analysis, three PD datasets were downloaded from the GEO database, two of which were merged and preprocessed as the internal training set and the remaining one as the external validation set. Based on the internal training set, differential analysis was performed to obtain differentially expressed genes (DEGs), and weighted gene co-expression network analysis (WGCNA) was conducted to obtain significant module genes. The genes obtained here were intersected to form the intersecting genes. The intersecting genes obtained from DEGs and WGCNA were intersected with cuproptosis-related genes (CRGs) to generate cuproptosis-related disease signature genes, and functional enrichment analysis was performed on Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, LASSO analysis of the cuproptosis-related disease signature genes was performed to identify key genes and construct a diagnostic and predictive model. Then, single sample gene set enrichment analysis (ssGSEA) was performed on the internal training set to further analyze the correlation between key genes and immune cells. Lastly, the results were validated using an external validation set. A total of 405 DEGs were obtained by differential analysis, and 6 gene modules were identified by WGCNA analysis. The genes in the most significant modules were intersected with the DEGs to obtain 21 intersecting genes. The functions of the intersecting genes were mainly enriched in neurotransmitter transport, GABA-ergic synapse, synaptic vesicle cycle, serotonergic synapse, phenylalanine metabolism, tyrosine metabolism, tryptophan metabolism, etc. Subsequently, the intersecting genes were intersected with CRGs, and LASSO regression analysis was performed to screen 3 key cuproptosis-related disease signature genes, namely, SLC18A2, SLC6A3, and SV2C. The calibration curve of the nomogram model constructed based on these 3 key genes to predict PD showed good agreement, with a C-index of 0.944 and an area under the ROC (AUC) of 0.944 (0.833-1.000). It was also validated by the external dataset that the model constructed with these 3 key genes had good diagnostic and predictive power for PD. The ssGSEA analysis revealed that neutrophils might be the potential core immune cells and that SLC18A2, SLC6A3, and SV2C were significantly negatively correlated with neutrophils, which was also verified in the validation set. PD diagnosis and prediction model based on CRGs (SLC18A2, SLC6A3, and SV2C) has good diagnostic and predictive performance and could be a useful tool in the diagnosis of PD.

Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model

Introduction: Pediatric sepsis (PS) is a life-threatening infection associated with high mortality rates, necessitating a deeper understanding of its underlying pathological mechanisms. Recently discovered programmed cell death induced by copper has been implicated in various medical conditions, but its potential involvement in PS remains largely unexplored. Methods: We first analyzed the expression patterns of cuproptosis-related genes (CRGs) and assessed the immune landscape of PS using the GSE66099 dataset. Subsequently, PS samples were isolated from the same dataset, and consensus clustering was performed based on differentially expressed CRGs. We applied weighted gene co-expression network analysis to identify hub genes associated with PS and cuproptosis. Results: We observed aberrant expression of 27 CRGs and a specific immune landscape in PS samples. Our findings revealed that patients in the GSE66099 dataset could be categorized into two cuproptosis clusters, each characterized by unique immune landscapes and varying functional classifications or enriched pathways. Among the machine learning approaches, Extreme Gradient Boosting demonstrated optimal performance as a diagnostic model for PS. Discussion: Our study provides valuable insights into the molecular mechanisms underlying PS, highlighting the involvement of cuproptosis-related genes and immune cell infiltration.

Identification and immunological characterization of cuproptosis-related molecular clusters in ischemic stroke

The present study elucidated cuproptosis-related molecular clusters involved in ischemic stroke and developed predictive models. Transcriptomic and immunological profiles of ischemic stroke-related datasets were extracted from the Gene Expression Omnibus database. Next, we conducted weighted gene co-expression network analysis to determine cluster-specific differentially expressed genes (DEGs). Models such as random forest and eXtreme gradient boosting (XGB) were evaluated to select the best prediction performance model. Subsequently, we validated the model's predictive efficiency by using nomograms, decision curve analysis, calibration curves, and receiver operating characteristic curve analysis with an external dataset. We identified two cuproptosis-related clusters involved in ischemic stroke. The DEGs in Cluster 2 were closely associated with amino acid metabolism, various immune responses, and cell proliferation pathways. The XGB model showed lower residuals, a smaller root mean square error, and a greater area under the curve value (AUC = 0.923), thus exhibiting the best discriminative performance. The AUC value for the external validation dataset was 0.921, thus confirming the high performance of the model. NFE2L2, NLRP3, GLS, LIPT1, and MTF1 were identified as potential cuproptosis predictors, thus shedding new light on ischemic stroke pathogenesis and heterogeneity.

