Cuproptosis scoring system to predict the clinical outcome and immune response in bladder cancer

Cuproptosis: Mechanism, role, and advances in urological malignancies

Prostate, bladder, and kidney cancers are the most common malignancies of the urinary system. Chemotherapeutic drugs are generally used as adjuvant treatment in the middle, late, or recurrence stages after surgery for urologic cancers. However, traditional chemotherapy is plagued by problems such as poor efficacy, severe side effects, and complications. Copper-containing nanomedicines are promising novel cancer treatment modalities that can potentially overcome these disadvantages. Copper homeostasis and cuproptosis play crucial roles in the development, adaptability, and therapeutic sensitivity of urological malignancies. Cuproptosis refers to the direct binding of copper ions to lipoylated components of the tricarboxylic acid cycle, leading to protein oligomerization, loss of iron-sulfur proteins, proteotoxic stress, and cell death. This review focuses on copper homeostasis and cuproptosis as well as recent findings on copper and cuproptosis in urological malignancies. Furthermore, we highlight the potential therapeutic applications of copper- and cuproptosis-targeted therapies to better understand cuproptosis-based drugs for the treatment of urological tumors in the future.

Bioinformatics Identification and Experimental Validation of a Prognostic Model for the Survival of Lung Squamous Cell Carcinoma Patients

Lung squamous cell carcinoma (LUSC) kills more than four million people yearly. Creating more trustworthy tumor molecular markers for LUSC early detection, diagnosis, prognosis, and customized treatment is essential. Cuproptosis, a novel form of cell death, opened up a new field of study for searching for trustworthy tumor indicators. Our goal was to build a risk model to assess drug sensitivity, monitor immune function, and predict prognosis in LUSC patients. The 19 cuproptosis-related genes were found in the literature, and patient genomic and clinical information was collected using the Cancer Genomic Atlas (TCGA) database. The LUSC patients were grouped using unsupervised clustering techniques, and 7626 differentially expressed genes were identified. Using univariate COX analysis, LASSO regression analysis, and multivariate COX analysis, a prognostic model for LUSC patients was developed. The tumor immune escape was evaluated using the Tumor Immune Dysfunction and Exclusion (TIDE) method. The R packages 'pRRophetic,' 'ggpubr,' and 'ggplot2' were utilized to examine drug sensitivity. For modeling, a 6-cuproptosis-based gene signature was found. Patients with high-risk LUSC had significantly worse survival rates than those with low-risk conditions. The possibility of tumor immunological escape was increased in patients with higher risk scores due to more immune cell inactivation. For patients with high-risk LUSC, we discovered seven potent potential drugs (AZD6482, CHIR.99021, CMK, Embelin, FTI.277, Imatinib, and Pazopanib). In conclusion, the cuproptosis-based genes predictive risk model can be utilized to predict outcomes, track immune function, and evaluate medication sensitivity in LUSC patients.

Breast cancer clinical outcomes and tumor immune microenvironment: cross-dialogue of multiple epigenetic modification profiles

The discovery of RNA methylation alterations associated with cancer holds promise for their utilization as potential biomarkers in cancer diagnosis, prognosis, and prediction. RNA methylation has been found to impact the immunological microenvironment of tumors, but the specific role of methylation-related genes (MRGs), particularly in breast cancer (BC), the most common cancer among women globally, within the tumor microenvironment remains unknown. In this study, we obtained data from TCGA and GEO databases to investigate the expression patterns of MRGs in both genomic and transcriptional domains in BC. By analyzing the data, we identified two distinct genetic groupings that were correlated with clinicopathological characteristics, prognosis, degree of TME cell infiltration, and other abnormalities in MRGs among patients. Subsequently, an MRG model was developed to predict overall survival (OS) and its accuracy was evaluated in BC patients. Additionally, a highly precise nomogram was created to enhance the practical usability of the MRG model. In low-risk groups, we observed lower TBM values and higher TIDE scores. We further explored how MRGs influence a patient's prognosis, clinically significant characteristics, response to therapy, and the TME. These risk signatures have the potential to improve treatment strategies for BC patients and could be applied in future clinical settings. Moreover, they may also be utilized to determine prognosis and biological features in these patients.

Epigenetic regulation of diverse cell death modalities in cancer: a focus on pyroptosis, ferroptosis, cuproptosis, and disulfidptosis

Tumor is a local tissue hyperplasia resulted from cancerous transformation of normal cells under the action of various physical, chemical and biological factors. The exploration of tumorigenesis mechanism is crucial for early prevention and treatment of tumors. Epigenetic modification is a common and important modification in cells, including DNA methylation, histone modification, non-coding RNA modification and m6A modification. The normal mode of cell death is programmed by cell death-related genes; however, recent researches have revealed some new modes of cell death, including pyroptosis, ferroptosis, cuproptosis and disulfidptosis. Epigenetic regulation of various cell deaths is mainly involved in the regulation of key cell death proteins and affects cell death by up-regulating or down-regulating the expression levels of key proteins. This study aims to investigate the mechanism of epigenetic modifications regulating pyroptosis, ferroptosis, cuproptosis and disulfidptosis of tumor cells, explore possible triggering factors in tumor development from a microscopic point of view, and provide potential targets for tumor therapy and new perspective for the development of antitumor drugs or combination therapies.

A novel network-based method identifies a cuproplasia-related pan-cancer gene signature to predict patient outcome

Copper is a vital micronutrient involved in many biological processes and is an essential component of tumour cell growth and migration. Copper influences tumour growth through a process called cuproplasia, defined as abnormal copper-dependent cell-growth and proliferation. Copper-chelation therapy targeting this process has demonstrated efficacy in several clinical trials against cancer. While the molecular pathways associated with cuproplasia are partially known, genetic heterogeneity across different cancer types has limited the understanding of how cuproplasia impacts patient survival. Utilising RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets, we generated gene regulatory networks to identify the critical cuproplasia-related genes across 23 different cancer types. From this, we identified a novel 8-gene cuproplasia-related gene signature associated with pan-cancer survival, and a 6-gene prognostic risk score model in low grade glioma. These findings highlight the use of gene regulatory networks to identify cuproplasia-related gene signatures that could be used to generate risk score models. This can potentially identify patients who could benefit from copper-chelation therapy and identifies novel targeted therapeutic strategies.

Cuproptosis/OXPHOS tendency prediction of prognosis and immune microenvironment of esophageal squamous cell carcinoma: Bioinformatics analysis and experimental validation

BACKGROUND

Cuproptosis is a newly discovered cell death mechanism that relies on mitochondrial respiration, for which oxidative phosphorylation (OXPHOS) is an essential part. However, the detailed mechanisms of cuproptosis associated with OXPHOS in esophageal squamous cell carcinoma (ESCC) and how this correlation affects prognosis still remains unclear.

METHODS

scRNA-seq data of ESCC were downloaded from SRA (Sequence Read Archive) database. "AUCell" algorithm was used to grouping epithelial cells according to cuproptosis and OXPHOS score. Cell-cell communication, Pseudo-time Trajectory and transcription factor enrichment analysis were repectively conducted by "CellChat", "monocle3" package and "pySCENIC" algorithm. Univariate and LASSO cox regression analysis were used to construct the prognostic cuproptosis-OXPHOS signature. Finally, CCK-8 assay and DCFH-DA staining assay were respectively validated the sensitive and ROS production of elesclomol.

RESULTS

scRNA-seq data were analyzed to identify 10 core cell types. According to the median scores for cuproptosis and OXPHOS, malignant epithelial cells were divided into double high, double low, and mixed groups. The double high group distributed at the end of the pseudo-time trajectory and harbored HMGA1(+) as specific transcriptional regulons. Knockdown of HMGA1 partly reversed the inhibition of cell viability visualized by CCK-8 assay, while reactive oxygen species (ROS) production by elesclomol was enhanced after HMGA1 silencing. Furthermore, the immunosuppressive signal was significantly increased in the double high group detected by 'CellChat' in single-cell data and 'ssGSEA' in bulk data followed by 'CIBERSORTx' algorithm. Finally, a new cuproptosis-OXPHOS prognostic signature (CNN2, ATP6V0E1, PSMD6, CCDC25, IGFBP2, MT1E, and RPS4Y1) was constructed for the prediction of the prognosis, and a high-risk group corresponding to a more sensitive tendency to erlotinib, dasatinib, and bosutinib treatment was identified.

CONCLUSIONS

Our study revealed the relationship between OXPHOS and tendency of cuproptosis in ESCC, and malignant cells with this characteristic exerted immunosuppressive signals and indicated poor prognosis. Furthermore, we constructed the regulatory network in high cuproptosis-OXPHOS ESCC and identified HMGA1 as a potential regulator molecule of cuproptosis mediated by elesclomol.

Analysis of cuproptosis-related genes in prognosis and immune infiltration in grade 4 diffuse gliomas

Background

Grade 4 diffuse gliomas are highly malignant tumours with poor prognosis. Cuproptosis is a novel form of cell death. Cuproptosis genes are associated with various tumours and affect the prognosis of patients with these tumours. However, the relationship between cuproptosis and grade 4 diffuse gliomas remains unclear.

Methods

Differentially expressed genes associated with cuproptosis in grade 4 diffuse gliomas were identified. Second, the prognostic model was established by univariate and multivariate COX regression analyses, and the genes (p < 0.05) were selected for subsequent analysis. The endpoint of the study was death. Single-gene analysis was performed in accordance with the expression levels of SLC31A1. Third, based on the expression levels of SLC31A1, gene function enrichment, drug sensitivity, and immune cell infiltration analyses were performed. Finally, the expression and biological functions of SLC31A1 in grade 4 diffuse gliomas were identified using immunohistochemical staining, qRT-PCR, and related biological experiments.

