Cuproptosis was a copper-dependent and unique kind of cell death that was separate from existing other forms of cell death. The last decade has witnessed a considerable increase in investigations of programmed cell death, and whether copper induced cell death was an independent form of cell death has long been argued until mechanism of cuproptosis has been revealed. After that, increasing number of researchers attempted to identify the relationship between cuproptosis and the process of cancer. Thus, in this review, we systematically detailed the systemic and cellular metabolic processes of copper and the copper-related tumor signaling pathways. Moreover, we not only focus on the discovery process of cuproptosis and its mechanism, but also outline the association between cuproptosis and cancers. Finally, we further highlight the possible therapeutic direction of employing copper ion ionophores with cuproptosis-inducing functions in combination with small molecule drugs for targeted therapy to treat specific cancers. 

/pdf/cuproptosis-mechanisms-and-links-with-cancers-18vfyq1k.pdf

Cuproptosis: mechanisms and links with cancers

Breast Cancer in Adolescent and Young Adult Women Under the Age of 40 Years.

Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS313) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS313 was quantified using Cox regression analyses. We assessed PRS313 interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS313 was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS313 was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10th percentile and 20.5% at the 90th percentile of PRS313. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS313 alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS313 is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.

Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk

Previous population-based studies have described first primary breast cancer tumor characteristics and their association with contralateral breast cancer (CBC) risk. However, information on influential covariates such as treatment, family history of breast cancer, and BRCA1/2 mutation carrier status was not available. In a large, population-based, case-control study, we evaluated whether tumor characteristics of the first primary breast cancer are associated with risk of developing second primary asynchronous CBC, overall and in subgroups of interest, including among BRCA1/2 mutation non-carriers, women who are not treated with tamoxifen, and women without a breast cancer family history. The Women’s Environmental Cancer and Radiation Epidemiology Study is a population-based case-control study of 1521 CBC cases and 2212 individually-matched controls with unilateral breast cancer. Detailed information about breast cancer risk factors, treatment for and characteristics of first tumors, including estrogen receptor (ER) and progesterone receptor (PR) status, was obtained by telephone interview and medical record abstraction. Multivariable risk ratios (RRs) and 95% confidence intervals (CIs) were estimated in conditional logistic regression models, adjusting for demographics, treatment, and personal medical and family history. A subset of women was screened for BRCA1/2 mutations. Lobular histology of the first tumor was associated with a 30% increase in CBC risk (95% CI 1.0–1.6). Compared to women with ER+/PR+ first tumors, those with ER-/PR- tumors had increased risk of CBC (RR = 1.4, 95% CI 1.1–1.7). Notably, women with ER-/PR- first tumors were more likely to develop CBC with the ER-/PR- phenotype (RR = 5.4, 95% CI 3.0–9.5), and risk remained elevated in multiple subgroups: BRCA1/2 mutation non-carriers, women younger than 45 years of age, women without a breast cancer family history, and women who were not treated with tamoxifen. Having a hormone receptor negative first primary breast cancer is associated with increased risk of CBC. Women with ER-/PR- primary tumors were more likely to develop ER-/PR- CBC, even after excluding BRCA1/2 mutation carriers. Hormone receptor status, which is routinely evaluated in breast tumors, may be used clinically to determine treatment protocols and identify patients who may benefit from increased surveillance for CBC.

/pdf/hormone-receptor-status-of-a-first-primary-breast-cancer-42djl4ua3j.pdf

Hormone receptor status of a first primary breast cancer predicts contralateral breast cancer risk in the WECARE study population

Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52–0.74; at 10 years, 0.53–0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62–1.37), and the calibration slope was 0.90 (95% PI: 0.73–1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52–0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4–10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.

