Analysis of the function, mechanism and clinical application prospect of TRPS1, a new marker for breast cancer.

It has been discovered that Trichorhinophalangeal Syndrome-1 (TRPS1), a novel member of the GATA transcription factor family, participates in both normal physiological processes and the development of numerous diseases. Recently, TRPS1 has been identified as a new biomarker to aid in cancer diagnosis and is very common in breast cancer (BC), especially in triple-negative breast cancer (TNBC). In this review, we discussed the structure and function of TRPS1 in various normal cells, focused on its role in tumorigenesis and tumor development, and summarize the research status of TRPS1 in the occurrence and development of BC. We also analyzed the potential use of TRPS1 in guiding clinically personalized precision treatment and the development of targeted drugs.

MRPL13 is a metastatic and prognostic marker of breast cancer: A silico analysis accompanied with experimental validation.

BACKGROUND: Although progress has been made in accurate diagnosis and targeted treatments, breast cancer (BC) patients with metastasis still present a grim prognosis. With the continuous emergence and development of new personalized and precision medicine targeting specific tumor biomarkers, there is an urgent need to find new metastatic and prognostic biomarkers for BC patients. METHODS: We were dedicated to identifying genes linked to metastasis and prognosis in breast cancer through a combination of in silico analysis and experimental validation. RESULTS: A total of 25 overlap differentially expressed genes were identified. Ten hub genes (namely MRPL13, CTR9, TCEB1, RPLP0, TIMM8B, METTL1, GOLT1B, PLK2, PARL and MANBA) were identified and confirmed. MRPL13, TCEB1 and GOLT1B were shown to be associated with the worse overall survival (OS) and were optionally chosen for further verification by western blot. Only MRPL13 was found associated with cell invasion, and the expression of MRPL13 in metastatic BC was significantly higher than in primary BC. CONCLUSION: We proposed MRPL13 could be a potential novel biomarker for the metastasis and prognosis of breast cancer.

Remembering Hypoxia: Uncovering the Long-Term Effects of Transient Oxygen Deprivation on IFN Signaling and Progression of Breast Cancer.

Hypoxia occurs in 90% of solid tumors and is strongly associated with an increased propensity for metastasis. Hypoxia induces tumor progression largely through inducing HIF-mediated transcription, resulting in alterations to tumor cell metabolism, as well as increases in migration and invasion. Hypoxia also results in a myriad of changes to the tumor microenvironment (TME). While many studies have examined the immediate effects of hypoxia on tumor cells and the associated TME, far fewer have focused on the long-term consequences of transient reductions in oxygen. In this issue of Cancer Research, Iriondo and colleagues examined whether short-term exposure to hypoxia leads to a "hypoxic memory" in the context of breast cancer. The authors used established cell lines and circulating tumor cell lines to demonstrate that these cells harbor a hypoxic memory that sustains downregulation of IFN signaling and antigen presentation (AP) pathways that contribute to tumor progression via alterations to tumor cells and the TME. The authors further showed that cells that have experienced hypoxia maintain the reduction in IFN signaling in vivo and are more aggressive. They determined that the hypoxic memory and reduction of IFN signaling can be reversed with a histone deacetylase inhibitor, entinostat, providing a potential means to reverse hypoxia-induced suppression of IFN signaling. As suppression of IFN signaling has the potential to influence both tumor cells and the TME, the identification of a strategy to inhibit long-term suppression of IFN signaling downstream of hypoxia could prove to be an effective means to target tumor progression. See related article by Iriondo et al., p. 3141.

Value of miR-31 and miR-150-3p as diagnostic and prognostic biomarkers for breast cancer.