Results

We identified six coproptosis genes in the grade 4 diffuse gliomas dataset (SLC31A1, PDHA1, GLS, FDX1, LIPT1, and ATP7B). The six key cuproptosis genes of grade 4 diffuse gliomas were analysed using univariate COX analysis. Basic patient data, including age, race, year of diagnosis, sex, and treatment, were included in the univariate COX analysis. Then, multivariate COX analysis was performed for the factors with p < 0.2 in the univariate COX analysis. Age, year of diagnosis, and SLC31A1, PDHA1, and FDX1 levels were found to be independent prognostic factors. A nomogram was constructed using these 5 factors. Through experiments, we found that SLC31A1 had a higher expression level in cancer tissue than that near cancer among the three genes, SLC31A1, PDHA1, and FDX1; therefore, we focused on SLC31A1. According on the expression level of SLC31A1, we performed gene function enrichment, drug sensitivity, and immune cell infiltration analyses. Navitoclax was the most sensitive drug. Differential gene function enrichment was observed for metalloendopeptidase activity. SLC31A1 is expressed in dendritic cells, macrophages, neutrophils, and CD8+T cells. SLC31A1 is highly expressed in grade 4 diffuse gliomas, whereas SLC31A1 knockdown significantly reduces cell proliferation and mobility.

Conclusions

Age, year of diagnosis, and SLC31A1, PDHA1, and FDX1 expression were independent prognostic factors. A nomogram was constructed based on age, year of diagnosis, and SLC31A1, PDHA1, and FDX1 levels. Through analysis and experimental verification, SLC31A1 was found to affect the prognosis and progression of patients with grade 4 diffuse gliomas and was associated with immune cell infiltration.

Independent prognostic value of CLDN6 in bladder cancer based on M2 macrophages related signature

M2 macrophages are associated with the prognosis of bladder cancer. CLDN6 has been linked to immune infiltration and is crucial for predicting the prognosis in multi-tumor. The effect of CLDN6 on M2 macrophages in bladder cancer remains elusive. Here, we compared a total of 40 machine learning algorithms, then selected optimal algorithm to develop M2 macrophages-related signature (MMRS) based on the identified M2 macrophages related module. MMRS predicted the prognosis better than other models and associated to immunotherapy response. CLDN6, as an important variable in MMRS, was an independent factor for poor prognosis. We found that CLDN6 was highly expressed and affected immune infiltration, immunotherapy response, and M2 macrophages polarization. Meanwhile, CLDN6 promoted the growth of bladder cancer and enhanced the carcinogenic effect by inducing polarization of M2 macrophages. In total, CLDN6 is an independent risk factor in MMRS to predict the prognosis of bladder cancer.

Harnessing ferroptosis for enhanced sarcoma treatment: mechanisms, progress and prospects

Sarcoma is a malignant tumor that originates from mesenchymal tissue. The common treatment for sarcoma is surgery supplemented with radiotherapy and chemotherapy. However, patients have a 5-year survival rate of only approximately 60%, and sarcoma cells are highly resistant to chemotherapy. Ferroptosis is an iron-dependent nonapoptotic type of regulated programmed cell death that is closely related to the pathophysiological processes underlying tumorigenesis, neurological diseases and other conditions. Moreover, ferroptosis is mediated via multiple regulatory pathways that may be targets for disease therapy. Recent studies have shown that the induction of ferroptosis is an effective way to kill sarcoma cells and reduce their resistance to chemotherapeutic drugs. Moreover, ferroptosis-related genes are related to the immune system, and their expression can be used to predict sarcoma prognosis. In this review, we describe the molecular mechanism underlying ferroptosis in detail, systematically summarize recent research progress with respect to ferroptosis application as a sarcoma treatment in various contexts, and point out gaps in the theoretical research on ferroptosis, challenges to its clinical application, potential resolutions of these challenges to promote ferroptosis as an efficient, reliable and novel method of clinical sarcoma treatment.

Creation of signatures and identification of molecular subtypes based on disulfidptosis-related genes for glioblastoma patients' prognosis and immunological activity

BACKGROUND

In recent times, disulfidptosis, an intricate form of cellular demise, has garnered attention due to its impact on prognosis, tumor progression and treatment response. Nevertheless, the exact significance of disulfidptosis-related genes (DisRGs) in glioblastoma (GBM) remains enigmatic.

METHODS

The GEO and TCGA databases provided transcriptional and clinically relevant data on tumor samples, while the GTEx database provided data on healthy tissues. Disulfidptosis-related genes (DisRGs) were procured from previous scholarly investigations. The expression profile of DisRGs was initially scrutinized among patients diagnosed with GBM, subsequent to which their prognostic value was explored. Through consensus clustering, we constructed DisRGs-related clusters and gene subtypes. Our results established that the DisRG-related clusters had differentially expressed genes, resulting in a DisulfidptosisScore model, which had a positive prognostic value.

RESULTS

The differential expression profile of 24 DisRGs between GBM samples and healthy samples was acquired. Through consensus cluster analysis, two distinct disulfidptosis subtypes, namely DisRGcluster A and DisRGcluster B, were identified. Then, the DisulfidptosisScore model including 4 characteristic genes was constructed.Notably, patients with GBM assigned with lower score demonstrated a considerably longer overall survival (OS) compared to those with higher score.

CONCLUSION

We have effectively devised a prognostic model associated with disulfidptosis, presenting autonomous prognostic predictions for patients with GBM. These findings serve as a valuable addition to the current comprehension of disulfidptosis and offer fresh theoretical substantiation for the development of enhanced treatment strategies.

Can uric acid affect the immune microenvironment in bladder cancer? A single-center multi-omics study

Metabolic abnormalities are one of the important factors in bladder cancer (BCa) progression and microenvironmental disturbance. As an important product of purine metabolism, uric acid's (UA) role in BCa metabolism and immunotherapy remains unclear. In this study, we conducted a retrospective analysis of a cohort comprising 39 BCa patients treated with PD-1 and 169 patients who underwent radical cystectomy at Shanghai Tenth People's Hospital. Kaplan-Meier curves and Cox regression analysis showed that the prognosis of patients with high UA is worse (p = 0.007), and high UA is an independent risk factor for cancer specific survival in patients with BCa (p = 0.025). We established a hyperuricemia mouse model with BCa subcutaneous xenografts in vivo. The results revealed that the subcutaneous tumors of hyperuricemia mice had a greater weight and volume in comparison with the control group. Through flow cytometric analysis, the proportion of CD8+ and CD4+ T cells in these subcutaneous tumors was seen to decline significantly. We also evaluated the relationship of UA and BCa by muti-omic analysis. UA related genes were significantly increased in the CD8+ T cell of non-responders to immunotherapy by single-cell sequencing. An 11-gene UA related signature was constructed and the risk score negatively correlated with various immune cells and immune checkpoints. Finally, a nomogram was established using a UA related signature to forecast the survival rate of patients with BCa. Collectively, this study demonstrated that UA was an independent prognostic biomarker for BCa and was associated with worse immunotherapy response.

Multiple programmed cell death patterns and immune landscapes in bladder cancer: Evidence based on machine learning and multi-cohorts

BACKGROUND

Bladder cancer (BLCA) is the most prevalent malignant neoplasm of the urinary tract, and ranks seventh as the most frequent systemic neoplasm in males. Dysregulation of programmed cell death (PCD) has been implicated in various stages of cancer progression, including tumorigenesis, invasion, and metastasis. However, the correlation between multiple PCD modes and BLCA is lacking. Thus, a risk prediction model was built based on 12 models of PCD to predict prognosis and immunotherapy response in patients with BLCA.

METHODS

The RNA sequencing transcriptome data of BLCA were collected from the Cancer Genome Atlas Program (TCGA) and GEO datasets. Univariate Cox and LASSO regression analyzes were performed to identify PCD-related genes (PCDRGs) significant for prognosis. Multivariate Cox regression analysis was used to develop a prognostic model for PCD. Survival analysis and chi-squared test were employed to analyze the survival variations between different risk groups. Univariate and multivariate Cox analyses were performed to evaluate the model as an independent prognostic predictor. A nomogram was formulated using both clinical data and the model to predict the survival rates of BLCA patients. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were performed to analyze and elucidate the molecular mechanisms and pathways operating within different risk score groups. Furthermore, the immune landscape was investigated and the efficacy of various anti-tumor drugs was evaluated for BLCA. Finally, consensus clustering analysis was adopted to explore the association between different PCD clusters and clinical characteristics.

RESULTS

Assessment of the public datasets and multivariate Cox analysis yielded 1254 PCDRGs, of which 10 PCDRGs for BLCA were identified. Based on the PCDRGs, a prognostic model was built for BLCA patient prognosis. Compared with the low-risk group, the high-risk group had a poorer prognosis. The model predicted area under the curve (AUC) values of 0.751, 0.753, and 0.763, respectively, for 1-, 3-, and 5-year survival of BLCA patients. The nomogram further demonstrated the credibility of the prognosis model. The low-risk group patients exhibited lower TIDE scores and higher TMB scores, implying better response of the low-risk group to immunotherapy. The consensus clustering analysis indicated that compared with PCD cluster A, PCD cluster B was significantly more expressed in PCDRGs, suggesting a closer relation of PCD cluster B to PCDRGs. Patients in PCD cluster B had lower risk scores.

CONCLUSION

To summarize, the effects of 12 PCD patterns on BLCA were synthesized and the correlation between PCD and BLCA was explored. These findings provide new and convincing evidence for individualized treatment of BLCA, and help guide the treatment strategy and improve the prognosis of BLCA patients.

Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients

Glioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment. The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. After collecting 18 RCD-related signatures from the opening literature, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. Here, based on bulk and single-cell sequencing samples, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed an RCD gene pair scoring system, named RCD.GP signature, showing a reliable and robust performance in predicting the prognosis of glioblastoma. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified that the SLC43A3 highly expressed in glioma grades and glioma cell lines through qRT-PCR. Our study provided comprehensive insights into the RCD roles in glioma, developed a robust RCD gene pair signature for predicting the prognosis of glioma patients, constructed a machine learning framework for screening the core candidates and identified the SLC43A3 as an oncogenic role and a prediction biomarker in glioblastoma.

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 Deposition in Polydopamine Nanostructure to Promote Cuproptosis by Catalytically Inhibiting Copper Exporters of Tumor Cells for Cancer Immunotherapy

Cuproptosis is an emerging programmed cell death, displaying great potential in cancer treatment. However, intracellular copper content to induce cuproptosis is unmet, which mainly ascribes to the intracellular pumping out equilibrium mechanism by copper exporter ATP7A and ATP7B. Therefore, it is necessary to break such export balance mechanisms for desired cuproptosis. Mediated by diethyldithiocarbamate (DTC) coordination, herein a strategy to efficiently assemble copper ions into polydopamine nanostructure (PDA-DTC/Cu) for reprogramming copper metabolism of tumor is developed. The deposited Cu2+ can effectively trigger the aggregation of lipoylated proteins to induce cuproptosis of tumor cells. Beyond elevating intracellular copper accumulation, PDA-DTC/Cu enables to break the balance of copper metabolism by disrupting mitochondrial function and restricting the adenosine triphosphate (ATP) energy supply, thus catalytically inhibiting the expressions of ATP7A and ATP7B of tumor cells to enhance cuproptosis. Meanwhile, the killed tumor cells can induce immunogenic cell death (ICD) to stimulate the immune response. Besides, PDA-DTC/Cu NPs can promote the repolarization of tumor-associated macrophages (TAMs ) to relieve the tumor immunosuppressive microenvironment (TIME). Collectively, PDA-DTC/Cu presented a promising "one stone two birds" strategy to realize copper accumulation and inhibit copper export simultaneously to enhance cuproptosis for 4T1 murine breast cancer immunotherapy.

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.

Molecular map of cGAS-STING pathway-related genes in bladder cancer: the perspective toward immune microenvironment and prognosis

BACKGROUND

The cGAS-STING pathway emerges as a pivotal innate immune pathway with the potential to profoundly influence all facets of tumor initiation and progression. The prognostic significance and immunological role of cGAS-STING pathway-related genes (CRGs) in individuals diagnosed with bladder cancer (BLCA) have not yet been fully elucidated.

METHODS

Performed unsupervised cluster analysis to identify distinct clusters. Utilizing LASSO and multivariate Cox regression analysis to construct a prognostic risk model. The IMvigor210, GSE13507 and GSE78220 cohorts were utilized to explore the potential value of risk score in immune therapy response and survival prediction.

RESULTS

A risk model was developed utilizing four CRGs in order to forecast the overall survival (OS) of BLCA patients. The risk score to be a standalone risk factor, which was further corroborated by the external validation set obtained from the GEO database (GSE13507). We established an integrated nomogram that combined risk scoring and clinical information, exhibiting commendable clinical practicality in predicting the overall survival period of BLCA patients. It is noteworthy that risk score could differentiate tumor microenvironments among different risk groups and individuals who were more responsive to immunotherapy in IMvigor210 and GSE13507 cohorts. In vitro experiments, we noted an up-regulation of IRF3 and IKBKB upon the activation of the cGAS-STING pathway. Conversely, the activation of the cGAS-STING pathway resulted in a down-regulation of POLR3G and CTNNB1.

CONCLUSIONS

CRG risk model shows promise as a potential stratification approach for bladder cancer patients.

Comprehensive analysis of the role of cuproptosis-related genes in the prognosis and immune infiltration of adrenocortical Carcinoma

Background

Cuproptosis is a recently discovered form of nonapoptotic programmed cell death. However, no research on cuproptosis in the context of adrenocortical carcinoma has been conducted, and the prognostic value of assessing cuproptosis remains unclear.

Methods

In this study, we established comprehensive models to assess gene expression changes, mutation status, and prognosis prediction and developed a prognostic nomogram for cuproptosis-related genes. Using data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Genotype-Tissue Expression (GTEx) databases, an analysis of 11 cuproptosis-related genes was performed. Additionally, a risk scoring method and nomogram were used to assess the relationships among cuproptosis-associated genes, transcript expression, clinical characteristics, and prognosis. The connections among tumors, immune checkpoints, and immune infiltration were also analyzed.

Results

The patterns observed in patients with adrenocortical carcinoma who were assessed using cuproptosis-associated risk scores provide useful information for understanding gene mutations, clinical outcomes, immune cell infiltration, and immune checkpoint analysis results. FDX1, LIPT1, MTF1, COX11, CYP2D6, DLAT, ATP7Band CDKN2A were differentially expressed in patients with adrenocortical carcinoma and normal controls. In addition, higher risk scores were significantly associated with poor overall survival and progression-free interval. The nomogram model subsequently developed to facilitate the clinical application of the analysis showed good predictive and calibration capabilities. GSE10927 and GSE33371 were used for independent cohort validation. Moreover, CDKN2A, FDX1, and other cuproptosis-related genes were significantly associated with immune infiltration and checkpoints.

Conclusion

We confirmed that our model had excellent predictive ability in patients with adrenocortical carcinoma. Therefore, an in-depth evaluation of patients using cuproptosis-related risk scores is clinically essential and can assist in therapy in the future.

Novel cuproptosis-related prognostic gene profiles in preeclampsia

BACKGROUND

Preeclampsia (PE) is a pregnancy-specific disorder with complex pathogenesis. Cuproptosis is a novel identified form of programmed cell death, however, the link between cuproptosis and clinical outcomes in PE is still not fully understood. In this study, we searched for cuproptosis-related genes (CRGs) in the placental tissues of normal and PE patients to clarify the importance of cuproptosis in the development of PE and provide potential predictive indicators for the occurrence of PE.

METHODS

Using RNA sequencing data in the GEO database, we conducted functional enrichment analysis of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA), supported by linear regression model and operating characteristic curve (ROC) curve analysis, and summarized the role of CRGs in preeclampsia.

RESULTS

A total of 2831 differentially expressed genes related to PE were screened through multiple database analyses. After further intersection with 19 reported CRGs, 5 CRGs have been closely associated with the pathogenesis of PE, including NFE2L2, PDHA1, PDHB, DLD and GLS. NFE2L2 was identified as a key central gene. Pearson correlation analysis showed that CRGs could be related to several maternal and fetal outcome factors, including the highest pregnancy blood pressure, placenta weight, umbilical blood flow pulsatility index (PI), and neonatal weight. Linear regression equation revealed that the expression of NFE2L2 is negatively correlated with the highest pregnancy blood pressure and umbilical blood flow PI but positively correlated with placental weight and neonatal weight. QRT-PCR showed that the expression of these CRGs was significantly lower in placental tissues.

CONCLUSIONS

This cuproptosis pattern may be a potential prognostic factor in patients with PE and could provide new insights into disease progression.

Identification of a new gene signature for prognostic evaluation in cervical cancer: based on cuproptosis-associated angiogenesis and multi-omics analysis

Patients with recurrent or metastatic cervical cancer are in urgent need of novel prognosis assessment or treatment approaches. In this study, a novel prognostic gene signature was discovered by utilizing cuproptosis-related angiogenesis (CuRA) gene scores obtained through weighted gene co-expression network analysis (WGCNA) of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. To enhance its reliability, the gene signature was refined by integrating supplementary clinical variables and subjected to cross-validation. Meanwhile, the activation of the VEGF pathway was inferred from an analysis of cell-to-cell communication, based on the expression of ligands and receptors in cell transcriptomic datasets. High-CuRA patients had less infiltration of CD8 + T cells and reduced expression of most of immune checkpoint genes, which indicated greater difficulty in immunotherapy. Lower IC50 values of imatinib, pazopanib, and sorafenib in the high-CuRA group revealed the potential value of these drugs. Finally, we verified an independent prognostic gene SFT2D1 was highly expressed in cervical cancer and positively correlated with the microvascular density. Knockdown of SFT2D1 significantly inhibited ability of the proliferation, migration, and invasive in cervical cancer cells. CuRA gene signature provided valuable insights into the prediction of prognosis and immune microenvironment of cervical cancer, which could help develop new strategies for individualized precision therapy for cervical cancer patients.

Identification of a 5-Gene Cuproptosis Signature Predicting the Prognosis for Colon Adenocarcinoma Based on WGCNA

Background: Colorectal cancer is a highly aggressive malignant tumor that primarily affects the digestive system. It is frequently diagnosed at an advanced stage. Cuproptosis is a copper-dependent form cell death mechanism, distinct from all other known pathways underlying cell death, tumor progression, prognosis, and immune response. Although the role of cuproptosis in colorectal cancer has been investigated over time, there is still an urgent need to explore new methods and insights to understand its potential function. Methods: The Gene Expression Omnibus and The Cancer Genome Atlas gene expression data were systematically explored to investigate the role of cuproptosis in colon adenocarcinoma. The weighted gene coexpression network analysis was used to construct a gene coexpression network and identify the critical module and cuproptosis-related genes correlated with colon adenocarcinoma prognosis. A cuproptosis-related genes prognostic signature for colon adenocarcinoma was identified and validated. To validate the identified gene signature, quantitative reverse transcription-polymerase chain reaction was performed. Cell proliferation assays were analyzed by CCK8 and cell cycle detection. In addition, reactive oxygen species assay was also analyzed. Results: Five hub cuproptosis-related genes (Dihydrolipoamide S-acetyltransferase, Cyclin-dependent kinase inhibitor 2A, ATOX1, VEGFA, and ULK1) were screened and a prognostic risk model for predicting overall survival was established based on these genes. The model was successfully tested in the validation cohort and the GEPIA database. Colon adenocarcinoma patients were categorized into high-risk and low-risk groups based on risk scores. The study revealed that patients with higher risk scores were more likely to have a poor prognosis. Moreover, Dihydrolipoamide S-acetyltransferase was a tumor suppressor gene that can induce cell death and affected the redox reactions in the colon cancer cell line. Conclusions: These findings suggest that the newly identified 5-gene signature may serve as a more reliable prognostic factor than clinical factors such as age and stage of disease. These findings offer a theoretical foundation for further investigation into potential cuproptosis-related biomarkers for predicting colon adenocarcinoma prognosis in the future.