Copper homeostasis and copper-induced cell death： Novel targeting for intervention in the pathogenesis of vascular aging

Copper-induced cell death, also known as cuproptosis, is distinct from other types of cell death such as apoptosis, necrosis, and ferroptosis. It can trigger the accumulation of lethal reactive oxygen species, leading to the onset and progression of aging. The significant increases in copper ion levels in the aging populations confirm a close relationship between copper homeostasis and vascular aging. On the other hand, vascular aging is also closely related to the occurrence of various cardiovascular diseases throughout the aging process. However, the specific causes of vascular aging are not clear, and different living environments and stress patterns can lead to individualized vascular aging. By exploring the correlations between copper-induced cell death and vascular aging, we can gain a novel perspective on the pathogenesis of vascular aging and enhance the prognosis of atherosclerosis. This article aims to provide a comprehensive review of the impacts of copper homeostasis on vascular aging, including their effects on endothelial cells, smooth muscle cells, oxidative stress, ferroptosis, intestinal flora, and other related factors. Furthermore, we intend to discuss potential strategies involving cuproptosis and provide new insights for copper-related vascular aging.

A glance through the effects of CD4+ T cells, CD8+ T cells, and cytokines on Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia. Unfortunately, despite numerous studies, an effective treatment for AD has not yet been established. There is remarkable evidence indicating that the innate immune mechanism and adaptive immune response play significant roles in the pathogenesis of AD. Several studies have reported changes in CD8+ and CD4+ T cells in AD patients. This mini-review article discusses the potential contribution of CD4+ and CD8+ T cells reactivity to amyloid β (Aβ) protein in individuals with AD. Moreover, this mini-review examines the potential associations between T cells, heme oxygenase (HO), and impaired mitochondria in the context of AD. While current mathematical models of AD have not extensively addressed the inclusion of CD4+ and CD8+ T cells, there exist models that can be extended to consider AD as an autoimmune disease involving these T cell types. Additionally, the mini-review covers recent research that has investigated the utilization of machine learning models, considering the impact of CD4+ and CD8+ T cells.

Identification and immunological characterization of PLA2G2A and cell death-associated molecular clusters in idiopathic pulmonary fibrosis

AIMS

Idiopathic pulmonary fibrosis (IPF) is a severe pulmonary interstitial pneumonia. Our study focuses on the role of PLA2 enzyme in the IPF to explore a more effective diagnosis and treatment mechanism of IPF.

MAIN METHODS

Transcriptome data of IPF from GEO database and bleomycin-induced pulmonary fibrosis mice were analyzed to identify PLA2 enzyme and their metabolite, lysophosphatidylcholines 18:0, in IPF. Based on PLA2G2A and PLA2G2D / PLA2G2A-associated cell death genes (PCDs), the consensus clustering analysis was used to identify the subtypes of IPF and the correlation between PLA2G2A and prognosis was analyzed. The machine learning (ML) models and artificial neural network (ANN) model was used to validate the diagnostic accuracy of PLA2s and PCDs in diagnosing IPF. The gene and protein expression of NLRP3, GSDMD, and CASP-1 was estimated in recombinant PLA2G2A protein induced MLE-12 cells.

KEY FINDINGS

The expression of PLA2G2D, PLA2G2A, and LPC18 significantly changed in IPF. Furtherly, PLA2G2A has a significant correlation with poor patient prognosis, which could predict the 2 or 3-years mortality rates of IPF. Two subtypes of IPF patients, identified based on PCDs, showed significant different immunoinfiltration. Recombinant PLA2G2A protein could induce the pyrotosis in the MLE-12 cell. The generalized linear model and ANN model of PLA2s or PCDs accurate diagnosis IPF.

SIGNIFICANCE

PLA2G2A is the most robustly associated gene with IPF among the PLA2s, which demonstrates a potential in diagnosing and prognostic value in IPF, and provides a foundation for further understanding and breakthroughs in IPF diagnosis and treatment.

Evaluation the role of cuproptosis-related genes in the pathogenesis, diagnosis and molecular subtypes identification of atherosclerosis

Background

At present, the pathogenesis of atherosclerosis has not been fully elucidated, and the diagnosis and treatment face great challenges. Cuproptosis is a novel cell death pattern that might be involved in the development of atherosclerosis. However, no research has reported the correlation between cuproptosis and atherosclerosis.

