Background Breast Cancer Patients Research Articles

Pro-inflammatory cytokines increase temporarily after adjuvant treatment for breast cancer in postmenopausal women: a longitudinal study

BackgroundBreast cancer patients have an increased risk of cardiometabolic disease and for many patients, adjuvant therapy causes an altered lipid profile, insulin resistance and inflammation. Previous follow-up studies are inconclusive regarding the duration of therapy-induced inflammation. We examined the acute and persistent changes of adjuvant chemotherapy on inflammatory and metabolic health markers in breast cancer patients.MethodsPlasma levels of IL-6, IL-8, IL-10, IFN-γ, TNF-α, high-sensitivity C-reactive protein (hsCRP) and metabolic health parameters were analyzed before, shortly after and every six months up to two years after adjuvant chemotherapy treatment in 51 postmenopausal early breast cancer (EBC) patients, as well as in 41 healthy age- and BMI-matched controls. A target-specific multiplex assay was applied for cytokine measurements.ResultsBefore initiation of adjuvant therapy, plasma IL-8 levels were higher in EBC patients (31%, p = 0.0001). Also, a larger proportion of the patients had a hsCRP level above 2 mg/L (41%) compared to the controls (17%, Χ2 = 5.15, p = 0.023). Plasma levels of all five cytokines, but not hsCRP, were significantly increased after compared to before adjuvant chemotherapy (15–48% increase; all p ≤ 0.05). Already six months after ending chemotherapy treatment, all plasma cytokine levels were significantly reduced and close to pre-chemotherapy levels. Adjuvant chemotherapy caused a worsened lipid profile (increased triglycerides, lower HDL levels), insulin resistance and increased plasma insulin levels that remained high during the first year after chemotherapy.ConclusionPostmenopausal women with EBC have temporarily increased plasma levels of pro-inflammatory cytokines after adjuvant chemotherapy. Although transient, the therapy-induced increase in plasma cytokine levels, together with dyslipidemia and insulin resistance, may contribute to cardiometabolic risk in breast cancer patients treated with adjuvant chemotherapy.Trial registration The clinical trial (registration number NCT03784651) was registered on www.clinicaltrials.gov on 24 December 2018.

CureMate: A clinical decision support system for breast cancer treatment

BackgroundBreast Cancer (BC) poses significant challenges in treatment decision-making. Multiple first treatment lines are currently available, determined by several patient-specific factors that need to be considered in the decision-making process. PurposeTo present CureMate, a Clinical Decision Support System to predict the most effective initial treatment for BC patients. Different artificial intelligence models based on demographic, anatomopathological and magnetic resonance imaging variables are studied. CureMate’s web application allows for easy use of the best model. MethodsA database of 232 BCE patients, each described by 29 variables, was established. Out of four machine learning algorithms, specifically Decision Tree Classifier (DTC), Gaussian Naïve Bayes (GNB), k-Nearest Neighbor (K-NN), and Support Vector Machine (SVM), the most suitable model for the task was identified, optimized and independently tested. ResultsSVM was identified as the best model for BC treatment planning, resulting in a test accuracy of 0.933. CureMate’s web application, including the SVM model, allows for introducing the relevant patient variables and displays the suggested first treatment step, as well as a diagram of the following steps. ConclusionThe results demonstrate CureMate’s high accuracy and effectiveness in clinical settings, indicating its potential to aid practitioners in making informed therapeutic decisions.

Age- and ethnic-driven molecular and clinical disparity of East Asian breast cancers

BackgroundBreast cancer (BC) is a complex disease with profound genomic aberrations. However, the underlying molecular disparity influenced by age and ethnicity remains elusive.MethodsIn this study, we aimed to investigate the molecular properties of 843 primary and metastatic BC patients enrolled in the K-MASTER program. By categorizing patients into two distinct age subgroups, we explored their unique molecular properties. Additionally, we leveraged large-scale genomic data from the TCGA and MSK-IMPACT studies to examine the ethnic-driven molecular and clinical disparities.ResultsWe observed a high prevalence of PI3KCA mutations in K-MASTER HER2 + tumors, particularly in older patients. Moreover, we identified increased mutation rates in DNA damage response molecules, including ARID1A, MSH6, and MLH1. The K-MASTER patients were mainly comprised of triple-negative breast cancer (TNBC) and HER2-positive tumors, while the TCGA and MSK-IMPACT cohorts exhibited a predominance of hormone receptor-positive (HR +) subtype tumors. Importantly, GATA3 mutations were less frequently observed in East Asian patients, which correlated with poor clinical outcomes. In addition to characterizing the molecular disparities, we developed a gradient-boosting multivariable model to identify a new molecular signature that could predict the therapeutic response to platinum-based chemotherapy.ConclusionsOur findings collectively provide unprecedented insights into the significance of age and ethnicity on the molecular and clinical characteristics of BC patients.