Anoikis regulator GLI2 promotes NC cell immunity escape by TGF-β-mediated non-classic hedgehog signaling in colorectal cancer: based on artificial intelligence and big data analysis

BACKGROUND

Anoikis is a speed-limited procedure to inhibit tumor metastasis during epithelial-mesenchymal transition (EMT). Previous studies have explored anoikis-related genes (ARG) in predicting prognosis and distinguishing tumoral immunity in many types of cancer. However, the role of ARGs in regulating NK cell exhaustion (NKE) and in predicting chemotherapy sensitivity is not clear. Therefore, it is necessary to work on it.

METHODS

Gene expression profiles and clinical features are collected from TCGA and GEO, and data analysis is performed in R4.2.0.

RESULTS

The ARGs-based no-supervised learning algorithm identifies three ARG subgroups, amongst which the prognosis is different. WCGNA and Artificial intelligence (AI) are applied to construct an NKE-related drug sensitivity stratification and prognosis identification model in digestive system cancer. Pathways association analysis screens out GLI2 is a key gene in regulating NKE by non-classic Hedgehog signaling (GLI2/TGF-β/IL6). In vitro experiments show that down-regulation of GLI2 enhances the CAPE-mediated cell toxicity and accompanies with down-regulation of PD-L1, tumor-derive IL6, and snial1 whereas the expression of cleaved caspas3, cleaved caspase4, cleaved PARP, and E-cadherin are up-regulated in colorectal cancer. Co-culture experiments show that GLI2- decreased colorectal tumor cells lead to down-regulation of TIM-3 and PD1 in NK cells, which are restored by TGF-bate active protein powder. Besides, the Elisa assay shows that GLI2-decreased colorectal tumor cells lead to up-regulation of IFN-gamma in NK cells.

BLCA prognostic model creation and validation based on immune gene-metabolic gene combination

BACKGROUND

Bladder cancer (BLCA) is a prevalent urinary system malignancy. Understanding the interplay of immunological and metabolic genes in BLCA is crucial for prognosis and treatment.

METHODS

Immune/metabolism genes were extracted, their expression profiles analyzed. NMF clustering found prognostic genes. Immunocyte infiltration and tumor microenvironment were examined. Risk prognostic signature using Cox/LASSO methods was developed. Immunological Microenvironment and functional enrichment analysis explored. Immunotherapy response and somatic mutations evaluated. RT-qPCR validated gene expression.

RESULTS

We investigated these genes in 614 BLCA samples, identifying relevant prognostic genes. We developed a predictive feature and signature comprising 7 genes (POLE2, AHNAK, SHMT2, NR2F1, TFRC, OAS1, CHKB). This immune and metabolism-related gene (IMRG) signature showed superior predictive performance across multiple datasets and was independent of clinical indicators. Immunotherapy response and immune cell infiltration correlated with the risk score. Functional enrichment analysis revealed distinct biological pathways between low- and high-risk groups. The signature demonstrated higher prediction accuracy than other signatures. qRT-PCR confirmed differential gene expression and immunotherapy response.

CONCLUSIONS

The model in our work is a novel assessment tool to measure immunotherapy's effectiveness and anticipate BLCA patients' prognosis, offering new avenues for immunological biomarkers and targeted treatments.

Copper homeostasis and cuproptosis in mitochondria

Mitochondria serve as sites for energy production and are essential for regulating various forms of cell death induced by metal metabolism, targeted anticancer drugs, radiotherapy and immunotherapy. Cuproptosis is an autonomous form of cell death that depends on copper (Cu) and mitochondrial metabolism. Although the recent discovery of cuproptosis highlights the significance of Cu and mitochondria, there is still a lack of biological evidence and experimental verification for the underlying mechanism. We provide an overview of how Cu and cuproptosis affect mitochondrial morphology and function. Through comparison with ferroptosis, similarities and differences in mitochondrial metabolism between cuproptosis and ferroptosis have been identified. These findings provide implications for further exploration of cuproptotic mechanisms. Furthermore, we explore the correlation between cuproptosis and immunotherapy or radiosensitivity. Ultimately, we emphasize the therapeutic potential of targeting cuproptosis as a novel approach for disease treatment.

Contribution of cuproptosis and Cu metabolism-associated genes to chronic obstructive pulmonary disease

Airway epithelial cell injury plays a crucial role in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, a novel form of Cu-induced programmed cell death known as cuproptosis has not yet been thoroughly investigated in the context of COPD. Clinical reports have suggested that high copper exposure may increase the risk of COPD. In this study, we aimed to determine the expression and potential functions of cuproptosis-related genes and genes associated with copper metabolism in COPD. We initially identified 52 copper metabolism-related genes based on a review of the literature. Subsequently, we calculated the expression levels of these genes using data from four GEO datasets. To gain insights into the activated signalling pathways and underlying mechanisms in COPD patients, we conducted Gene Ontology (GO) and KEGG pathway analyses, examined protein-protein interactions, and performed weighted correlation network analysis. Our findings revealed that 18 key copper metabolism-related genes, including 5 cuproptosis-related genes, were significantly enriched in signalling pathways and biological processes associated with the development of COPD. Further analysis of clinical data and animal experiments confirmed the high expression of certain cuproptosis key regulators, such as DLD and CDKN2A, in both healthy smokers and COPD smokers. Additionally, these regulators exhibited abnormal expression in a COPD rat model. Notably, copper content was found to be elevated in the lung tissues of COPD rats, suggesting its potential involvement in cuproptosis. These findings provide an experimental foundation for further research into the role of cuproptosis in COPD. Targeting copper metabolism-related genes may represent an effective approach for the treatment of COPD.

Different types of cell death in diabetic endothelial dysfunction

Diabetes mellitus is a metabolic disease caused by disorders of insulin secretion and utilization. Long-term hyperglycemia, insulin resistance, and disorders of glucose and lipid metabolism cause vascular endothelial cell damage. Endothelial dysfunction is a key feature of diabetic vascular complications such as diabetic nephropathy, retinopathy, neuropathy, and atherosclerosis. Importantly, cell death is thought to be a key factor contributing to vascular endothelial injury. Morphologically, cell death can be divided into three forms: type I apoptosis, type II autophagy, and type III necrosis. According to the difference in function, cell death can be divided into accidental cell death (ACD) and regulated cell death (RCD). RCD is a controlled process involving numerous proteins and precise signaling cascades. Multiple subroutines covered by RCD may be involved in diabetic endothelial dysfunction, including apoptosis, autophagy, necroptosis, pyroptosis, entosis, ferroptosis, ferroautophagy, parthanatos, netotic cell death, lysosome-dependent cell death, alkaliptosis, oxeiptosis, cuproptosis, and PANoptosis. This article briefly reviews the mechanism and significance of cell death associated with diabetic endothelial dysfunction, which will help deepen the understanding of diabetic endothelial cell death and provide new therapeutic ideas.

Comprehensive exploration of the involvement of cuproptosis in tumorigenesis and progression of neuroblastoma

BACKGROUND

Copper-induced cell death, or "cuproptosis," as an apoptotic process, has recently received much attention in human diseases. Recent studies on cuproptosis have provided novel insights into the pathogenesis of various diseases, especially cancers. However, the association between neuroblastoma (NB) and cuproptosis in terms of their clinical outcomes, tumorigenesis, and treatment response remains unclear.

METHODS

To determine the role of cuproptosis in NB tumorigenesis and progression, this study employed a systematic technique to explore the characteristic patterns of 10 key cuproptosis-related genes (CUGs) in NB. Consensus clustering analysis of the TARGET and GEO databases divided the NB patients into two subgroups that showed different clinicopathological attributes, molecular patterns, survival outcomes, disease-associated pathways, tumor immune microenvironment (TIME) features, and treatment responses. Moreover, a cuproptosis scoring scheme was established, which divided the patients with NB into two groups with high scores and low scores as per the median score. Furthermore, this research developed a nomogram and risk signature on the basis of this cuproptosis score to better elucidate its function in predicting NB prognosis. In vitro experiments were carried out using Transwell Assay, HLECs tube formation assay, Colony formation assay, Western Blotting Assay, Immunohistochemical (IHC) Staining, Immunofluorescence (IF) Staining and Flow Cytometry Analysis.

RESULTS

The results demonstrated that the established cuproptosis score and prediction model could effectively distinguish between the individuals in low and high-risk groups and had a high predictive value. Lastly, bioinformatics analysis and in vitro experiments enabled the identification of PDHA1, a key CUG, which was involved in both DNA replication-related pathways and the cell cycle. It was also associated with tumorigenesis and progression of NB.

CONCLUSION

Cuproptosis, especially PDHA1, play a crucial role in the TIME characteristics, tumor progression, and long-term prognosis of NB. The patterns of cuproptosis assessed in this research may improve the understanding of the overall concept of NB tumorigenesis, thus facilitating the development of more effective therapeutic interventions.

Identification of metabolism-related gene signature in lung adenocarcinoma

AIM

Lung cancer is one of the most common cancers in China and has a high mortality rate. Most patients who are diagnosed have lost the opportunity to undergo surgery. Aberrant metabolism is closely associated with tumorigenesis. We aimed to identify an effective metabolism-related prediction model for assessing prognosis based on the cancer genome atlas (TCGA) and GSE116959 databases.

