INF2-mediated actin polymerization at ER-organelle contacts regulates organelle size and movement.

Proper regulation of organelle dynamics and inter-organelle contacts is critical for cellular health and function. Both the endoplasmic reticulum (ER) and actin cytoskeleton are known to regulate organelle dynamics, but how, when, and where these two subcellular components are coordinated to control organelle dynamics remains unclear. Here, we show that ER-associated actin consistently marks mitochondrial, endosomal, and lysosomal fission sites. We also show that actin polymerization by the ER-anchored isoform of the formin protein INF2 is a key regulator of the morphology and mobility of these organelles. Together, our findings establish a mechanism by which INF2-mediated polymerization of ER-associated actin at ER-organelle contacts regulates organelle dynamics.

Serum and Urine Metabolic Fingerprints Characterize Renal Cell Carcinoma for Classification, Early Diagnosis, and Prognosis.

Renal cell carcinoma (RCC) is a substantial pathology of the urinary system with a growing prevalence rate. However, current clinical methods have limitations for managing RCC due to the heterogeneity manifestations of the disease. Metabolic analyses are regarded as a preferred noninvasive approach in clinics, which can substantially benefit the characterization of RCC. This study constructs a nanoparticle-enhanced laser desorption ionization mass spectrometry (NELDI MS) to analyze metabolic fingerprints of renal tumors (n = 456) and healthy controls (n = 200). The classification models yielded the areas under curves (AUC) of 0.938 (95% confidence interval (CI), 0.884-0.967) for distinguishing renal tumors from healthy controls, 0.850 for differentiating malignant from benign tumors (95% CI, 0.821-0.915), and 0.925-0.932 for classifying subtypes of RCC (95% CI, 0.821-0.915). For the early stage of RCC subtypes, the averaged diagnostic sensitivity of 90.5% and specificity of 91.3% in the test set is achieved. Metabolic biomarkers are identified as the potential indicator for subtype diagnosis (p < 0.05). To validate the prognostic performance, a predictive model for RCC participants and achieve the prediction of disease (p = 0.003) is constructed. The study provides a promising prospect for applying metabolic analytical tools for RCC characterization.

Exosome-based anticancer vaccines: From Bench to bedside.

Exosomes, a subset of extracellular vesicles, are released by all active cells and play a crucial role in intercellular communications. Exosomes could facilitate the transfer of various biologically active molecules, such as DNA, non-coding RNAs, and proteins, from donor to recipient cells, thereby participating in diverse biological and pathological processes. Besides, exosomes possess unique characteristics, including non-toxicity, low-immunogenicity, and stability within biological systems, rendering them highly advantageous for cancer drug development. Meanwhile, accumulating evidence suggests that exosomes originating from tumor cells and immune cells possess distinct composition profiles that play a direct role in anticancer immunotherapy. Of note, exosomes can transport their contents to specific cells, thereby exerting an impact on the phenotype and immune-regulatory functions of targeted cells. Therapeutic cancer vaccines, an emerging therapeutics of immunotherapy, could enhance antitumor immune responses by delivering a large number of tumor antigens, thereby augmenting the immune response against tumor cells. Therefore, the therapeutic rationale of cancer vaccines and exosome-based immunotherapy are almost similar to some extent, but some challenges have hindered their application in the clinical setting. Here, in this review, we first summarized the biogenesis, structure, compositions, and biological functions of exosomes. Then we described the roles of exosomes in cancer biology, particularly in tumor immunity. We also comprehensively reviewed current exosome-based anticancer vaccine development and we divided them into three types. Finally, we give some insights into clinical translation and clinical trial progress of exosome-based anticancer vaccines for future direction.

Objective monitoring of loneliness levels using smart devices: A multi-device approach for mental health applications.

Loneliness is linked to wide ranging physical and mental health problems, including increased rates of mortality. Understanding how loneliness manifests is important for targeted public health treatment and intervention. With advances in mobile sending and wearable technologies, it is possible to collect data on human phenomena in a continuous and uninterrupted way. In doing so, such approaches can be used to monitor physiological and behavioral aspects relevant to an individual's loneliness. In this study, we proposed a method for continuous detection of loneliness using fully objective data from smart devices and passive mobile sensing. We also investigated whether physiological and behavioral features differed in their importance in predicting loneliness across individuals. Finally, we examined how informative data from each device is for loneliness detection tasks. We assessed subjective feelings of loneliness while monitoring behavioral and physiological patterns in 30 college students over a 2-month period. We used smartphones to monitor behavioral patterns (e.g., location changes, type of notifications, in-coming and out-going calls/text messages) and smart watches and rings to monitor physiology and sleep patterns (e.g., heart-rate, heart-rate variability, sleep duration). Participants reported their loneliness feeling multiple times a day through a questionnaire app on their phone. Using the data collected from their devices, we trained a random forest machine learning based model to detect loneliness levels. We found support for loneliness prediction using a multi-device and fully-objective approach. Furthermore, behavioral data collected by smartphones generally were the most important features across all participants. The study provides promising results for using objective data to monitor mental health indicators, which could provide a continuous and uninterrupted source of information in mental healthcare applications.