Methods

The differential cuproptosis-related genes (CRGs) between atherosclerosis group and control group (A-CRGs) were discovered via differential expression analysis. The correlation analysis, PPI network analysis, GO, KEGG and GSEA analysis were performed to investigate the function of A-CRGs. The differences of biological function between atherosclerosis group and control group were investigated via immune infiltration analysis and GSVA. The LASSO regression, nomogram and machine learning models were constructed to predict atherosclerosis risk. The atherosclerosis molecular subtypes clusters were discovered via unsupervised cluster analysis. Subsequently, we used the above research methods to analyze the differential CRGs between clusters (M-CRGs) and evaluate the molecular subtypes identification performance of M-CRGs. Finally, we verified the diagnostic value for atherosclerosis and role in cuproptosis of these CRGs through the validation set and in vitro experiments.

Results

Five A-CRGs were identified and they were mainly related to the biological function of copper ion metabolism and immune inflammatory response. The diagnostic models and nomogram of atherosclerosis based on 5 A-CRGs indicated that these genes had well diagnostic value. A total of two molecular subtypes clusters were obtained in the atherosclerosis group. There were many differences in biological functions between these two molecular subtypes clusters, such as mitochondrial outer membrane permeabilization and primary immunodeficiency. In addition, 3 M-CRGs were identified in the 2 clusters. Machine learning models and nomogram constructed based on M-CRGs showed that these genes had well molecular subtypes identification efficacy. In the end, the results of in vitro experiment and validation set confirmed the diagnostic value for atherosclerosis and role in cuproptosis of these genes.

Conclusion

The cuproptosis may be a potential pathogenesis of atherosclerosis and CRGs may be promising markers for the diagnosis and molecular subtypes identification of atherosclerosis.

Machine learning identification and immune infiltration of disulfidptosis-related Alzheimer's disease molecular subtypes

BACKGROUND

Alzheimer's disease (AD) is a common neurodegenerative disorder. Disulfidptosis is a newly discovered form of programmed cell death that holds promise as a therapeutic strategy for various disorders. However, the functional roles of disulfidptosis-related genes (DRGs) in AD remain unknown.

METHODS

Microarray data and clinical information from patients with AD and healthy controls were downloaded from the Gene Expression Omnibus database. A thorough examination of DRG expression and immune characteristics in both groups was performed. Based on the identified DRGs, we performed an unsupervised clustering analysis to categorize the AD samples into various disulfidptosis-related molecular clusters. Weighted gene co-expression network analysis was performed to select hub genes specific to disulfidptosis-related AD clusters. The performances of various machine learning models were compared to determine the optimal predictive model. The predictive ability of the optimal model was assessed using nomogram analysis and five external datasets.

RESULTS

Eight DRGs showed differential expression between the AD and control samples. Two different molecular clusters were identified. The immune cell infiltration analysis revealed distinct differences in the immune microenvironment of the two clusters. The support vector machine model showed the highest performance, and a panel of five signature genes was identified, which showed excellent performance on the external validation datasets. The nomogram analysis also showed high accuracy in predicting AD.

CONCLUSION

We identified disulfidptosis-related molecular clusters in AD and established a novel risk model to assess the likelihood of developing AD. These findings revealed a complex association between disulfidptosis and AD, which may aid in identifying potential therapeutic targets for this debilitating disorder.

Identification of immunological characteristics and cuproptosis-related molecular clusters in Rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterised by progressive articular damage, functional loss, and comorbidities. The relationship between cuproptosis, a form of programmed cell death, and RA remains unknown. Therefore, this study aimed to explore cuproptosis-related molecular clusters in RA.

METHODS

Gene expression profiles of GSE93272 were downloaded from the Gene Expression Omnibus to identify the expression profiles of cuproptosis regulators and the immune infiltration characteristics of RA. The molecular clusters of cuproptosis-related genes and the related immune cell infiltration were explored. Cluster-specific differentially expressed genes were identified using the weighted gene co-expression network analysis. Further, an external dataset (GSE15573) was used, and an enzyme-linked immunosorbent assay was performed to validate the predictive efficiency.