METHODS

TCGA and GSE116959 datasets from Gene Expression Omnibus were used to obtain lung adenocarcinoma (LUAD) data. Additionally, we captured metabolism-related genes (MRGs) from the GeneCards database. First, we extracted differentially expressed genes using R to analyze the LUAD data. We then selected the same differentially expressed genes, including 168 downregulated and 77 upregulated genes. Finally, 218 differentially expressed MRGs (DEMRGs) were included to perform functional enrichment analysis and construct a protein-protein interaction network with the help of Cytoscape and Search Tool for the Retrieval of Interacting Genes database. Cytoscape was used to visualize the intensive intervals in the network. Then univariate and Least Absolute Shrinkage and Selection Operator Cox regression analyses, which assisted in identifying the overall survival (OS)-related DEMRGs and building a 10-DEMRG prognosis model, were performed. The prognostic values, tumor immunity relevance, and molecular mechanism were further investigated. A nomogram incorporating signature, age, gender, and TNM stage was established.

RESULTS

A 10-DEMRG model was established to forecast the OS of LUAD through Least Absolute Shrinkage and Selection Operator regression analysis. This prognostic signature stratified LUAD patients into low-risk and high-risk groups. The receiver operating characteristic curve and K-M analysis indicated good performance of the DEMRGs signature at predicting OS in the TCGA dataset. Univariate and multivariate Cox regression also revealed that the DEMRGs signature was an independent prognosis factor in LUAD. We noticed that the risk score was substantially related to the clinical parameters of LUAD patients, covering age and stage. Immune analysis results showed that risk score was associated with some immune cells and immune checkpoints. Nomogram also verified the clinical value of the DEMRGs signature.

CONCLUSION

In this study, we constructed a DEMRGs signature and established a prognostic nomogram that is robust and reliable to predict OS in LUAD. Overall, the findings could help with therapeutic customization and personalized therapies.

Development and validation of a model based on immunogenic cell death related genes to predict the prognosis and immune response to bladder urothelial carcinoma

Background

Immunogenic cell death (ICD) has been categorized as a variant of regulated cell death that is capable of inducing an adaptive immune response. A growing body of evidence has indicated that ICD can modify the tumor immune microenvironment by releasing danger signals or damage-associated molecular patterns (DAMPs), potentially enhancing the efficacy of immunotherapy. Consequently, the identification of biomarkers associated with ICD that can classify patients based on their potential response to ICD immunotherapy would be highly advantageous. Therefore the goal of the study is to better understand and identify what patients with bladder urothelial carcinoma (BLCA) will respond to immunotherapy by analyzing ICD signatures and investigate ICD-related prognostic factors in the context of BLCA.

Methods

The data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases regarding BLCA and normal samples was categorized based on ICD-related genes (IRGs). Specifically, we conducted an immunohistochemical (IHC) experiment to validate the expression levels of Calreticulin (CALR) in both tumor and adjacent tissues, and evaluated its prognostic significance using the Kaplan-Meier (KM) curve. Subsequently, the samples from TCGA were divided into two subtypes using consensus clustering. To obtain a more comprehensive comprehension of the biological functions, we utilized Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). The calculation of immune landscape between two subtypes was performed through ESTIMATE and CIBERSORT. Risk models were constructed using Cox and Lasso regression and their prognosis predictive ability was evaluated using nomogram, receiver operating characteristic (ROC), and calibration curves. Finally, Tumor Immune Dysfunction and Exclusion (TIDE) algorithms was utilized to predict the response to immunotherapy.

Results

A total of 34 IRGs were identified, with most of them exhibiting upregulation in BLCA samples. The expression of CALR was notably higher in BLCA compared to the adjacent tissue, and this increase was associated with an unfavorable prognosis. The differentially expressed genes (DEGs) associated with ICD were linked to various immune-related pathways. The ICD-high subtypes exhibited an immune-activated tumor microenvironment (TME) compared to the ICD-low subtypes. Utilizing three IRGs including CALR, IFNB1, and IFNG, a risk model was developed to categorize BLCA patients into high- and low-risk groups. The overall survival (OS) was considerably greater in the low-risk group compared to the high-risk group, as evidenced by both the TCGA and GEO cohorts. The risk score was identified as an independent prognostic parameter (all p < 0.001). Our model demonstrated good predictive ability (The area under the ROC curve (AUC), AUC1-year= 0.632, AUC3-year= 0.637, and AUC5-year =0.653). Ultimately, the lower risk score was associated with a more responsive immunotherapy group.

Conclusion

The potential of the ICD-based risk signature to function as a marker for evaluating the prognosis and immune landscape in BLCA suggests its usefulness in identifying the suitable population for effective immunotherapy against BLCA.

The effect of cuproptosis-relevant genes on the immune infiltration and metabolism of gynecological oncology by multiply analysis and experiments validation

Gynecological cancers are a leading cause of mortality for women, including ovarian cancer (OC), cervical squamous cell carcinoma (CESC), and uterine corpus endometrial carcinoma (UCEC). Nevertheless, these gynecological cancer types have not elucidated the role of cuproptosis and the correlated tumor microenvironment (TME) infiltration features. CRGs had important potential molecular functions and prognostic significance in gynecological cancers, especially in UCEC. Hub CRG, FDX1, was correlated with the CD8+ T cell immune infiltration in UCEC and CESC. FDX1 OE could significantly repress the proliferation ability in UCEC cells by MTT, EdU, and clone formation. High levels of FDX1 could repress ATP and lactic acid but enhance ROS and glucose levels by metabolism assay. The xenograft tumor model indicated that FDX1 OE significantly inhibited the growth of UCEC and attenuated the PCNA, HK2, PKM2, and Ki-67 expression. These CRGs are significant roles that could be potential markers and treatment targets to optimize the TME immune cell infiltration features for gynecological cancer types. FDX1 is a hub CRGs in UCEC to promote immune infiltration and attenuate proliferation and metabolism.

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.

Comprehensive analysis of cuproptosis genes and cuproptosis-related genes as prognosis factors in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a common invasive and pernicious cancer with a low five-year survival rate. To identify potential therapeutic targets, we first investigated the characteristics of cuproptosis genes (CUGs) in ESCC. The expression patterns of 10 CUGs (FDX1, LIPT1, LIAS, DLAT, DLD, PDHA1, PDHB, GLS, MTF1, and CDKN2A) were analyzed to identify ESCC-relevant targets. Weighted correlation network analysis (WGCNA) was performed to obtain CUG-related genes (CRGs). A total of seven differentially expressed genes were identified (FDX1, DLAT, LIAS, PDHB, MTF1, GLS, and CDKN2A). DLAT was upregulated in stage III, and LIPT1 was upregulated in N0 + N1 cancers. The high expression of CDKN2A, and PDHA1, was related to better overall survival, whereas the low expression of LIAS was related to better clinical outcomes. WGCNA was performed to get CUG-related genes (CRGs) and showed three key modules that related to FDX1, DLAT, and LIPT1. Moreover, CRGs (BTLA, CT47A1, and PRRX1) were selected to construct a risk score model in order to predict the survival and prognosis of patients with ESCC. Additionally, the cuproptosis score based on CUGs and a nomogram constructed based on it helped accurately predict the prognosis of patients with ESCC; thus, maybe it can be used for the clinical diagnosis of ESCC. The results also showed that milciclib might inhibit the proliferation and migration of KYSE150 and KYSE510 cells by targeting CDKN2A. In conclusion, the abovementioned CUGs and CRGs play a crucial role in tumorigenesis and cancer progression in ESCC, indicating their potential as therapeutic targets.

Molecular subtypes identified by multiomics analysis based on cuproptosis-related genes precisely predict response to immunotherapy and chemotherapy in colorectal cancer

Cuproptosis is a newly reported type of programmed cell death that is involved in the progression of various diseases. Some studies have reported its potential significance in multiple tumors. Colorectal cancer (CRC) is one of the malignant tumors with high incidence and mortality. The purpose of this study was to further explore the importance of cuproptosis in the CRC development and treatment. We analyzed the expression, alterations, and promoter methylation of cuproptosis-related genes (CRGs) in patients with CRC. Three machine learning methods was used to determine cuproptosis-related feature genes and a diagnostic model was built based on them. Using the unsupervised clustering, patients with CRC were classified into distinct clusters. Then, the LASSO method was used to establish a cuproptosis risk model. We analyzed the association of risk scores with outcomes, immune microenvironment, response to immunotherapy, and sensitivity to chemotherapeutic drugs. The results showed that the expression of CRGs was dysregulated in CRC. The diagnostic model based on cuproptosis-related feature genes showed great clinical value. The patients in two clusters displayed different prognosis and microenvironment. Furthermore, the risk score was correlated with clinical characteristics, immune infiltration and response to immunotherapy and chemotherapy. Above all, the present findings revealed the involvement of cuproptosis in CRC development and provided a diagnostic tool to evaluate CRC occurrence risk. The immune infiltration and drug sensitivity analysis results helped to predict the response of patients in different subtypes of CRC to immunotherapy and chemotherapy.

CDKN2A was a cuproptosis-related gene in regulating chemotherapy resistance by the MAGE-A family in breast cancer: based on artificial intelligence (AI)-constructed pan-cancer risk model

BACKGROUND

Before the discovery of cuproptosis, copper-loaded nanoparticle is a wildly applied strategy for enhancing the tumor-cell-killing effect of chemotherapy. Although copper(ii)-related researches are wide, details of cuproptosis-related bioprocess in pan-cancer are not clear yet now, especially for prognosis and drug sensitivity prediction yet now.

METHODS

In this study, VOSviewer is used for the literature review, and R4.2.0 is used for data analysis. Public data are collected from TCGA and GEO, local breast cancer cohort is collected to verify the expression level of CDKN2A.