Anoikis-related genes in breast cancer patients: reliable biomarker of prognosis

BackgroundBreast cancer (BC) is the most common cancer in women, and its progression is closely related to the phenomenon of anoikis. Anoikis, the specific programmed death resulting from a lack of contact between cells and the extracellular matrix, has recently been recognized as playing a critical role in tumor initiation, maintenance, and treatment. The ability of cancer cells to resist anoikis leads to cancer progression and metastatic colonization. However, the impact of anoikis on the prognosis of BC patients remains unclear.MethodThis study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect transcriptome and clinical data of BC patients. Anoikis-related genes (ARGs) were classified into subtypes A and B through consensus clustering. Subsequently, survival prognosis analysis, immune cell infiltration analysis, and functional enrichment analysis were performed for both subtypes. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a set of 10 ARGs related to prognosis was identified. Immune cell infiltration and tumor microenvironment analyses were conducted on these 10 ARGs to develop a prognostic model. Furthermore, single-cell data analysis and real-time polymerase chain reaction (RT-PCR) analysis were employed to study the expression of the 10 identified prognostic ARGs in BC cells.ResultsOne hundred thirty-five ARGs were identified as differentially expressed genes in the TCGA and GEO databases, with 42 of them associated with the survival prognosis of BC patients. Analyses involving Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) revealed distinct expression patterns of ARGs between types A and B. Patients in type A exhibited worse survival prognosis and lower immune cell infiltration compared to type B. Subsequent analyses identified 10 key ARGs (YAP1, PIK3R1, BAK1, PHLDA2, EDA2R, LAMB3, CD24, SLC2A1, CDC25C, and SLC39A6) relevant to BC prognosis. Kaplan–Meier analysis indicated that high-risk patients based on these ARGs had a poorer BC prognosis. Additionally, Cox regression analysis established gender, age, T (tumor), N (nodes), and risk score as predictive factors in a nomogram model for BC. The model demonstrated diagnostic value for BC patients at 1, 3, and 5 years. Decision curve analysis (DCA) verified the risk score as a reliable predictor of BC patient survival rates. Moreover, RT-PCR results confirmed differential expressions of YAP1, PIK3R1, BAK1, PHLDA2, CD24, SLC2A1, and CDC25C in BC cells, with SLC39A6, EDA2R, and LAMB3 showing low expression levels.ConclusionARGs markers can be used as BC biomarkers for risk stratification and survival prediction in BC patients. Besides, ARGs can be used as stratification factors for individualized and precise treatment of BC patients.

Serum exosomal miR-200c is a potential diagnostic biomarker for breast cancer

Background Breast cancer (BC) is one of the most common malignancies in women. Exosomes are widely found in body fluids and carry microRNAs (miRNAs) that reflect the biological properties of the parental cells. Our study aimed to investigate the differential expression of miR-200c in BC serum exosomes and its diagnostic value. Methodology miRNA profiles in culture supernatant exosomes of normal mammary epithelial cells MCF-10A and BC cells (MCF-7, MDA-MB-231, MCF-7 Taxol) were examined by miRNA deep sequencing to screen for significantly differentially expressed miRNAs; Transmission electron microscopy (TEM), Nanoparticle tracking analysis (NTA), and Western blot were used to identify exosomes; qPCR was used to detect the expression level of miR-200c in cellular exosomes and serum exosomes; The efficacy of individual and combined tests of each indicator to diagnose BC was evaluated using receiver operating characteristic (ROC) curves. Results We identified typical exosome features by TEM, NTA and Western blot, indicating successful exosome extraction. Then our miRNA sequencing results and qRT-PCR experiments showed that miR-200c was significantly down-regulated in BC cell exosomes. In addition, we divided the clinical serum samples into two cohorts according to region, and in independent cohort I, the serum exosomal miR-200c levels of BC patients were significantly lower than those of healthy controls. In cohort II, serum exosomal miR-200c expression was significantly lower in the BC group than in the control and benign breast disease (BBD) groups, whereas miR-200c expression in the BBD group was not statistically different from that in the control group. ROC analyses in both independent cohorts confirmed that serum exosomal miR-200c could differentiate between patients with and without BC disease and could be used as an early diagnostic marker for BC disease. Conclusion Serum exosome miR-200c can be used as a potential biomarker for the diagnosis of BC, and combined with conventional serum diagnostic markers AFP, CA125 and CA153 can help to improve diagnostic efficiency.

