Cuproptosis/ferroptosis-related gene signature is correlated with immune infiltration and predict the prognosis for patients with breast cancer

Identification of biomarkers for chronic renal fibrosis and their relationship with immune infiltration and cell death

BACKGROUND

Chronic kidney disease (CKD) represents a significant global public health challenge. This study aims to identify biomarkers of renal fibrosis and elucidate the relationship between unilateral ureteral obstruction (UUO), immune infiltration, and cell death.

METHODS

Gene expression matrices for UUO were retrieved from the gene expression omnibus (GSE36496, GSE79443, GSE217650, and GSE217654). Seven genes identified through Protein-Protein Interaction (PPI) network and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) analysis were validated using qRT-PCR in both in vivo and in vitro UUO experiments. WB assays were employed to investigate the role of Clec4n within NF-κB signaling pathway in renal fibrosis. The composition of immune cells in UUO was assessed using CIBERSORT, and gene set variant analysis (GSVA) was utilized to evaluate prevalent signaling pathways and cell death indices.

RESULTS

GO and KEGG enrichment analyses revealed numerous inflammation-related pathways significantly enriched in UUO conditions. Bcl2a1b, Clec4n, and Col1a1 were identified as potential diagnostic biomarkers for UUO. Analysis of immune cell infiltration indicated a correlation between UUO and enhanced mast cell activation. Silencing Clec4n expression appeared to mitigate the inflammatory response in renal fibrosis. GSVA results indicated elevated inflammatory pathway scores in UUO, with significant differences in disulfiram and cuproptosis scores compared to those in the normal murine kidney group.

CONCLUSION

Bcl2a1b, Clec4n, and Col1a1 may serve as biomarkers for diagnosing UUO. UUO development is closely linked to immune cell infiltration, activation of inflammatory pathways, disulfiram, and cuproptosis processes.

Integration of cuproptosis-related gene signatures in stomach adenocarcinoma: implications for prognostic prediction and therapeutic strategies in cancer drug resistance

BACKGROUND

Stomach adenocarcinoma (STAD) is a prevalent and aggressive cancer, often diagnosed at later stages, which poses challenges for effective treatment. Despite advancements in cancer therapies, the phenomenon of tumor drug resistance remains a critical hurdle. Recent studies have highlighted cuproptosis, a copper-dependent regulated cell death process, as a potential mechanism in various cancers, including STAD. This study integrates cuproptosis-related gene signatures with clinical features to better predict prognosis and explore potential therapeutic targets, focusing on the role of cuproptosis in overcoming tumor resistance mechanisms.

METHODS

Using comprehensive datasets from TCGA-STAD (n = 375 tumor samples, 32 normal samples), GTEx (n = 211 normal gastric tissues), and GEO (GSE84437 and GSE29272), we analyzed the expression of genes associated with cuproptosis. We examined genetic alterations, immune infiltration, and constructed multivariate Cox regression models with clinicopathological covariates (age, gender, TNM stage, histological grade, residual tumor status) to assess the relationship between cuproptosis gene expression and patient survival outcomes, including overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). Drug sensitivity analysis was performed using the Genomics of Drug Sensitivity in Cancer (GDSC) database.

RESULTS

Our analysis identified significant upregulation of several cuproptosis-related genes, including FDX1, which was correlated with improved prognosis and immune cell infiltration patterns. High expression of FDX1 was associated with better OS and DSS outcomes. Further genetic alterations, notably in CDKN2A, were frequent and linked to poor prognosis, highlighting the complexity of tumor drug resistance in STAD. Prognostic models incorporating FDX1, PDHA1, and LIAS expression stratified patients into distinct risk categories, emphasizing their potential as biomarkers for personalized therapeutic strategies.

CONCLUSIONS

This study underscores the importance of cuproptosis-related genes, particularly FDX1, in the prognosis and therapeutic response of STAD. By integrating molecular features with clinical data, we offer insights into the potential for overcoming drug resistance in cancer therapy. These findings lay the groundwork for future research into targeted treatments that modulate cuproptosis, offering a novel approach to tackling tumor progression and resistance in STAD.