RESULTS

7036 published articles exhibited a time-dependent linear relationship (R=0.9781, p<0.0001), and breast cancer (33.4%) is the most researched topic. Cuproptosis-related-genes (CRGs)-based unsupervised clustering divides pan-cancer subgroups into four groups (CRG subgroup) with differences in prognosis and tumor immunity. 44 tumor-driver-genes (TDGs)-based prediction model of drug sensitivity and prognosis is constructed by artificial intelligence (AI). Based on TDGs and clinical features, a nomogram is (C- index: 0.7, p= 6.958e- 12) constructed to predict the prognosis of breast cancer. Importance analysis identifies CDKN2A has a pivotal role in AI modeling, whose higher expression indicates worse prognosis in breast cancer. Furthermore, inhibition of CDKN2A down-regulates decreases Snail1, Twist1, Zeb1, vimentin and MMP9, while E-cadherin is increased. Besides, inhibition of CDKN2A also decreases the expression of MEGEA4, phosphorylated STAT3, PD-L1, and caspase3, while cleaved-caspase3 is increased. Finally, we find down-regulation of CDKN2A or MAGEA inhibits cell migration and wound healing, respectively.

CONCLUSIONS

AI identified CRG subgroups in pan-cancer based on CRGs-related TDGs, and 44-gene-based AI modeling is a novel tool to identify chemotherapy sensitivity in breast cancer, in which CDKN2A/MAGEA4 pathway played the most important role.

Cuproptosis-immunotherapy using PD-1 overexpressing T cell membrane-coated nanosheets efficiently treats tumor

The cuproptosis cell death pathway brings fresh opportunities for tumor therapy. However, efficient and targeted cuproptosis induction in tumors is still a challenge. Unfortunately, the well-known cuproptosis initiator, disulfiram and copper complex (DSF/Cu2+), also increases PD-L1 level in tumors, which may diminish the final therapeutic outcome. In this study, DSF/Cu2+-loading MXene nanosheets are coated with PD-1 overexpressing T cell membrane to generate CuX-P system. CuX-P could recognize and stick to PD-L1 on tumor cells like a patch, which promotes the endocytosis of both CuX-P and PD-L1 by tumor cells. Following internalization and release of DSF/Cu2+ in the cytoplasm, PD-L1 expression is upregulated. However, due to the presence of CuX-P in the tumor microenvironment, the then supplemented PD-L1 on tumor surface again binds CuX-P for internalization. This feedback loop keeps blocking and consuming the PD-L1 on tumor surface and promotes the enrichment of CuX-P in tumors to induce cuproptosis. After CuX-P treatment with laser irradiation, strong anti-tumor immune responses are stimulated in a mouse model with triple-negative breast cancer. Thus, this study develops a tumor-targeted biomimetic system that offers simultaneous cuproptosis killing, photothermal therapy (PTT) and immunotherapy in mice.

A risk prognostic model for patients with esophageal squamous cell carcinoma basing on cuproptosis and ferroptosis

BACKGROUND

Cuproptosis, a form of copper-dependent programmed cell death recently presented by Tsvetkov et al., have been identified as a potential therapeutic target for refractory cancers and ferroptosis, a well-known form describing iron-dependent cell death. However, whether the crossing of cuproptosis-related genes and ferroptosis-related genes can introduce some new idea, thus being used as a novel clinical and therapeutic predictor in esophageal squamous cell carcinoma (ESCC) remains unknown.

METHODS

We collected ESCC patient data from the Gene Expression Omnibus and the Cancer Genome Atlas databases and used Gene Set Variation Analysis to score each sample based on cuproptosis and ferroptosis. We then performed weighted gene co-expression network analysis to identify cuproptosis and ferroptosis-related genes (CFRGs) and construct a ferroptosis and cuproptosis-related risk prognostic model, which we validated using a test group. We also investigated the relationship between the risk score and other molecular features, such as signaling pathways, immune infiltration, and mutation status.

RESULTS

Four CFRGs (MIDN, C15orf65, COMTD1 and RAP2B) were identified to construct our risk prognostic model. Patients were classified into low- and high-risk groups based on our risk prognostic model and the low-risk group showed significantly higher survival possibilities (P < 0.001). We used the "GO", "cibersort" and "ESTIMATE" methods to the above-mentioned genes to estimate the relationship among the risk score, correlated pathways, immune infiltration, and tumor purity.

CONCLUSION

We constructed a prognostic model using four CFRGs and demonstrated its potential clinical and therapeutic guidance value for ESCC patients.

Cuproptosis-related gene subtypes predict prognosis in patients with head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. A novel form of copper-dependent and reactive oxygen species (ROS)-dependent cell death, cuproptosis, has been described in many cancers. The roles and potential mechanisms of cuproptosis-related genes (CRGs) are still unclear in HNSCC.

METHOD

We downloaded TCGA datasets of HNSCC genomic mutations and clinic data from The Cancer Genome Atlas. Based on the Cuproptosis-related differentially expressed genes in HNSCC, we constructed a prognostic signature.

RESULTS

Eight CRGs have been identified as associated with the prognosis of HNSCC. According to Kaplan-Meier analyses, HNSCC with a high Risk Score had a poor prognosis. Furthermore, the AUC of the Risk Score for the 1-, 3-, and 5- year overall survival was respectively, 0.70, 0.71, and 0.68. TCGA data revealed that T cell functions, such as HLA, cytolytic activity, inflammation regulation, co-inhibition, and co-stimulation, differed significantly between members of the low and high groups. The immune checkpoint genes PD-L1, PD-L1, and CTLA-4 were also expressed differently in the two risk groups.

CONCLUSIONS

A CRG signature was defined that is associated with the prognosis of patients with HNSCC.

Identification of cuproptosis-related genes for predicting the development of prostate cancer

Copper can be toxic at very high intracellular concentrations and can inhibit prostate cancer (PCa) progression. Recently, a study reported the mechanism of cuproptosis and the potentially associated genes. However, the function of these cuproptosis-related genes in PCa remains unknown. Based on the RNA sequence and clinical data from public databases, we analyzed the clinical value of cuproptosis-related genes in PCa. DLD, DLAT, PDHA1, and CDKN2A were expressed differently between normal and PCa tissues. The FDX1, LIAS, DLAT, GLS, and CDKN2A genes can affect PCa progression, while PDHA1 and CDKN2A influence the patients' disease-free survival (DFS) status. The expression of LIAS, LIPT1, DLAT, and PDHB did not alter upon the incidence of PCa in Chinese patients. A constructed regression model showed that FDX1, PDHA1, MTF1, and CDKN2A can be risk factors leading to PCa in both Western and Chinese patients with PCa. The lasso regression model reflected that these genes can affect the patients' DFS status. Additionally, the cuproptosis-related genes were associated with immune cell infiltration. We also verified the high expression of PDHA1 and CDKN2A, in clinical samples. In conclusion, we identified a novel cuproptosis-related gene signature for predicting the development of PCa.

Grade scoring system reveals distinct molecular subtypes and identifies KIF20A as a novel biomarker for predicting temozolomide treatment efficiency in gliomas

BACKGROUND

The importance of molecular diagnostics is increasingly emphasized in the 2021 WHO guidelines for gliomas. There is considerable variability in molecular features and prognosis among glioma patients with the same pathological WHO grade.

METHODS

mRNA data and clinical information of human glioma patients were obtained from TCGA and CGGA databases, while expression profiles and TMZ resistance phenotypes of human glioma stem cells were acquired from the GEO database. Differentially expressed genes were identified across distinct WHO grades. Unsupervised clustering was performed on glioma patients based on DEG expression profiles. The Boruta algorithm was employed to identify feature genes for distinct molecular subtypes, and PCA was used to reduce the dimensionality of the feature gene expression data. Grade scores for each sample were calculated and correlated with patients' clinical molecular pathological features and immune microenvironment. Gene set enrichment analysis identified grade score-related functional pathways. Weighted gene co-expression network analysis identified grade score-associated biomarkers. The impact of the hub gene on malignant glioma behavior was validated through in vitro experiments, including CCK-8, EdU, colony formation, Transwell, wound healing, and immunofluorescence assays.

RESULTS

A total of 672 and 687 samples were screened from TCGA and CGGA databases, respectively, along with 6 control, 24 low-grade, and 40 glioblastoma samples from our hospital. Two robust gene clusters were identified based on the expression profiles of 4,476 DEGs among grades 2, 3, and 4 tissues, revealing distinct prognoses. The grade scores exhibited significant heterogeneity across different WHO grade samples, representing diverse immune microenvironments. Grade scores served as independent risk factors for predicting patient prognosis, with higher sensitivity than traditional biomarkers. KIF20A, identified as a grade score-related biomarker, was independently associated with glioma prognosis. Exclusively expressed in tumor cells, KIF20A knockdown significantly inhibited tumor growth, invasion, and EMT biological behavior in glioma cells. Furthermore, KIF20A could serve as a biological marker for predicting patient response to TMZ treatment.

CONCLUSION

The grade scoring system enhances our understanding of the glioma tumor microenvironment. KIF20A, a novel biomarker for predicting TMZ treatment efficiency, influences malignant tumor behavior by affecting the EMT biological behavior of glioma cells.