The role of HGH1 in breast cancer prognosis: a study on immune response and cell cycle

BackgroundBreast cancer (BRCA) remains to be among the main causes of cancer-associated mortality in women globally. HGH1 homolog (HGH1) has been reported to be associated with tumor immunity. However, the function of HGH1 in BRCA remains unclear. Therefore, the present study examined the potential role of HGH1 in BRCA.MethodsThe Cancer Genome Atlas (TCGA) databases and Gene Expression Omnibus (GEO) were used to obtain RNA-seq data for BRCA. A protein localization of HGH1 was determined by using the Human Protein Atlas (HPA), and immunohistochemistry (IHC) staining revealed an upregulation in the expression of HGH1 in clinical BRCA tissues. Xenograft mice were used to test tumor growth and HGH1 expression in breast cancer cells. The protein interaction information of HGH1 was analyzed using the GeneMANIA website. Based on univariate Cox regression and Kaplan-Meier methods, we evaluated the role of HGH1 in BRCA prognosis. HGH1-related differentially expressed genes were analyzed using GO, KEGG, and GSEA. We also examined the relationship between HGH1 expression, immune checkpoints, and immune infiltration. CCK-8, EdU, and colony formation assays were used to measure cell proliferation, and western blot analysis was used to evaluate HGH1’s role in BRCA.ResultsIHC results showed that the expression of HGH1 was significantly upregulated in BRCA tissues compared to normal tissues. High levels of HGH1 expression was associated with worse clinical features and a worse prognosis. HGH1 expression was an independent predictor of BRCA outcomes in both univariate and multivariate analyses. Functionally, western blot analysis showed that HGH1 is implicated in cell cycle. As well, knocking down HGH1 significantly reduced BRCA cells’ proliferative abilities. Crucially, HGH1 expression levels were positively correlated with Th2 cell infiltration and negatively correlated with Tcm cell infiltration.ConclusionBiomarkers such as HGH1 can reliably predict prognosis in BRCA patients.

RBM8A, a new target of TEAD4, promotes breast cancer progression by regulating IGF1R and IRS-2

BackgroundBreast cancer (BC) is the most common malignant tumor in women worldwide, and further elucidation of the molecular mechanisms involved in BC pathogenesis is essential to improve the prognosis of BC patients. RNA Binding Motif Protein 8 A (RBM8A), with high affinity to a myriad of RNA transcripts, has been shown to play a crucial role in genesis and progression of multiple cancers. We attempted to explore its functional significance and molecular mechanisms in BC.MethodsBioinformatics analysis was performed on publicly available BC datasets. qRT-PCR was used to determine the expression of RBM8A in BC tissues. MTT assay, clone formation assay and flow cytometry were employed to examine BC cell proliferation and apoptosis in vitro. RNA immunoprecipitation (RIP) and RIP-seq were used to investigate the binding of RBM8A/EIF4A3 to the mRNA of IGF1R/IRS-2. RBM8A and EIF4A3 interactions were determined by co-immunoprecipitation (Co-IP) and immunofluorescence. Chromatin immunoprecipitation (Ch-IP) and dual-luciferase reporter assay were carried out to investigate the transcriptional regulation of RBM8A by TEAD4. Xenograft model was used to explore the effects of RBM8A and TEAD4 on BC cell growth in vivo.ResultsIn this study, we showed that RBM8A is abnormally highly expressed in BC and knockdown of RBM8A inhibits BC cell proliferation and induces apoptosis in vitro. EIF4A3, which phenocopy RBM8A in BC, forms a complex with RBM8A in BC. Moreover, EIF4A3 and RBM8A complex regulate the expression of IGF1R and IRS-2 to activate the PI3K/AKT signaling pathway, thereby promoting BC progression. In addition, we identified TEAD4 as a transcriptional activator of RBM8A by Ch-IP, dual luciferase reporter gene and a series of functional rescue assays. Furthermore, we demonstrated the in vivo pro-carcinogenic effects of TEAD4 and RBM8A by xenograft tumor experiments in nude mice.ConclusionCollectively, these findings suggest that TEAD4 novel transcriptional target RBM8A interacts with EIF4A3 to increase IGF1R and IRS-2 expression and activate PI3K/AKT signaling pathway, thereby further promoting the malignant phenotype of BC cells.

