Targeting Riboflavin Kinase Induces Ferroptosis and Apoptosis in Triple-Negative Breast Cancer

Targeting Riboflavin Kinase Induces Ferroptosis and Apoptosis in Triple-Negative Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Targeting Riboflavin Kinase Induces Ferroptosis and Apoptosis in Triple-Negative Breast Cancer Yi Liu, Dongin Kim, Seul-Ah Kim, Inhye Moon, Seojeong Kim, Jinhue Jeong, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7709970/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Triple-negative breast cancer (TNBC) is a highly aggressive and heterogeneous subtype of breast cancer lacking estrogen receptor, progesterone receptor, and HER2 expression. Due to the absence of actionable molecular targets, patients rely heavily on chemotherapy, often facingearly recurrence and poor prognosis. There is an urgent need for novel therapeutic strategies that utilize alternative cell death mechanisms beyond conventional apoptosis. Methods: To identify metabolic vulnerabilities specific to TNBC, we employed an integrative strategy combining genome-scale metabolic modeling based on patient transcriptomic data with CRISPR-Cas9 dependency datasets. Riboflavin kinase (RFK), an enzyme that converts riboflavin into FMN and FAD, was identified as a top-ranked candidate target. Functional validation was conducted via genetic knockdown and pharmacological inhibition using roseoflavin. Cellular proliferation was assessed by WST assay and crystal violet staining. Apoptosis and ferroptosis were evaluated by Annexin V/PI flow cytometry, western blotting, JC-1 and C11-BODIPY fluorescence, ROS and MDA assays, and glutathione quantification. In vivo efficacy was tested in orthotopic xenograft models using RFK-silenced TNBC cells or roseoflavin-treated mice. Immunohistochemical analyses (Ki67, 4-HNE, TUNEL) were used to assess tumor proliferation, ferroptosis, and apoptosis, respectively. Results: RFK suppression significantly inhibited TNBC cell proliferation in vitro and in vivo . Mechanistically, RFK loss reduced glutathione levels, increased intracellular ROS accumulation,and enhanced lipid peroxidation, resulting in mitochondrial dysfunction and concurrent induction of ferroptosis and apoptosis. In TNBC xenograft models, RFK knockdown or roseoflavin treatment markedly reduced tumor growth, enhanced lipid peroxidation, and increased cell death. Transcriptomic analyses suggest that TNBC tumors, exhibiting heightened ferroptosis susceptibility, may engage in metabolic reprogramming,characterized by upregulation of genes involved in riboflavin uptake, flavin cofactor biosynthesis, and glutathione synthesis, as a compensatory adaptation toenhance redox buffering capacity and resist ferroptotic stress. Conclusions: Our study identifiedRFK as a TNBC-specific metabolic vulnerability, regulatingredox homeostasis and cell death pathways. Targeting RFK represents a promising therapeutic strategy for TNBC, as it induced both ferroptosis and apoptosis. These findings underscore the potential of exploiting the riboflavin–FMN/FAD–glutathione axis as a redox metabolic checkpoint in ferroptosis-prone TNBC. Cancer Biology Oncology Triple-negative breast cancer (TNBC) riboflavin kinase (RFK) cell death ferroptosis Genome-scale metabolic models (GEMs) flavin cofactors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction According to GLOBOCAN 2022, breast cancer is the most commonly diagnosed cancer (23.8%) and the leading cause of cancer-related death (15.4%) among women worldwide [ 1 ]. Triple-negative breast cancer (TNBC), characterized by the lack or low expression of estrogen receptor (ER), progesterone receptor (PR), and HER2 amplification, accounts for approximately 15–20% of all breast cancer cases [ 2 ]. Due to its high heterogeneity and complex pathogenesis, TNBC treatment faces considerable challenges, with patients often showing poor responses to chemotherapy and experiencing significant side effects, which limit treatment options [ 3 ]. These challenges highlight the urgent need for novel and more effective therapeutic strategies. Programmed cell death (PCD) has emerged as a critical determinant of tumor progression and a key therapeutic mechanism in cancer [ 4 ]. Among various forms of PCD, ferroptosis—an iron-dependent, lipid peroxidation-driven cell death—has gained increasing attention for its role in cancer therapy [ 5 ], particularly in TNBC [ 6 ]. Recent studies have shown that TNBC cells possess a distinct metabolic profile characterized by altered iron metabolism and glutathione homeostasis, making them more susceptible to ferroptosis than other breast cancer subtypes [ 7 , 8 ]. Genome-scale metabolic models (GEMs) have emerged as powerful computational frameworks for identifying context-specific metabolic vulnerabilities in cancer. By integrating high-throughput omics data with curated human metabolic networks, GEMs enable the construction of patient- or tumor-type-specific models that simulate cellular metabolism under various genetic or environmental perturbations [ 9 ]. Recent studies have utilized in silico gene knockout simulations to systematically predict cancer-essential metabolic genes, offering a rational strategy for the discovery of druggable targets [ 10 ]. This modeling approach is particularly valuable in cancers like TNBC, where conventional targeted therapies are limited due to the absence of hormone or HER2 receptors [ 11 ]. In the present study, we employed personalized GEM reconstruction and large-scale simulation using the cancer genome atlas (TCGA)-derived tumor samples to uncover TNBC-specific essential genes, which were further validated using experimental CRISPR screening data, highlighting riboflavin kinase (RFK) as a promising therapeutic target. RFK, also known as flavokinase, catalyzes the first step of riboflavin (RF) bioactivation, converting RF to flavin mononucleotide (FMN). FMN is subsequently transformed into flavin adenine dinucleotide (FAD) by FAD synthetase (FADS) in an ATP-dependent reaction [ 12 ]. FAD serves as an essential coenzyme for glutathione reductase (GR), which regenerates reduced glutathione (GSH) from its oxidized form (GSSG), thereby playing a crucial role in protecting cells from oxidative stress [ 13 ]. Previous studies in prostate cancer cells have shown that RFK overexpression confers resistance to cisplatin, hydrogen peroxide, and diamide, whereas RFK knockdown promotes apoptosis, suggesting that RFK plays a role in oxidative stress defense and malignant progression [ 14 ]. However, the functional role of RFK in TNBC has not yet been investigated. To address this gap, we employed a GEM-based modeling approach that identified RFK as a potential vulnerability associated with TNBC cell death. Based on this prediction, we conducted comprehensive in vitro and in vivo experiments using both genetic knockdown and pharmacological inhibition strategies to examine the role of RFK in regulating cell death pathways, specifically ferroptosis and apoptosis. Our findings underscore the therapeutic potential of targeting RFK as a novel metabolic vulnerability in TNBC. Methods and Materials Cell cultures and reagents Human female TNBC cell lines MDA-MB-231 and MDA-MB-468 were obtained from ATCC and cultured at 37°C with 5% CO 2 in Roswell Park Memorial Institute 1640 (RPMI 1640, #LM011-01, WELGENE, Gyeongsan-si, Korea) or Dulbecco’s Modified Eagle’s Medium (DMEM, #LM001-05, WELGENE) supplemented with 10% fetal bovine serum (FBS, #35-015-CV, Corning, NY, USA), and 1% penicillin/streptomycin (#SV30010, HyClone, MA, USA). Cell lines were authenticated by short tandem repeat profiling, routinely tested for mycoplasma contamination, and maintained for a maximum of 30 passages after thawing. Roseoflavin (#SML1583, Sigma-Aldrich, Darmstadt, Germany) was dissolved in DMSO. Reconstruction of patient-specific cancer GEMs To generate personalized GEMs for cancer patients, we first retrieved gene expression profiles from 9,911 primary tumor samples across 33 cancer types in TCGA ( https://www.cancer.gov/tcga ). Among these, 115 TNBC samples were analyzed separately to identify TNBC-specific metabolic dependencies. Next, 9,911 patient-specific GEMs were reconstructed by integrating significantly expressed genes into the universal human genome-scale metabolic model, Human-GEM [ 15 ], using the integrative metabolic analysis tool (iMAT) algorithm [ 16 ]. The iMAT algorithm employs a mixed-integer linear programming (MILP) framework to identify a steady-state flux distribution that adheres to stoichiometric and thermodynamic constraints while maximizing agreement between gene expression profiles and metabolic reaction activity. To ensure biological relevance, each reconstructed model was further constrained to sustain flux through a biomass reaction, thereby conforming its capacity to support cell proliferation. In silico gene knockout simulation of GEMs To evaluate the metabolic essentiality of individual genes across reconstructed GEMs, we performed in silico gene knockout simulations by systematically deleting one gene at a time in each patient-specific GEM. A gene was defined as essential if its deletion abolished the model’s ability to carry flux through the biomass reaction, simulating loss of cell viability. In each knockout simulation, all reactions associated with the target gene were constrained to zero flux, effectively mimicking a gene deletion. Flux balance analysis (FBA) [ 17 ] was then performed to maximize biomass production. Genes whose deletion resulted in a maximal biomass flux of zero were classified as essential for that specific GEM. All simulations were conducted using the COBRApy package [ 18 ], with Gurobi [ 19 ] employed as the solver. Calculation of essentiality frequency and TNBC specificity score For each gene, an essentiality frequency was calculated within each cancer type, defined as the proportion of patient-derived GEMs in which the gene was predicted