Identification of hub cuproptosis related genes and immune cell infiltration characteristics in periodontitis

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

Comparing Regularized Logistic Regression and Stochastic Gradient Descent in Predicting Drug-Gene Interactions of Inhibitors of Apoptosis Proteins in Periodontitis

OBJECTIVE

Periodontitis, characterized by inflammation linked to apoptosis dysregulation, underscores the role of inhibitors of apoptosis proteins (IAPs) like survivin and cIAP1, implicated in disease progression and treatment resistance across various conditions. Our study aims to analyze the prediction of drug-gene interactions by machine learning techniques, combining regularized logistic regression and stochastic gradient descent (SGD) for efficient classification.

METHODS

Data from Probes-Drugs.org on IAP-based drug-protein interactions underwent rigorous annotation and outlier removal. A data robot tool trained machine learning models, regularized logistic regression and SGD (https://app.datarobot.com/new). Network analysis employed Cytoscape to construct and analyze the IAP network, identifying key hub nodes crucial in periodontitis pathogenesis.

RESULTS

The constructed IAP network comprised 376 nodes and 556 edges, revealing intricate drug-gene interactions with an average of 2957 neighbors per node. Ten hub nodes were identified as pivotal in regulating biological processes specific to periodontitis, suggesting their potential as therapeutic targets and biomarkers. Predictive models demonstrated high accuracy, with gradient descent achieving 93% and regularized logistic regression achieving 92% in identifying drug-gene interactions within the IAP network.

CONCLUSIONS

These findings highlight the utility of computational methods in elucidating molecular mechanisms underlying periodontitis, offering insights into potential therapeutic strategies targeting IAP-related pathways. Future research should focus on validating hub genes experimentally and integrating multi-omics data to advance precision medicine approaches in periodontitis treatment.

A comprehensive analysis of the role of cuproptosis in periodontitis through integrated analysis of single-cell and bulk RNA sequencing

Introduction

Trace mineral micronutrients are essential for periodontal health, and unbalanced levels of trace minerals are related to chronic periodontitis, including copper. We attempted to explore the relationship between cuproptosis and periodontitis.

Methods

Bulk RNA-seq and single-cell RNA sequencing data were used to analyze expression of cuproptosis-related genes (CRGs) in periodontitis. A mouse periodontitis model was established to verify the expression of key genes.

Results

Cuproptosis-related genes were differentially expressed in periodontitis. RT-PCR results confirmed the alteration of five key genes in the mouse model.

Conclusions

Cuproptosis is an important player in the pathophysiological process of periodontitis.

Copper Chelation Therapy Attenuates Periodontitis Inflammation through the Cuproptosis/Autophagy/Lysosome Axis

Periodontitis development arises from the intricate interplay between bacterial biofilms and the host's immune response, where macrophages serve pivotal roles in defense and tissue homeostasis. Here, we uncover the mitigative effect of copper chelator Tetrathiomolybdate (TTM) on periodontitis through inhibiting cuproptosis, a newly identified form of cell death which is dependent on copper. Our study reveals concurrent cuproptosis and a macrophage marker within murine models. In response to lipopolysaccharide (LPS) stimulation, macrophages exhibit elevated cuproptosis-associated markers, which are mitigated by the administration of TTM. TTM treatment enhances autophagosome expression and mitophagy-related gene expression, countering the LPS-induced inhibition of autophagy flux. TTM also attenuates the LPS-induced fusion of autophagosomes and lysosomes, the degradation of lysosomal acidic environments, lysosomal membrane permeability increase, and cathepsin B secretion. In mice with periodontitis, TTM reduces cuproptosis, enhances autophagy flux, and decreases Ctsb levels. Our findings underscore the crucial role of copper-chelating agent TTM in regulating the cuproptosis/mitophagy/lysosome pathway during periodontitis inflammation, suggesting TTM as a promising approach to alleviate macrophage dysfunction. Modulating cuproptosis through TTM treatment holds potential for periodontitis intervention.