/pdf/prediction-and-clinical-utility-of-a-contralateral-breast-15rapbzokk.pdf

Prediction and clinical utility of a contralateral breast cancer risk model

Background Cuproptosis is a new modality of cell death regulation that is currently considered as a new cancer treatment strategy. Nevertheless, the prognostic predictive value of cuproptosis-related lncRNAs in breast cancer (BC) remains unknown. Using cuproptosis-related lncRNAs, this study aims to predict the immune microenvironment and prognosis of BC patients. and develop new therapeutic strategies that target the disease. Methods The Cancer Genome Atlas (TCGA) database provided the RNA-seq data along with the corresponding clinical and prognostic information. Univariate and multivariate Cox regression analyses were performed to acquire lncRNAs associated with cuproptosis to establish predictive features. The Kaplan-Meier method was used to calculate the overall survival rate (OS) in the high-risk and low-risk groups. High risk and low risk gene sets were enriched to explore functional discrepancies among risk teams. The mutation data were analyzed using the “MAFTools” r-package. The ties of predictive characteristics and immune status had been explored by single sample gene set enrichment analysis (ssGSEA). Last, the correlation between predictive features and treatment condition in patients with BC was analyzed. Based on prognostic risk models, we assessed associations between risk subgroups and immune scores and immune checkpoints. In addition, drug responses in at-risk populations were predicted. Results We identified a set of 11 Cuproptosis-Related lncRNAs (GORAB-AS1, AC 079922.2, AL 589765.4, AC 005696.4, Cytor, ZNF 197-AS1, AC 002398.1, AL 451085.3, YTH DF 3-AS1, AC 008771.1, LINC 02446), based on which to construct the risk model. In comparison to the high-risk group, the low-risk patients lived longer (p < 0.001). Moreover, cuproptosis-related lncRNA profiles can independently predict prognosis in BC patients. The AUC values for receiver operating characteristics (ROC) of 1-, 3-, and 5-year risk were 0.849, 0.779, and 0.794, respectively. Patients in the high-risk group had lower OS than those in the low-risk group when they were divided into groups based on various clinicopathological variables. The tumor burden mutations (TMB) correlation analysis showed that high TMB had a worse prognosis than low-TMB, and gene mutations were found to be different in high and low TMB groups, such as PIK3CA (36% versus 32%), SYNE1 (4% versus 6%). Gene enrichment analysis indicated that the differential genes were significantly concentrated in immune-related pathways. The predictive traits were significantly correlated with the immune status of BC patients, according to ssGSEA results. Finally, high-risk patients showed high sensitivity in anti-CD276 immunotherapy and conventional chemotherapeutic drugs such as imatinib, lapatinib, and pazopanib. Conclusion We successfully constructed of a cuproptosis-related lncRNA signature, which can independently predict the prognosis of BC patients and can be used to estimate OS and clinical treatment outcomes in BRCA patients. It will serve as a foundation for further research into the mechanism of cuproptosis-related lncRNAs in breast cancer, as well as for the development of new markers and therapeutic targets for the disease.

/pdf/identification-of-novel-cuproptosis-related-lncrna-mbesp2ys.pdf

Identification of novel cuproptosis-related lncRNA signatures to predict the prognosis and immune microenvironment of breast cancer patients

Obesity has been reported to be a risk factor for breast cancer, but how obesity affects breast cancer (BC) remains unclear. Although body mass index (BMI) is the most commonly used reference for obesity, it is insufficient to evaluate the obesity-related pathophysiological changes in breast tissue. The purpose of this study is to establish a DNA-methylation-based biomarker for BMI (DM-BMI) and explore the connection between obesity and BC. Using DNA methylation data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we developed DM-BMI to evaluate the degree of obesity in breast tissues. In tissues from non-BC and BC population, the DM-BMI model exhibited high accuracy in BMI prediction. In BC tissues, DM-BMI correlated with increased adipose tissue content and BC tissues with increased DM-BMI exhibited higher expression of pro-inflammatory adipokines. Next, we identified the gene expression profile relating to DM-BMI. Using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we observed that the DM-BMI-related genes were mostly involved in the process of cancer immunity. DM-BMI is positively correlated with T cell infiltration in BC tissues. Furthermore, we observed that DM-BMI was positively correlated with immune checkpoint inhibitors (ICI) response markers in BC. Collectively, we developed a new biomarker for obesity and discovered that BC tissues from obese individuals exhibit an increased degree of immune cell infiltration, indicating that obese BC patients might be the potential beneficiaries for ICI treatment.

