Appropriate level of cuproptosis may be involved in alleviating pulmonary fibrosis

Exploration of a Predictive Model for Keloid and Potential Therapeutic Drugs Based on Immune Infiltration and Cuproptosis-Related Genes

The aim of this study was to investigate the correlation between cuproptosis-related genes and immunoinfiltration in keloid, develop a predictive model for keloid occurrence, and explore potential therapeutic drugs. The microarray datasets (GSE7890 and GSE145725) were obtained from Gene Expression Omnibus database to identify the differentially expressed genes (DEGs) between keloid and nonkeloid samples. Key genes were identified through immunoinfiltration analysis and DEGs and then analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, followed by the identification of protein-protein interaction networks, transcription factors, and miRNAs associated with key genes. Additionally, a logistic regression analysis was performed to develop a predictive model for keloid occurrence, and potential candidate drugs for keloid treatment were identified. Three key genes (FDX1, PDHB, and DBT) were identified, showing involvement in acetyl-CoA biosynthesis, mitochondrial matrix, oxidoreductase activity, and the tricarboxylic acid cycle. Immune infiltration analysis suggested the involvement of B cells, Th1 cells, dendritic cells, T helper cells, antigen-presenting cell coinhibition, and T cell coinhibition in keloid. These genes were used to develop a logistic regression-based nomogram for predicting keloid occurrence with an area under the curve of 0.859 and good calibration. We identified 32 potential drug molecules and extracted the top 10 compounds based on their P-values, showing promise in targeting key genes and potentially effective against keloid. Our study identified some genes in keloid pathogenesis and potential therapeutic drugs. The predictive model enhances early diagnosis and management. Further research is needed to validate and explore clinical implications.

Copper homeostasis and cuproptosis in radiation-induced injury

Radiation therapy for cancer treatment brings about a series of radiation injuries to normal tissues. In recent years, the discovery of copper-regulated cell death, cuproptosis, a novel form of programmed cell death, has attracted widespread attention and exploration in various biological functions and pathological mechanisms of copper metabolism and cuproptosis. Understanding its role in the process of radiation injury may open up new avenues and directions for exploration in radiation biology and radiation oncology, thereby improving tumor response and mitigating adverse reactions to radiotherapy. This review provides an overview of copper metabolism, the characteristics of cuproptosis, and their potential regulatory mechanisms in radiation injury.

Copper homeostasis dysregulation in respiratory diseases: a review of current knowledge

Cu is an essential micronutrient for various physiological processes in almost all human cell types. Given the critical role of Cu in a wide range of cellular processes, the local concentrations of Cu and the cellular distribution of Cu transporter proteins in the lung are essential for maintaining a steady-state internal environment. Dysfunctional Cu metabolism or regulatory pathways can lead to an imbalance in Cu homeostasis in the lungs, affecting both acute and chronic pathological processes. Recent studies have identified a new form of Cu-dependent cell death called cuproptosis, which has generated renewed interest in the role of Cu homeostasis in diseases. Cuproptosis differs from other known cell death pathways. This occurs through the direct binding of Cu ions to lipoylated components of the tricarboxylic acid cycle during mitochondrial respiration, leading to the aggregation of lipoylated proteins and the subsequent downregulation of Fe-S cluster proteins, which causes toxic stress to the proteins and ultimately leads to cell death. Here, we discuss the impact of dysregulated Cu homeostasis on the pathogenesis of various respiratory diseases, including asthma, chronic obstructive pulmonary disease, idiopathic interstitial fibrosis, and lung cancer. We also discuss the therapeutic potential of targeting Cu. This study highlights the intricate interplay between copper, cellular processes, and respiratory health. Copper, while essential, must be carefully regulated to maintain the delicate balance between necessity and toxicity in living organisms. This review highlights the need to further investigate the precise mechanisms of copper interactions with infections and immune inflammation in the context of respiratory diseases and explore the potential of therapeutic strategies for copper, cuproptosis, and other related effects.

