Background Breast Cancer Patients Research Articles

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.

Minimally Invasive Breast Biopsy After Neoadjuvant Systemic Treatment to Identify Breast Cancer Patients with Residual Disease for Extended Neoadjuvant Treatment: A New Concept

BackgroundBreast cancer patients with residual disease after neoadjuvant systemic treatment (NAST) have a worse prognosis compared with those achieving a pathologic complete response (pCR). Earlier identification of these patients might allow timely, extended neoadjuvant treatment strategies. We explored the feasibility of a vacuum-assisted biopsy (VAB) after NAST to identify patients with residual disease (ypT+ or ypN+) prior to surgery.MethodsWe used data from a multicenter trial, collected at 21 study sites (NCT02948764). The trial included women with cT1-3, cN0/+ breast cancer undergoing routine post-neoadjuvant imaging (ultrasound, MRI, mammography) and VAB prior to surgery. We compared the findings of VAB and routine imaging with the histopathologic evaluation of the surgical specimen.ResultsOf 398 patients, 34 patients with missing ypN status and 127 patients with luminal tumors were excluded. Among the remaining 237 patients, tumor cells in the VAB indicated a surgical non-pCR in all patients (73/73, positive predictive value [PPV] 100%), whereas PPV of routine imaging after NAST was 56.0% (75/134). Sensitivity of the VAB was 72.3% (73/101), and 74.3% for sensitivity of imaging (75/101).ConclusionResidual cancer found in a VAB specimen after NAST always corresponds to non-pCR. Residual cancer assumed on routine imaging after NAST corresponds to actual residual cancer in about half of patients. Response assessment by VAB is not safe for the exclusion of residual cancer. Response assessment by biopsies after NAST may allow studying the new concept of extended neoadjuvant treatment for patients with residual disease in future trials.

Comprehensive evaluation of cell death-related genes as novel diagnostic biomarkers for breast cancer

BackgroundBreast cancer (BRCA) ranks first among cancers in terms of incidence and mortality rates in women, primarily owing to metastasis, chemo-resistance, and heterogeneity. To predict long-term prognosis and design novel therapies for BRCA, more sensitive markers need to be explored. MethodsData from 1089 BRCA patients were downloaded from TCGA database. Pearson's correlation analysis and univariate and multivariate Cox regression analyses were performed to assess the role of cell death-related genes (CDGs) in predicting BRCA prognosis. Kaplan–Meier survival curves were generated to compare the overall survival in the two subgroups. A nomogram was constructed using risk scores based on the five CDGs and other clinicopathological features. CCK-8, EdU incorporation, and colony formation assays were performed to verify the inhibitory effect of NFKBIA on BRCA cell proliferation. Transwell assay, flow cytometry, and immunohistochemistry analyses were performed to ascertain the biological function of NFKBIA. ResultsFive differentially expressed CDGs were detected among 156 CDGs. The risk score for each patient was then calculated based on the expression levels of the five CDGs. Distinct differences in immune infiltration, expression of immune-oncological targets, mutation status, and half-maximal inhibitory concentration values of some targeted drugs were observed between the high- and low-risk groups. Finally, in vitro cell experiments verified that NFKBIA overexpression suppresses the proliferation and migration of BRCA cells. ConclusionsOur study revealed that some CDGs, especially NFKBIA, could serve as sensitive markers for predicting the prognosis of patients with BRCA and designing more personalized clinical therapies.

Exosomal Linc00969 induces trastuzumab resistance in breast cancer by increasing HER-2 protein expression and mRNA stability by binding to HUR