to be essential. To assess TNBC- specific vulnerabilities, we defined a TNBC specificity score as the difference between the essentiality frequency in TNBC and the mean essentiality frequency across all non-TNBC cancer types. The precise formulas used in these calculations are provided in the Fig. 1 A annotations. DepMap CRISPR knockout dependency analysis Genome-wide CRISPR screening data were obtained from the cancer dependency map (DepMap) portal (DepMap release 22Q2; https://depmap.org/portal ). We utilized the CRISPR_gene_dependency.csv dataset, which contains normalized probabilistic estimates of gene dependency derived from genome-wide CRISPR knockout screens. These dependency probabilities reflect the likelihood that a given gene is essential for the survival of each cancer cell line. Following DepMap guidelines, a gene was considered essential in a specific cell line if its dependency probability exceeded 0.5. To identify TNBC-specific dependencies, we selected cell lines classified as TNBC. TNBC was defined based on the metadata fields lineage_sub_subtype as "ERneg_HER2neg" and lineage_molecular_subtype as "Basal", "Basal A", or "Basal B". This resulted in 21 TNBC cell lines with available dependency probability data. We analyzed 11 candidate genes identified from the simulation-based screening. For each gene, we visualized the distribution of dependency probabilities across the 21 TNBC cell lines using box-and-dot plots. We then counted, for each gene, the number of TNBC cell lines in which the gene showed a dependency probability > 0.5. In addition, we generated a heatmap (cell lines × candidate genes) to visualize the gene dependency probability patterns for the 11 candidate genes across the 21 TNBC cell lines. All analyses were performed in Python 3.8. The following packages were used: pandas for data handling and preprocessing, numpy for numerical operations, and matplotlib and seaborn for data visualization. These analyses allowed us to validate in silico GEM-predicted essential genes using independent experimental CRISPR datasets and further prioritize RFK as potential TNBC-specific vulnerabilities. Gene expression analysis Gene expression profiles were obtained from the UCSC Xena Toil RNA-seq Recompute dataset ( https://toil.xenahubs.net ), which provides uniformly processed and batch-corrected RNA-sequencing data from the cancer genome atlas breast invasive carcinoma cohort (TCGA-BRCA) and the genotype-tissue expression (GTEx) project. The Toil pipeline performs transcript alignment using STAR and quantification with RSEM, followed by batch correction across TCGA and GTEx samples, enabling direct cross-cohort comparisons [ 20 ]. For independent validation, two publicly available breast cancer microarray datasets (GSE76250 and GSE45827) were retrieved from the gene expression omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/ ). PAM50 molecular subtype annotations for TCGA-BRCA samples were obtained from the UCSC Xena database. Tumors were categorized into basal-like and non-basal-like groups, the latter comprising luminal A, luminal B, HER2-enriched, and normal-like subtypes. Gene expression comparisons were performed across four groups: basal-like tumors, non-basal-like tumors, TCGA adjacent-normal tissues, and GTEx normal breast tissues. Ferroptosis-related genes were selected based on previously reported experimental evidence for their role in ferroptosis regulation and were categorized as ferroptosis-protective (e.g., GPX4, SLC40A1 ) or ferroptosis-promoting genes (e.g., TFRC , ACSL4 ). For the GSE76250 and GSE45827 datasets, statistical analysis between the two groups was conducted using a two-tailed unpaired Student’s t -test. Because the GSE datasets involved only two groups, two-sample comparisons were performed with a two-tailed unpaired Student’s t -test (a standard approach generally robust to modest deviations from normality). For the TCGA-BRCA and GTEx data, normality was assessed using the Shapiro–Wilk or D’Agostino–Pearson test (according to sample size), and homogeneity of variances was evaluated with Levene’s test across the four groups. Because both assumptions were violated ( p < 0.05), nonparametric methods were applied: overall group differences were analyzed using the Kruskal–Wallis test, and post hoc pairwise comparisons were conducted using the Mann–Whitney U test with Bonferroni correction for multiple testing. All tests were two-tailed, and statistical significance was defined as an adjusted p < 0.05. All statistical analyses and data visualizations were performed in Python 3.8 using pandas, numpy, scipy, matplotlib, and seaborn. Survival analysis Relapse-free survival (RFS) and overall survival (OS) analyses were conducted using the KM-Plotter tool ( http://kmplot.com ), which integrates gene expression and clinical outcome data from multiple breast cancer cohorts. As KM-Plotter supports OS analysis for RNA-seq but not RFS in breast cancer cohorts, we evaluated OS using RNA-seq data (GSE96058) and RFS using microarray data aggregated from multiple GEO datasets within the platform [ 21 ]. For genes with multiple Affymetrix probe sets, the Jetset algorithm was applied to select the optimal “best probe” for analysis. Analyses were restricted to patients with basal-like tumors, as defined by PAM50 classification. As an exception, OS for GPX4 was assessed using microarray data because, in the RNA-seq dataset, dichotomizing GPX4 expression into “high” and “low” yielded highly unbalanced group sizes, making the comparison unreliable; thus, GPX4 OS was derived from the microarray cohorts. Kaplan–Meier curves were generated, and hazard ratios (HRs) with 95% confidence intervals and log-rank p -values were reported. Generation of stable RFK knockdown cell lines To generate stable RFK knockdown cell lines, lentiviral particles encoding shRNA targeting human RFK (shRFK; #TRCN0000037599, sequence: CCCATATTACAAGAATACGAA; Sigma-Aldrich) were transduced into TNBC cells. Control cells were transduced with empty vector (pLKO.1-puro). Stable clones were selected using puromycin and established through single-cell cloning. Mouse models and in vivo experiments All animal experiments were conducted in compliance with institutional guidelines and were approved by the institutional animal care and use committee of the national cancer center (IACUC-NCC-24-1053). Four-week-old female BALB/c nude mice (ORIENT, Gyeonggi-do, Korea) were used for in vivo experiments. To evaluate the effect of RFK knockdown, MDA-MB-231 (5 × 10⁶) or MDA-MB-468 (1 × 10⁷) control cells or shRFK-transduced cells were suspended in 100 µL of PBS and orthotopically injected into the mammary fat pads of mice. Tumor volumes were measured every three days using digital calipers. After five weeks, mice were euthanized and tumors were collected for imaging, weighing, and immunohistochemistry (IHC) analysis. To assess the therapeutic potential of roseoflavin, parental MDA-MB-231 or MDA-MB-468 cells were injected using the same protocol. When tumor volumes reached approximately 250 mm³, mice were randomized into two groups to receive either vehicle (saline) or roseoflavin (30 mg/kg/day) via daily intraperitoneal injection for 30 days. At the study endpoint, tumors were excised for imaging, weighing, and IHC evaluation. Cell proliferation assay Stable RFK-knockdown TNBC cells and corresponding control cells were seeded into 96-well plates at a density of 5 × 10³ cells per well and cultured for 24, 48, or 72 h. At each time point, 5 µL of EZ-CytoX reagent (#EZ-3000P, DoGen, Seoul, Korea) was added to each well and incubated at 37°C for 3 h in the dark. Optical density (OD) was then measured at 450 nm using a microplate reader (Infinite 200 PRO, Tecan Group Ltd., Switzerland). To evaluate the effect of pharmacological RFK inhibition, parental TNBC cells were seeded into 96-well plates (5 × 10³ cells/well) and allowed to attach for 24 h. Cells were then treated with increasing concentrations of roseoflavin (0, 0.05, 0.1, 0.5, 1, 2, or 5 µM) for 48 h, followed by the addition of EZ-CytoX reagent as described above. Live cell counting Stable RFK-knockdown and control TNBC cells were seeded into 6-well plates at a density of 1 × 10 5 cells per well and cultured for 48, 72, and 96 h. At each time point, cells were harvested using trypsin, resuspended in complete medium, and mixed with 0.4% trypan blue solution (#LS014-01, WELGENE). Viable and non-viable cells were quantified using a Luna II automated cell counter (Logos Biosystems, Inc., Anyang-si, Korea). Clonogenic assay Stable RFK-knockdown cells and control TNBC cells were seeded into 6-well plates at a density of 1,000 cells per well and cultured for 14 days, with medium refreshed every 3 days. For pharmacological inhibition experiments, parental TNBC cells were seeded at the same density and allowed to adhere for 24 h prior to treatment with roseoflavin (0, 5, or 10 µM) for 48 h. Following roseoflavin exposure, the medium was replaced with fresh drug-free medium and renewed every 3 days. At the end of incubation period, colonies were gently washed with PBS, fixed with 100% ethanol for 1 h, and stained with 0.5% crystal violet solution (#000C1066, Samchun Chemical, Seoul, Korea). Plates were washed with distilled water to remove excess dye and air-dried before obtaining the image results. Reactive oxygen species (ROS) analysis Stable RFK-knockdown and control TNBC cells were seeded into 96-well black bottom plates at a density of 1 × 10 4 cells per well and cultured for 48 h. Cells were then stained with DCFDA (#C6827, Invitrogen, Waltham, USA) and incubated at 37°C in the dark for 30 min. Fluorescence was measured using a microplate reader at excitation/emission (Ex/Em) wavelengths of 485/535 nm. For pharmacological inhibition experiments, parental TNBC cells were seeded at the same density and allowed to adhere for 24 h, followed by treatment with roseoflavin (0, 5, or 10 µM) for 