Cuproptosis-associated hub gene identification and immune cell infiltration patterns in silicosis

Recent research has hinted at a potential connection between silicosis, a fibrotic lung disease caused by exposure to crystalline silica particles, and cuproptosis. The aim of the study was to explore how cuproptosis-related genes (CRGs) may influence the development of silicosis and elucidate the underlying mechanisms. An analysis of genes associated with both silicosis and cuproptosis was conducted. Key gene identification was achieved through the application of two machine learning techniques. Additionally, the correlation between these key genes and immune cell populations was explored and the critical pathways were discerned. To corroborate our findings, the expression of key genes was verified in both a publicly available silica-induced mouse model and our own silicosis mouse model. A total of 12 differentially expressed CRGs associated with silicosis were identified. Further analysis resulted in the identification of 6 CRGs, namely LOX, SPARC, MOXD1, ALB, MT-CO2, and AOC2. Elevated immune cell infiltration of CD8 T cells, regulatory T cells, M0 macrophages, and neutrophils in silicosis patients compared to healthy controls was indicated. Validation in a silica-induced pulmonary fibrosis mouse model supported SPARC and MT-CO2 as potential signature genes for the prediction of silicosis. These findings highlight a strong association between silicosis and cuproptosis. Among CRGs, LOX, SPARC, MOXD1, ALB, MT-CO2, and AOC2 emerged as pivotal players in the context of silicosis by modulating CD8 T cells, regulatory T cells, M0 macrophages, and neutrophils.

The role of epithelial cells in fibrosis: Mechanisms and treatment

Fibrosis is a pathological process that affects multiple organs and is considered one of the major causes of morbidity and mortality in multiple diseases, resulting in an enormous disease burden. Current studies have focused on fibroblasts and myofibroblasts, which directly lead to imbalance in generation and degradation of extracellular matrix (ECM). In recent years, an increasing number of studies have focused on the role of epithelial cells in fibrosis. In some cases, epithelial cells are first exposed to external physicochemical stimuli that may directly drive collagen accumulation in the mesenchyme. In other cases, the source of stimulation is mainly immune cells and some cytokines, and epithelial cells are similarly altered in the process. In this review, we will focus on the multiple dynamic alterations involved in epithelial cells after injury and during fibrogenesis, discuss the association among them, and summarize some therapies targeting changed epithelial cells. Especially, epithelial mesenchymal transition (EMT) is the key central step, which is closely linked to other biological behaviors. Meanwhile, we think studies on disruption of epithelial barrier, epithelial cell death and altered basal stem cell populations and stemness in fibrosis are not appreciated. We believe that therapies targeted epithelial cells can prevent the progress of fibrosis, but not reverse it. The epithelial cell targeting therapies will provide a wonderful preventive and delaying action.

Peroxisome proliferator-activated receptors signature reveal the head and neck squamous cell carcinoma energy metabolism phenotype and clinical outcome

BACKGROUND

Peroxisome proliferator activating receptors (PPARs) are important regulators of nuclear hormone receptor function, and they play a key role in biological processes such as lipid metabolism, inflammation and cell proliferation. However, their role in head and neck squamous cell carcinoma (HNSC) is unclear.

METHODS

We used multiple datasets, including TCGA-HNSC, GSE41613, GSE139324, PRJEB23709 and IMVigor, to perform a comprehensive analysis of PPAR-related genes in HNSC. Single-cell sequencing data were preprocessed using Seurat packets, and intercellular communication was analyzed using CellChat packets. Functional enrichment analysis of PPAR-related genes was performed using ClusterProfile and GSEA. Prognostic models were constructed using LASSO and Cox regression models, and immunohistochemical analyses were performed using human protein mapping (The Human Protein Atlas).

RESULTS

Our single-cell RNA sequencing analysis revealed distinct cell populations in HNSC, with T cells having the most significant transcriptome differences between tumors and normal tissues. The PPAR features were higher in most cell types in tumor tissues compared with normal tissues. We identified 17 PPAR-associated differentially expressed genes between tumors and normal tissues. A prognostic model based on seven PPAR-associated genes was constructed with high accuracy in predicting 1, 2 and 3 year survival in patients with HNSC. In addition, patients with a low risk score had a higher immune score and a higher proportion of T cells, CD8+ T cells and cytotoxic lymphocytes. They also showed higher immune checkpoint gene expression, suggesting that they might benefit from immunotherapy. PPAR-related genes were found to be closely related to energy metabolism.