BackgroundBreast cancer (BC) is the most common malignant disease in female patients worldwide. In HER-2+ BC patients, trastuzumab therapy is associated with a better prognosis. However, many HER-2+ BC patients experience recurrence or metastasis because of trastuzumab resistance. The mechanisms underlying trastuzumab resistance remain unclear. Recently, substantial evidence has suggested that exosomes are associated with drug resistance, and lncRNAs have attracted increasing attention due to their potential role in the regulation of trastuzumab resistance.MethodsWe collected the exosomes from the plasma of BC patients with and without trastuzumab resistance, sequenced the whole transcriptomes, identified differentially expressed lncRNAs, and identified lncRNA Linc00969, which was overexpressed in trastuzumab-resistant patients. Then, we established trastuzumab-resistant BC cell lines and explored the role of exosomal Linc00969 in trastuzumab resistance in vitro and in vivo by silencing or overexpressing Linc00969 and performing a series of functional analyses. Furthermore, to explore the mechanism by which exosomal Linc00969 contributes to trastuzumab resistance, we measured changes in HER-2, HUR and autophagy-related protein expression levels after regulating Linc00969 expression. In addition, we investigated the interaction between Linc00969 and HUR via pull-down and RIP assays and the effect of HUR on HER-2 expression and trastuzumab resistance after blocking HUR.ResultsWe first found that exosomal lncRNA Linc00969 was overexpressed in trastuzumab-resistant BC patients and that exosome-mediated Linc00969 transfer could disseminate trastuzumab resistance in BC. Then, we found that silencing Linc00969 could reduce trastuzumab resistance and that overexpressing Linc00969 could enhance trastuzumab resistance. Furthermore, our results showed that Linc00969 could upregulate HER-2 expression at the protein level and maintain the stability of HER-2 mRNA by binding to HUR. Additionally, we found that exosomal Linc00969 could regulate trastuzumab resistance by inducing autophagy.ConclusionsIn this study, we first identified that exosomal lncRNA Linc00969 could induce trastuzumab resistance by increasing HER-2 protein expression and mRNA stability by binding to HUR, and Linc00969 might also be involved in trastuzumab resistance by inducing autophagy. Our results elucidate a novel mechanism underlying trastuzumab resistance, and Linc00969 might be a new target for improving the treatment of HER-2+ BC patients.

Development and testing of a random forest-based machine learning model for predicting events among breast cancer patients with a poor response to neoadjuvant chemotherapy

BackgroundBreast cancer (BC) is the most common malignant tumor around the world. Timely detection of the tumor progression after treatment could improve the survival outcome of patients. This study aimed to develop machine learning models to predict events (defined as either (1) the first tumor relapse locally, regionally, or distantly; (2) a diagnosis of secondary malignant tumor; or (3) death because of any reason.) in BC patients post-treatment.MethodsThe patients with the response of stable disease (SD) and progressive disease (PD) after neoadjuvant chemotherapy (NAC) were selected. The clinicopathological features and the survival data were recorded in 1 year and 5 years, respectively. Patients were randomly divided into the training set and test set in the ratio of 8:2. A random forest (RF) and a logistic regression were established in both of 1-year cohort and the 5-year cohort. The performance was compared between the two models. The models were validated using data from the Surveillance, Epidemiology, and End Results (SEER) database.ResultsA total of 315 patients were included. In the 1-year cohort, 197 patients were divided into a training set while 87 were into a test set. The specificity, sensitivity, and AUC were 0.800, 0.833, and 0.810 in the RF model. And 0.520, 0.833, and 0.653 of the logistic regression. In the 5-year cohort, 132 patients were divided into the training set while 33 were into the test set. The specificity, sensitivity, and AUC were 0.882, 0.750, and 0.829 in the RF model. And 0.882, 0.688, and 0.752 of the logistic regression. In the external validation set, of the RF model, the specificity, sensitivity, and AUC were 0.765, 0.812, and 0.779. Of the logistics regression model, the specificity, sensitivity, and AUC were 0.833, 0.376, and 0.619.ConclusionThe RF model has a good performance in predicting events among BC patients with SD and PD post-NAC. It may be beneficial to BC patients, assisting in detecting tumor recurrence.

Identification of a novel five ferroptosis-related gene signature as a promising prognostic model for breast cancer

BackgroundBreast cancer (BCa) is a major challenge for women’s health worldwide. Ferroptosis is closely related to tumorigenesis and cancer progression. However, the prognostic value of ferroptosis-related genes in BCa remains unclear, and more accurate prognostic models are urgently needed.MethodsGene expression profiles and clinical information of BCa patients were collected from public databases. LASSO and multivariate Cox regression analysis were utilized to construct the prognostic gene signature. Kaplan–Meier plotter, receiver operating characteristic (ROC) curves, and nomogram were used to validate the prognostic value of the gene signature. Gene set enrichment analysis was performed to explore the molecular functions and signaling pathways.ResultsDifferentially expressed ferroptosis-related genes between BCa samples and normal tissues were obtained. A novel five-gene signature including BCL2, SLC40A1, TFF1, APOOL, and PRAME was established for prognosis prediction. Patients stratified into high-risk or low-risk group displayed significantly different survival. Kaplan–Meier and ROC curves showed a good performance for survival prediction in different cohorts. Biological function analysis revealed that the five-gene signature was associated with cancer progression, immune infiltration, immune response, and drug resistance. Nomogram including the five-gene signature was established.ConclusionA novel five ferroptosis-related gene signature and nomogram could be used for prognostic prediction in BCa.