24 h. ROS levels were then measured as described above. Quantitative real-time polymerase chain reaction (qRT-PCR) Total RNA was extracted from cells using TRIzol reagent (#FATRR001, Invitrogen), and cDNA was synthesized using the PrimeScript RT reagent kit (#RR037A, TaKaRa, Kusatsu, Japan) according to the manufacturer’s instructions. qRT-PCR was conducted using SYBR Premix Ex Taq II (#RR820A, TaKaRa) on a CFX Maestro system (Bio-Rad Laboratories, Inc. CA, USA), and relative gene expression was analyzed using the ΔΔCt method. Primer sequences used were as follows: RFK (forward: 5′-ACTATGGTTGGGCCAGTGTT-3′; reverse: 5′-ATGGCCACATTGAGGATTTC-3′); GAPDH (forward: 5′-GAGTCAACGGATTTGGTCGT-3′; reverse: 5′-GACAAGCTTCCCGTTCTCAG-3′). Western blotting Stable RFK-knockdown and control TNBC cells (2 × 10⁵ cells/well) were seeded into 6-well plates and cultured for 48 h under normal conditions. For pharmacological inhibition, parental TNBC cells (2 × 10⁵ cells/well) were seeded and allowed to adhere for 24 h, then treated with roseoflavin (0, 5, or 10 µM) in serum-free medium for 48 h prior to lysis. Cells were lysed in ice-cold RIPA buffer (#9806s, Cell Signaling Technology, MA, USA) with protease inhibitor (#P3100-005, GenDEPOT, TX, USA) and phosphatase inhibitor (#04906845001, Roche, Basel, Switzerland). Lysates were sonicated on ice for 2 s and centrifuged at 13,000 rpm for 20 min at 4°C. Total protein concentration was determined using a BCA protein assay kit (#23225, Thermo Fisher Scientific, MA, USA). Equal amounts of protein (30 µg) were separated on 8%–12% SDS-PAGE and transferred to PVDF membranes (#BSP0161, Pall Life Science, CA, USA). Membranes were blocked in 5% skim milk (#232100, BD Biosciences, NJ, USA) in 0.1% Tris-buffered saline with Tween-20 (TBST) for 1 h at room temperature (RT), and then incubated overnight at 4°C with primary antibodies diluted in 5% bovine serum albumin (BSA, #C0082-100, Research and Diagnostic Technology, USA). The following antibodies were used: RFK (#CAB9141, 1:1000, Assay Genie, Dublin, Ireland), cleaved caspase-7 (#9491S, 1:2000, Cell Signaling Technology), cleaved PARP (#9541S, 1:2000, Cell Signaling Technology), and β-actin (#PM-053,1:2000, MBL, Nagoya, Japan) as a loading control. After washing three times with TBST, membranes were incubated for 3 h at RT with HRP-conjugated secondary antibodies: anti-mouse (#GTX213111-01, 1:5000, GeneTex, CA, USA) or anti-rabbit (#GTX213110-01, 1:5000, GeneTex). Protein bands were visualized using an enhanced chemiluminescence (ECL) detection reagent (#RPN2235, Cytiva, Buckinghamshire, UK) and imaged using a ChemiDoc MP imaging system (Bio-Rad Laboratories, Inc.). Band intensity was quantified using ImageJ software (NIH, USA), and normalized to β-actin. Flow cytometry analysis Cell samples were prepared as described in Western blotting. For apoptosis detection, cells were stained with Annexin V-FITC (#556420, BD Bioscience, CA, USA) and propidium iodide (PI; #556463, BD Bioscience) for 10 min at RT in the dark. Apoptotic cell populations were analyzed using a NovoCyte 2060R flow cytometry (Agilent Technologies, CA, USA) via FITC and PE channels. For lipid peroxidation analysis, cells were stained with 2 µM BODIPY 581/591-C11 dye (#D3861, Invitrogen, Thermo Fisher Scientific) at 37°C for 30 min in the dark. After staining, cells were washed, resuspended in PBS, and immediately analyzed by flow cytometry using the FITC channel to detect oxidized BODIPY fluorescence. GSH/GSSG measurement RFK-knockdown/control or roseoflavin-treated cells (2 × 10⁵ cells/well) were cultured under the same conditions as for Western blotting. Intracellular concentrations of GSH and GSSG were quantified using the GSH/GSSG assay kit (#ab138881, Abcam, Cambridge, UK). Fluorescence was measured at 490/520 nm using a microplate reader. GSH levels were directly proportional to fluorescence, and GSSG values were scaled by a factor of ½ to reflect the two moles of GSH generated per GSSG reduction. Malondialdehyde (MDA) quantification RFK-knockdown/control or roseoflavin-treated cells (2 × 10⁵ cells/well) were cultured under the same conditions as for Western blotting. MDA levels were quantified using a lipid peroxidation assay kit (#ab118970, Abcam) according to the manufacturer’s instructions. Fluorescence was measured at 532/553 nm. MDA levels were normalized to total protein content and expressed as nmol/µg relative to control. Mitochondrial membrane potential (MMP) assay RFK-knockdown/control or roseoflavin-treated cells (2 × 10⁵ cells/well) were cultured under the same conditions as described in Western blotting. To assess MMP, cells were stained with 2 µM JC-1 dye (#ab113850, Abcam) at 37°C for 30 min, followed by washing with 1 × PBS. Fluorescence was observed using a fluorescence microscope (Axio Observer 7, Carl Zeiss, Oberkochen, Germany) at 100× magnification. MMP status was determined by the JC-1 fluorescence shift, with the red/green fluorescence intensity ratio used to indicate mitochondrial polarization–higher ratios reflecting polarized (intact) mitochondria, and lower ratios indicating depolarized (damaged) mitochondria. Immunohistochemistry (IHC) analysis Tumor tissues were harvested, fixed in 4% paraformaldehyde (PFA), and embedded in paraffin. Tissue sections were subjected to IHC staining using primary antibodies against RFK (#PA5-51222, 1:200, Thermo Fisher Scientific), 4-HNE (#MHN-020P, 1:200, JaICA, Japan), and Ki67 (#M7240, 1:100, Dako, Carpinteria, CA). Visualization was performed using 3,3-diaminobenzidine (DAB; #K3468, Dako), followed by hematoxylin counterstaining. Images were captured at 200× magnification using a DM1000 LED Ergonomic system microscope (Leica, Wetzlar, Germany). Quantification was performed using ImageJ software, based on three representative fields per section, and presented as the percentage of positively stained area (% area). TUNEL assay Apoptosis in paraffin-embedded tumor sections was assessed using the in situ cell death detection kit (#11684795910, Roche, Basel, Switzerland) following the manufacturer’s instructions. Sections were incubated with the TUNEL reaction mixture at 37°C for 1 h, followed by counterstaining with DAPI at RT for 30 s. Fluorescence images were acquired using a Zeiss Axio Observer 7 microscope at 200× magnification. TUNEL-positive cells (green) and nuclei (blue) were quantified using ImageJ software from three representative fields per section. Statistical analysis All statistical analyses were performed using a GraphPad Prism 10.0 (GraphPad Software, CA, USA). Data are presented as mean ± standard deviation (SD). Each experiment was independently repeated at least three times with one or more technical replicates per condition. Unless otherwise specified, for comparisons between two groups, a two-tailed unpaired Student’s t- test was used. For comparisons involving more than two groups, one-way or two-way analysis of variance (ANOVA) was applied as needed. Statistical significance was defined as p < 0.05. Exact p -values were reported unless p < 0.0001, which is indicated as such. Only biologically relevant p -values are shown, as specified in the figure legends. Results Integrative Genome-Scale Metabolic Modeling and CRISPR Screening Identify RFK as a TNBC-Specific Essential Gene To identify functionally essential genes in TNBC, we constructed patient-specific GEMs for 115 TNBC patients using gene expression data in TCGA [ 22 ]. In silico gene knockout simulations were then conducted on each model to predict essential genes. A union of 107 genes were defined as essential in at least one TNBC model, based on criteria described in the Methods section. We first assessed the essentiality frequency of each gene across the 115 TNBC models (Fig. S1). Notably, 85% (91/107) were essential in all models, suggesting they function as pan-essential housekeeping genes. To determine TNBC-specific essentiality, we expanded our analysis to include genome-scale knockout simulations across 9,911 patient-derived models representing 33 TCGA cancer types. Genes essential across all models from all 33 TCGA cancer types were considered pan-essential and excluded. This filtering yielded 16 candidate genes with potential TNBC-specific essentiality. The 91 genes essential in all TNBC models were also universally essential across all TCGA models (Fig. S2). Next, we quantified the essentiality frequency of each of the 16 candidates in all cancer types and calculated a TNBC specificity score, defined as the difference between essentiality frequency in TNBC models and the mean frequency across non-TNBC cancers. Among these, 11 genes with positive TNBC specificity scores-indicating higher essentiality in TNBC-were retained (Fig. 1 A). These genes were subsequently validated using experimental gene dependency data from the DepMap project [ 23 ]. To complement the simulation-based predictions, we analyzed CRISPR knockout screening data from the DepMap project ( https://depmap.org/portal ). Unlike raw gene effect scores, which indicate the magnitude of cell viability loss upon gene deletion but can be affected by experimental variability, DepMap provides gene dependency probabilities, which are normalized model-based estimates of gene essentiality. A gene dependency probability > 0.5 is generally considered indicative of essentiality in a given cell line. Applying this threshold, we assessed the dependency of the 11 candidate genes across 21 TNBC cell lines. Three genes including RFK , FOSL1 , and PGM3 were identified as essential in at least one TNBC cell line, with a dependency score exceeding 0.5. RFK exhibited the highest mean dependency score (Fig. 1 B). Specifically, RFK was identified as essential in 13 TNBC cell lines, FOSL1 in 12, and PGM3 in 5 (Fig. 1 C). Notably, RFK exhibited essentiality across a broad spectrum of TNBC subtypes, including representative basal-like 1 (BL1) subtypes such as MDA-MB-468 and HCC1937, and basal-like 