Construction and validation of cuproptosis-related lncRNA prediction signature for bladder cancer and immune infiltration analysis

Bladder cancer (BC) is a common urologic tumor with a high recurrence rate. Cuproptosis and long noncoding RNAs (lncRNAs) have demonstrated essential roles in the tumorigenesis of many malignancies. Nevertheless, the prognostic value of cuproptosis-related lncRNA (CRLs) in BC is still unclear. The public data used for this study were acquired from the Cancer Genome Atlas database. A comprehensive exploration of the expression profile, mutation, co-expression, and enrichment analyses of cuproptosis-related genes was performed. A total of 466 CRLs were identified using Pearson's correlation analysis. 16 prognostic CRLs were then retained by univariate Cox regression. Unsupervised clustering divided the patients into two clusters with diverse survival outcomes. The signature consists of 7 CRLs was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Survival curves and receiver operating characteristics showed the prognostic signature possessed good predictive value, which was validated in the testing and entire sets. The reliability and stability of our signature were further confirmed by stratified analysis. Additionally, the signature-based risk score was confirmed as an independent prognostic factor. Gene set enrichment analysis showed molecular alteration in the high-risk group was closely associated with cancer. We then developed the clinical nomogram using independent prognostic indicators. Notably, the infiltration of immune cells and expression of immune checkpoints were higher in the high-risk group, suggesting that they may benefit more from immunotherapy. In summary, the prognostic signature might effectively predict the prognosis and provide new insight into the clinical treatment of BC patients.

Cuproptosis-related gene SLC31A1 expression correlates with the prognosis and tumor immune microenvironment in glioma

Cuproptosis is a newly discovered form of cell death. It is regulated by a string of genes. The genes are identified to influence the tumor progression, but in glioma, the cuproptosis-related genes are little studied. The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) were used to screen for SLC31A1 gene expression in glioma and healthy tissue samples. The results were validated using the Gene Expression Omnibus (GEO) and quantitative real-time polymerase chain reaction (qPCR). The Human Protein Atlas (HPA) and the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) were used to validate our results at the protein level. Multivariable analysis and Kaplan-Meier survival curves were used to examine the relationship among SLC31A1 gene expression, clinical parameters, and survival rates. The online Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) was used to find the genes and proteins that correlate to SLC31A1. The immune infiltration analysis was performed using the Tumor Immune Estimation Resource (TIMER) databases. Small interfering RNA was used to knock down the SLC31A1 expression, and the cell proliferation, apoptosis, and migration were analyzed using cell counting kit-8, flow cytometry, and transwell. The glioma patients have higher SLC31A1 expression levels, which increase as the World Health Organization (WHO) grade escalates. The survival analysis illustrates that the SLC31A1 gene expression negatively correlates with overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS). The immune infiltration analysis shows the SLC31A1 gene positively correlates with T helper 2 (Th2) cells, macrophages, and M2-type macrophages and negatively correlates with plasmacytoid dendritic cells (pDCs), natural killer (NK) CD56bright cells, and CD8 T cells. The in vitro KD experiment shows the SLC31A1 knockdown depressed the glioma cell proliferation and migration and promoted the apoptosis rate. The SLC31A1 gene expression can shorten the survival time of glioma patients. In vitro study shows that SLC31A1 can promote cell proliferation, and migration, and depress the cell apoptosis of glioma cells. It also can promote the formation of a tumor-suppressive microenvironment.

The signature of cuproptosis-related immune genes predicts the tumor microenvironment and prognosis of prostate adenocarcinoma

Background

Cuproptosis plays a crucial role in cancer, and different subtypes of cuproptosis have different immune profiles in prostate adenocarcinoma (PRAD). This study aimed to investigate immune genes associated with cuproptosis and develop a risk model to predict prognostic characteristics and chemotherapy/immunotherapy responses of patients with PRAD.

Methods

The CIBERSORT algorithm was used to evaluate the immune and stromal scores of patients with PRAD in The Cancer Genome Atlas (TCGA) cohort. Validation of differentially expressed genes DLAT and DLD in benign and malignant tissues by immunohistochemistry, and the immune-related genes of DLAT and DLD were further screened. Univariable Cox regression were performed to select key genes. Least absolute shrinkage and selection operator (LASSO)-Cox regression analyse was used to develop a risk model based on the selected genes. The model was validated in the TCGA, Memorial Sloan-Kettering Cancer Center (MSKCC) and Gene Expression Omnibus (GEO) datasets, as well as in this study unit cohort. The genes were examined via functional enrichment analysis, and the tumor immune features, tumor mutation features and copy number variations (CNVs) of patients with different risk scores were analysed. The response of patients to multiple chemotherapeutic/targeted drugs was assessed using the pRRophetic algorithm, and immunotherapy was inferred by the Tumor Immune Dysfunction and Exclusion (TIDE) and immunophenoscore (IPS).

Results

Cuproptosis-related immune risk scores (CRIRSs) were developed based on PRLR, DES and LECT2. High CRIRSs indicated poor overall survival (OS), disease-free survival (DFS) in the TCGA-PRAD, MSKCC and GEO datasets and higher T stage and Gleason scores in TCGA-PRAD. Similarly, in the sample collected by the study unit, patients with high CRIRS had higher T-stage and Gleason scores. Additionally, higher CRIRSs were negatively correlated with the abundance of activated B cells, activated CD8+ T cells and other stromal or immune cells. The expression of some immune checkpoints was negatively correlated with CRIRSs. Tumor mutational burden (TMB), mutant-allele tumor heterogeneity (MATH) and copy number variation (CNV) scores were all higher in the high-CRIRS group. Multiple chemotherapeutic/targeted drugs and immunotherapy had better responsiveness in the low-CRIRS group.

Conclusion

Overall, lower CRIRS indicated better response to treatment strategies and better prognostic outcomes.

Multi-omics analysis uncovers clinical, immunological, and pharmacogenomic implications of cuproptosis in clear cell renal cell carcinoma

OBJECTIVE

The latest research proposed a novel copper-dependent programmed cell death named cuproptosis. We aimed to elucidate the influence of cuproptosis in clear cell renal cell carcinoma (ccRCC) from a multi-omic perspective.

METHODS

This study systematically assessed mRNA expression, methylation, and genetic alterations of cuproptosis genes in TCGA ccRCC samples. Through unsupervised clustering analysis, the samples were classified as different cuproptosis subtypes, which were verified through NTP method in the E-MTAB-1980 dataset. Next, the cuproptosis score (Cuscore) was computed based on cuproptosis-related genes via PCA. We also evaluated clinical and immunogenomic features, drug sensitivity, immunotherapeutic response, and post-transcriptional regulation.

RESULTS

Cuproptosis genes presented multi-layer alterations in ccRCC, and were linked with patients' survival and immune microenvironment. We defined three cuproptosis subtypes [C1 (moderate cuproptosis), C2 (low cuproptosis), and C3 (high cuproptosis)], and the robustness and reproducibility of this classification was further proven. Overall survival was best in C3, moderate in C1, and worst in C2. C1 had the highest sensitivity to pazopanib, and sorafenib, while C2 was most sensitive to sunitinib. Furthermore, C1 patients benefited more from anti-PD-1 immunotherapy. Patients with high Cuscore presented the notable survival advantage. Cuscore was highly linked with immunogenomic features, and post-transcriptional events that contributed to ccRCC development. Finally, several potential compounds and druggable targets (NMU, RARRES1) were selected for low Cuscore group.

CONCLUSION

Overall, our study revealed the non-negligible role of cuproptosis in ccRCC development. Evaluation of the cuproptosis subtypes improves our cognition of immunogenomic features and better guides personalized prognostication and precision therapy.

Unraveling Colorectal Cancer and Pan-cancer Immune Heterogeneity and Synthetic Therapy Response Using Cuproptosis and Hypoxia Regulators by Multi-omic Analysis and Experimental Validation

Cuproptosis, a new type of programmed cell death (PCD), is closely related to cellular tricarboxylic acid cycle and cellular respiration, while hypoxia can modulate PCD. However, their combined contribution to tumor subtyping remains unexplored. Here, we applied a multi-omics approach to classify TCGA_COADREAD based on cuproptosis and hypoxia. The classification was validated in three colorectal cancer (CRC) cohorts and extended to a pan-cancer analysis. The results demonstrated that pan-cancers, including CRC, could be divided into three distinct subgroups (cuproptosis-hypoxia subtypes, CHSs): CHS1 had active metabolism and poor immune infiltration but low fibrosis; CHS3 had contrasting characteristics with CHS1; CHS2 was intermediate. CHS1 may respond well to cuproptosis inducers, and CHS3 may benefit from a combination of immunotherapy and anti-fibrosis/anti-hypoxia therapies. In CRC, the CHSs also showed a significant difference in prognosis and sensitivity to classic drugs. Organoid-based drug sensitivity assays validated the results of transcriptomics. Cell-based assays indicated that masitinib and simvastatin had specific effects on CHS1 and CHS3, respectively. A user-friendly website based on the classifier was developed (https://fan-app.shinyapps.io/chs_classifier/) for accessibility. Overall, the classifier based on cuproptosis and hypoxia was applicable to most pan-cancers and could aid in personalized cancer therapy.

Construction and validation of a cuproptosis-related five-lncRNA signature for predicting prognosis, immune response and drug sensitivity in breast cancer

BACKGROUND

Despite advances in treatment, recurrence and mortality rates from breast cancer (BrCa) continue to rise, clinical effectiveness is limited, and prognosis remains disappointing, especially for patients with HER2-positive, triple-negative, or advanced breast cancer. Based on cuproptosis-related long noncoding RNAs (CRLs), this study aims to create a predictive signature to assess the prognosis in patients with BrCa.

METHODS

The related CRLs RNA-seq data clinicopathological data were collected from The Cancer Genome Atlas (TCGA) database, and the predictive model was constructed after correlation analysis. Subsequently, we examined and validated connections and changes in the CRLs model with prognostic features (including risk curves, ROC curves and nomograms), pathway and functional enrichment, tumor mutation (TMB), tumor immune dysfunction and exclusion (TIDE) and treatment sensitivity.