RESULTS

Thirteen cuproptosis-related genes and activated immune responses were identified between patients with RA and controls. Immune infiltration revealed significant immunological heterogeneity in the two cuproptosis-related molecular clusters in RA. Functional enrichment indicated that Cluster1 and Cluster2 were predominantly enriched in the toll-like receptor signalling pathway and regulation of autophagy, respectively. Further, the performance of FAM96A and CGRRF1 genes in the external validation dataset was observed to be relatively satisfactory (area under the receiver operating characteristic curve = 0.687 and 0.674, respectively). Based on our serum samples, FAM96A and CGRRF1 both exhibited higher expression levels in patients with RA (p = 0.001; p = 0.000).

CONCLUSIONS

Our study systematically illustrated the involvement of cuproptosis in the progression of RA, and explored the pathogenic mechanisms and novel therapeutic strategies for RA, targeting FAM96A and CGRRF1.

Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning

BACKGROUND AND AIMS

Cuproptosis has been identified as a key player in the development of several diseases. In this study, we investigate the potential role of cuproptosis-related genes in the pathogenesis of nonalcoholic fatty liver disease (NAFLD).

METHOD

The gene expression profiles of NAFLD were obtained from the Gene Expression Omnibus database. Differential expression of cuproptosis-related genes (CRGs) were determined between NAFLD and normal tissues. Protein-protein interaction, correlation, and function enrichment analyses were performed. Machine learning was used to identify hub genes. Immune infiltration was analyzed in both NAFLD patients and controls. Quantitative real-time PCR was employed to validate the expression of hub genes.

RESULTS

Four datasets containing 115 NAFLD and 106 control samples were included for bioinformatics analysis. Three hub CRGs (NFE2L2, DLD, and POLD1) were identified through the intersection of three machine learning algorithms. The receiver operating characteristic curve was plotted based on these three marker genes, and the area under the curve (AUC) value was 0.704. In the external GSE135251 dataset, the AUC value of the three key genes was as high as 0.970. Further nomogram, decision curve, calibration curve analyses also confirmed the diagnostic predictive efficacy. Gene set enrichment analysis and gene set variation analysis showed these three marker genes involved in multiple pathways that are related to the progression of NAFLD. CIBERSORT and single-sample gene set enrichment analysis indicated that their expression levels in macrophages, mast cells, NK cells, Treg cells, resting dendritic cells, and tumor-infiltrating lymphocytes were higher in NAFLD compared with control liver samples. The ceRNA network demonstrated a complex regulatory relationship between the three hub genes. The mRNA level of these hub genes were further confirmed in a mouse NAFLD liver samples.

CONCLUSION

Our study comprehensively demonstrated the relationship between NAFLD and cuproptosis, developed a promising diagnostic model, and provided potential targets for NAFLD treatment and new insights for exploring the mechanism for NAFLD.

Cuproptosis-related genes prediction feature and immune microenvironment in major depressive disorder

Background

Major depressive disorder (MDD) is a severe, unpredictable, ill-cured, relapsing neuropsychiatric disorder. A recently identified type of death called cuproptosis has been linked to a number of illnesses. However, the influence of cuproptosis-related genes in MDD has not been comprehensively assessed in prior study.

Aim

This investigation intends to shed light on the predictive value of cuproptosis-related genes for MDD and the immunological microenvironment.

Methods

GSE38206, GSE76826, GSE9653 databases were used to analyze cuproptosis regulators and immune characteristics. To find the genes that were differently expressed, weighted gene co-expression network analysis was employed. We calculated the effectiveness of the random forest model, generalized linear model, and limit gradient lifting to arrive at the best machine prediction model. Nomogram, calibration curve, and decision curve analysis were used to show the anticipated MDD's accuracy.

Results

This study found that there were activated immune responses and cuproptosis-related genes that were dysregulated in people with MDD compared to healthy controls. Considering the test performance of the learned model and validation on subsequent datasets, the RF model (including OSBPL8, VBP1, MTM1, ELK3, and SLC39A6) was considered to have the best discriminative performance. (AUC = 0.875).

Conclusion

Our study constructed a prediction model to predict MDD risk and clarified the potential connection between cuproptosis and MDD.

Verification of cuproptosis-related diagnostic model associated with immune infiltration in rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a chronic autoimmune disease closely related to inflammation. Cuproptosis is a newly discovered unique type of cell death, and it has been found that it may play an essential role in the occurrence and development of RA. Therefore, we intend to explore the potential association between cuproptosis-related genes (CRGs) and RA to provide a new biomarker for the treatment and prognosis of RA.