Predictive biomarkers in the era of immunotherapy for gastric cancer: current achievements and future perspectives

Gastric cancer (GC) is one of the primary contributors to cancer-related mortality on a global scale. It holds a position within the top five most prevalent malignancies both in terms of occurrence and fatality rates. Immunotherapy, as a breakthrough cancer treatment, brings new hope for GC patients. Various biomarkers, such as the expression of programmed death ligand-1 (PD-L1), the microsatellite instability (MSI) status, tumor mutational burden (TMB), and Epstein-Barr virus (EBV) infection, demonstrate potential to predict the effectiveness of immunotherapy in treating GC. Nevertheless, each biomarker has its own limitations, which leads to a significant portion of patients continue to be unresponsive to immunotherapy. With the understanding of the tumor immune microenvironment (TIME), genome sequencing technology, and recent advances in molecular biology, new molecular markers, such as POLE/POLD1mutations, circulating tumor DNA, intestinal flora, lymphocyte activation gene 3 (LAG-3), and lipid metabolism have emerged. This review aims to consolidate clinical evidence to offer a thorough comprehension of the existing and emerging biomarkers. We discuss the mechanisms, prospects of application, and limitations of each biomarker. We anticipate that this review will open avenues for fresh perspectives in the investigation of GC immunotherapy biomarkers and promote the precise choice of treatment modalities for gastric cancer patients, thereby advancing precision immuno-oncology endeavors.

Integrated Analysis of Bulk and Single-Cell RNA Sequencing Data Reveal a Novel Prognostic Signature of Combining Cuproptosis- and Ferroptosis-Related Genes in Hepatocellular Carcinoma

As a common malignancy, hepatocellular carcinoma (HCC) proliferation and metastasis could be promoted by ferroptosis and cuproptosis. In this study, we screened out the differentially expressed cuproptosis- and ferroptosis-related genes (CFRGs) and identified the 17 informative prognosis-associated genes. A CFRG scoring model was constructed based on the subtypes identified by consensus clustering analysis and principal component analysis (PCA). Furthermore, the immune profile, expression of immune checkpoint genes (ICGs) and drug susceptibility were also compared between the two CFRG score groups. The results showed that patients with a high CFRG score had higher survival probabilities. The correlation analysis suggested that CFRG scores were negatively correlated with activated CD4.T.cell. The expression patterns of thirty ICGs and the half-maximal inhibitory concentration (IC50) values of 128 drugs displayed significant differences between the two CFRG score groups. A statistically significant difference in the efficacy of sorafenib was found between the two CFRG score groups. Moreover, based on multivariate COX regression analysis and weighted gene co-expression network analysis (WGCNA), we screened DLAT and SLC2A1 as signature genes. Molecular docking analysis revealed that DLAT and SLC2A1 had a strong binding affinity toward camptothecin, rapamycin, dactolisib, and luminespib. The correlation between the CFRG score and single-cell characteristics was further explored. The study depended on our understanding of the biological function of CFRGs in HCC and provided new insights for developing treatment strategies.

Targeting the initiator to activate both ferroptosis and cuproptosis for breast cancer treatment: progress and possibility for clinical application

Breast cancer is the most commonly diagnosed cancer worldwide. Metal metabolism is pivotal for regulating cell fate and drug sensitivity in breast cancer. Iron and copper are essential metal ions critical for maintaining cellular function. The accumulation of iron and copper ions triggers distinct cell death pathways, known as ferroptosis and cuproptosis, respectively. Ferroptosis is characterized by iron-dependent lipid peroxidation, while cuproptosis involves copper-induced oxidative stress. They are increasingly recognized as promising targets for the development of anticancer drugs. Recently, compelling evidence demonstrated that the interplay between ferroptosis and cuproptosis plays a crucial role in regulating breast cancer progression. This review elucidates the converging pathways of ferroptosis and cuproptosis in breast cancer. Moreover, we examined the value of genes associated with ferroptosis and cuproptosis in the clinical diagnosis and treatment of breast cancer, mainly outlining the potential for a co-targeting approach. Lastly, we delve into the current challenges and limitations of this strategy. In general, this review offers an overview of the interaction between ferroptosis and cuproptosis in breast cancer, offering valuable perspectives for further research and clinical treatment.