Adaptive designs were primarily used but inadequately reported in early phase drug trials.

BACKGROUND: Faced with the high cost and limited efficiency of classical randomized controlled trials, researchers are increasingly applying adaptive designs to speed up the development of new drugs. However, the application of adaptive design to drug randomized controlled trials (RCTs) and whether the reporting is adequate are unclear. Thus, this study aimed to summarize the epidemiological characteristics of the relevant trials and assess their reporting quality by the Adaptive designs CONSORT Extension (ACE) checklist. METHODS: We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov from inception to January 2020. We included drug RCTs that explicitly claimed to be adaptive trials or used any type of adaptative design. We extracted the epidemiological characteristics of included studies to summarize their adaptive design application. We assessed the reporting quality of the trials by Adaptive designs CONSORT Extension (ACE) checklist. Univariable and multivariable linear regression models were used to the association of four prespecified factors with the quality of reporting. RESULTS: Our survey included 108 adaptive trials. We found that adaptive design has been increasingly applied over the years, and was commonly used in phase II trials (n = 45, 41.7%). The primary reasons for using adaptive design were to speed the trial and facilitate decision-making (n = 24, 22.2%), maximize the benefit of participants (n = 21, 19.4%), and reduce the total sample size (n = 15, 13.9%). Group sequential design (n = 63, 58.3%) was the most frequently applied method, followed by adaptive randomization design (n = 26, 24.1%), and adaptive dose-finding design (n = 24, 22.2%). The proportion of adherence to the ACE checklist of 26 topics ranged from 7.4 to 99.1%, with eight topics being adequately reported (i.e., level of adherence ≥ 80%), and eight others being poorly reported (i.e., level of adherence ≤ 30%). In addition, among the seven items specific for adaptive trials, three were poorly reported: accessibility to statistical analysis plan (n = 8, 7.4%), measures for confidentiality (n = 14, 13.0%), and assessments of similarity between interim stages (n = 25, 23.1%). The mean score of the ACE checklist was 13.9 (standard deviation [SD], 3.5) out of 26. According to our multivariable regression analysis, later published trials (estimated ß = 0.14, p < 0.01) and the multicenter trials (estimated ß = 2.22, p < 0.01) were associated with better reporting. CONCLUSION: Adaptive design has shown an increasing use over the years, and was primarily applied to early phase drug trials. However, the reporting quality of adaptive trials is suboptimal, and substantial efforts are needed to improve the reporting.

Ceratophyllum demersum alleviates microplastics uptake and physiological stress responses in aquatic organisms, an overlooked ability.

It has been demonstrated that microplastics (MPs) may be inadvertently ingested by aquatic animals, causing harm to their physiological functions and potentially entering the food chain, thereby posing risks to human food safety. To achieve an environmentally friendly and efficient reduction of MPs in freshwater environments, this experiment investigates the depuration effect of C. demersum on MPs using three common aquatic animals: Macrobrachium nipponense, Corbicula fluminea, and Bellamya aeruginosa as research subjects. The amounts of MPs, digestive enzyme activity, oxidative stress index, and energy metabolism enzyme activity in the digestive and non-digestive systems of three aquatic animals were measured on exposure days 1, 3, and 7 and on depuration days 1 and 3. The results indicated that the depuration effect of C. demersum and the species interaction were significant for the whole individual. Concerning digestive tissue, C. demersum was the most effective in purifying B. aeruginosa. When subjected to short-term exposure to MPs, C. demersum displayed a superior depuration effect. Among non-digestive tissues, C. demersum exhibited the earliest purifying effect on C. fluminea. Additionally, C. demersum alleviated physiological responses caused by MPs. In conclusion, this study underscores C. demersum as a promising new method for removing MPs from aquatic organisms.

The IL-33-ST2 axis plays a vital role in endometriosis via promoting epithelial-mesenchymal transition by phosphorylating ß-catenin.