2 (BL2) subtype like SUM149PT and HCC1806. Furthermore, RFK dependency was also observed in mesenchymal (M) and mesenchymal stem-like (MSL) subtypes, represented by CAL-51, CAL-120, MDA-MB-231, and MDA-MB-157, respectively [ 24 , 25 ]. Although FOSL1 is a well- characterized oncogene extensively implicated in TNBC progression and chemoresistance [ 26 ], RFK was selected for further investigation in this study due to its novel and unexplored role in TNBC. Ferroptosis-Sensitizing Transcriptomic and Prognostic Signatures in the Basal-like Subtype Support RFK as a TNBC-Specific Therapeutic Target To further explore the clinical relevance of RFK , we analyzed publicly available breast cancer datasets in GEO (GSE76250 and GSE45827) and found that RFK expression was significantly elevated in TNBC tissues compared to normal breast tissues (Fig. 2 A). We next examined whether this upregulation was specific to a particular molecular subtype using PAM50-based classification in the TCGA breast cancer dataset. As the basal-like subtype largely overlaps with TNBC at the molecular level, we compared RFK mRNA levels between basal-like and non-basal-like tumors. Interestingly, no significant difference was observed ( p > 0.05, Fig. 2 B), suggesting that RFK overexpression may be a general feature of breast cancer rather than uniquely enriched in basal-like tumors. Although RFK expression did not differ significantly between basal and non-basal breast cancer subtypes, basal-like tumors are known to exhibit heightened sensitivity to ferroptosis compared to other breast cancer subtypes [ 6 ]. To explore this, we investigated whether basal-like tumors exhibit a ferroptosis-sensitizing gene expression signature using a curated set of ferroptosis-related genes previously reported to be differentially regulated in basal-like breast cancer [ 27 ]. These genes were categorized into ferroptosis-protective (e.g., GPX4, SLC40A1 ) or ferroptosis-promoting (e.g., TFRC , ACSL4 ) groups based on their experimentally validated roles. Our analysis revealed that basal-like tumors exhibited a ferroptosis-prone transcriptional pattern driven by both impaired antioxidant defense and dysregulated iron/lipid metabolism. Expression of GPX4 , encoding glutathione peroxidase 4 (a GSH-dependent peroxidase that detoxifies lipid hydroperoxides), was significantly downregulated in basal-like tumors compared to non-basal-like subtypes ( p < 0.0001, Fig. 2 B). Decreased GPX4 expression is known to impair lipid peroxide detoxification and lower the threshold for ferroptotic cell death [ 28 , 29 ]. In terms of iron metabolism, basal-like tumors demonstrated upregulation of TFRC (transferrin receptor 1), which promotes cellular iron uptake [ 30 ], and downregulation of SLC40A1 (ferroportin), the key iron exporter [ 31 ] ( p < 0.0001, Fig. 2 B), resulting in an anticipated increase in intracellular labile iron pools and enhanced susceptibility to iron-catalyzed lipid peroxidation [ 32 ]. Furthermore, for lipid metabolism, ACSL4 , a lipid-activating enzyme that incorporates polyunsaturated fatty acids (PUFAs) into membrane phospholipids, the primary substrates for lipid peroxidation [ 33 ], was markedly upregulated in basal-like tumors compared to non-basal-like subtypes ( p < 0.0001, Fig. 2 B). Taken together, these coordinated alterations in GPX4 , TFRC , SLC40A1 , and ACSL4 suggest that basal-like tumors harbor an intrinsic ferroptosis-sensitizing molecular profile, characterized by impaired peroxide detoxification and enhanced lipid peroxidation potential. These findings align with prior reports that TNBC, particularly the basal-like subtype, is intrinsically vulnerable to ferroptotic cell death due to concurrent dysregulation of glutathione metabolism, iron homeostasis, and PUFA-enriched phospholipid biosynthesis [ 27 ]. Consistent with this notion, comparison of basal-like tumors with both normal and tumor-adjacent normal breast tissues revealed similar expression trends: GPX4 was downregulated, TFRC was upregulated, and SLC40A1 was downregulated, while ACSL4 showed no significant difference compared to normal tissue but was modestly decreased relative to adjacent-normal tissue (Fig. 2 B). These data reinforce the hypothesis that TNBC exhibits a ferroptosis-sensitizing transcriptomic state not only relative to other subtypes but also compared to non-malignant breast tissues, supporting the rationale for exploiting RFK inhibition as a metabolic vulnerability in this context. Although RFK expression was not significantly different between basal-like and non-basal-like breast cancer subtypes (Fig. 2 B), Kaplan–Meier analysis revealed that elevated RFK expression in basal-like tumors was marginally associated with worse RFS (HR = 1.24, p = 0.051; Fig. 2 C), whereas no such association was observed in other subtypes (all p > 0.1, Fig. S3). This subtype-specific prognostic trend likely reflects the intrinsic ferroptosis sensitivity of basal-like tumors. While such sensitivity could typically promote tumor suppression via ferroptotic cell death, elevated RFK expression may mitigate this vulnerability by sustaining redox homeostasis and reducing ferroptotic stress. In this context, RFK may contribute to tumor survival and disease progression in TNBC. Taken together, these findings indicate that TNBC is characterized by a ferroptosis-prone transcriptomic profile and a distinct prognostic vulnerability marked by RFK . Combined with prior evidence of RFK’s functional essentiality in TNBC cell models, our results support the therapeutic potential of targeting RFK to exploit the ferroptosis susceptibility of TNBC. RFK Knockdown Suppresses TNBC Cell Proliferation and Induces Apoptosis To evaluate the functional role of RFK in TNBC, we performed genetic knockdown of RFK in two representative TNBC cell lines, MDA-MB-231 and MDA-MB-468 (Fig. 3 A, B), which were selected based on their high dependency scores in Fig. 1 C. RFK knockdown led to a significant reduction in cell proliferation under both short-term culture conditions (Fig. 3 C, D) and long-term colony-forming conditions (Fig. 3 E). Immunoblot analysis further revealed upregulation of apoptosis-related markers, and flow cytometry analysis confirmed a marked increase in apoptotic cell populations upon RFK knockdown (Fig. 3 F, G). These findings indicate that RFK is essential for TNBC cell survival and that its depletion triggers apoptotic cell death. RFK Knockdown Induces Ferroptosis Through Oxidative Stress and Redox Imbalance In addition to promoting apoptosis, RFK knockdown triggered hallmark features of ferroptosis in TNBC cells. Lipid peroxidation levels were significantly elevated, as demonstrated by increased BODIPY-C11 oxidation (Fig. 4 A, B) and malondialdehyde (MDA) accumulation (Fig. 4 C). Intracellular reactive oxygen species (ROS) levels were also elevated following RFK depletion (Fig. 4 D), and mitochondrial membrane potential (MMP) was markedly reduced (Fig. 4 E, F), indicating mitochondrial dysfunction. Furthermore, RFK knockdown significantly decreased total and reduced glutathione (GSH) levels (Fig. 4 G), reflecting impaired redox homeostasis. Collectively, these findings indicate that RFK depletion in TNBC cells promotes ferroptosis by elevating oxidative stress, impairing mitochondrial function, and disrupting glutathione-mediated redox homeostasis. RFK Knockdown Suppresses TNBC Tumor Growth and Induces Ferroptotic and Apoptotic Cell Death in Vivo To assess the role of RFK in tumor progression in vivo , we established orthotopic xenograft models by injecting control or RFK-knockdown TNBC cells into the mammary fat pads of immunodeficient mice. Tumors derived from RFK-depleted cells exhibited significantly slower growth, with markedly reduced tumor volume and weight compared to controls (Fig. 5 A-C). Importantly, mouse body weights remained stable throughout the experiment, indicating non-significant systemic toxicity from RFK depletion (Fig. 5 D). To assess tumor cell death and proliferative activity, we performed immunohistochemical analyses and TUNEL assays. RFK expression was markedly reduced in tumors from the knockdown group, confirming efficient target silencing. Ki-67 staining showed a substantial decrease in proliferative activity, while 4-hydroxynonenal (4-HNE) levels, a marker of lipid peroxidation and ferroptosis, were significantly elevated. In parallel, TUNEL staining confirmed an increased number of apoptotic cells in RFK-deficient tumors (Fig. 5 E, F). These findings demonstrate that RFK promotes TNBC tumor growth in vivo and that its depletion induces both ferroptotic and apoptotic cell death. Pharmacological Inhibition of RFK by Roseoflavin Induces Apoptosis in TNBC Cells Roseoflavin, a structural analog of riboflavin, competitively inhibits the biosynthesis of flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD), thereby functioning as an RFK inhibitor [ 34 , 35 ]. To investigate the therapeutic potential of targeting RFK, we evaluated the effects of roseoflavin on TNBC cell proliferation. Roseoflavin treatment significantly suppressed the growth of MDA-MB-231 and MDA-MB-468 cells (Fig. 6 A), as further confirmed by a marked reduction in colony-forming ability (Fig. 6 B, C). Additionally, Western blot analysis revealed elevated levels of apoptosis-related proteins, including cleaved caspase-7 and cleaved PARP (Fig. 6 D, E). Flow cytometry analysis further demonstrated a substantial increase in apoptotic cell populations following roseoflavin treatment (Fig. 6 F, G). These findings suggest that pharmacological inhibition of RFK activity suppresses TNBC progression through the induction of apoptosis. Roseoflavin-Induced RFK Inhibition Triggers Ferroptosis in TNBC Cells To determine whether pharmacological inhibition of RFK also induces ferroptosis, we treated TNBC cells with roseoflavin and evaluated ferroptosis-associated parameters. Roseoflavin treatment led to a significant increase