Hub metastatic gene signature and risk score of breast cancer patients with small tumor sizes using WGCNA

BackgroundBreast cancer (BC) is the most common cancer in women and accounts for approximately 15% of all cancer deaths among women globally. The underlying mechanism of BC patients with small tumor size and developing distant metastasis (DM) remains elusive in clinical practices.MethodsWe integrated the gene expression of BCs from ten RNAseq datasets from Gene Expression Omnibus (GEO) database to create a genetic prediction model for distant metastasis-free survival (DMFS) in BC patients with small tumor sizes (≤ 2 cm) using weighted gene co-expression network (WGCNA) analysis and LASSO cox regression.ResultsABHD11, DDX39A, G3BP2, GOLM1, IL1R1, MMP11, PIK3R1, SNRPB2, and VAV3 were hub metastatic genes identified by WGCNA and used to create a risk score using multivariable Cox regression. At the cut-point value of the median risk score, the high-risk score (≥ median risk score) group had a higher risk of DM than the low-risk score group in the training cohort [hazard ratio (HR) 4.51, p < 0.0001] and in the validation cohort (HR 5.48, p = 0.003). The nomogram prediction model of 3-, 5-, and 7-year DMFS shows good prediction results with C-indices of 0.72–0.76. The enriched pathways were immune regulation and cell–cell signaling. EGFR serves as the hub gene for the protein–protein interaction network of PIK3R1, IL1R1, MMP11, GOLM1, and VAV3.ConclusionPrognostic gene signature was predictive of DMFS for BCs with small tumor sizes. The protein–protein interaction network of PIK3R1, IL1R1, MMP11, GOLM1, and VAV3 connected by EGFR merits further experiments for elucidating the underlying mechanisms.Graphical abstract

Unmutated RRAS2 emerges as a key oncogene in post-partum-associated triple negative breast cancer

BackgroundBreast cancer (BC) is the most common cancer in women, with triple negative BC (TNBC) accounting for 20% of cases. While early detection and targeted therapies have improved overall life expectancy, TNBC remains resistant to current treatments. Although parity reduces the lifetime risk of developing BC, pregnancy increases the risk of developing TNBC for years after childbirth. Although numerous gene mutations have been associated with BC, no single gene alteration has been identified as a universal driver. RRAS2 is a RAS-related GTPase rarely found mutated in cancer.MethodsConditional knock-in mice were generated to overexpress wild type human RRAS2 in mammary epithelial cells. A human sample cohort was analyzed by RT-qPCR to measure RRAS2 transcriptional expression and to determine the frequency of both a single-nucleotide polymorphism (SNP rs8570) in the 3’UTR region of RRAS2 and of genomic DNA amplification in tumoral and non-tumoral human BC samples.ResultsHere we show that overexpression of wild-type RRAS2 in mice is sufficient to develop TNBC in 100% of females in a pregnancy-dependent manner. In human BC, wild-type RRAS2 is overexpressed in 68% of tumors across grade, location, and molecular type, surpassing the prevalence of any previously implicated alteration. Still, RRAS2 overexpression is notably higher and more frequent in TNBC and young parous patients. The increased prevalence of the alternate C allele at the SNP position in tumor samples, along with frequent RRAS2 gene amplification in both tumors and blood of BC patients, suggests a cause-and-effect relationship between RRAS2 overexpression and breast cancer.ConclusionsHigher than normal expression of RRAS2 not bearing activating mutations is a key driver in the majority of breast cancers, especially those of the triple-negative type and those linked to pregnancy.

Radiomic analysis reveals diverse prognostic and molecular insights into the response of breast cancer to neoadjuvant chemotherapy: a multicohort study

BackgroundBreast cancer patients exhibit various response patterns to neoadjuvant chemotherapy (NAC). However, it is uncertain whether diverse tumor response patterns to NAC in breast cancer patients can predict survival outcomes. We aimed to develop and validate radiomic signatures indicative of tumor shrinkage and therapeutic response for improved survival analysis.MethodsThis retrospective, multicohort study included three datasets. The development dataset, consisting of preoperative and early NAC DCE-MRI data from 255 patients, was used to create an imaging signature-based multitask model for predicting tumor shrinkage patterns and pathological complete response (pCR). Patients were categorized as pCR, nonpCR with concentric shrinkage (CS), or nonpCR with non-CS, with prediction performance measured by the area under the curve (AUC). The prognostic validation dataset (n = 174) was used to assess the prognostic value of the imaging signatures for overall survival (OS) and recurrence-free survival (RFS) using a multivariate Cox model. The gene expression data (genomic validation dataset, n = 112) were analyzed to determine the biological basis of the response patterns.ResultsThe multitask learning model, utilizing 17 radiomic signatures, achieved AUCs of 0.886 for predicting tumor shrinkage and 0.760 for predicting pCR. Patients who achieved pCR had the best survival outcomes, while nonpCR patients with a CS pattern had better survival than non-CS patients did, with significant differences in OS and RFS (p = 0.00012 and p = 0.00063, respectively). Gene expression analysis highlighted the involvement of the IL-17 and estrogen signaling pathways in response variability.ConclusionsRadiomic signatures effectively predict NAC response patterns in breast cancer patients and are associated with specific survival outcomes. The CS pattern in nonpCR patients indicates better survival.