Identification of cuproptosis-related gene clusters and immune cell infiltration in major burns based on machine learning models and experimental validation

Introduction

Burns are a global public health problem. Major burns can stimulate the body to enter a stress state, thereby increasing the risk of infection and adversely affecting the patient's prognosis. Recently, it has been discovered that cuproptosis, a form of cell death, is associated with various diseases. Our research aims to explore the molecular clusters associated with cuproptosis in major burns and construct predictive models.

Methods

We analyzed the expression and immune infiltration characteristics of cuproptosis-related factors in major burn based on the GSE37069 dataset. Using 553 samples from major burn patients, we explored the molecular clusters based on cuproptosis-related genes and their associated immune cell infiltrates. The WGCNA was utilized to identify cluster-specific genes. Subsequently, the performance of different machine learning models was compared to select the optimal model. The effectiveness of the predictive model was validated using Nomogram, calibration curves, decision curves, and an external dataset. Finally, five core genes related to cuproptosis and major burn have been was validated using RT-qPCR.

Results

In both major burn and normal samples, we determined the cuproptosis-related genes associated with major burns through WGCNA analysis. Through immune infiltrate profiling analysis, we found significant immune differences between different clusters. When K=2, the clustering number is the most stable. GSVA analysis shows that specific genes in cluster 2 are closely associated with various functions. After identifying the cross-core genes, machine learning models indicate that generalized linear models have better accuracy. Ultimately, a generalized linear model for five highly correlated genes was constructed, and validation with an external dataset showed an AUC of 0.982. The accuracy of the model was further verified through calibration curves, decision curves, and modal graphs. Further analysis of clinical relevance revealed that these correlated genes were closely related to time of injury.

Conclusion

This study has revealed the intricate relationship between cuproptosis and major burns. Research has identified 15 cuproptosis-related genes that are associated with major burn. Through a machine learning model, five core genes related to cuproptosis and major burn have been selected and validated.

Novel application of the ferroptosis-related genes risk model associated with disulfidptosis in hepatocellular carcinoma prognosis and immune infiltration

Hepatocellular carcinoma (HCC) stands as the prevailing manifestation of primary liver cancer and continues to pose a formidable challenge to human well-being and longevity, owing to its elevated incidence and mortality rates. Nevertheless, the quest for reliable predictive biomarkers for HCC remains ongoing. Recent research has demonstrated a close correlation between ferroptosis and disulfidptosis, two cellular processes, and cancer prognosis, suggesting their potential as predictive factors for HCC. In this study, we employed a combination of bioinformatics algorithms and machine learning techniques, leveraging RNA sequencing data, mutation profiles, and clinical data from HCC samples in The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) databases, to develop a risk prognosis model based on genes associated with ferroptosis and disulfidptosis. We conducted an unsupervised clustering analysis, calculating a risk score (RS) to predict the prognosis of HCC using these genes. Clustering analysis revealed two distinct HCC clusters, each characterized by significantly different prognostic and immune features. The median RS stratified HCC samples in the TCGA, GEO, and ICGC cohorts into high-and low-risk groups. Importantly, RS emerged as an independent prognostic factor in all three cohorts, with the high-risk group demonstrating poorer prognosis and a more active immunosuppressive microenvironment. Additionally, the high-risk group exhibited higher expression levels of tumor mutation burden (TMB), immune checkpoints (ICs), and human leukocyte antigen (HLA), suggesting a heightened responsiveness to immunotherapy. A cancer stem cell infiltration analysis revealed a higher similarity between tumor cells and stem cells in the high-risk group. Furthermore, drug sensitivity analysis highlighted significant differences in response to antitumor drugs between the two risk groups. In summary, our risk prognostic model, constructed based on ferroptosis-related genes associated with disulfidptosis, effectively predicts HCC prognosis. These findings hold potential implications for patient stratification and clinical decision-making, offering valuable theoretical insights in this field.