in lipid peroxidation, as detected by BODIPY 581/591 C11 staining and elevated MDA levels (Fig. 7 A, B). Intracellular ROS levels were markedly increased (Fig. 7 C), while MMP was significantly reduced (Fig. 7 D), indicating mitochondrial dysfunction. Furthermore, intracellular levels of total glutathione and GSH were significantly decreased (Fig. 7 E), suggesting impaired antioxidant capacity. Together, these findings indicate that roseoflavin-induced RFK inhibition promotes ferroptosis in TNBC cells by enhancing oxidative stress and disrupting redox homeostasis. Pharmacological Inhibition of RFK by Roseoflavin Suppresses TNBC Tumor Growth In Vivo To evaluate the therapeutic potential of RFK inhibition in vivo , orthotopic xenograft models of TNBC were established by injecting MDA-MB-231 or MDA-MB-468 cells into the mammary fat pads of immunodeficient mice. Once tumors were established, mice were administered roseoflavin (30 mg/kg/day) or vehicle daily. Roseoflavin treatment significantly suppressed tumor growth, as evidenced by reduced tumor volume and weight compared to the control group (Fig. 8 A–C). Notably, there were no significant changes in body weight (Fig. 8 D), and serum levels of liver toxicity markers, alanine aminotransferase (ALT) and aspartate aminotransferase (AST), remained within normal ranges (Fig. 8 E), indicating good tolerability. Immunohistochemical analysis of tumor tissues revealed a marked reduction in Ki-67 expression, indicating decreased proliferative activity, along with increased levels of 4-HNE, a marker of lipid peroxidation and ferroptosis. In addition, TUNEL staining demonstrated enhanced apoptotic cell death in roseoflavin-treated tumors (Fig. 8 F, G). These findings suggest that pharmacological inhibition of RFK by roseoflavin not only suppresses TNBC tumor growth but also induces both ferroptosis and apoptosis in vivo , without overt toxicity. Discussion TNBC is one of the most aggressive and therapeutically challenging subtypes of breast cancer, characterized by high recurrence rates, poor prognosis, and a lack of actionable molecular targets [ 36 ]. Targeted therapy options remain limited: PARP inhibitors provide benefit only for BRCA1/2-mutated patients, while immune checkpoint inhibitors (e.g., pembrolizumab) are approved for a subset of PD-L1-positive tumors [ 37 ]. Given the suboptimal efficacy of current treatments, there is an urgent need to develop new therapeutic strategies that engage alternative cell death pathways. In this regard, increasing attention has been paid toward non-apoptotic cell death pathways, especially ferroptosis, an iron-dependent regulated cell death driven by lipid peroxidation [ 38 , 39 ]. Emerging evidence suggests that TNBC cells possess a unique metabolic dependency on iron handling and glutathione homeostasis, which may underlie their increased sensitivity to ferroptosis compared to other breast cancer subtypes [ 27 ]. Several ferroptosis inducers, including GPX4 inhibitors, have shown selective lethality in TNBC models [ 40 , 41 ], positioning ferroptosis as a promising therapeutic vulnerability in this subtype. Consistent with this notion, Kaplan–Meier analysis of breast cancer data in KM-Plotter revealed that higher GPX4 expression was significantly associated with worse RFS and OS specifically in basal-like tumors (Fig. S4). These findings further support the clinical relevance of GPX4-mediated ferroptosis resistance in TNBC and underscore the therapeutic potential of targeting ferroptosis in this subtype. To identify metabolic vulnerabilities capable of triggering both apoptosis and ferroptosis, we employed an integrative approach combining GEM-based simulation and experimental validation. This approach revealed RFK as a TNBC-specific metabolic dependency. Patient-derived GEM simulations, constructed from transcriptomic data of TNBC tumors, predicted that RFK knockout would efficiently impair cell survival in this subtype. These predictions were subsequently validated in vitro and in vivo experiments. Genetic silencing or pharmacological inhibition of RFK using roseoflavin not only suppressed TNBC tumor growth but also induced hallmark features of apoptosis and ferroptosis. These included increased levels of cleaved caspase-7 and cleaved PARP, elevated ROS accumulation, glutathione depletion, lipid peroxidation, mitochondrial dysfunction, and ultimately, cell death. These dual phenotypic changes underscore the pivotal role of RFK in maintaining redox homeostasis and regulating multiple cell death pathways in TNBC. Simulation-based gene knockout predictions using patient-derived GEMs allow for large-scale prediction of gene essentiality across heterogeneous tumor microenvironments modeled from patient-specific transcriptomes. However, a major limitation of this approach is the experimental impracticality of performing genome-wide CRISPR screening directly in primary human tumors. In contrast, CRISPR-based functional genomics screening in established cancer cell lines provides direct experimental evidence of gene dependency, but its physiological relevance is limited and may not fully recapitulate the complexity and interpatient heterogeneity of human tumors. To overcome these complementary limitations, we integrated patient-derived GEM-based predictions with CRISPR dependency datasets from the DepMap portal, thereby enhancing biological relevance through patient-level modeling and improving empirical validity via experimental gene dependency. This integrative approach provided a converging line of evidence identifying RFK as an essential and druggable metabolic node in TNBC, underscoring its potential as a therapeutic target for precision oncology. Despite its central role in flavin metabolism, the function of RFK in cancer biology remains poorly understood [ 42 ]. Prior studies in prostate cancer demonstrated that RFK overexpression conferred resistance to cisplatin and oxidative stress-inducing agents such as hydrogen peroxide and diamide. In contrast, RFK knockdown sensitized prostate cancer cells to these agents and induced apoptotic cell death. Mechanistically, RFK overexpression increased intracellular levels of FAD, FMN, and total glutathione, alongside the upregulation of antioxidant enzymes such as GR and glutathione S-transferase π (GSTπ). Furthermore, RFK expression was positively correlated with the Gleason score, implicating a role in oxidative stress resistance and malignant progression in prostate cancer [ 14 ]. Our transcriptomic and survival analyses identified that, beyond GPX4, additional ferroptosis-protective genes exhibit distinct expression and prognostic patterns in the basal-like subtype. Specifically, GCLM, the modifier subunit of glutamate–cysteine ligase (the rate-limiting enzyme for glutathione biosynthesis) [ 43 ], was significantly upregulated (Fig. S5A), as was SLC52A2 (Fig. S5B), encoding a riboflavin importer [ 44 ]. In contrast, ABCG2, which mediates riboflavin efflux [ 45 ], was downregulated in basal-like tumors compared to other subtypes (Fig. S5C). Notably, high expression of GCLM (HR = 1.79, p = 0.02) and SLC52A2 (HR = 2.31, p = 0.0011) was associated with poorer overall survival specifically in basal-like tumors, but not in other subtypes (Fig. S6, 7). Mechanistically, these alterations likely represent compensatory adaptations to the heightened ferroptosis susceptibility observed in basal-like subtypes, which are characterized by reduced GPX4 expression, elevated levels of intracellular labile iron, and an abundance of PUFA-containing phospholipids. Upregulation of GCLM may serve to expand the intracellular glutathione pool, thereby fortifying antioxidant defenses and mitigating lipid peroxidation. Similarly, increased expression of SLC52A2, in combination with downregulation of ABCG2, is predicted to elevate intracellular riboflavin availability. This, in turn, enhances the production of FMN and FAD, critical cofactors required for glutathione biosynthesis enzymes, thereby strengthening redox buffering capacity. We propose that this adaptive remodeling, herein termed the riboflavin–FMN/FAD–glutathione axis, functions as a metabolic safeguard against ferroptotic stress and supports tumor cell survival in TNBC. Previous studies have shown that RFK activity is highly sensitive to the relative concentrations of its substrate, riboflavin [ 46 ]. In TNBC, increased riboflavin uptake (via SLC52A2) and reduced efflux (via ABCG2) likely lead to elevated intracellular riboflavin, thereby enhancing RFK activity and driving FMN/FAD production. This, in turn, supports glutathione-dependent redox buffering. Such a metabolic context implies that TNBC may be particularly dependent on RFK-mediated flavin cofactor synthesis to counter ferroptotic stress. This reliance offers a mechanistic rationale for the TNBC-specific prognostic association of RFK and highlights it as a selective therapeutic target in TNBC. Beyond oncology, RFK has been widely studied for its role in antioxidant defense in neurodegenerative and ischemic diseases. In Parkinson’s disease models, both RFK and its metabolic product FMN have been shown to alleviate oxidative stress and prevent dopaminergic neurons from degeneration, emphasizing the neuroprotective role of riboflavin metabolism [ 47 ]. Similarly, in models of ischemic neuronal injury, RFK overexpression attenuated neuronal apoptosis and necrosis induced by oxygen-glucose deprivation, whereas RFK silencing exacerbated neuronal damage [ 48 ]. These findings reinforce the concept that RFK functions as a redox-regulating enzyme with broader cytoprotective effects across diverse pathological conditions. As a key enzyme in riboflavin metabolism, RFK catalyzes the phosphorylation of riboflavin to generate FMN, which is further converted to FAD [ 34 ]. These flavin cofactors, FMN and FAD, are essential for the activity of more than 100 human flavoproteins, many of which are involved in