Uncertainty affects cancer-related fatigue among breast cancer women undergoing peripherally inserted central catheter chemotherapy: the chain mediating role of psychological resilience and self-care

BackgroundBreast cancer patients undergoing chemotherapy via peripherally inserted central catheter often experience serious behavioral and psychological challenges, with uncertainty and cancer-related fatigue being prevalent issues that profoundly impact prognosis. Therefore, this study aimed to investigate the relationship between uncertainty and cancer-related fatigue by employing a chain mediation model to examine the potential mediating roles of psychological resilience and self-care.MethodsA cross-sectional study was conducted with 223 breast cancer patients receiving peripherally inserted central catheter chemotherapy at two tertiary affiliated hospitals of China Medical University in Liaoning, China, from February 2021 to December 2022. Participants completed self-reported questionnaires to assess uncertainty, psychological resilience, self-care, and cancer-related fatigue. The collected data were subsequently analyzed using Pearson’s correlation analysis, hierarchical regression analysis, and mediation analysis.ResultsUncertainty exhibited a significant positive correlation with cancer-related fatigue (p < 0.01) and a negative correlation with psychological resilience (p < 0.01) and self-care (p < 0.01). Uncertainty was found to impact cancer-related fatigue through three pathways: psychological resilience mediated the relationship between uncertainty and cancer-related fatigue (mediating effect = 0.240, 95% confidence interval: 0.188 to 0.298, effect ratio = 53.22%); self-care also mediated this relationship (mediating effect = 0.080, 95% confidence interval: 0.044 to 0.121, effect ratio = 17.74%); furthermore, there was a significant joint mediating effect of psychological resilience and self-care on the association between uncertainty and cancer-related fatigue (mediating effect = 0.042, 95% confidence interval: 0.021 to 0.068, effect ratio o = 9.31%).ConclusionThe findings of this study revealed that uncertainty not only directly influenced cancer-related fatigue, but also operated through the mediating effect of psychological resilience, self-care, and sequential mediation of psychological resilience and self-care. Interventions tailored for breast cancer patients receiving peripherally inserted central catheter chemotherapy should target these factors to help alleviate uncertainty, enhance psychological resilience, and improve self-care practices, thereby ameliorating cancer-related fatigue.

Age-Related Variations in Breast Cancer Complications: Insights from Physical Medicine and Rehabilitation Clinical Practice.

Background Breast cancer patients often experience complications related to the disease or its treatment. With the rising average life expectancy, age is becoming less of a factor in treatment decisions for this condition. This study aims to evaluate differences in breast cancer complications among various age groups in patients referred to a hospital's physical medicine and rehabilitation (PMR) department. Methodology A retrospective study was conducted among all breast cancer patients evaluated in a PMR department between November 2019 and March 2021. Data were collected from patients' clinical files. SPSS® version 24 (IBM Corp., Armonk, NY, USA) was used for data analysis. Results We assessed 85 patients with a mean age of 56 years, finding that shoulder pain was the primary referral reason, reported by 85.9% of patients, followed by lymphedema in 32% of cases. Patients aged 56-65 years exhibited greater deficits in shoulder mobility compared to those between 66 and 75 years old, as well as greater functional limitations compared to other age groups. Most patients reported symptoms post-surgery, with an average delay of 24 months in PMR consultation. Despite this, nearly all patients (89.3%) reported clinical improvement following interventions. Conclusions We found that individuals in the 56-65-year age group were more prone to develop functional and shoulder mobility limitations. Despite delayed consultation, the majority of patients experienced clinical improvement, highlighting the intervention effectiveness of PMR interventions. These findings suggest that age alone may not be a determining factor in the reported breast cancer sequelae, implying the influence of other contributing factors in patient management. Further research is needed to elucidate the underlying mechanisms contributing to the diverse burden of disease sequelae observed across different age groups and to devise tailored interventions.