OBJECTIVES: Interleukin 33 (IL-33) is a crucial inflammatory factor that functions as an alarm signal in endometriosis (EMs). Epithelial-mesenchymal transition (EMT), a process related to inflammatory signals, intracellular reactive oxygen species (ROS) production, and lipid peroxidation, have been proposed as potential mechanisms that contribute to the development and progression of EMs. IL-33 is highly upregulated in the ectopic milieu. Moreover, ectopic endometrial cells constitutively express interleukin-33 receptor ST2 (IL-33R). However, the role of IL-33/ST2 in the EMT of EMs remains largely unknown. In this study, we aimed to mechanistically determine the role of IL-33/ST2 in EMs-associated fibrosis. MATERIALS AND METHODS: We established a non-lethal oxidative stress model to explore the conditions that trigger IL-33 induction. We performed α-smooth muscle actin (α-SMA) protein detection, cell counting kit-8 (CCK-8) assays, and scratch assays to analyze the impact of IL-33 on primary endometrial stromal cells (ESCs) proliferation and invasion. Clinical samples from patients with or without EMs were subjected to immunohistochemical (IHC) and and immunofluorescence(IF) staining to assess the clinical relevance of IL-33 receptor ST2 and EMT-related proteins. Furthermore, we used the ectopic human endometrial epithelial cell line 12Z and normal human epithelial cell line EEC to evaluate the effects of IL-33 on Wnt/ß-catenin signaling. The effect of IL-33 on EMT-associated fibrosis was validated in vivo by intraperitoneal injections of IL-33 and antiST2. RESULTS: We observed that ectopic milieu, characterized by ROS, TGF-ß1, and high level of estrogen, triggers the secretion of IL-33 from ectopic ESCs. Ectopic endometrial lesions exhibited higher level of fibrotic characteristics and ST2 expression than that in the normal endometrium. Exogenous recombinant human (rhIL-33) enhanced ESC migration and survival. Similarly, 12Z cells displayed a higher degree of EMT characteristics with elevated expression of CCN4 and Fra-1, downstream target genes of the WNT/ß-catenin pathway, than that observed in EECs. Conversely, blocking IL-33 with neutralizing antibodies, knocking down ST2 or ß-catenin with siRNA, and ß-catenin dephosphorylation abolished its effects on EMT promotion. In vivo validation demonstrated that IL-33 significantly promotes EMs-related fibrosis through the activation of Wnt/ß-catenin signaling. CONCLUSION: Our data strongly support the vital role of the IL-33/ST2 pathway in EMs-associated fibrosis and emphasize the importance of the EMT in the pathophysiology of fibrosis. Targeting the IL-33/ST2/Wnt/ß-catenin axis may hold promise as a feasible therapeutic approach for controlling fibrosis in EMs.

Tungsten doped FeCoP2 nanoparticles embedded into carbon for highly efficient oxygen evolution reaction.

Designing active and stable electrocatalysts with economic efficiency for oxygen evolution reaction (OER) is essential for developing water splitting process at an industrial scale. Herein, we rationally designed a tungsten doped iron cobalt phosphide incorporated with carbon (Wx-FeCoP2/C), prepared by a mechanochemical approach. X-ray photoelectron spectroscopy (XPS) revealed that the doping of W led to an increasing of Co3+/Co2+ and Fe3+/Fe2+ molar ratios, which contributed to the enhanced OER performance. As a result, a current density of 10 mA cm-2 was achieved in 1 M KOH at an overpotential of 264 mV on the optimized W0.1-FeCoP2/C. Moreover, at high current density of 100 mA cm-2, the overpotential value was 310 mV, and the corresponding Tafel slope was measured to be 48.5 mV dec-1, placing it among the best phosphide-based catalysts for OER. This work is expected to enlighten the design strategy of highly efficient phosphide-based OER catalysts.

Comprehensive multi-omics analysis provides biological insights and therapeutic strategies for small-cell lung cancer.

Integration of multi-omics analysis into small-cell lung cancer (SCLC) research. In the research of small-cell lung cancer, the integration of multi-omics analysis has become an important research direction. Multi-omics analysis includes the study of genomics, transcriptomics, proteomics, metabolomics, and other levels, which can help us to understand the pathogenesis and development process of diseases more comprehensively as well as develop novel therapeutics and biomarkers for further precision oncology.

Genetically predicted blood metabolites mediate the association between circulating immune cells and pancreatic cancer: A Mendelian randomization study.