Interplay of Mendelian and polygenic risk factors in Arab breast cancer patients

BackgroundBreast cancer patients from the indigenous Arab population present much earlier than patients from Western countries and have traditionally been underrepresented in cancer genomics studies. The contribution of polygenic and Mendelian risk toward the earlier onset of breast cancer in the population remains elusive.MethodsWe performed low-pass whole genome sequencing (lpWGS) and whole-exome sequencing (WES) from 220 female breast cancer patients unselected for positive family history from the indigenous Arab population. Using publicly available resources, we imputed population-specific variants and calculated breast cancer burden-sensitive polygenic risk scores (PRS). Variant pathogenicity was also evaluated on exome variants with high coverage.ResultsVariants imputed from lpWGS showed high concordance with paired exome (median dosage correlation: 0.9459, Interquartile range: 0.9410–0.9490). After adjusting the PRS to the Arab population, we found significant associations between PRS performance in risk prediction and first-degree relative breast cancer history prediction (Spearman rho=0.43, p = 0.03), where breast cancer patients in the top PRS decile are 5.53 (95% CI 1.76–17.97, p = 0.003) times more likely also to have a first-degree relative diagnosed with breast cancer compared to those in the middle deciles. In addition, we found evidence for the genetic liability threshold model of breast cancer where among patients with a family history of breast cancer, pathogenic rare variant carriers had significantly lower PRS than non-carriers (p = 0.0205, Mann-Whitney U test) while for non-carriers every standard deviation increase in PRS corresponded to 4.52 years (95% CI 8.88–0.17, p = 0.042) earlier age of presentation.ConclusionsOverall, our study provides a framework to assess polygenic risk in an understudied population using lpWGS and identifies common variant risk as a factor independent of pathogenic variant carrier status for earlier age of onset of breast cancer among indigenous Arab breast cancer patients.

Effectiveness of home-based exercise in breast cancer survivors: a randomized clinical trial

BackgroundBreast cancer patients are recommended to engage in regular exercise. In developing countries, where there is a lack of facilities to offer specialized, supervised exercise for this population, regularly exercising might be a challenge. We aimed to evaluate the effectiveness of a home-based intervention in this population.MethodsBreast cancer survivors were randomly assigned to either the home-based exercise program or the usual care group. Exercise intervention included walking, balance, and stretch exercises, along with weekly follow-up telephone calls. Quality of life (QOL) was evaluated using EORTC QLQ-C30 and EORTC QLQ-BR23 questionnaires and the predicted VO2 peak was measured using the Ebbeling submaximal treadmill test.ResultsEighty-nine patients were enrolled in the study. Reported minutes of exercise gradually increased from 40.7 min per week in week 1 to 116.9 min per week in week 12. This intervention improved global QOL (P = 0.001), social functioning (P = 0.04), and the predicted VO2 peak (P = 0.01).ConclusionThis home-based exercise regime effectively increased quality of life and physical activity levels.Trial registryIranian Registry of Clinical Trials identifier: IRCT20140810018746N1, prospectively registered 08/01/2018, https://en.irct.ir/trial/27959.

GSG2 facilitates the progression of human breast cancer through MDM2-mediated ubiquitination of E2F1

BackgroundBreast cancer (BC) has posed a great threat to world health as the leading cause of cancer death among women. Previous evidence demonstrated that germ cell-specific gene 2 (GSG2) was involved in the regulation of multiple cancers. Thus, the clinical value, biological function and underlying mechanism of GSG2 in BC were investigated in this study.MethodsThe expression of GSG2 in BC was revealed by immunohistochemistry (IHC), qPCR and western blotting. Secondly, the biological function of GSG2 in BC was evaluated by MTT assay, flow cytometry, Transwell assay and wound healing assay. Furthermore, the potential molecular mechanism of GSG2 regulating the progression of BC by co-immunoprecipitation (Co-IP) and protein stability detection.ResultsOur data indicated that GSG2 was frequently overexpressed in BC. Moreover, there was a significant correlation between the GSG2 expression and the poor prognosis of BC patients. Functionally, GSG2 knockdown inhibited the malignant progression of BC characterized by reduced proliferation, enhanced apoptosis and attenuated tumor growth. Migration inhibition of GSG2 knockdown BC cells via epithelial-mesenchymal transition (EMT), such as downregulation of Vimentin and Snail. In addition, E2F transcription factor 1 (E2F1) was regarded as a target protein of GSG2. Downregulation of E2F1 attenuated the promoting role of GSG2 on BC cells. Mechanistically, knockdown of GSG2 accelerated the ubiquitination of E2F1 protein, which was mediated by E3 ubiquitin ligase MDM2.ConclusionsGSG2 facilitated the development and progression of BC through MDM2-mediated ubiquitination of E2F1, which may be a promising candidate target with potential therapeutic value.