Non-apoptotic regulatory cell death scoring system to predict the clinical outcome and drug choices in breast cancer

BackgroundBreast cancer (BC), the most common cancer among women globally, has been shown by numerous studies to significantly involve non-apoptotic regulatory cell death (RCD) in its pathogenesis and progression. MethodsWe obtained the RNA sequences and clinical data of BC patients from The Cancer Genome Atlas (TCGA) database for the training set, while datasets GSE96058, GSE86166, and GSE20685 from The Gene Expression Omnibus (GEO) database were utilized as validation cohorts. Initially, we performed non-negative matrix factorization (NMF) clustering analysis on the BC samples from the TCGA database to discern non-apoptotic RCD-related molecular subtypes. To identify prognostically-relevant non-apoptotic RCD genes (NRGs) and construct a prognostic model, we implemented three machine learning algorithms: lasso regression, random forest, and XGBoost analysis. The expression of selected genes was verified using real-time quantitative polymerase chain reaction (RT-qPCR), single-cell RNA-sequencing (scRNA-seq) analysis, and The Human Protein Atlas (HPA) database. The risk signature was evaluated concerning clinical characteristics and drug sensitivity. Furthermore, we developed a nomogram to predict BC patient survival. ResultsThe NMF method successfully compartmentalized patients from the TCGA database into three distinct non-apoptotic RCD-related subtypes, with significant variations observed in immune characteristics and prognostic stratification across these subtypes. We identified 5 differentially expressed NRGs used in establishing the risk signature. Patients with different risk groups exhibited distinct clinicopathological features, drug sensitivity, and prognostic outcomes. A nomogram was subsequently developed, incorporating the NRGs-related risk signature, age, T stage, and N stage, to aid clinical decision-making. ConclusionWe identified a novel NRGs-related risk signature, which was expected to become a potential prognostic marker in BC.

The association of FSCN1 (rs852479, rs1640233) and HOTAIR (rs920778) polymorphisms with the risk of breast cancer in Egyptian women

BackgroundBreast cancer (BC) is one of the most prevalent cancers that contribute to mortality among women worldwide. Despite contradictory findings, considerable evidence suggests that single nucleotide polymorphisms (SNPs) in the FSCN1 and HOTAIR genes may have a causative impact on the development of BC. This case–control study was conducted to evaluate the association of genotype frequency in FSCN1 rs852479, rs1640233, and HOTAIR rs920778 with susceptibility and prognosis of BC, as well as the impact of clinical stages and hormonal features.Methods and resultsFSCN1 (rs852479, rs1640233) and HOTAIR (rs920778) were genotyped using TaqMan real-time PCR assay in 200 BC patients and 200 cancer-free controls, all representing Egyptian women. Genotypic analyses in association with clinicopathological factors and disease risk were assessed. As a result, a significant association with BC risk was observed for CC genotype frequency of FSCN1 rs852479 A > C (OR = 0.395, 95% CI 0.204–0.76, p-value = 0.005). However, no significant correlation was detected between the FSCN1 rs1640233 C > T and HOTAIR rs920778 C > T polymorphic variants and susceptibility to BC. Interestingly, CC genotype of FSCN1 rs1640233 was more likely to progress tumor size and lymph node invasion in BC cases (p-value = 0.04 and 0.02, respectively). Moreover, it was revealed that there was a non-significant correlation between the haplotype distributions of FSCN1 rs852479 and rs1640233 and the probability of BC.ConclusionsBased on the sample size and genetic characteristics of the subjects involved in the present study, our findings indicated that FSCN1 rs852479 may contribute to BC susceptibility in a sample of the Egyptian population.

Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer

BackgroundBreast Cancer (BC) is a highly heterogeneous and complex disease. Personalized treatment options require the integration of multi-omic data and consideration of phenotypic variability. Radiogenomics aims to merge medical images with genomic measurements but encounter challenges due to unpaired data consisting of imaging, genomic, or clinical outcome data. In this study, we propose the utilization of a well-trained conditional generative adversarial network (cGAN) to address the unpaired data issue in radiogenomic analysis of BC. The generated images will then be used to predict the mutations status of key driver genes and BC subtypes.MethodsWe integrated the paired MRI and multi-omic (mRNA gene expression, DNA methylation, and copy number variation) profiles of 61 BC patients from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). To facilitate this integration, we employed a Bayesian Tensor Factorization approach to factorize the multi-omic data into 17 latent features. Subsequently, a cGAN model was trained based on the matched side-view patient MRIs and their corresponding latent features to predict MRIs for BC patients who lack MRIs. Model performance was evaluated by calculating the distance between real and generated images using the Fréchet Inception Distance (FID) metric. BC subtype and mutation status of driver genes were obtained from the cBioPortal platform, where 3 genes were selected based on the number of mutated patients. A convolutional neural network (CNN) was constructed and trained using the generated MRIs for mutation status prediction. Receiver operating characteristic area under curve (ROC-AUC) and precision-recall area under curve (PR-AUC) were used to evaluate the performance of the CNN models for mutation status prediction. Precision, recall and F1 score were used to evaluate the performance of the CNN model in subtype classification.ResultsThe FID of the images from the well-trained cGAN model based on the test set is 1.31. The CNN for TP53, PIK3CA, and CDH1 mutation prediction yielded ROC-AUC values 0.9508, 0.7515, and 0.8136 and PR-AUC are 0.9009, 0.7184, and 0.5007, respectively for the three genes. Multi-class subtype prediction achieved precision, recall and F1 scores of 0.8444, 0.8435 and 0.8336 respectively. The source code and related data implemented the algorithms can be found in the project GitHub at https://github.com/mattthuang/BC_RadiogenomicGAN.ConclusionOur study establishes cGAN as a viable tool for generating synthetic BC MRIs for mutation status prediction and subtype classification to better characterize the heterogeneity of BC in patients. The synthetic images also have the potential to significantly augment existing MRI data and circumvent issues surrounding data sharing and patient privacy for future BC machine learning studies.

A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis.

Breast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients. BRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the 'limma' package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function. Our study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells. Our 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics.

CYP4B1 polymorphisms and the risk of breast cancer in Chinese women: a case-control study

BackgroundBreast cancer (BC) is one of the malignant diseases threatening the life and health of women worldwide. The CYP4B1 gene was abnormally expressed in BC and was associated with the prognosis of BC patients. This study aimed to explore the relationship between CYP4B1 single nucleotide polymorphisms (SNPs) and BC risk in Chinese women.MethodsA case-control study of 1,143 women (571 patients and 572 healthy individuals) was conducted. Rs2297813 G/T, rs12142787 G/A, and rs3766197 C/T in CYP4B1 were selected and genotyped by MassARRAY system. The relationships between these SNPs and the risk of BC were assessed by logistic regression analysis. In addition, multi-factor dimensionality reduction (MDR) was used to analyze SNP-SNP interactions.ResultsCYP4B1 rs2297813 had a risk-increasing effect on BC in women with body mass index (BMI) ≤ 24 kg/m2 (OR = 1.72, p = 0.026). CYP4B1 rs12142787 was associated with an increased BC risk in smokers (AA: OR = 1.32, p = 0.045). Among non-drinkers, rs2297813 (OR = 1.69, p = 0.009) and rs12142787 (OR = 1.51, p = 0.020) were related to an increased incidence of BC. CYP4B1 rs3766197 (OR = 1.61p = 0.031) was associated with a higher risk of advanced stages (III/IV stage) of BC. Besides, the contributions of CYP4B1 rs2297813 (OR = 1.55, p = 0.021) and rs12142787 (OR = 1.53, p = 0.033) to BC risk might be associated with more than one birth in patients with BC. The three-locus model consisting of rs2297813, rs12142787, and rs3766197 was regarded as the best predictive model for BC risk.ConclusionCYP4B1 SNPs were associated with BC risk in Chinese women, especially in patients with BMI ≤ 24 kg/m2, smokers, non-drinkers, patients in advanced stages (III/IV stage), and patients who reproduced once. These findings shed light on the relationship between CYP4B1 SNPs and BC risk in Chinese women.

LncRNA TDRKH-AS1 promotes breast cancer progression via the miR-134-5p/CREB1 axis