Clinical and Immunological Significance of ANKRD52 in Pan-Cancer

Ankyrin repeat domain 52 (ANKRD52) is a regulatory component of the protein phosphatase 6 (PP6) holoenzyme. Evidence has emerged to suggest involvement of ANKRD52 in tumor metastases and cancer cell escape from T cell-mediated elimination and immunotherapy but there has been no research across different cancer types. The current study explored the biological functions of ANKRD52 by combining data from many databases. The aim was to expose new diagnostic or treatment biomarkers for malignant tumors. The roles of ANKRD52 with respect to immunotherapy in 33 human cancer types were analyzed by combining data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), UCSC Xena, the Tumor Immune Estimation Resource (TIMER), TISIDB and Cellminer. Bioinformatics methods were used to analyze the association between ANKRD52 expression and prognosis, immunological indicators (immune cell infiltration, ESTIMATE scores and tumor microenvironment (TME) signatures), tumor mutational burden (TMB), microsatellite instability (MSI) and drug sensitivity. ANKRD52 expression was generally higher in 24 tumor tissues than in normal tissues and was associated with poor prognosis, especially in kidney chromophobe (KICH). Lower expression was observed in advanced cancer. ANKRD52 expression was strongly linked to major immunological indicators, such as immune cell infiltration, ESTIMATE scores, TME signatures, as well as expression of immune and tumor-related genes. Expression was also associated with indicators of immunotherapy efficacy and outcome, such as TMB in 7 cancer types and MSI in 12. In addition, ANKRD52 expression was linked to sensitivity to a number of anticancer drugs. ANKRD52 had a distinct immune function in breast invasive carcinoma (BRCA) that correlated negatively with most immune indicators. Expression was enriched in proliferation-, differentiation- and metabolism-related pathways and linked to other immune cells and TME signatures. A nomogram to predict 3- or 5-year overall survival (OS) of patients with BRCA was constructed. ANKRD52 may have utility as an oncological and immunological biomarker. New insights into oncogenesis are presented and the development of ANKRD52-targeting to increase the therapeutic efficacy of immunotherapy combined with chemotherapy explored.

Identification of JAZF1, KNOP1, and PLEKHA1 as causally associated genes and drug targets for Alzheimer's disease: a summary data-based Mendelian randomization study

BACKGROUND

There is a growing body of evidence indicating the significant role of the immune system and immune cells in the progression of Alzheimer's disease (AD). However, the exact role of genes from various immune cell types in AD remains unclear. We aimed to utilize summary data-based Mendelian randomization (SMR) to explore the potential causal relationships between genes in specific immune cells and the risk of AD.

METHODS

By utilizing data sets of expression quantitative trait loci (eQTL) for 14 different immune cell types and large-scale AD genome-wide association study (GWAS), we employed SMR to identify key genes associated with AD within specific immune cells. Sensitivity analyses, including F-statistic, colocalization, and assessment of horizontal pleiotropy, were further conducted to validate the discovered genes. In addition, replication analyses were performed in AD GWAS from the FinnGen consortium. Finally, we further identified existing drugs that target or interact with the druggable genes and reviewed the studies about the associations between these drugs and AD.

RESULTS

SMR analysis revealed 342 genes associated with AD across 14 immune cell types. Further sensitivity analyses identified nine genes, CTSH, FCER1G, FNBP4, HLA-E, JAZF1, KNOP1, PLEKHA1, RP11-960L18.1, and ZNF638 that had significant associations with AD across nine specific immune cell types. JAZF1, KNOP1 and PLEKHA1 were replicated in an independent analysis using the GWAS data. The review on gene-related drugs also supported these findings.

CONCLUSIONS

Our research suggests that the expression of the genes JAZF1, KNOP1, and PLEKHA1 in specific immune cell types is related to the risk of AD.

Unveiling the role of TGF-β signaling pathway in breast cancer prognosis and immunotherapy

Introduction

The TGF-β signaling pathway (TSP) is pivotal in tumor progression. Nonetheless, the connection between genes associated with the TSP and the clinical outcomes of breast cancer, as well as their impact on the tumor microenvironment and immunotherapeutic responses, remains elusive.

Methods

Breast cancer transcriptomic and single-cell sequencing data were obtained from the The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. We identified 54 genes associated with the TSP from the Molecular Signatures Database (MSigDB) and analyzed both data types to evaluate TSP activity. Using weighted gene co-expression network analysis (WGCNA), we identified modules linked to TSP activity. To assess patient risk, we used 101 machine learning algorithms to develop an optimal TGF-β pathway-related prognostic signature (TSPRS). We then examined immune activity and response to immune checkpoint inhibitors and chemotherapy in these groups. Finally, we validated ZMAT3 expression levels clinically and confirmed its relevance in breast cancer using CCK-8 and migration assays.