RESULTS

A prediction model formula composed of 5 CRLs was obtained, and divided breast cancer patients into high and low risk subgroups according to the obtained risk scores. The results showed that the overall survival (OS) of patients in the high-risk group was lower than that in the low-risk group, and the AUC of all samples at 1, 3 and 5 years were 0.704, 0.668 and 0.647, respectively. It was indicated that CRLs prognostic model could independently predict prognostic indicators of BrCa patients. In addition, analysis of gene set enrichment, immune function, TMB, and TIDE showed that these differentially expressed CRLs had a wealth of related pathways and functions, and might be closely related to immune response and immune microenvironment. Additionally, TP53 was found to have the highest mutation frequency in high-risk group (40%), while PIK3CA was found to have the highest mutation frequency in low-risk group (42%), which might become new targets for targeted therapy. Finally, we compared susceptibility to anticancer agents to identify potential treatment options for breast cancer. Lapatinib, Sunitinib, Phenformin, Idelalisib, Ruxolitinib, Cabozantinib were more sensitive to patients in the low-risk group, while Sorafenib, Vinorelbine, Pyrimethamine were more sensitive to patients in high-risk group, namely, these drugs could potentially be used in the future to treat breast cancer patients grouped according to the risk model.

CONCLUSION

This study identified CRLs associated with breast cancer and provided a tailored tool for predicting prognosis, immune response, and drug sensitivity in patients with BrCa.

Identification and validation of long noncoding RNA AC083900.1 and RP11-283C24.1 for prediction of progression of osteosarcoma

BACKGROUND

The role of cuproptosis, an emerging cell death pathway that makes a remarkable contribution to tumor progression, remains elusive in osteosarcoma (OS), in addition to its regulator, including long-no-coding RNAs (lncRNAs) that are also a critical factor for fueling OS.

METHODS

Transcriptome and clinical data from 70 normal human bone tissue samples and 84 frozen clinical osteosarcoma samples were included in this study. Cuproptosis-associated lncRNAs (CRlncs) were identified through differential expression and co-expression analyses. Univariate Cox regression was performed to screen for prognostic lncRNAs, then we used least absolute shrinkage and selection operator regression to distinguish prognosis-related CRlncs (AC083900.1 and RP11-283C24.1) for modeling the CRlncs prognostic signature (CLPS) by multivariate Cox regression using the stepwise method. CLPS performance was tested by independent prognostic analyses, survival curve and receiver operating characteristic (ROC) curve. In addition, the molecular and immune mechanisms that underlie the unfavorable prognosis of CLPS-identified high-risk group were elucidated.

RESULT

AC083900.1 and RP11-283C24.1 have been identified as the most important CRlncs for OS progression (hazard ratio: 3.498 and 2.724, respectively), and the derived CLPS demonstrated outstanding performance for the prediction of OS prognosis (AUC of 0.799 and 0.778 in the training and test sets, both adj-p < 0.05 in survival curve). As was anticipated, CLPS also outperformed a recent clinical prognostic approach that only achieved an AUC of 0.682 [metastasis]. It is notable that AC083900.1 progressed OS metastasis, evidenced by its high expression in metastatic OS, its high correlation to metastasis-related genes, and its high AUC of 0.683 for the prediction of metastasis. Mechanistically, AC083900.1 and RP11-283C24.1 dysregulated many critical biological processes regarding humoral immune response, immunoglobulin complex, etc.; while reducing the infiltration of many cytotoxic immune cells (B-cells, TIL, neutrophils, etc.). It is encouraging that BMS-509744 and KIN001-135 demonstrated high therapeutic implications for CLPS-identified high-risk OS, and the low-risk counterpart was sensitive to SB-216763. Quantitative RT-PCR analysis showed that both AC083900.1 and RP11-283C24.1 were significantly upregulated in different osteosarcoma cell lines.

CONCLUSION

This study elucidated the roles and mechanisms of AC083900.1 and RP11-283C24.1 in the development of OS, fostering a reliable prognostic approach and treatment for OS patients.

Global status and research trends of cuprotosis research: A bibliometrics study via CiteSpace

Cuproptosis, a novel copper ion-dependent cell death type being regulated in cells, has raised concerns but lacks scientific analysis. Therefore, this study aimed to analyze the global status and emerging trends in cuprotosis research using bibliometric methods. Publications related to cuprotosis were systematically retrieved from the Web of Science Core Collection and then screened according to the inclusion criteria. Next, CiteSpace and Microsoft Excel 2021 were used to measure and visualize annual publications, categories, journals, countries, institutions, authors, co-cited references, and keywords to identify future global status and trends. A total of 2776 publications on cuprotosis were included, and the overall trend in the number of publications exhibited a rapid increase over the years. Biochemistry and Molecular Biology is the most common category, whereas the Journal of Inorganic Biochemistry is the most active. The United States is the country that produces the most articles, and University of Melbourne in Australia is the core institution involved in this field. Furthermore, Chan Pak of Stanford University is the most prolific author. Oxidative stress and antioxidant, the toxicity of copper in vitro, anticancer mechanism, and brain injury in neurological diseases are hot topics. The research frontiers are copper complexes, anticancer activity, DeoxyriboNucleic Acid binding, inflammation, and nanoparticles. This study provides the current status and trends in cuprotosis research. It may help researchers to identify hot topics and get ideas for future research directions in this field, focusing on copper complexes, anticancer activity, DeoxyriboNucleic Acid binding, inflammation, and nanoparticles.

The therapeutic and prognostic role of cuproptosis-related genes in triple negative breast cancer

BACKGROUND

This study aimed to observe the potential impact of known cuproptosis-related genes (CRGs) on triple negative breast cancer (TNBC) development, as well as their associated molecular mechanisms, immune infiltration mechanisms and potential therapeutic agents.

RESULTS

Based on the Cox Proportional Hazard Model, 11 CRGs may be especially important in TNBC development and progression (considered as the Key-TNBC-CRGs). The expression of several Key-TNBC-CRGs (e.g., ATP7A, PIK3CA, LIAS, and LIPT) are associated with common mutations. The SCNA variation of 11 Key-TNBC-CRGs are related to differences immune infiltration profiles. In particular, depletion of ATP7A, ATP7B, CLS, LIAS, and SCL31A1 and while high amplification of NLRP3 and LIPT2 are correlated with decreased immune infiltration. In our Cox proportional hazards regression model, there is a significant difference in the overall survival between high-risk and low-risk groups. The HR in the high-risk group is 3.891 versus the low-risk group. And this model has a satisfactory performance in Prediction of 5-15-year survival, in particular in the 10-year survival (AUC = 0.836). Finally, we discovered some potential drugs for TNBC treatment based on the strategy of targeting 11 Key-TNBC-CRGs, such as Dasatinib combined with ABT-737, Erastin or Methotrexate, and Docetaxel/Ispinesib combination.

CONCLUSION

In conclusion, CRGs may play important roles in TNBC development, and they can impact tumor immune microenvironment and patient survival. The Key-TNBC-CRGs interact mutually and can be influenced by common BC-related mutations. Additionally, we established a 11-gene risk model with a robust performance in prediction of 5-15-year survival. As well, some new drugs are proposed potentially effective in TNBC based on the CRG strategy.

BAIAP2L2 is a novel prognostic biomarker related to migration and invasion of HCC and associated with cuprotosis

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, and its pathophysiological mechanisms remain unknown. IRSp53 family members, such as BAIAP2L1, participate in the progression of multiple tumors. However, the role of BAIAP2L2 in HCC remains unclear. This study comprehensively analyzed the potential role of BAIAP2L2 in HCC using bioinformatic techniques. The expression of BAIAP2L2 in HCC was analyzed using The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC), and Human Protein Atlas (HPA) databases and in vitro experiments. In addition, the prognostic value of BAIAP2L2 in HCC was analyzed using the TCGA database. TCGA and GEO database were used to analyze the role of BAIAP2L2 in immune features. We also explored the function of BAIAP2L2 in methylation and cuprotosis. The CellMiner database was used to analyze the relationship between BAIAP2L2 expression and drug sensitivity. Our study revealed that BAIAP2L2 is overexpressed in HCC and promotes the migration and invasion of HCC cells. BAIAP2L2 may affect the prognosis of HCC by regulating immunity, methylation, and cuprotosis. BAIAP2L2 is a novel HCC prognostic gene involved in immune infiltration associated with cuprotosis and may be a potential prognosis and therapeutic target for HCC.

KIF20A as a potential biomarker of renal and bladder cancers based on bioinformatics and experimental verification

BACKGROUND

Bladder cancer (BC) is a malignant tumor that occurs in the bladder wall and often appears in elderly individuals. Renal cancer (RC) arises from the renal tubular epithelium, but its molecular mechanism remains unclear.

METHODS

We downloaded RC datasets (GSE14762 and GSE53757) and a BC dataset (GSE121711) to screen differentially expressed genes (DEGs). We also performed weighted gene coexpression network analysis (WGCNA). We created a protein-protein interaction (PPI) network and performed functional enrichment analysis, such as gene set enrichment analysis (GSEA). Heatmaps were made for gene expression. Survival analysis and immunoinfiltration analysis were performed. Comparative toxicogenomics database (CTD) analysis was performed to find the relationship between disease and hub genes. Western blotting was performed to verify the role of KIF20A in apoptosis.

RESULTS

A total of 764 DEGs were identified. The GSEA showed that the DEGs were mainly enriched in organic acid metabolism, drug metabolism, mitochondria, and metabolism of cysteine and methionine. The PPI network in GSE121711 showed that KIF20A was a hub gene of renal clear cell carcinoma. Where the expression level of KIF20A was higher, the prognosis of patients was worse. CTD analysis showed that KIF20A was associated with inflammation, proliferation, and apoptosis. KIF20A expression in the RC group was upregulated, as shown by western blotting. The core proteins (including pRB Ser 780, CyclinA, E2F1, CCNE1, and CCNE2) in the pRB Ser 780/CyclinA signaling pathway were also upregulated in the RC group.

CONCLUSIONS

KIF20A might be a novel biomarker for researching renal and bladder cancers.