BACKGROUND: Pancreatic cancer is characterized by metabolic dysregulation and unique immunological profiles. Nevertheless, the comprehensive understanding of immune and metabolic dysregulation of pancreatic cancer remains unclear. In the present study, we aimed to investigate the causal relationship of circulating immune cells and pancreatic cancer and identify the blood metabolites as potential mediators. METHODS: The exposure and outcome genome-wide association studies (GWAS) data used in the present study were obtained from the GWAS open-access database (https://gwas.mrcieu.ac.uk). The study used 731 circulating immune cell features, 1400 types of blood metabolites and pancreatic cancer from GWAS. We then performed bidirectional Mendelian randomization (MR) analyses to explore the causal relationships between the circulating immune cells and pancreatic cancer, and two-step MR to discover potential mediating blood metabolites in this process. All statistical analyses were performed in R software. The STROBE-MR (i.e. Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization) checklist for the reporting of MR studies was also used. RESULTS: MR analysis identified seven types of circulating immune cells causally associated with pancreatic cancer. Furthermore, there was no strong evidence that genetically predicted pancreatic cancer had an effect on these seven types of circulating immune cells. Further two-step MR analysis found 10 types of blood metabolites were causally associated with pancreatic cancer and the associations between circulating CD39+CD8+ T cells and pancreatic cancer were mediated by blood orotates with proportions of 5.18% (p = 0.016). CONCLUSIONS: The present study provides evidence supporting the causal relationships between various circulating immune cells, especially CD39+CD8+ T cells, and pancreatic cancer, with a potential effect mediated by blood orotates. Further research is needed on additional risk factors as potential mediators and establish a comprehensive immunity-metabolism network in pancreatic cancer.

Pan-cancer analysis of polo-like kinase family genes reveals polo-like kinase 1 as a novel oncogene in kidney renal papillary cell carcinoma.

Background: Polo-like kinases (PLKs) are a kinase class of serine/threonine with five members that play crucial roles in cell cycle regulation. However, their biological functions, regulation, and expression remain unclear. This study revealed the molecular properties, oncogenic role, and clinical significance of PLK genes in pan-cancers, particularly in kidney renal papillary cell carcinoma (KIRP). Methods: We evaluated the mutation landscape, expression level, and prognostic values of PLK genes using bioinformatics analyses and explored the association between the expression level of PLK genes and tumor microenvironment (TME), immune subtype, cancer immunotherapy, tumor stemness, and drug sensitivity. Finally, we verified the prognostic value in patients with KIRP through univariate and multivariate analyses and nomogram construction. Results: PLK genes are extensively altered in pan-cancer, which may contribute to tumorigenesis. These genes are aberrantly expressed in some types of cancer, with PLK1 being overexpressed in 31 cancers. PLK expression is closely associated with the prognosis of various cancers. The expression level of PLK genes is related with sensitivity to diverse drugs and cancer immunity as well as cancer immunotherapy. Importantly, we verified that PLK1 was overexpressed in KIRP tissues and could be an unfavorable prognostic biomarker in patients with KIRP. Hence, PLK1 may serve as an oncogenic gene in KIRP and should be explored in future studies. Conclusions: Our study comprehensively reports the molecular characteristics and biological functions of PLK family gens across human cancers and recommends further investigation of these genes as potential biomarkers and therapeutic targets, especially in KIRP.

Deleting SUV39H1 in CAR-T cells epigenetically enhances the antitumor function.

SUV39H1 ablation in CAR-T cells epigenetically enhances the antitumor function (by Figdraw). (A) Schematic illustration of SUV39H1 ablation-mediated enhanced antitumor function of CAR-T cells. Functional CAR-T cells eventually transformed into dysfunctional exhausted CAR-T cells under the exposure of chronic tumor antigens, accompanied by reduced proliferation level, effector function, and stemness/memory characteristics, thereby limiting the antitumor activity so as to cause the recurrence of solid tumors. Upon genetic engineering of SUV39H1 ablation, SUV KO CAR-T cells are endowed with increased proliferation level and stemness/memory properties, accompanied by reduced effector/exhausted phenotype. Augmented SUV KO CAR-T cells after in vitro expansion intravenously infusion to mice achieved stronger and more persistent tumor rejection. (B) SUV39H1 ablation-mediated epigenetic reprogramming mechanism of CAR-T cells. Epigenetically, under the stimulation of chronic tumor antigens, exhausted CAR-T cells were characterized by downregulation of proliferation, effector and stemness/memory-associated genes and upregulation of exhaustion-associated genes. SUV39H1 genetic ablation increased chromatin accessibility of stemness/memory-associated genes and reduced chromatin accessibility of inhibitory receptors and effector-associated genes in SUV KO CAR-T cells, epigenetically reprogramming human T cells to express higher levels of stemness/memory genes such as KLF2, LEF1 and TCF7 and lower levels of effector/exhaustion genes.