Results

At the single-cell level, TSP activity was most notable in endothelial cells, with higher activity in normal tissues compared to tumors. TSPRS was developed. This signature's accuracy was confirmed through internal and external validations. A nomogram incorporating the TSPRS was created to improve prediction accuracy. Further studies showed that breast cancer patients categorized as low-risk by the TSPRS had higher immune phenotype scores and more immune cell infiltration, leading to better prognosis and enhanced immunotherapy response. Additionally, a strong link was found between the TSPRS risk score and the effectiveness of anti-tumor agents. Silencing the ZMAT3 gene in the TSPRS significantly reduced the proliferation and invasiveness of breast cancer cells.

Discussion

Our study developed a TSPRS, which emerges as a potent predictive instrument for the prognosis of breast cancer, offering novel perspectives on the immunotherapeutic approach to the disease.

Development of prognostic model incorporating a ferroptosis/cuproptosis-related signature and mutational landscape analysis in muscle-invasive bladder cancer

BACKGROUND

Muscle-invasive bladder cancer (MIBC) is a prevalent and aggressive malignancy. Ferroptosis and cuproptosis are recently discovered forms of programmed cell death (PCD) that have attracted much attention. However, their interactions and impacts on MIBC overall survival (OS) and treatment outcomes remain unclear.

METHODS

Data from the TCGA-BLCA project (as the training set), cBioPortal database, and GEO datasets (GSE13507 and GSE32894, as the test sets) were utilized to identify hub ferroptosis/cuproptosis-related genes (FRGs and CRGs) and develop a prognostic signature. Differential expression analysis (DEA) was conducted, followed by univariate and multivariate Cox's regression analyses and multiple machine learning (ML) techniques to select genetic features. The performance of the ferroptosis/cuproptosis-related signature was evaluated using Kaplan-Meier (K-M) survival analysis and receiver-operating characteristics (ROC) curves. Mutational and tumour immune microenvironment landscapes were also explored. Real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) experiments confirmed the expression patterns of the hub genes, and functional assays assessed the effects of SCD knockdown on cell viability, proliferation, and migration.

RESULTS

DEA revealed dysregulated FRGs and CRGs in the TCGA MIBC cohort. SCD, DDR2, and MT1A were identified as hub genes. A prognostic signature based on the sum of the weighted expression of these genes demonstrated strong predictive efficacy in the training and test sets. Nomogram incorporating this signature accurately predicted 1-, 3-, and 5-year survival probabilities in the TCGA cohort and GSE13507 dataset. Copy number variation (CNV) and tumour immune microenvironment analysis revealed that high risk score level groups were associated with immunosuppression and lower tumour purity. The associations of risk scores with immunotherapy and chemical drugs were also explored, indicating their potential for guiding treatment for MIBC patients. The dysregulated expression patterns of three hub genes were validated by RT-qPCR experiments.

CONCLUSIONS

Targeting hub FRGs and CRGs could be a promising therapeutic approach for MIBC. Our prognostic model offers a new framework for MIBC subtyping and can inform personalized therapeutic strategies.

Single-cell and bulk RNA-seq unveils the immune infiltration landscape associated with cuproptosis in cerebral cavernous malformations

BACKGROUND

Cerebral cavernous malformations (CCMs) are vascular abnormalities associated with deregulated angiogenesis. Their pathogenesis and optimal treatment remain unclear. This study aims to investigate the molecular signatures of cuproptosis, a newly identified type of cell death, associated with CCMs development.

METHODS

Bulk RNA sequencing (RNA-seq) from 15 CCM and 6 control samples were performed with consensus clustering and clustered to two subtypes based on expression levels of cuproptosis-related genes (CRGs). Differentially expressed genes and immune infiltration between subtypes were then identified. Machine learning algorithms including the least absolute shrinkage and selection operator and random forest were employed to screen for hub genes for CCMs associated with cuproptosis. Furthermore, Pathway enrichment and correlation analysis were used to explore the functions of hub genes and their association with immune phenotypes in CCMs. An external dataset was then employed for validation. Finally, employing the Cellchat algorithm on a single-cell RNA-seq dataset, we explored potential mechanisms underlying the participation of these hub genes in cell-cell communication in CCMs.