BACKGROUND: The most prevalent malignancy among women is breast cancer (BC). MicroRNAs (miRNAs) play a role in the initiation and progression of BC by influencing breast cancer stem cells (BCSCs) but the diagnostic and prognostic roles of those miRNAs on BC patients are still unknown. It was aimed to investigate expression profiles, diagnostic and prognostic potentials of BCSC-related miRNAs in different subtypes (Luminal A and B, HER2 + and TNBC) of BC patients. METHODS AND RESULTS: Expression analysis of 15 BCSC-related miRNAs was performed in 50 breast tumor tissues and 20 adjacent non-tumor tissues obtained from BC patients using the qRT-PCR method. The expression levels of miR-31 and miR-150-3p were significantly upregulated in the tumor tissues compared to the adjacent non-tumor tissues (p < 0.05). miR-31 expression upregulated in the Luminal A and Luminal B group compared to non-tumor tissue (p < 0.05). miR-31 expression was determined to be significantly higher in the Luminal group (Luminal A and B) compared to the aggressive group (HER2 + and TNBC) (p < 0.05). According to the ROC analysis, the area under the curve (AUC) of miR-31 and miR-150-3p were 0.66 with a sensitivity of 68% and a specificity of 70%. A significant inverse correlation was observed between miR-31 expression with metastatic carcinoma status, in situ component, and Ki67 value in tumors, and high miR-150-3p expression was correlated with p63 expression (p < 0.05). CONCLUSION: miR-31 and miR-150-3p have the potential to serve as biomarkers for guiding diagnosis, evaluating prognosis, and metastatic process in patients with BC.

Single-cell transcriptome analysis reveals immune microenvironment changes and insights into the transition from DCIS to IDC with associated prognostic genes.

BACKGROUND: Ductal carcinoma in situ (DCIS) of the breast is an early stage of breast cancer, and preventing its progression to invasive ductal carcinoma (IDC) is crucial for the early detection and treatment of breast cancer. Although single-cell transcriptome analysis technology has been widely used in breast cancer research, the biological mechanisms underlying the transition from DCIS to IDC remain poorly understood. RESULTS: We identified eight cell types through cell annotation, finding significant differences in T cell proportions between DCIS and IDC. Using this as a basis, we performed pseudotime analysis on T cell subpopulations, revealing that differentially expressed genes primarily regulate immune cell migration and modulation. By intersecting WGCNA results of T cells highly correlated with the subtypes and the differentially expressed genes, we identified six key genes: FGFBP2, GNLY, KLRD1, TYROBP, PRF1, and NKG7. Excluding PRF1, the other five genes were significantly associated with overall survival in breast cancer, highlighting their potential as prognostic biomarkers. CONCLUSIONS: We identified immune cells that may play a role in the progression from DCIS to IDC and uncovered five key genes that can serve as prognostic markers for breast cancer. These findings provide insights into the mechanisms underlying the transition from DCIS to IDC, offering valuable perspectives for future research. Additionally, our results contribute to a better understanding of the biological processes involved in breast cancer progression.

Pericardial cyst unveiling: a case of unusual chest symptoms in a young woman with a family history of cancer: a case report and review of literature.

BACKGROUND: Pericardial cysts, though rare and benign, can present with various clinical symptoms depending on their size and location in the body. The detection of these cysts typically relies on imaging studies for a conclusive diagnosis, with surgical removal being the definitive treatment. CASE PRESENTATION: This case report details the clinical journey of a 32-year-old Iranian woman with a family history of breast and lung cancer, who experienced left-sided chest pain. Utilizing a combination of clinical history review, mammography, echocardiography, and computed tomography, a precise diagnosis of a 10 cm × 3.5 cm pericardial cyst was achieved. The patient underwent median sternotomy for complete cyst excision. CONCLUSIONS: While pericardial cysts are often asymptomatic and benign, they can lead to life-threatening complications. Hence, regular follow-up is advised, and in certain instances, minimally invasive interventions or surgery may be necessary.

Deciphering the role of LGALS2: insights into tertiary lymphoid structure-associated dendritic cell activation and immunotherapeutic potential in breast cancer patients.

Recent advances in cancer research have highlighted the pivotal role of tertiary lymphoid structures (TLSs) in modulating immune responses, particularly in breast cancer (BRCA). Here, we performed an integrated analysis of bulk transcriptome data from over 6000 BRCA samples using biological network-based computational strategies and machine learning (ML) methods, and identified LGALS2 as a key marker within TLSs. Single-cell sequencing and spatial transcriptomics uncover the role of LGALS2 in TLS-associated dendritic cells (DCs) stimulation and reveal the complexity of the tumor microenvironment (TME) at both the macro and micro levels. Elevated LGALS2 expression correlates with prolonged survival, which is associated with a robust immune response marked by diverse immune cell infiltration and active anti-tumor pathways leading to a 'hot' tumor microenvironment. The colocalization of LGALS2 with TLS-associated DCs and its role in immune activation in BRCA were confirmed by hematoxylin-eosin (HE), immunohistochemistry (IHC), and in vivo validation analyses. The identification of LGALS2 as a key factor in BRCA not only highlights its therapeutic potential in novel TLS-directed immunotherapy but also opens new avenues in patient stratification and treatment selection, ultimately improving clinical management.