Comparing various Bayesian random-effects models for pooling randomized controlled trials with rare events.

The meta-analysis of rare events presents unique methodological challenges owing to the small number of events. Bayesian methods are often used to combine rare events data to inform decision-making, as they can incorporate prior information and handle studies with zero events without the need for continuity corrections. However, the comparative performances of different Bayesian models in pooling rare events data are not well understood. We conducted a simulation to compare the statistical properties of four parameterizations based on the binomial-normal hierarchical model, using two different priors for the treatment effect: weakly informative prior (WIP) and non-informative prior (NIP), pooling randomized controlled trials with rare events using the odds ratio metric. We also considered the beta-binomial model proposed by Kuss and the random intercept and slope generalized linear mixed models. The simulation scenarios varied based on the treatment effect, sample size ratio between the treatment and control arms, and level of heterogeneity. Performance was evaluated using median bias, root mean square error, median width of 95% credible or confidence intervals, coverage, Type I error, and empirical power. Two reviews are used to illustrate these methods. The results demonstrate that the WIP outperforms the NIP within the same model structure. Among the compared models, the model that included the treatment effect parameter in the risk model for the control arm did not perform well. Our findings confirm that rare events meta-analysis faces the challenge of being underpowered, highlighting the importance of reporting the power of results in empirical studies.

Tenecteplase versus alteplase for acute ischemic stroke: a systematic review and meta-analysis of randomized and non-randomized studies.

OBJECTIVE: Alteplase is the current standard of care for acute ischemic stroke. Tenecteplase is a newer fibrinolytic agent with preferable administration and lower costs; however, its comparative effectiveness to alteplase remains uncertain. We set out to perform a systematic review and meta-analysis to establish the benefits and harms of tenecteplase versus alteplase for acute ischemic stroke. METHODS: We searched PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov from inception to April 2023 for randomized and non-randomized studies that compared tenecteplase versus alteplase for acute ischemic stroke. Paired reviewers independently assessed risk of bias and extracted data. We performed both conventional meta-analyses and Bayesian network meta-analyses (NMA) with random-effects models and used the GRADE approach to evaluate the certainty of evidence. Our primary efficacy outcome was excellent functional outcome at 3 months, defined as a score of 0-1 on the modified Rankin Scale. Our primary safety outcomes were symptomatic intracranial hemorrhage and all-cause mortality. RESULTS: Thirty-six studies were eligible for review, including 12 randomized (n = 5533) and 24 non-randomized studies (n = 44,956). Moderate certainty evidence showed that there was no difference between tenecteplase and alteplase in increasing the proportion of patients achieving excellent functional outcome at 3 months (odds ratio [OR], 1.10; 95% CI 0.98-1.23; risk difference [RD] 2.4%, 95% CI - 0.5 to 5.2), while moderate certainty evidence from NMA suggested that 0.25 mg/kg tenecteplase significantly improved excellent functional outcome at 3 months (OR, 1.16; 95% credible interval 1.02-1.32). Moderate certainty evidence showed that, compared to alteplase, tenecteplase may make little to no difference in the prevalence of symptomatic intracranial hemorrhage (OR, 1.12; 95% CI 0.79-1.59; RD 0.3%, 95% CI - 0.5 to 1.4), and probably reduces all-cause mortality (adjusted odds ratio [aOR], 0.44; 95% CI 0.30-0.64; RD - 4.6%; 95% CI - 5.8 to - 2.9). CONCLUSIONS: Moderate certainty evidence suggested that there was little to no difference between tenecteplase and alteplase in increasing the proportion of patients achieving excellent functional outcome at 3 months and the risk of symptomatic intracranial hemorrhage, while compared to alteplase, tenecteplase probably reduce all-cause mortality. Administration of 0.25 mg/kg tenecteplase after acute ischemic stroke is suggestive of increasing the proportion of patients that achieve excellent functional outcome at 3 months.

Improved beluga whale optimization algorithm based cluster routing in wireless sensor networks.