BackgroundBreast cancer (BC) is a prevalent malignancy with complex etiology and varied clinical behavior. Long non-coding RNAs (lncRNAs) have emerged as key regulators in cancer progression, including BC. Among these, lncRNA TDRKH-AS1 has been implicated in several cancers, but its role in BC remains unclear.MethodsWe conducted a comprehensive investigation to elucidate the role of TDRKH-AS1 in BC. Clinical samples were collected from BC patients, and BC cell lines were cultured. Bioinformatics analysis using the starBase database was carried out to assess TDRKH-AS1 expression levels in BC tissue samples. Functional experiments, including knockdown, colony formation, CCK-8, Transwell, and wound-healing assays, were conducted to determine the role of TDRKH-AS1 in BC cell proliferation and invasion. Luciferase reporter and RIP assays were used to examine the interactions between TDRKH-AS1 and miR-134-5p. In addition, the downstream target gene of miR-134-5p, cAMP response element-binding protein 1 (CREB1), was identified and studied using various methods, including RT-qPCR, immunoprecipitation, and rescue experiments. In vivo experiments using mouse tumor xenograft models were conducted to examine the role of TDRKH-AS1 in BC tumorigenesis.ResultsTDRKH-AS1 was found to be significantly upregulated in BC tissues and cell lines. High TDRKH-AS1 expression correlated with advanced BC stages and worse patient outcomes. Knockdown of TDRKH-AS1 led to decreased BC cell proliferation and invasion. Mechanistically, TDRKH-AS1 acted as a sponge for miR-134-5p, thereby reducing the inhibitory effects of miR-134-5p on CREB1 expression. Overexpression of CREB1 partially rescued the effects of TDRKH-AS1 knockdown in BC cells. In vivo studies further confirmed the tumor-promoting role of TDRKH-AS1 in BC.ConclusionsOur study unveiled a novel regulatory axis involving TDRKH-AS1, miR-134-5p, and CREB1 in BC progression. TDRKH-AS1 functioned as an oncogenic lncRNA by promoting BC cell proliferation and invasion through modulation of the miR-134-5p/CREB1 axis. These findings highlighted TDRKH-AS1 as a potential diagnostic biomarker and therapeutic target for BC treatment.

The Impact of Medicaid Expansion Under the Affordable Care Act on the Gap Between American Indians and Whites in Breast Cancer Management and Prognosis

BackgroundBreast cancer (BC) death rates in the USA have not significantly declined for American Indians (AIs) in comparison to Whites. Our objective was to determine whether Medicaid Expansion as part of the Affordable Care Act led to improved BC outcomes for AIs relative to Whites. Patients and MethodsUsing the National Cancer Database, we conducted a retrospective cohort study. Included were BC patients who were AI and White; 40 to 64 years of age; diagnosed in 2009 to 2016; lived in states that expanded Medicaid in January 2014, and states that did not expand Medicaid. Our outcomes were stage at diagnosis, insurance status, timely treatment, and 3-year mortality. ResultsThere were 359,484 newly diagnosed BC patients, 99.49% White, 0.51% AI. Uninsured rates declined more in the expansion states than in the nonexpansion states (OR = 0.44, 95% CI: 0.15-0.97, P < 0.001). Lower rates of Stage I BC diagnosis was found in AIs compared to Whites (46.58% vs. 55.33%, P < .001); these differential rates did not change after Medicaid expansion. Rates of definitive treatment initiation within 30 days of diagnosis declined after Medicaid expansion (P < .001); there was a smaller decline in the expansion states (OR 1.118, 95% CI: 1.09, 1.15, P < .001). Three year mortality was not different between expansion and nonexpansion states post Medicaid expansion. ConclusionsIn newly diagnosed BCs, uninsured rates declined more in the states that expanded Medicaid in January 2014. Timely treatment post Medicaid expansion declined less in states that expanded Medicaid. There was no differential benefit of Medicaid expansion in the 2 races.

Construction of a prognostic 6-gene signature for breast cancer based on multi-omics and single-cell data

BackgroundBreast cancer (BC) is one of the females’ most common malignant tumors there are large individual differences in its prognosis. We intended to uncover novel useful genetic biomarkers and a risk signature for BC to aid determining clinical strategies.MethodsA combined significance (pcombined) was calculated for each gene by Fisher’s method based on the RNA-seq, CNV, and DNA methylation data from TCGA-BRCA. Genes with a pcombined&amp;lt; 0.01 were subjected to univariate cox and Lasso regression, whereby an RS signature was established. The predicted performance of the RS signature would be assessed in GSE7390 and GSE20685, and emphatically analyzed in triple-negative breast cancer (TNBC) patients, while the expression of immune checkpoints and drug sensitivity were also examined. GSE176078, a single-cell dataset, was used to validate the differences in cellular composition in tumors between TNBC patients with different RS.ResultsThe RS signature consisted of C15orf52, C1orf228, CEL, FUZ, PAK6, and SIRPG showed good performance. It could distinguish the prognosis of patients well, even stratified by disease stages or subtypes and also showed a stronger predictive ability than traditional clinical indicators. The down-regulated expressions of many immune checkpoints, while the decreased sensitivity of many antitumor drugs was observed in TNBC patients with higher RS. The overall cells and lymphocytes composition differed between patients with different RS, which could facilitate a more personalized treatment.ConclusionThe six genes RS signature established based on multi-omics data exhibited well performance in predicting the prognosis of BC patients, regardless of disease stages or subtypes. Contributing to a more personalized treatment, our signature might benefit the outcome of BC patients.