mitochondrial energy production and oxidative stress responses [ 49 ]. Riboflavin has been shown to exert antioxidant effects by promoting mitochondrial integrity, maintaining glutathione homeostasis, and enhancing the activity of antioxidant enzymes such as glutathione peroxidase (GPx), superoxide dismutase (SOD), and catalase. Supplementation with riboflavin reduces oxidative stress markers such as MDA and protects against lipid peroxidation [ 49 ]. Notably, riboflavin deficiency induces apoptosis through multiple mechanisms, including redox imbalance, mitochondrial dysfunction, endoplasmic reticulum (ER) stress, and lipotoxicity [ 50 ]. These effects align with the observed consequences of RFK depletion, which limits the availability of FMN and FAD and compromises antioxidant capacity. In the context of ferroptosis, several FMN- or FAD-dependent flavoproteins contribute to regulation of this non-apoptotic, iron-dependent cell death. These include GR, apoptosis-inducing factor mitochondria-associated 2 (AIFM2/FSP1), NAD(P)H:quinone oxidoreductase 1 (NQO1), and cytochrome P450 oxidoreductase (POR) [ 51 ]. Among them, GR plays a critical role in maintaining intracellular glutathione levels, which are essential for the activity of GPX4, the enzyme responsible for reducing toxic lipid peroxides and blocking ferroptosis. Disruption of this GR-GPX4 axis leads to unchecked lipid peroxidation and ferroptotic death. Moreover, GR has been implicated in cancer aggressiveness and therapy resistance. For example, GR expression has been reported as a marker of malignancy in hepatocellular carcinoma [ 52 ], and pharmacological inhibition of GR with carmustine promotes ferroptosis in vivo and enhances the antitumor efficacy of sorafenib [ 53 ]. GR is also required for colorectal cancer cell survival under acidic microenvironments, further emphasizing its importance in in redox adaptation [ 54 ]. In this study, RFK was identified as a candidate therapeutic target through TNBC-specific genome-scale metabolic modeling. Functional experiments revealed that both genetic knockdown and pharmacological inhibition of RFK disrupted redox homeostasis in TNBC cells, leading to glutathione depletion, ROS accumulation, mitochondrial dysfunction, and enhanced lipid peroxidation, which are hallmarks of both apoptosis and ferroptosis. These findings highlight RFK as a dual regulator of redox signaling and cell death, linking its metabolic activity to the survival of TNBC cells. While our data support the tumor-suppressive effects of RFK inhibition, the detailed molecular pathways by which RFK governs ferroptosis and apoptosis remain to be elucidated. Future investigations will focus on identifying downstream effectors and interacting partners that mediate RFK-dependent cell fate decisions. Moreover, combinatorial strategies involving RFK inhibition alongside ferroptosis inducers, immune checkpoint blockade, or conventional chemotherapy may offer promising avenues to overcome therapeutic resistance and improve clinical outcomes in TNBC. Collectively, our findings highlight RFK as a metabolic vulnerability and a redox checkpoint in the ferroptosis-apoptosis axis of TNBC. Abbreviations AIFM2/FSP1 apoptosis-inducing factor mitochondria-associated 2 / ferroptosis suppressor protein 1 BSA bovine serum albumin DAB 3,3-diaminobenzidine DepMap Dependency Map DMEM Dulbecco’s Modified Eagle’s Medium ER estrogen receptor FAD flavin adenine dinucleotide FBA flux balance analysis FBS fetal bovine serum FMN flavin mononucleotide GEO Gene Expression Omnibus GEMs genome-scale metabolic models GR glutathione reductase GSM genome-scale metabolic GSH glutathione GSSG oxidized glutathione GSTπ glutathione S-transferase-π GTEx Genotype-Tissue Expression HR hazard ratio IHC immunohistochemistry i.p. intraperitoneal KO knockout MDA malondialdehyde MILP mixed integer linear programming MMP mitochondrial membrane potential NQO1 NAD(P)H:quinone oxidoreductase 1 OD optical density OS overall survival PCD programmed cell death PFA paraformaldehyde PI propidium iodide POR cytochrome P450 oxidoreductase PR progesterone receptor RF riboflavin RFK riboflavin kinase RFS relapse-free survival RoF roseoflavin ROS reactive oxygen species RPMI 1640 Roswell Park Memorial Institute 1640 TBST Tris-buffered saline with Tween-20 TCGA-BRCA Cancer Genome Atlas breast invasive carcinoma cohort TNBC triple-negative breast cancer TUNEL terminal deoxynucleotidyl transferase dUTP nick end labeling Declarations Ethics approval and consent to participate All animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC) at the National Cancer Center (IACUC-NCC-24-1053). Consent for publication Not applicable. Data availability No datasets were generated during the current study. Materials or additional information used in this study are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding This work was supported by grants from the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) (2022R1A2C2092053 and RS-2024-00431505) and by Korea Basic Science Institute (National research Facilities and Equipment Center) grant funded by the Ministry of Education (2021R1A6C101A442). Authors’ contributions Y.L. performed the experiments, contributed to the experimental design, and drafted the manuscript. D.K. and T.K. conducted GEMs, Depmap analysis, interpreted related data, carried out bioinformatics analysis and co-wrote the manuscript. SA.K., I.M. and S.K. performed experiments, acquired data, contributed to data analysis. J.J. performed animal experiments and acquired data. S.Y. and Y.K. conceived and designed the study, supervised all aspects of the project, and secured funding. All authors reviewed and approved the final manuscript. Acknowledgements Y.L. gratefully acknowledges financial support from the China Scholarship Council (CSC) Grant (201306120117). The authors also thank the Ewha Drug Development Research Core Center for providing analytical support using the NanoDrop spectrophotometer (NFEC-2023-03-286005), flow cytometer (NFEC-2019-03-254735), microplate reader (NFEC-2021-08-272460), fluorescence microscope (NFEC-2021-08-272462), and real-time PCR system(NFEC-2021-08-272451). Authors' information 1 College of Pharmacy, Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, 03760, South Korea. Yi Liu ( [email protected] ); Seul-Ah Kim ( [email protected] ); Inhye Moon ( [email protected] ); Seojeong Kim ( [email protected] ); Youngjoo Kwon ( [email protected] ) 2 Standigm Inc., 6F, 182 Dogok-ro, Gangnam-gu, Seoul, Republic of Korea. Dongin Kim ( [email protected] ) Tae Yong Kim ( [email protected] ); Sojeong Yun ( [email protected] ) 3 Graduate Program in Innovative Biomaterials Convergence, Ewha Womans University, Seoul, 03760, Republic of Korea. Seul-Ah Kim ( [email protected] ); Inhye Moon ( [email protected] ); Youngjoo Kwon ( [email protected] ) 4 National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang-si Gyeonggi-do, 10408, Republic of Korea. Jinhue Jeong ( [email protected] ) References Bray F et al (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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10:48:47","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":158459,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7709970/v1/783c3d973125ac2b7ece06d2.html"},{"id":92252020,"identity":"4848ba9a-9d82-4b85-b5c2-a5703b8fb443","added_by":"auto","created_at":"2025-09-26 10:48:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":418128,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrative genome-scale metabolic modeling and CRISPR screening identify \u003cem\u003eRFK\u003c/em\u003e as a TNBC-specific essential gene.\u003cstrong\u003e A.\u003c/strong\u003e Heatmap showing the essentiality frequency of 16 candidate genes across 33 TCGA cancer types, based on \u003cem\u003ein silico\u003c/em\u003e knockout simulations using transcriptomic data from GEMs. Each row represents a gene and each column represents a cancer type. The side color bar indicates the TNBC specificity score, calculated as the difference between essentiality frequency in TNBC and the average frequency across all non-TNBC cancer types. Genes enclosed by a black rectangle exhibit TNBC specificity \u0026gt;\u003cem\u003e \u003c/em\u003e0, indicating selective essentiality in TNBC. Essentiality frequency\u003cem\u003e \u003c/em\u003eis defined as the proportion of models in which a gene is predicted to be essential within each cancer type. \u003cstrong\u003eB.\u003c/strong\u003e Box and dot plots displaying CRISPR-based gene dependency scores for 11 candidate genes (with TNBC specificity score \u0026gt; 0) across 21 TNBC cell lines in the DepMap database. Genes with at least one TNBC cell line exhibiting a dependency score \u0026gt; 0.5 (threshold for functional dependency) are highlighted in red. The dashed line represents the 0.5 cutoff. Genes are ranked in descending order of TNBC specificity. \u003cstrong\u003eC. \u003c/strong\u003eHeatmap displaying gene dependency scores of the same 11 candidate genes across 21 TNBC cell lines. Black checkmarks indicate cell lines in which the gene shows functional dependency (score \u0026gt; 0.5). The bar graph on the right summarizes the number of TNBC cell lines in which each gene is essential.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7709970/v1/461d07dcea5724586703bd0f.png"},{"id":92252021,"identity":"86ad7278-ac7d-4464-b5f8-fe4a0a94bd4e","added_by":"auto","created_at":"2025-09-26 10:48:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":491393,"visible":true,"origin":"","legend":"\u003cp\u003eExpression patterns of \u003cem\u003eRFK\u003c/em\u003e and ferroptosis-related genes in TNBC and breast cancer molecular subtypes and prognostic significance of \u003cem\u003eRFK\u003c/em\u003e. \u003cstrong\u003eA.\u003c/strong\u003e \u003cem\u003eRFK\u003c/em\u003e mRNA levels in TNBC versus normal breast tissues, analyzed using GEO datasets (GSE76250 and GSE45827). \u003cstrong\u003eB.