Methods

Download GSE93777 datasets from the GEO database. Variance analysis was performed on the CRGs that had been reported. Then, the random forest (RF) model and nomogram of differentially expressed CRGs were constructed, and the ROC curve was used to evaluate the accuracy of the diagnostic model. Next, RA patients were subtyped by consensus clustering, and immune infiltration was analyzed in each subgroup to confirm the correlation between CRGs and abundance of immune cells. The expression levels of CRGs were verified by qRT-PCR.

Results

Eight differentially expressed CRGs (DLST, DLD, PDHB, PDHA1, ATP7A, CDKN2A, LIAS, DLAT) were screened out by differential analysis to construct an RF model. The ROC curve proved that this model had good diagnostic accuracy. Based on the above eight significant CRGs, a nomogram was built to predict effective and high-precision results. The consensus clustering method identified two CRG patterns. Most of the immune cells were enriched in cluster A, indicating that cluster A may be related to the development of RA. Finally, qRT-PCR verified the expression of eight key genes, further confirming our findings.

Conclusion

The diagnosis model of RA based on the above eight CRGs has excellent diagnostic potential. Based on these, patients can be divided into two different molecular subtypes; it is expected to develop a new treatment strategy for RA.

Identification of immune infiltration and cuproptosis-related molecular clusters in tuberculosis

Background

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb) infection. Cuproptosis is a novel cell death mechanism correlated with various diseases. This study sought to elucidate the role of cuproptosis-related genes (CRGs) in TB.

Methods

Based on the GSE83456 dataset, we analyzed the expression profiles of CRGs and immune cell infiltration in TB. Based on CRGs, the molecular clusters and related immune cell infiltration were explored using 92 TB samples. The Weighted Gene Co-expression Network Analysis (WGCNA) algorithm was utilized to identify the co-expression modules and cluster-specific differentially expressed genes. Subsequently, the optimal machine learning model was determined by comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB). The predictive performance of the machine learning model was assessed by generating calibration curves and decision curve analysis and validated in an external dataset.

Results

11 CRGs were identified as differentially expressed cuproptosis genes. Significant differences in immune cells were observed in TB patients. Two cuproptosis-related molecular clusters expressed genes were identified. Distinct clusters were identified based on the differential expression of CRGs and immune cells. Besides, significant differences in biological functions and pathway activities were observed between the two clusters. A nomogram was generated to facilitate clinical implementation. Next, calibration curves were generated, and decision curve analysis was conducted to validate the accuracy of our model in predicting TB subtypes. XGB machine learning model yielded the best performance in distinguishing TB patients with different clusters. The top five genes from the XGB model were selected as predictor genes. The XGB model exhibited satisfactory performance during validation in an external dataset. Further analysis revealed that these five model-related genes were significantly associated with latent and active TB.

Conclusion

Our study provided hitherto undocumented evidence of the relationship between cuproptosis and TB and established an optimal machine learning model to evaluate the TB subtypes and latent and active TB patients.

Identification of cuproptosis-related molecular subtypes as a biomarker for differentiating active from latent tuberculosis in children

BACKGROUND

Cell death plays a crucial role in the progression of active tuberculosis (ATB) from latent infection (LTBI). Cuproptosis, a novel programmed cell death, has been reported to be associated with the pathology of various diseases. We aimed to identify cuproptosis-related molecular subtypes as biomarkers for distinguishing ATB from LTBI in pediatric patients.

METHOD

The expression profiles of cuproptosis regulators and immune characteristics in pediatric patients with ATB and LTBI were analyzed based on GSE39939 downloaded from the Gene Expression Omnibus. From the 52 ATB samples, we investigated the molecular subtypes based on differentially expressed cuproptosis-related genes (DE-CRGs) via consensus clustering and related immune cell infiltration. Subtype-specific differentially expressed genes (DEGs) were found using the weighted gene co-expression network analysis. The optimum machine model was then determined by comparing the performance of the eXtreme Gradient Boost (XGB), the random forest model (RF), the general linear model (GLM), and the support vector machine model (SVM). Nomogram and test datasets (GSE39940) were used to verify the prediction accuracy.