/pdf/integrative-analysis-of-gene-expression-and-dna-methylation-12d0cgda.pdf

Integrative Analysis of Gene Expression and DNA Methylation Depicting the Impact of Obesity on Breast Cancer

Breast cancer (BC) is the most diagnosed cancer and the leading cause of cancer-related deaths in women. The purpose of this study was to develop a prognostic model based on BC-related DNA methylation pattern. A total of 361 BC incidence-related probes (BCIPs) were differentially methylated in blood samples from women at high risk of BC and BC tissues. Twenty-nine of the 361 BCIPs that significantly correlated with BC outcomes were selected to establish the BCIP score. BCIP scores based on BC-related DNA methylation pattern were developed to evaluate the mortality risk of BC. The correlation between overall survival and BCIP scores was assessed using Kaplan–Meier, univariate, and multivariate analyses. In BC, the BCIP score was significantly correlated with malignant BC characteristics and poor outcomes. Furthermore, we assessed the BCIP score-related gene expression profile and observed that genes with expressions associated with the BCIP score were involved in the process of cancer immunity according to GO and KEGG analyses. Using the ESTIMATE and CIBERSORT algorithms, we discovered that BCIP scores were negatively correlated with both T cell infiltration and immune checkpoint inhibitor response markers in BC tissues. Finally, a nomogram comprising the BCIP score and BC prognostic factors was used to establish a prognostic model for patients with BC, while C-index and calibration curves were used to evaluate the effectiveness of the nomogram. A nomogram comprising the BCIP score, tumor size, lymph node status, and molecular subtype was developed to quantify the survival probability of patients with BC. Collectively, our study developed the BCIP score, which correlated with poor outcomes in BC, to portray the variation in DNA methylation pattern related to BC incidence.

A Prognostic Model for Breast Cancer Based on Cancer Incidence-Related DNA Methylation Pattern

Population-based estimates are lacking for the temporal trends in the contralateral breast cancer (CBC) risk for patients with breast cancer (BC). Data for BC patients diagnosed with CBC were collected from the Surveillance, Epidemiology, and End Results database. CBC incidence was calculated using the Kaplan-Meier method and the temporal trend in CBC incidence was assessed using joinpoint regression. Survival analysis was calculated using propensity scoring (PS) and multivariate Cox regression with a competing risk model. We found that 10,944 of 212,630 patients with early-stage BC were subsequently diagnosed with secondary BC in the contralateral breast. The 5-, 10-, 15-, and 20-year cumulative CBC incidences were 1.9, 4.6, 7.6, and 10.5%, respectively. Being younger (<40 years), black, hormone receptor-negative, and having undergone radiotherapy were correlated with a high risk of CBC occurrence. CBC incidence increased continuously in the first 11 years after the initial cancer diagnosis, and the upward trend slowed from years 11 to 21, and tended to decline from years 21 to 24. CBC diagnosis was significantly and negatively associated with survival. We reported population-based estimates of the CBC occurrence pattern and risk factors. Patients are at high risk of developing CBC in the first 21 years after the initial BC diagnosis.

Patterns of Occurrence and Outcomes of Contralateral Breast Cancer: Analysis of SEER Data.


 Background: Obesity has been reported to be a risk factor for breast cancer, but how obesity affects BC is unclear. Although BMI is the most commonly used marker for obesity, it is insufficient to evaluate the obesity-related pathophysiological changes in breast tissue. The purpose of this study was to establish a DNA-methylation-based biomarker for obesity and to explore the association of obesity with BC. Methods: Lasso regression was used to developed DNA-methylation-based BMI index (DM-BMI). Linear regression and paired t-test were used to assess the accuracy of DM-BMI in BMI prediction. A deconvolution algorithm based on DNA methylation data was used to calculate the proportion of adipose cell in tissues. The Estimate and Cibersort algorithm were used to assess the degree of tumor-infiltrating immune cells.Results: Using DNA methylation data from TCGA and GEO, we developed DM-BMI to evaluate the degree of obesity in breast tissues. In tissues from non-BC and BC population, the DM-BMI model exhibited high accuracy in BMI prediction. In BC tissues, DM-BMI correlated with increased adipose tissue content and BC tissues with increased DM-BMI exhibited higher expression of pro-inflammatory adipokines. Next, we identified the gene expression profile relating to DM-BMI. Using GO and KEGG database, we observed that the DM-BMI-related genes mostly involve in the process of cancer immunity. DM-BMI is positively correlated with T cell infiltration in BC tissues. Further, we observed that DM-BMI positively correlated with immune checkpoint inhibitors [1] response markers in BC. Conclusions: Collectively, we developed a new biomarker for obesity and discovered that BC tissues from obese individuals exhibit an increased degree of immune cell infiltration indicating that obese BC patients might be the potential beneficiaries for ICI treatment.

Integrative Analysis of Gene Expression and DNA Methylation Depicts the Impact of Obesity on Breast Cancer

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