Comprehensive research into prognostic and immune signatures of transcription factor family in breast cancer

BackgroundBreast cancer (BRCA) is the most common malignancy with high morbidity and mortality in women, and transcription factor (TF) is closely related to the occurrence and development of BRCA. This study was designed to identify a prognostic gene signature based on TF family to reveal immune characteristics and prognostic survival of BRCA.MethodsIn this study, RNA-sequence with corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and GSE42568. Prognostic differentially expressed transcription factor family genes (TFDEGs) were screened to construct a risk score model, after which BRCA patients were stratified into low-risk and high-risk groups based on their corresponding risk scores. Kaplan–Meier (KM) analysis was applied to evaluate the prognostic implication of risk score model, and a nomogram model was developed and validated with the TCGA and GSE20685. Furthermore, the GSEA revealed pathological processes and signaling pathways enriched in the low-risk and high-risk groups. Finally, analyses regarding levels of immune infiltration, immune checkpoints and chemotactic factors were all completed to investigate the correlation between the risk score and tumor immune microenvironment (TIME).ResultsA prognostic 9-gene signature based on TFDEGs was selected to establish a risk score model. According to KM analyses, high-risk group witnessed a significantly worse overall survival (OS) than low-risk group in both TCGA-BRCA and GSE20685. Furthermore, the nomogram model proved great possibility in predicting the OS of BRCA patients. As indicted in GSEA analysis, tumor-associated pathological processes and pathways were relatively enriched in high-risk group, and the risk score was negatively correlated with ESTIMATE score, infiltration levels of CD4+ and CD8+T cells, as well as expression levels of immune checkpoints and chemotactic factors.ConclusionsThe prognostic model based on TFDEGs could distinguish as a novel biomarker for predicting prognosis of BRCA patients; in addition, it may also be utilized to identify potential benefit population from immunotherapy in different TIME and predict potential drug targets.

Loss of TTC17 promotes breast cancer metastasis through RAP1/CDC42 signaling and sensitizes it to rapamycin and paclitaxel

BackgroundBreast cancer (BC) metastasis is the leading cause of poor prognosis and therapeutic failure. However, the mechanisms underlying cancer metastasis are far from clear.MethodsWe screened candidate genes related to metastasis through genome-wide CRISPR screening and high-throughput sequencing of patients with metastatic BC, followed by a panel of metastatic model assays. The effects of tetratricopeptide repeat domain 17 (TTC17) on migration, invasion, and colony formation ability together with the responses to anticancer drugs were investigated in vitro and in vivo. The mechanism mediated by TTC17 was determined by RNA sequencing, Western blotting, immunohistochemistry, and immunofluorescence. The clinical significance of TTC17 was evaluated using BC tissue samples combined with clinicopathological data.ResultsWe identified the loss of TTC17 as a metastasis driver in BC, and its expression was negatively correlated with malignancy and positively correlated with patient prognosis. TTC17 loss in BC cells promoted their migration, invasion, and colony formation capacity in vitro and lung metastasis in vivo. Conversely, overexpressing TTC17 suppressed these aggressive phenotypes. Mechanistically, TTC17 knockdown in BC cells resulted in the activation of the RAP1/CDC42 pathway along with a disordered cytoskeleton in BC cells, and pharmacological blockade of CDC42 abolished the potentiation of motility and invasiveness caused by TTC17 silencing. Research on BC specimens demonstrated reduced TTC17 and increased CDC42 in metastatic tumors and lymph nodes, and low TTC17 expression was linked to more aggressive clinicopathologic characteristics. Through screening the anticancer drug library, the CDC42 inhibitor rapamycin and the microtubule-stabilizing drug paclitaxel showed stronger inhibition of TTC17-silenced BC cells, which was confirmed by more favorable efficacy in BC patients and tumor-bearing mice receiving rapamycin or paclitaxel in the TTC17Low arm.ConclusionsTTC17 loss is a novel factor promoting BC metastasis, that enhances migration and invasion by activating RAP1/CDC42 signaling and sensitizes BC to rapamycin and paclitaxel, which may improve stratified treatment strategies under the concept of molecular phenotyping-based precision therapy of BC.