CONCLUSIONS

Our study provides a comprehensive understanding of the role of PPAR related genes in HNSC. The identified PPAR features and constructed prognostic models may serve as potential biomarkers for HNSC prognosis and treatment response. In addition, our study found that PPAR-related genes can differentiate energy metabolism and distinguish energy metabolic heterogeneity in HNSC, providing new insights into the molecular mechanisms of HNSC progression and therapeutic response.

A comprehensive cuproptosis score and associated gene signatures reveal prognostic and immunological features of idiopathic pulmonary fibrosis

Background

Cuproptosis, the most recently identified and regulated cell death, depends on copper ions in vivo. Copper regulates the pathogenesis of Idiopathic pulmonary fibrosis (IPF), but the mechanism of action underlying cuproptosis in IPF remains unclear.

Methods

We identified three cuproptosis patterns based on ten cuproptosis-related genes using unsupervised consensus clustering. We quantified these patterns using a PCA algorithm to construct a cuproptosis score. ssGSEA and the Cibersort algorithm assessed the immune profile of IPF patients. GSEA and GSVA were used to analyze the functional differences in different molecular patterns. Drug susceptibility prediction based on cuproptosis scores and meaningful gene markers was eventually screened in combination with external public data sets,in vitro experiments and our cases.

Results

Of the three types of cuproptosis-related clusters identified in the study, patients in the clusterA, geneclusterB, and score-high groups showed improved prognoses. Moreover, each cluster exhibited differential immune characteristics, with the subtype showing a poorer prognosis associated with an immune overreaction. Cuproptosis score can be an independent risk factor for predicting the prognosis of IPF patients. GSEA showed a significant functional correlation between the score and cuproptosis. The genes AKAP9, ANK3, C6orf106, LYRM7, and MBNL1, were identified as prognostic-related signatures in IPF patients. The functional role of immune regulation in IPF was further explored by correlating essential genes with immune factors. Also, the nomogram constructed by cumulative information from gene markers and cuproptosis score showed reliable clinical application.

Conclusions

Cuproptosis patterns differ significantly in the prognosis and immune characteristics of IPF patients. The cuproptosis score and five gene signatures can provide a reliable reference in the prognosis and diagnosis of IPF.

Cuproptosis: emerging biomarkers and potential therapeutics in cancers

The sustenance of human life activities depends on copper, which also serves as a crucial factor for vital enzymes. Under typical circumstances, active homeostatic mechanisms keep the intracellular copper ion concentration low. Excess copper ions cause excessive cellular respiration, which causes cytotoxicity and cell death as levels steadily rise above a threshold. It is a novel cell death that depends on mitochondrial respiration, copper ions, and regulation. Cuproptosis is now understood to play a role in several pathogenic processes, including inflammation, oxidative stress, and apoptosis. Copper death is a type of regulatory cell death(RCD).Numerous diseases are correlated with the development of copper homeostasis imbalances. One of the most popular areas of study in the field of cancer is cuproptosis. It has been discovered that cancer angiogenesis, proliferation, growth, and metastasis are all correlated with accumulation of copper ions. Copper ion concentrations can serve as a crucial marker for cancer development. In order to serve as a reference for clinical research on the product, diagnosis, and treatment of cancer, this paper covers the function of copper ion homeostasis imbalance in malignant cancers and related molecular pathways.

Collagen-related gene expression level predicts the prognosis and immune therapy response

BACKGROUND

Gastric cancer patients responded differently to the same treatment strategy and had various prognoses for the lack of biomarkers to guide the therapy choice.

METHODS

RNA data of a local gastric cancer cohort with 103 patients were processed and used to explore potential treatment guiding factors. Cluster analysis was performed by non-negative matrix factorization. The expression level of collagen-related genes was evaluated by ssGSEA named collagen score (CS). Data from TCGA, ACRG, and an immune therapy cohort were utilized to explore prognosis and efficacy. Prognostic predictive power of CS was assessed using the nomogram.

RESULTS

In our study, local RNA data were processed by cluster analysis, and it was found that cluster 2 contained a worse tumor infiltration status. The GSEA result showed that collagen-related pathways were differentially activated in two clusters. In TCGA and ACRG cohorts, the CS can be used as an independent prognostic factor (TCGA OS: p = 0.018, HR = 3.5; ACRG OS: p = 0.014, HR = 4.88). An immunotherapy cohort showed that the patients with higher CS had a significantly worse ORR (p = 0.0025). The high CS group contained several cell death pathways down-regulated and contained the worse tumor microenvironment. The nomogram demonstrated the survival prediction capability of collagen score.