SMARCD1 is an essential expression-restricted metastasis modifier.

Breast cancer is the most frequently diagnosed cancer worldwide, constituting 15% of cases in 2023. The predominant cause of breast cancer-related mortality is metastasis, and a lack of metastasis-targeted therapies perpetuates dismal outcomes for late-stage patients. By using meiotic genetics to study inherited transcriptional network regulation, we have identified, to the best of our knowledge, a new class of "essential expression-restricted" genes as potential candidates for metastasis-targeted therapeutics. Building upon previous work implicating the CCR4-NOT RNA deadenylase complex in metastasis, we demonstrate that RNA-binding proteins NANOS1, PUM2, and CPSF4 also regulate metastatic potential. Using various models and clinical data, we pinpoint Smarcd1 mRNA as a target of all three RNA-BPs. Strikingly, both high and low expression of Smarcd1 correlate with positive clinical outcomes, while intermediate expression significantly reduces the probability of survival. Applying the theory of "essential genes" from evolution, we identify 50 additional genes that require precise expression levels for metastasis to occur. Specifically, small perturbations in Smarcd1 expression significantly reduce metastasis in mouse models and alter splicing programs relevant to the ER+/HER2-enriched breast cancer. Identification subtype-specific essential expression-restricted metastasis modifiers introduces a novel class of genes that, when therapeutically "nudged" in either direction, may significantly improve late-stage breast cancer patients.

RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques.

PROBLEM: Breast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process. AIM: This study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data's high dimensionality and complexity. METHODS: We introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model's performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India. RESULTS: The proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls. CONCLUSION: Our findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.

Targeting mutant p53: a key player in breast cancer pathogenesis and beyond.

The p53 mutation is the most common genetic mutation associated with human neoplasia. TP53 missense mutations, which frequently arise early in breast cancer, are present in over thirty percent of breast tumors. In breast cancer, p53 mutations are linked to a more aggressive course of the disease and worse overall survival rates. TP53 mutations are mostly seen in triple-negative breast cancer, a very diverse kind of the disease. The majority of TP53 mutations originate in the replacement of individual amino acids within the p53 protein's core domain, giving rise to a variety of variations referred to as "mutant p53s." In addition to gaining carcinogenic qualities through gain-of-function pathways, these mutants lose the typical tumor-suppressive features of p53 to variable degrees. The gain-of-function impact of stabilized mutant p53 causes tumor-specific dependency and resistance to therapy. P53 is a prospective target for cancer therapy because of its tumor-suppressive qualities and the numerous alterations that it experiences in tumors. Phenotypic abnormalities in breast cancer, notably poorly differentiated basal-like tumors are frequently linked to high-grade tumors. By comparing data from cell and animal models with clinical outcomes in breast cancer, this study investigates the molecular mechanisms that convert gene alterations into the pathogenic consequences of mutant p53's tumorigenic activity. The study delves into current and novel treatment approaches aimed at targeting p53 mutations, taking into account the similarities and differences in p53 regulatory mechanisms between mutant and wild-type forms, as well.

Understanding genetic variations associated with familial breast cancer.

BACKGROUND: Breast cancer is the most frequent cancer among women. Genetics are the main risk factor for breast cancer. Statistics show that 15-25% of breast cancers are inherited among those with cancer-prone relatives. BRCA1, BRCA2, TP53, CDH1, PTEN, and STK11 are the most frequent genes for familial breast cancer, which occurs 80% of the time. In rare situations, moderate-penetrance gene mutations such CHEK2, BRIP1, ATM, and PALB2 contribute 2-3%. METHODS: A search of the PubMed database was carried out spanning from 2005 to July 2024, yielding a total of 768 articles that delve into the realm of familial breast cancer, concerning genes and genetic syndromes. After exclusion 150 articles were included in the final review. RESULTS: We report on a set of 20 familial breast cancer -associated genes into high, moderate, and low penetrance levels. Additionally, 10 genetic disorders were found to be linked with familial breast cancer. CONCLUSION: Familial breast cancer has been linked to several genetic diseases and mutations, according to studies. Screening for genetic disorders is recommended by National Comprehensive Cancer Network recommendations. Evaluation of breast cancer candidate variations and risk loci may improve individual risk assessment. Only high- and moderate-risk gene variations have clinical guidelines, whereas low-risk gene variants require additional investigation. With increasing use of NGS technology, more linkage with rare genes is being discovered.