\u003c/strong\u003e Comparative expression profiles of \u003cem\u003eRFK\u003c/em\u003e and representative ferroptosis-related genes including \u003cem\u003eGPX4\u003c/em\u003e (ferroptosis-protective), \u003cem\u003eTFRC\u003c/em\u003e and \u003cem\u003eSLC40A1\u003c/em\u003e (iron metabolism), and \u003cem\u003eACSL4\u003c/em\u003e(lipid metabolism) across four tissue categories: GTEx-derived normal breast tissues, tumor-adjacent normal breast tissues from TCGA Breast Invasive Carcinoma (TCGA-BRCA) cohort, non-basal-like primary tumors, and basal-like primary tumors (PAM50 classification) from TCGA-BRCA. Significance was assessed using two-tailed unpaired Student’s \u003cem\u003et\u003c/em\u003e-test (A) and Kruskal–Wallis with post hoc Mann–Whitney U tests with Bonferroni correction (B): **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.005, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001. \u003cstrong\u003eC. \u003c/strong\u003eKaplan–Meier analysis of \u003cem\u003eRFK\u003c/em\u003e expression and relapse-free survival (RFS) in basal-like breast cancer. Analyses were performed in KM-Plotter using basal-like (PAM50) tumors from the integrated breast cancer microarray data cohort available in KM-Plotter (derived from multiple GEO datasets). Patients were dichotomized at the median \u003cem\u003eRFK\u003c/em\u003emRNA expression. Higher \u003cem\u003eRFK\u003c/em\u003eexpression showed a marginal association with worse RFS (HR = 1.24, \u003cem\u003ep\u003c/em\u003e = 0.051).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7709970/v1/023afb1abb45ce89a188ab03.png"},{"id":92252814,"identity":"7baf9a37-068e-4505-b37f-03842bda8472","added_by":"auto","created_at":"2025-09-26 10:56:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":244519,"visible":true,"origin":"","legend":"\u003cp\u003eRFK knockdown suppresses TNBC cell proliferation and induces apoptosis. \u003cstrong\u003eA, B.\u003c/strong\u003e Protein and mRNA expression levels of RFK were assessed by Western blot and RT-PCR, respectively, in MDA-MB-468 and MDA-MB-231 cells following shRNA-mediated RFK knockdown. \u003cstrong\u003eC.\u003c/strong\u003e Cell proliferation was measured using WST-8 assays. \u003cstrong\u003eD.\u003c/strong\u003e Viable cell numbers were determined by trypan blue exclusion assay. \u003cstrong\u003eE.\u003c/strong\u003e Clonogenic assays were performed to evaluate long-term cell proliferative capacity. \u003cstrong\u003eF.\u003c/strong\u003e Western blot analysis of apoptosis markers, including cleaved caspase-7 and cleaved PARP, in RFK-depleted TNBC cells. \u003cstrong\u003eG.\u003c/strong\u003e Apoptotic cell populations were quantified by flow cytometry using Annexin V/PI staining. Data are represented as mean ± SD (n = 3 per group), *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.005, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7709970/v1/65abb7f6339180805242bd08.png"},{"id":92252024,"identity":"7b1e4acd-5b9b-43f8-ae88-5d07e60176e2","added_by":"auto","created_at":"2025-09-26 10:48:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":754815,"visible":true,"origin":"","legend":"\u003cp\u003eRFK knockdown induces ferroptosis through oxidative stress and redox imbalance. \u003cstrong\u003eA, B.\u003c/strong\u003e Lipid peroxidation was assessed in MDA-MB-468 and MDA-MB-231 cells after\u003cem\u003e RFK\u003c/em\u003e knockdown using BODIPY 581/591 C11 staining. \u003cstrong\u003eC.\u003c/strong\u003e MDA levels were measured to confirm lipid peroxidation. \u003cstrong\u003eD.\u003c/strong\u003e Intracellular ROS levels were quantified using DCFH-DA fluorescence. \u003cstrong\u003eE, F.\u003c/strong\u003e MMP was detected by JC-1 staining; scale bar = 100 μM. \u003cstrong\u003eG.\u003c/strong\u003e Intracellular levels of total glutathione, reduced glutathione (GSH), and oxidized-glutathione (GSSG) were measured using a GSH/GSSG assay kit. Data are represented as mean ± SD (n = 3-5 per group), **\u003cem\u003ep \u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.005, ****\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7709970/v1/0b9aadef3656026b24652936.png"},{"id":92252028,"identity":"b3820648-2cb3-4e77-a95d-1fa0abbec840","added_by":"auto","created_at":"2025-09-26 10:48:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1230708,"visible":true,"origin":"","legend":"\u003cp\u003eRFK knockdown suppresses TNBC tumor growth and induces ferroptotic and apoptotic cell death \u003cem\u003ein vivo\u003c/em\u003e. \u003cstrong\u003eA-C\u003c/strong\u003e. Tumor volumes and weightswere significantly reduced in mice bearing shRFK xenografts compared to controls (n = 6 per group). \u003cstrong\u003eD. \u003c/strong\u003eBody weights of mice were measured throughout the experimental period to assess treatment-related toxicity (n = 6 per group). \u003cstrong\u003eE, F\u003c/strong\u003e. Immunohistochemical staining was performed on tumor sections to evaluate RFK expression, proliferation (Ki-67), and lipid peroxidation (4-HNE). Apoptotic cell death was assessed using TUNEL staining (n = 5 per group). Scale bar = 50 μm. Data are represented as mean ± SD. *\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, ****\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7709970/v1/6f1b179f2806ecbfd757ba45.png"},{"id":92252820,"identity":"b4cb054d-1f25-43c2-9675-9ed2e7334e01","added_by":"auto","created_at":"2025-09-26 10:56:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":322007,"visible":true,"origin":"","legend":"\u003cp\u003ePharmacological inhibition of RFK by roseoflavin induces apoptosis in TNBC cells. \u003cstrong\u003eA.\u003c/strong\u003eCell proliferation was assessed by WST-8 assay following 48-hour treatment with roseoflavin (RoF) in MDA-MB-231 and MDA-MB-468 cells. \u003cstrong\u003eB, C.\u003c/strong\u003e Clonogenic assays were conducted to evaluate long-term proliferative capacity after RoF treatment. \u003cstrong\u003eD, E.\u003c/strong\u003e Western blot analysis of cleaved caspase-7 and cleaved PARP was performed to assess apoptosis induction following RoF exposure. \u003cstrong\u003eF.\u003c/strong\u003eApoptotic cell populations were quantified by flow cytometry using Annexin V/PI staining after RoF treatment. Data are presented as mean ± SD (n = 3 per group), *\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.005, ****\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7709970/v1/8bf9c80e79260daa076d4ac7.png"},{"id":92252034,"identity":"115ac7d0-62ab-4bd3-aa0b-b45088b545b1","added_by":"auto","created_at":"2025-09-26 10:48:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":843959,"visible":true,"origin":"","legend":"\u003cp\u003eRoseoflavin-induced RFK inhibition triggers ferroptosis in TNBC cells.\u003cstrong\u003e A.\u003c/strong\u003e Lipid peroxidation levels were measured in MDA-MB-468 and MDA-MB-231 cells after 48-hour treatment with roseoflavin (RoF; 5 or 10µM) using BODIPY 581/591 C11 staining.\u003cstrong\u003e B. \u003c/strong\u003eMDA levels were quantified following RoF treatment.\u003cstrong\u003e C. \u003c/strong\u003eIntracellular ROS levels were evaluated using DCFH-DA fluorescence\u003cstrong\u003e. D, E. \u003c/strong\u003eMMP was assessed using JC-1 staining; scale bar = 100 μm. \u003cstrong\u003eF. \u003c/strong\u003eIntracellular total glutathione, GSH, and GSSG levels were measured using a GSH/GSSG assay kit. Data are presented as mean ± SD (n = 3-5 per group, as indicated by the individual data points in each graph). *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.005, ****p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7709970/v1/085a7419cc05058552cafb3e.png"},{"id":92252823,"identity":"9e1a0db9-3c76-41e0-9125-9d341b9cbb0b","added_by":"auto","created_at":"2025-09-26 10:56:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1415819,"visible":true,"origin":"","legend":"\u003cp\u003ePharmacological inhibition of RFK by roseoflavin suppresses TNBC tumor growth \u003cem\u003ein vivo\u003c/em\u003e. \u003cstrong\u003eA-C. \u003c/strong\u003eTumor volume and weight were measured in mice treated with RoF (30 mg/kg/day) or vehicle.\u003cstrong\u003e D. \u003c/strong\u003eBody weight was monitored throughout the treatment period. \u003cstrong\u003eE.\u003c/strong\u003e Serum levels of liver enzymes, ALT and AST, were measured to assess potential hepatic toxicity.\u003cstrong\u003e F, G. \u003c/strong\u003eImmunohistochemical staining was performed to evaluate Ki-67 (proliferation marker) and 4-HNE (ferroptosis marker) expression, and TUNEL assay was used to detect apoptotic cells in tumor tissues . Scale bar = 50 μM. Data are presented as mean ± SD (n = 5-6 per group, as indicated). *p \u0026lt; 0.05, **p \u0026lt; 0.01, ****p \u0026lt; 0.001, ns: not significant.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7709970/v1/3fa9731914cf22368e0b956b.png"},{"id":92254106,"identity":"d36445eb-9351-47fe-a5da-0d89edd2ce66","added_by":"auto","created_at":"2025-09-26 11:12:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6373774,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7709970/v1/6db7d32d-6e9d-4a00-8cc3-118e008cf942.pdf"},{"id":92252026,"identity":"6575eb04-0110-49f2-8944-ff19d7bfca72","added_by":"auto","created_at":"2025-09-26 10:48:46","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":263435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical abstract\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCreated in https://BioRender.com\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7709970/v1/680edcc6a8534ca9a0bd6dac.