CONCLUSION

CS was verified as an independent prognostic factor and potentially reflected the therapeutic effect of immunotherapy. The CS could provide a new way to evaluate the clinical prognosis and response information helping develop the collagen-targeted treatment.

Deciphering the contributions of cuproptosis in the development of hypertrophic scar using single-cell analysis and machine learning techniques

Hypertrophic scar (HS) is a chronic inflammatory skin disease characterized by excessive deposition of extracellular matrix, but the exact mechanisms related to its formation remain unclear, making it difficult to treat. This study aimed to investigate the potential role of cuproptosis in the information of HS. To this end, we used single-cell sequencing and bulk transcriptome data, and screened for cuproptosis-related genes (CRGs) using differential gene analysis and machine learning algorithms (random forest and support vector machine). Through this process, we identified a group of genes, including ATP7A, ULK1, and MTF1, as novel therapeutic targets for HS. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to confirm the mRNA expression of ATP7A, ULK1, and MTF1 in both HS and normal skin (NS) tissues. We also constructed a diagnostic model for HS and analyzed the immune infiltration characteristics. Additionally, we used the expression profiles of CRGs to perform subgroup analysis of HS. We focused mainly on fibroblasts in the transcriptional profile at single-cell resolution. By calculating the cuproptosis activity of each fibroblast, we found that cuproptosis activity of normal skin fibroblasts increased, providing further insights into the pathogenesis of HS. We also analyzed the cell communication network and transcription factor regulatory network activity, and found the existence of a fibroblast-centered communication regulation network in HS, where cuproptosis activity in fibroblasts affects intercellular communication. Using transcription factor regulatory activity network analysis, we obtained highly active transcription factors, and correlation analysis with CRGs suggested that CRGs may serve as potential target genes for transcription factors. Overall, our study provides new insights into the pathophysiological mechanisms of HS, which may inspire new ideas for the diagnosis and treatment.

Identification and immunological characterization of cuproptosis-related molecular clusters in idiopathic pulmonary fibrosis disease

Background

Idiopathic pulmonary fibrosis (IPF) has attracted considerable attention worldwide and is challenging to diagnose. Cuproptosis is a new form of cell death that seems to be associated with various diseases. However, whether cuproptosis-related genes (CRGs) play a role in regulating IPF disease is unknown. This study aims to analyze the effect of CRGs on the progression of IPF and identify possible biomarkers.

Methods

Based on the GSE38958 dataset, we systematically evaluated the differentially expressed CRGs and immune characteristics of IPF disease. We then explored the cuproptosis-related molecular clusters, the related immune cell infiltration, and the biological characteristics analysis. Subsequently, a weighted gene co-expression network analysis (WGCNA) was performed to identify cluster-specific differentially expressed genes. Lastly, the eXtreme Gradient Boosting (XGB) machine-learning model was chosen for the analysis of prediction and external datasets validated the predictive efficiency.

Results

Nine differentially expressed CRGs were identified between healthy and IPF patients. IPF patients showed higher monocytes and monophages M0 infiltration and lower naive B cells and memory resting T CD4 cells infiltration than healthy individuals. A positive relationship was found between activated dendritic cells and CRGs of LIPT1, LIAS, GLS, and DBT. We also identified cuproptosis subtypes in IPF patients. Go and KEGG pathways analysis demonstrated that cluster-specific differentially expressed genes in Cluster 2 were closely related to monocyte aggregation, ubiquitin ligase complex, and ubiquitin-mediated proteolysis, among others. We also constructed an XGB machine model to diagnose IPF, presenting the best performance with a relatively lower residual and higher area under the curve (AUC= 0.700) and validated by external validation datasets (GSE33566, AUC = 0.700). The analysis of the nomogram model demonstrated that XKR6, MLLT3, CD40LG, and HK3 might be used to diagnose IPF disease. Further analysis revealed that CD40LG was significantly associated with IPF.

Conclusion

Our study systematically illustrated the complicated relationship between cuproptosis and IPF disease, and constructed an effective model for the diagnosis of IPF disease patients.