Chaperone-mediated autophagy modulates Snail protein stability: implications for breast cancer metastasis.

Breast cancer remains a significant health concern, with triple-negative breast cancer (TNBC) being an aggressive subtype with poor prognosis. Epithelial-mesenchymal transition (EMT) is important in early-stage tumor to invasive malignancy progression. Snail, a central EMT component, is tightly regulated and may be subjected to proteasomal degradation. We report a novel proteasomal independent pathway involving chaperone-mediated autophagy (CMA) in Snail degradation, mediated via its cytosolic interaction with HSC70 and lysosomal targeting, which prevented its accumulation in luminal-type breast cancer cells. Conversely, Snail predominantly localized to the nucleus, thus evading CMA-mediated degradation in TNBC cells. Starvation-induced CMA activation downregulated Snail in TNBC cells by promoting cytoplasmic translocation. Evasion of CMA-mediated Snail degradation induced EMT, and enhanced metastatic potential of luminal-type breast cancer cells. Our findings elucidate a previously unrecognized role of CMA in Snail regulation, highlight its significance in breast cancer, and provide a potential therapeutic target for clinical interventions.

Personal history of irradiation and risk of breast cancer: A Mendelian randomisation study.

Background: Studies on the relationship between personal history of irradiation and breast cancer have been reported for a long time. Still, epidemiological studies have not been conclusive, and the causal relationship is unclear. To address this issue, we employed Mendelian randomisation (MR) analysis to examine the association between individual radiation exposure history and breast cancer. Methods: We used a series of quality control methods to select single nucleotide polymorphism (SNP) closely related to exposure. Meanwhile, several analysis methods were used to analyse the sample data to make the conclusion more reliable. To evaluate the horizontal pleiotropy, heterogeneity and stability of SNPs for breast cancer, the MR-Egger intercept test, Cochran's Q test and 'leave one' sensitivity analysis were used. Finally, the outlier variation determined by the Mendelian Randomisation Pleiotropy RESidual Sum and Outlier test is gradually eliminated to reduce the influence of heterogeneity and horizontal pleiotropy. Results: After implementing rigorous quality control procedures, we carefully chose 102 qualified instrumental variables closely associated with the selected exposure for sensitivity analysis. This was conducted to evaluate the heterogeneity, level multiplicity, and stability of SNPs in the context of personal radiation history and its correlation with breast cancer. The results of the inverse variance weighted method analysis revealed a positive correlation between personal radiation and a heightened risk of breast cancer (odds ratio (OR) = 1.52; 95% confidence interval (CI) = 1.30-1.77). We also validated on another data set; the results were similar (OR = 1.51; 95% CI = 1.27-1.81). Furthermore, the findings from the sensitivity analysis were consistent. At the genetic level, our research demonstrated that personal radiation exposure is associated with an elevated risk of breast cancer. Conclusions: Using genetic data provides evidence and strengthens the causal link that personal radiation causes breast cancer.

Deciphering the multi-scale mechanism of herbal phytoconstituents in targeting breast cancer: a computational pharmacological perspective.

Breast Cancer (BC) is the most common cause of cancer-associated deaths in females worldwide. Despite advancements in BC treatment driven by extensive characterization of its molecular hallmarks, challenges such as drug resistance, tumor relapse, and metastasis persist. Therefore, there is an urgent need for alternative treatment approaches with multi-modal efficacy to overcome these hurdles. In this context, natural bioactives are increasingly recognized for their pivotal role as anti-cancer compounds. This study focuses on predicting molecular targets for key herbal phytoconstituents-gallic acid, piperine, quercetin, resveratrol, and beta-sitosterol-present in the polyherbal formulation, Krishnadi Churna. Using an in-silico network pharmacology model, key genes were identified and docked against these marker compounds and controls. Mammary carcinoma emerged as the most significant phenotype of the putative targets. Analysis of an online database revealed that out of 135 predicted targets, 134 were mutated in breast cancer patients. Notably, ESR1, CYP19A1, and EGFR were identified as key genes which are known to regulate the BC progression. Docking studies demonstrated that the herbal phytoconstituents had similar or better docking scores than positive controls for these key genes, with convincing protein-ligand interactions confirmed by molecular dynamics simulations, MM/GBSA and free energy landscape (FEL) analysis. Overall, this study highlights the predictive potential of herbal phytoconstituents in targeting BC genes, suggesting their promise as a basis for developing new therapeutic formulations for BC.