RESULTS

Nine DE-CRGs (NFE2L2, NLRP3, FDX1, LIPT1, PDHB, MTF1, GLS, DBT, and DLST) associated with active immune responses were ascertained between ATB and LTBI patients. Two cuproptosis-related molecular subtypes were defined in ATB pediatrics. Single sample gene set enrichment analysis suggested that compared with Subtype 2, Subtype 1 was characterized by decreased lymphocytes and increased inflammatory activation. Gene set variation analysis showed that cluster-specific DEGs in Subtype 1 were closely associated with immune and inflammation responses and energy and amino acids metabolism. The SVM model exhibited the best discriminative performance with a higher area under the curve (AUC = 0.983) and relatively lower root mean square and residual error. A final 5-gene-based (MAN1C1, DKFZP434N035, SIRT4, BPGM, and APBA2) SVM model was created, demonstrating satisfactory performance in the test datasets (AUC = 0.905). The decision curve analysis and nomogram calibration curve also revealed the accuracy of differentiating ATB from LTBI in children.

CONCLUSION

Our study suggested that cuproptosis might be associated with the immunopathology of Mycobacterium tuberculosis infection in children. Additionally, we built a satisfactory prediction model to assess the cuproptosis subtype risk in ATB, which can be used as a reliable biomarker for the distinguishment between pediatric ATB and LTBI.

The impact of serum copper on the risk of epilepsy: a mendelian randomization study

BACKGROUND

The relationship between serum copper and epilepsy has been elucidated in observational studies. In this study, we aimed to explore the causal relationship between serum copper and epilepsy using Mendelian randomization (MR) analysis.

METHODS

Single nucleotide polymorphisms (SNPs) associated with serum copper were used as instrumental variables for MR analysis to evaluate their causal effects on epilepsy. The main MR results were obtained by using the inverse variance weighting (IVW) method, supplemented by weighted median and MR-Egger regression. In addition, sensitivity analyses such as Cochran's Q test and pleiotropy test were used to assess these SNPs on epilepsy in terms of horizontal pleiotropy and heterogeneity.

RESULTS

The IVW method revealed that the serum copper was associated with an increased risk of generalized epilepsy (OR= 1.07; 95% CI 1.01- 1.14; P = 0.032), and the sensitivity analysis further supports the robustness of the results.

CONCLUSIONS

The current study reveals a possible causal role for serum copper in increasing the risk of generalized epilepsy, which provide guidance for identifying potential risk factors for epilepsy.

A Concise Review on Oxidative Stress-Mediated Ferroptosis and Cuproptosis in Alzheimer's Disease

Alzheimer's disease (AD), which was first identified more than a century ago, has become a pandemic that exacts enormous social burden and economic tolls as no measure of combating devastated AD is currently available. Growing etiopathological, genetic, and biochemical data indicate that AD is a heterogeneous, polygenic, multifactorial, and complex disease. However, its exact etiopathology remains to be determined. Numerous experimental data show that cerebral iron and copper dyshomeostasis contribute to Aβ amyloidosis and tauopathy, two neuropathological hallmarks of AD. Moreover, increasing experimental evidence suggests ferroptosis, an iron-dependent and nonapoptotic form of cell death, may be involved in the neurodegenerative process in the AD brain. Thus, the anti-ferroptosis approach may be an efficacious therapeutic strategy for AD patients. Furthermore, it remains to be further determined whether cuproptosis, a copper-dependent and distinct form of regulated cell death, also plays a contributing role in AD neurodegeneration. We hope this concise review of recent experimental studies of oxidative stress-mediated ferroptosis and cuproptosis in AD may spur further investigations on this timely and essential line of research.

Cuproptosis-related gene CDKN2A as a molecular target for IPF diagnosis and therapeutics

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a progressive chronic interstitial lung disease with limited therapeutic options. Cuproptosis is a recently proposed novel form of programmed cell death, which has been strongly implicated in the development of various human diseases. However, the prognostic and therapeutic value of cuproptosis-related genes (CRGs) in IPF remains to be elucidated.

METHODS

In the present study, weighted gene co-expression network analysis (WGCNA) was employed to identify the key CRGs associated with the development of IPF. The subsequent GSEA, immune cell correlation analysis, and single-cell RNA-Seq analysis were conducted to explore the potential role of the identified CRGs in IPF. In addition, ROC curves and survival analysis were used to assess the prognostic value of the key CRGs in IPF. Moreover, we explored the molecular mechanisms of participation of identified key CRGs in the development of pulmonary fibrogenesis through in vivo and in vitro experiments.