CircDNAJC11 interacts with TAF15 to promote breast cancer progression via enhancing MAPK6 expression and activating the MAPK signaling pathway

BackgroundBreast cancer (BC) is a common malignant tumor in women worldwide. Circular RNA (circRNA) has been proven to play a critical role in BC progression. However, the exact biological functions and underlying mechanisms of circRNAs in BC remain largely unknown.MethodsHere, we first screened for differentially expressed circRNAs in 4 pairs of BC tissues and adjacent non-tumor tissues using a circRNA microarray. Functionally, gain- and loss-of-function experiments in vitro and in vivo showed that circDNAJC11 promoted BC cell proliferation, migration, invasion, and tumor growth. Mechanistically, RNA pull-down, mass spectrum, RNA immunoprecipitation, fluorescence in situ hybridization assays, and rescue experiments were executed.ResultsWe found that circDNAJC11 was significantly upregulated in triple-negative breast cancer tissues and cells. Clinical data revealed that the high expression of circDNAJC11 was closely correlated with a poor prognosis of BC patients and could be an independent risk factor for BC prognosis. Functionally, gain- and loss-of-function experiments in vitro and in vivo showed that circDNAJC11 promoted BC cell proliferation, migration, invasion, and tumor growth. Mechanistically, RNA pull-down, mass spectrum, RNA immunoprecipitation, fluorescence in situ hybridization assays, and rescue experiments were executed. We demonstrated that circDNAJC11 combined with TAF15 to promote BC progression via stabilizing MAPK6 mRNA and activating the MAPK signaling pathway.ConclusionsThe circDNAJC11/TAF15/MAPK6 axis played a crucial role in the progression and development of BC, suggesting that circDNAJC11 might be a novel biomarker and therapeutical target for BC.

Comprehensive analysis of nicotinamide metabolism-related signature for predicting prognosis and immunotherapy response in breast cancer.

Breast cancer (BC) is the most common malignancy among women. Nicotinamide (NAM) metabolism regulates the development of multiple tumors. Herein, we sought to develop a NAM metabolism-related signature (NMRS) to make predictions of survival, tumor microenvironment (TME) and treatment efficacy in BC patients. Transcriptional profiles and clinical data from The Cancer Genome Atlas (TCGA) were analyzed. NAM metabolism-related genes (NMRGs) were retrieved from the Molecular Signatures Database. Consensus clustering was performed on the NMRGs and the differentially expressed genes between different clusters were identified. Univariate Cox, Lasso, and multivariate Cox regression analyses were sequentially conducted to develop the NAM metabolism-related signature (NMRS), which was then validated in the International Cancer Genome Consortium (ICGC) database and Gene Expression Omnibus (GEO) single-cell RNA-seq data. Further studies, such as gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, SubMap, and Immunophenoscore (IPS) algorithm, cancer-immunity cycle (CIC), tumor mutation burden (TMB), and drug sensitivity were performed to assess the TME and treatment response. We identified a 6-gene NMRS that was significantly associated with BC prognosis as an independent indicator. We performed risk stratification according to the NMRS and the low-risk group showed preferable clinical outcomes (P < 0.001). A comprehensive nomogram was developed and showed excellent predictive value for prognosis. GSEA demonstrated that the low-risk group was predominantly enriched in immune-associated pathways, whereas the high-risk group was enriched in cancer-related pathways. The ESTIMATE and CIBERSORT algorithms revealed that the low-risk group had a higher abundance of anti-tumor immunocyte infiltration (P < 0.05). Results of Submap, IPS, CIC, TMB, and external immunotherapy cohort (iMvigor210) analyses showed that the low-risk group were indicative of better immunotherapy response (P < 0.05). The novel signature offers a promising way to evaluate the prognosis and treatment efficacy in BC patients, which may facilitate clinical practice and management.