Mitochondrial-related genes as prognostic and metastatic markers in breast cancer: insights from comprehensive analysis and clinical models.

Background: Breast cancer (BC) constitutes a significant peril to global women's health. Contemporary research progressively suggests that mitochondrial dysfunction plays a pivotal role in both the inception and advancement of BC. However, investigations delving into the correlation between mitochondrial-related genes (MRGs) and the prognosis and metastasis of BC are still infrequent. Methods: Utilizing data from the TCGA database, we employed the "limma" R package for differential expression analysis. Subsequently, both univariate and multivariate Cox regression analyses were executed, alongside LASSO Cox regression analysis, to pinpoint prognostic MRGs and to further develop the prognostic model. External validation (GSE88770 merged GSE425680) and internal validation were further conducted. Our investigation delved into a broad spectrum of analyses that included functional enrichment, metabolic and immune characteristics, immunotherapy response prediction, intratumor heterogeneity (ITH), mutation, tumor mutational burden (TMB), microsatellite instability (MSI), cellular stemness, single-cell, and drug sensitivity analysis. We validated the protein and mRNA expressions of prognostic MRGs in tissues and cell lines through immunohistochemistry and qRT-PCR. Moreover, leveraging the GSE102484 dataset, we conducted differential gene expression analysis to identify MRGs related to metastasis, subsequently developing metastasis models via 10 distinct machine-learning algorithms and then selecting the best-performing model. The division between training and validation cohorts was set at 70% and 30%, respectively. Results: A prognostic model was constructed by 9 prognostic MRGs, which were DCTPP1, FEZ1, KMO, NME3, CCR7, ISOC2, STAR, COMTD1, and ESR2. Patients within the high-risk group experienced more adverse outcomes than their counterparts in the low-risk group. The ROC curves and constructed nomogram showed that the model exhibited an excellent ability to predict overall survival (OS) for patients and the risk score was identified as an independent prognostic factor. The functional enrichment analysis showed a strong correlation between metabolic progression and MRGs. Additional research revealed that the discrepancies in outcomes between the two risk categories may be attributed to a variety of metabolic and immune characteristics, as well as differences in intratumor heterogeneity (ITH), tumor mutational burden (TMB), and cancer stemness indices. ITH, TIDE, and IPS analyses suggested that patients possessing a low-risk score may exhibit enhanced responsiveness to immunotherapy. Additionally, distant metastasis models were established by PDK4, NRF1, DCAF8, CHPT1, MARS2 and NAMPT. Among these, the XGBoost model showed the best predicting ability. Conclusion: In conclusion, MRGs significantly influence the prognosis and metastasis of BC. The development of dual clinical prediction models offers crucial insights for tailored and precise therapeutic strategies, and paves the way for exploring new avenues in understanding the pathogenesis of BC.

Cohort profile: a nationwide study in Dutch CHEK2 c.1100delC families using the infrastructure of the HEreditary Breast and Ovarian cancer study Netherlands - Hebon-CHEK2.