RESULTS

Our study revealed two distinct CCM subtypes with differential pattern of CRG expression and immune infiltration. Three hub genes (BTBD10, PFDN4, and CEMIP) were identified and validated, which may significantly associate with CCM pathogenesis. These genes were found to be significantly upregulated in CCM endothelial cells (ECs) and were validated through immunofluorescence and western blot analysis. Single-cell RNA-seq analysis revealed the cellular co-expression patterns of these hub genes, particularly highlighting the high expression of BTBD10 and PFDN4 in ECs. Additionally, a significant co-localization was also observed between BTBD10 and the pivotal cuproptosis gene FDX1 in Mki67+ tip cells, indicating the crucial role of cuproptosis for angiogenesis in CCMs. The study also explored the cell-cell communication between subcluster of ECs expressing these hub genes and immune cells, particularly M2 macrophages, suggesting a role for these interactions in CCM pathogenesis.

CONCLUSION

This study identifies molecular signatures linking cuproptosis to CCMs pathogenesis. Three hub genes-PFDN4, CEMIP, and BTBD10-may influence disease progression by modulating immunity. Further research is needed to understand their precise disease mechanisms and evaluate their potential as biomarkers or therapeutic targets for CCMs.

Cuproptosis: unveiling a new frontier in cancer biology and therapeutics

Copper plays vital roles in numerous cellular processes and its imbalance can lead to oxidative stress and dysfunction. Recent research has unveiled a unique form of copper-induced cell death, termed cuproptosis, which differs from known cell death mechanisms. This process involves the interaction of copper with lipoylated tricarboxylic acid cycle enzymes, causing protein aggregation and cell death. Recently, a growing number of studies have explored the link between cuproptosis and cancer development. This review comprehensively examines the systemic and cellular metabolism of copper, including tumor-related signaling pathways influenced by copper. It delves into the discovery and mechanisms of cuproptosis and its connection to various cancers. Additionally, the review suggests potential cancer treatments using copper ionophores that induce cuproptosis, in combination with small molecule drugs, for precision therapy in specific cancer types.

Crosstalk between ferroptosis and cuproptosis: From mechanism to potential clinical application

Ferroptosis and cuproptosis, regulated forms of cell death resulting from metal ion accumulation, are closely related in terms of occurrence, cell metabolism, signaling pathways, and drug resistance. Notably, it is now understood that these processes play crucial roles in regulating physiological and pathological processes, especially in tumor development. Consequently, ferroptosis and cuproptosis have gained increasing significance as potential targets for anti-cancer drug development. This article systematically outlines the molecular mechanisms and cross-talk components of both ferroptosis and cuproptosis, elucidating their impacts on cancer. Furthermore, it investigates the clinical perspective of targeted ferroptosis and cuproptosis in cancer chemotherapy, immunotherapy, and radiotherapy. Our discussion extends to a comparative analysis of nanoparticles developed based on the mechanisms of ferroptosis and cuproptosis in cancer, contrasting them with current conventional therapies. Opportunities and challenges in cancer treatment are explored, emphasizing the potential therapeutic direction of co-targeting ferroptosis and cuproptosis. The article also attempts to analyze the clinical applications of this co-targeting approach for cancer treatment while summarizing the existing barriers that require overcoming.

A 15-Gene-Based Risk Signature for Predicting Overall Survival in SCLC Patients Who Have Undergone Surgical Resection

Small cell lung cancer (SCLC) is a malignancy with a poor prognosis whose treatment has not progressed for decades. The survival benefit of surgery and the selection of surgical candidates are still controversial in SCLC. This study is the first report to identify transcriptomic alterations associated with prognosis and propose a gene expression-based risk signature that can be used to predict overall survival (OS) in SCLC patients who have undergone potentially curative surgery. An integrative transcriptome analysis of three gene expression datasets (GSE30219, GSE43346, and GSE149507) revealed 1734 up-regulated and 2907 down-regulated genes. Cox-Mantel test, Cox regression, and Lasso regression analyses were used to identify genes to be included in the risk signature. EGAD00001001244 and GSE60052-cohorts were used for internal and external validation, respectively. Overall survival was significantly poorer in patients with high-risk scores compared to the low-risk group. The discriminatory performance of the risk signature was superior to other parameters. Multivariate analysis showed that the risk signature has the potential to be an independent predictor of prognosis. The prognostic genes were enriched in pathways including regulation of transcription, cell cycle, cell metabolism, and angiogenesis. Determining the roles of the identified prognostic genes in the pathogenesis of SCLC may contribute to the development of new treatment strategies. The risk signature needs to be validated in a larger cohort of patients to test its usefulness in clinical decision-making.