RESULTS

The expression level of cyclin-dependent kinase inhibitor 2A (CDKN2A) is upregulated in the lung tissues of IPF patients and associated with disease severity. Notably, CDKN2A was constitutively expressed by fibrosis-promoting M2 macrophages. Decreased CDKN2A expression sensitizes M2 macrophages to elesclomol-induced cuproptosis in vitro. Inhibition of CDKN2A decreases the number of viable macrophages and attenuates bleomycin-induced pulmonary fibrosis in mice.

CONCLUSION

These findings indicate that CDKN2A mediates the resistance of fibrosis-promoting M2 macrophages to cuproptosis and promotes pulmonary fibrosis in mice. Our work provides fresh insights into CRGs in IPF with potential value for research in the pathogenesis, diagnosis, and a new therapy strategy for IPF.

Role of cuproptosis in understanding diseases

Cell death is involved in a wide range of physiological and pathological processes. Recently, the term "cuproptosis" was coined to describe a novel type of cell death. This type of cell death, characterized by copper accumulation and proteotoxic stress, is a copper-dependent manner of death. Despite the progress achieved toward a better understanding of cuproptosis, mechanisms and related signaling pathways in physiology and pathology across various diseases remain to be proved. This mini review summarizes current research on cuproptosis and diseases, providing insights into prospective clinical therapies via targeting cuproptosis.

A novel Alzheimer's disease prognostic signature: identification and analysis of glutamine metabolism genes in immunogenicity and immunotherapy efficacy

Alzheimer's disease (AD) is characterized as a distinct onset and progression of cognitive and functional decline associated with age, as well as a specific neuropathology. It has been discovered that glutamine (Gln) metabolism plays a crucial role in cancer. However, a full investigation of its role in Alzheimer's disease is still missing. This study intended to find and confirm potential Gln-related genes associated with AD using bioinformatics analysis. The discovery of GlnMgs was made possible by the intersection of the WGCNA test and 26 Gln-metabolism genes (GlnMgs). GlnMgs' putative biological functions and pathways were identified using GSVA. The LASSO method was then used to identify the hub genes as well as the diagnostic efficiency of the four GlnMgs in identifying AD. The association between hub GlnMgs and clinical characteristics was also studied. Finally, the GSE63060 was utilized to confirm the levels of expression of the four GlnMgs. Four GlnMgs were discovered (ATP5H, NDUFAB1, PFN2, and SPHKAP). For biological function analysis, cell fate specification, atrioventricular canal development, and neuron fate specification were emphasized. The diagnostic ability of the four GlnMgs in differentiating AD exhibited a good value. This study discovered four GlnMgs that are linked to AD. They shed light on potential new biomarkers for AD and tracking its progression.

Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning

BACKGROUND

Bronchopulmonary Dysplasia (BPD) has a high incidence and affects the health of preterm infants. Cuproptosis is a novel form of cell death, but its mechanism of action in the disease is not yet clear. Machine learning, the latest tool for the analysis of biological samples, is still relatively rarely used for in-depth analysis and prediction of diseases.

METHODS AND RESULTS

First, the differential expression of cuproptosis-related genes (CRGs) in the GSE108754 dataset was extracted and the heat map showed that the expression of NFE2L2 gene was significantly higher in the control group whereas the expression of GLS gene was significantly higher in the treatment group. Chromosome location analysis showed that both the genes were positively correlated and associated with chromosome 2. The results of immune infiltration and immune cell differential analysis showed differences in the four immune cells, significantly in Monocytes cells. Five new pathways were analyzed through two subgroups based on consistent clustering of CRG expression. Weighted correlation network analysis (WGCNA) set the screening condition to the top 25% to obtain the disease signature genes. Four machine learning algorithms: Generalized Linear Models (GLM), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) were used to screen the disease signature genes, and the final five marker genes for disease prediction. The models constructed by GLM method were proved to be more accurate in the validation of two datasets, GSE190215 and GSE188944.

CONCLUSION

We eventually identified two copper death-associated genes, NFE2L2 and GLS. A machine learning model-GLM was constructed to predict the prevalence of BPD disease, and five disease signature genes NFATC3, ERMN, PLA2G4A, MTMR9LP and LOC440700 were identified. These genes that were bioinformatics analyzed could be potential targets for identifying BPD disease and treatment.