PURPOSE: CHEK2 c.1100delC is associated with an increased breast cancer risk in women. While this variant is prevalent in the Netherlands (1% in the general population), knowledge of aetiology and prognosis of breast cancer and other tumours in CHEK2 c.1100delC carriers is lacking. The nationwide HEreditary Breast and Ovarian cancer study the Netherlands (Hebon) cohort aims to answer study questions in families with an increased risk of breast cancer and ovarian cancer. While initially focusing on BRCA1/2-variant families, Hebon gradually expanded to include pathogenic variants in other genes associated with breast and/or ovarian cancer over time. This provides an excellent setting to establish a cohort to ultimately study the impact of CHEK2 c.1100delC on cancer risk prediction and surveillance, breast cancer treatment and prognosis. PARTICIPANTS: We invited all heterozygous and homozygous CHEK2 c.1100delC indexes and tested female relatives. 1802 women were included, of whom 1374 were heterozygotes and 938 were breast cancer cases. Pedigrees were collected from all clinical genetic departments. Furthermore, participants completed a detailed questionnaire on hormonal and lifestyle factors, family history, cancer diagnosis and treatment. FINDINGS TO DATE: Mean age at study inclusion was 53 years. Linkage with the Netherlands Cancer Registry showed a younger age at diagnosis in homozygotes (mean age 41.7 years) and heterozygotes (47.9 years) than non-carriers (51.2 years). Furthermore, carriers were more often diagnosed with grade 2, oestrogen receptor-positive breast cancer and more often developed contralateral breast cancer than non-carriers. Most women consumed alcohol regularly and about half never smoked. FUTURE PLANS: Further data linkages with the Netherlands Cancer Registry will allow prospective follow-up and breast cancer risk assessment in unaffected women at the time of genetic testing, risk of contralateral breast cancer and survival in patients with breast cancer. Also, linkage with the nationwide network and registry of histopathology and cytopathology in The Netherlands (PALGA) allows us to retrieve tumour samples to study tumourigenesis.

Systematic screening of protein-coding gene expression identified VWF as a potential key regulator in anthracycline-based chemotherapy-exacerbated metastasis of breast cancer.

BACKGROUND: Breast cancer is the most commonly diagnosed cancer worldwide. Although major treatments represented by chemotherapy have shown effectiveness at the initial period, recurrence and metastasis still occur later after treatments. The alternation of the tumor microenvironment by chemotherapy is confirmed as a trigger of the elevated proliferation and migration of the remaining tumor cells. METHODS: Using bioinformatic methods, differential gene expression analysis was used to determine DEGs between post-chemotherapy and pre-chemotherapy samples of breast cancer patients, followed by survival analysis and ELISA analysis of the potential key genes. An in vitro model of 2 breast cancer cells lines was used to demonstrate the role of VWF in the evasion and migration of breast cancer cells, using cell migration, evasion and wound healing assays, PCR and molecular docking analysis. RESULTS: 19 hub genes were further identified using GO and KEGG pathway analyses and WGCNA. The 5 secreted protein-coding genes with reported carcinogenesis effects (VWF, SVEP1, DPT, ADIPOQ, and LPL) were further analyzed in breast cancer patients and VWF was identified as a potential key regulator in the anthracycline-based chemotherapy-exacerbated metastasis. It was further confirmed that anthracycline-based chemotherapeutics doxorubicin exacerbated VWF upregulation and the evasion and migration of breast cancer cells. Based on molecular docking analysis and previous study, berberine was used as an inhibitor of VWF, and showed an effective inhibition of the doxorubicin-exacerbated VWF upregulation, migration and evasion in breast cancer. CONCLUSIONS: Doxorubicin-exacerbated evasion and migration through VWF upregulation. Berberine as an inhibitor of VWF was able to reversed the doxorubicin-exacerbated VWF upregulation and evasion and migration in breast cancer cells.

Elucidating the power of arginine restriction: taming type I interferon response in breast cancer via selective autophagy.

BACKGROUND: Type I interferons (IFN-I) are potent alarm factors that initiate cancer cell elimination within tumors by the immune system. This critical immune response is often suppressed in aggressive tumors, thereby facilitating cancer immune escape and unfavorable patient outcome. The mechanisms underpinning IFN-I suppression in tumors are incompletely understood. Arginase-1 (ARG1)-expressing immune cells that infiltrate tumors can restrict arginine availability by ARG1-mediated arginine degradation. We hypothesized that arginine restriction suppresses the IFN-I response in tumors. METHODS: Comprehensive, unbiased open approach omics analyses, various in vitro techniques, including microscopy, qPCR, immunoblotting, knock-down experiments, and flow cytometry were employed, as well as ex vivo analysis of tumor tissue from mice. Several functional bioassays were utilized to assess metabolic functions and autophagy activity in cancer cells. RESULTS: Arginine restriction potently induced expression of selective autophagy receptors, enhanced bulk and selective autophagy and strongly suppressed the IFN-I response in cancer cells in an autophagy-dependent manner. CONCLUSION: Our study proposes a mechanism for how tumor-infiltrating immune cells can promote cancer immune escape by dampening the IFN-I response. We suggest ARG1 and autophagy as putative therapeutic targets to activate the IFN-I response in tumors.