Cluster routing is a critical routing approach in wireless sensor networks (WSNs). However, the uneven distribution of selected cluster head nodes and impractical data transmission paths can result in uneven depletion of network energy. For this purpose, we introduce a new routing strategy for clustered wireless sensor networks that utilizes an improved beluga whale optimization algorithm, called tCBWO-DPR. In the selection process of cluster heads, we introduce a new excitation function to evaluate and select more suitable candidate cluster heads by establishing the correlation between the energy of node and the positional relationship of nodes. In addition, the beluga whale optimization (BWO) algorithm has been improved by incorporating the cosine factor and t-distribution to enhance its local and global search capabilities, as well as to improve its convergence speed and ability. For the data transmission path, we use Prim's algorithm to construct a spanning tree and introduce DPR for determining the optimal route between cluster heads based on the correlation distances of cluster heads. This effectively shortens the data transmission path and enhances network stability. Simulation results show that the improved beluga whale optimization based algorithm can effectively improve the survival cycle and reduce the average energy consumption of the network.

Design and statistical analysis reporting among interrupted time series studies in drug utilization research: a cross-sectional survey.

INTRODUCTION: Interrupted time series (ITS) design is a commonly used method for evaluating large-scale interventions in clinical practice or public health. However, improperly using this method can lead to biased results. OBJECTIVE: To investigate design and statistical analysis characteristics of drug utilization studies using ITS design, and give recommendations for improvements. METHODS: A literature search was conducted based on PubMed from January 2021 to December 2021. We included original articles that used ITS design to investigate drug utilization without restriction on study population or outcome types. A structured, pilot-tested questionnaire was developed to extract information regarding study characteristics and details about design and statistical analysis. RESULTS: We included 153 eligible studies. Among those, 28.1% (43/153) clearly explained the rationale for using the ITS design and 13.7% (21/153) clarified the rationale of using the specified ITS model structure. One hundred and forty-nine studies used aggregated data to do ITS analysis, and 20.8% (31/149) clarified the rationale for the number of time points. The consideration of autocorrelation, non-stationary and seasonality was often lacking among those studies, and only 14 studies mentioned all of three methodological issues. Missing data was mentioned in 31 studies. Only 39.22% (60/153) reported the regression models, while 15 studies gave the incorrect interpretation of level change due to time parameterization. Time-varying participant characteristics were considered in 24 studies. In 97 studies containing hierarchical data, 23 studies clarified the heterogeneity among clusters and used statistical methods to address this issue. CONCLUSION: The quality of design and statistical analyses in ITS studies for drug utilization remains unsatisfactory. Three emerging methodological issues warranted particular attention, including incorrect interpretation of level change due to time parameterization, time-varying participant characteristics and hierarchical data analysis. We offered specific recommendations about the design, analysis and reporting of the ITS study.

Identification and validation of a copper homeostasis-related gene signature for the predicting prognosis of breast cancer patients via integrated bioinformatics analysis.

The prognostic value of copper homeostasis-related genes in breast cancer (BC) remains largely unexplored. We analyzed copper homeostasis-related gene profiles within The Cancer Genome Atlas Program breast cancer cohorts and performed correlation analysis to explore the relationship between copper homeostasis-related mRNAs (chrmRNA) and lncRNAs. Based on these results, we developed a gene signature-based risk assessment model to predict BC patient outcomes using Cox regression analysis and a nomogram, which was further validated in a cohort of 72 BC patients. Using the gene set enrichment analysis, we identified 139 chrmRNAs and 16 core mRNAs via the Protein-Protein Interaction network. Additionally, our copper homeostasis-related lncRNAs (chrlncRNAs) (PINK1.AS, OIP5.AS1, HID.AS1, and MAPT.AS1) were evaluated as gene signatures of the predictive model. Kaplan-Meier survival analysis revealed that patients with a high-risk gene signature had significantly poorer clinical outcomes. Receiver operating characteristic curves showed that the prognostic value of the chrlncRNAs model reached 0.795 after ten years. Principal component analysis demonstrated the capability of the model to distinguish between low- and high-risk BC patients based on the gene signature. Using the pRRophetic package, we screened out 24 anticancer drugs that exhibited a significant relationship with the predictive model. Notably, we observed higher expression levels of the four chrlncRNAs in tumor tissues than in the adjacent normal tissues. The correlation between our model and the clinical characteristics of patients with BC highlights the potential of chrlncRNAs for predicting tumor progression. This novel gene signature not only predicts the prognosis of patients with BC but also suggests that targeting copper homeostasis may be a viable treatment strategy.