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTargeting Riboflavin Kinase Induces Ferroptosis and Apoptosis in Triple-Negative Breast Cancer\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to GLOBOCAN 2022, breast cancer is the most commonly diagnosed cancer (23.8%) and the leading cause of cancer-related death (15.4%) among women worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Triple-negative breast cancer (TNBC), characterized by the lack or low expression of estrogen receptor (ER), progesterone receptor (PR), and HER2 amplification, accounts for approximately 15\u0026ndash;20% of all breast cancer cases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Due to its high heterogeneity and complex pathogenesis, TNBC treatment faces considerable challenges, with patients often showing poor responses to chemotherapy and experiencing significant side effects, which limit treatment options [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These challenges highlight the urgent need for novel and more effective therapeutic strategies.\u003c/p\u003e\u003cp\u003eProgrammed cell death (PCD) has emerged as a critical determinant of tumor progression and a key therapeutic mechanism in cancer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among various forms of PCD, ferroptosis\u0026mdash;an iron-dependent, lipid peroxidation-driven cell death\u0026mdash;has gained increasing attention for its role in cancer therapy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], particularly in TNBC [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recent studies have shown that TNBC cells possess a distinct metabolic profile characterized by altered iron metabolism and glutathione homeostasis, making them more susceptible to ferroptosis than other breast cancer subtypes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGenome-scale metabolic models (GEMs) have emerged as powerful computational frameworks for identifying context-specific metabolic vulnerabilities in cancer. By integrating high-throughput omics data with curated human metabolic networks, GEMs enable the construction of patient- or tumor-type-specific models that simulate cellular metabolism under various genetic or environmental perturbations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Recent studies have utilized \u003cem\u003ein silico\u003c/em\u003e gene knockout simulations to systematically predict cancer-essential metabolic genes, offering a rational strategy for the discovery of druggable targets [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This modeling approach is particularly valuable in cancers like TNBC, where conventional targeted therapies are limited due to the absence of hormone or HER2 receptors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In the present study, we employed personalized GEM reconstruction and large-scale simulation using the cancer genome atlas (TCGA)-derived tumor samples to uncover TNBC-specific essential genes, which were further validated using experimental CRISPR screening data, highlighting riboflavin kinase (RFK) as a promising therapeutic target.\u003c/p\u003e\u003cp\u003eRFK, also known as flavokinase, catalyzes the first step of riboflavin (RF) bioactivation, converting RF to flavin mononucleotide (FMN). FMN is subsequently transformed into flavin adenine dinucleotide (FAD) by FAD synthetase (FADS) in an ATP-dependent reaction [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. FAD serves as an essential coenzyme for glutathione reductase (GR), which regenerates reduced glutathione (GSH) from its oxidized form (GSSG), thereby playing a crucial role in protecting cells from oxidative stress [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious studies in prostate cancer cells have shown that RFK overexpression confers resistance to cisplatin, hydrogen peroxide, and diamide, whereas RFK knockdown promotes apoptosis, suggesting that RFK plays a role in oxidative stress defense and malignant progression [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the functional role of RFK in TNBC has not yet been investigated. To address this gap, we employed a GEM-based modeling approach that identified RFK as a potential vulnerability associated with TNBC cell death. Based on this prediction, we conducted comprehensive \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments using both genetic knockdown and pharmacological inhibition strategies to examine the role of RFK in regulating cell death pathways, specifically ferroptosis and apoptosis. Our findings underscore the therapeutic potential of targeting RFK as a novel metabolic vulnerability in TNBC.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCell cultures and reagents\u003c/h2\u003e\u003cp\u003eHuman female TNBC cell lines MDA-MB-231 and MDA-MB-468 were obtained from ATCC and cultured at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e in Roswell Park Memorial Institute 1640 (RPMI 1640, #LM011-01, WELGENE, Gyeongsan-si, Korea) or Dulbecco\u0026rsquo;s Modified Eagle\u0026rsquo;s Medium (DMEM, #LM001-05, WELGENE) supplemented with 10% fetal bovine serum (FBS, #35-015-CV, Corning, NY, USA), and 1% penicillin/streptomycin (#SV30010, HyClone, MA, USA). Cell lines were authenticated by short tandem repeat profiling, routinely tested for mycoplasma contamination, and maintained for a maximum of 30 passages after thawing. Roseoflavin (#SML1583, Sigma-Aldrich, Darmstadt, Germany) was dissolved in DMSO.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eReconstruction of patient-specific cancer GEMs\u003c/h3\u003e\n\u003cp\u003eTo generate personalized GEMs for cancer patients, we first retrieved gene expression profiles from 9,911 primary tumor samples across 33 cancer types in TCGA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/tcga\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/tcga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Among these, 115 TNBC samples were analyzed separately to identify TNBC-specific metabolic dependencies. Next, 9,911 patient-specific GEMs were reconstructed by integrating significantly expressed genes into the universal human genome-scale metabolic model, Human-GEM [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], using the integrative metabolic analysis tool (iMAT) algorithm [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The iMAT algorithm employs a mixed-integer linear programming (MILP) framework to identify a steady-state flux distribution that adheres to stoichiometric and thermodynamic constraints while maximizing agreement between gene expression profiles and metabolic reaction activity. To ensure biological relevance, each reconstructed model was further constrained to sustain flux through a biomass reaction, thereby conforming its capacity to support cell proliferation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn silico\u003c/b\u003e \u003cb\u003egene knockout simulation of GEMs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the metabolic essentiality of individual genes across reconstructed GEMs, we performed \u003cem\u003ein silico\u003c/em\u003e gene knockout simulations by systematically deleting one gene at a time in each patient-specific GEM. A gene was defined as essential if its deletion abolished the model\u0026rsquo;s ability to carry flux through the biomass reaction, simulating loss of cell viability. In each knockout simulation, all reactions associated with the target gene were constrained to zero flux, effectively mimicking a gene deletion. Flux balance analysis (FBA) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] was then performed to maximize biomass production. Genes whose deletion resulted in a maximal biomass flux of zero were classified as essential for that specific GEM. All simulations were conducted using the COBRApy package [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], with Gurobi [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] employed as the solver.\u003c/p\u003e\n\u003ch3\u003eCalculation of essentiality frequency and TNBC specificity score\u003c/h3\u003e\n\u003cp\u003eFor each gene, an essentiality frequency was calculated within each cancer type, defined as the proportion of patient-derived GEMs in which the gene was predicted to be essential. To assess TNBC- specific vulnerabilities, we defined a TNBC specificity score as the difference between the essentiality frequency in TNBC and the mean essentiality frequency across all non-TNBC cancer types. The precise formulas used in these calculations are provided in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA annotations.\u003c/p\u003e\n\u003ch3\u003eDepMap CRISPR knockout dependency analysis\u003c/h3\u003e\n\u003cp\u003eGenome-wide CRISPR screening data were obtained from the cancer dependency map (DepMap) portal (DepMap release 22Q2; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://depmap.org/portal\u003c/span\u003e\u003cspan address=\"https://depmap.org/portal\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We utilized the \u003cem\u003eCRISPR_gene_dependency.csv\u003c/em\u003e dataset, which contains normalized probabilistic estimates of gene dependency derived from genome-wide CRISPR knockout screens. These dependency probabilities reflect the likelihood that a given gene is essential for the survival of each cancer cell line. Following DepMap guidelines, a gene was considered essential in a specific cell line if its dependency probability exceeded 0.5. To identify TNBC-specific dependencies, we selected cell lines classified as TNBC. TNBC was defined based on the metadata fields lineage_sub_subtype as \"ERneg_HER2neg\" and lineage_molecular_subtype as \"Basal\", \"Basal A\", or \"Basal B\". This resulted in 21 TNBC cell lines with available dependency probability data.\u003c/p\u003e\u003cp\u003eWe analyzed 11 candidate genes identified from the simulation-based screening. For each gene, we visualized the distribution of dependency probabilities across the 21 TNBC cell lines using box-and-dot plots. We then counted, for each gene, the number of TNBC cell lines in which the gene showed a dependency probability\u0026thinsp;\u0026gt;\u0026thinsp;0.5. In addition, we generated a heatmap (cell lines \u0026times; candidate genes) to visualize the gene dependency probability patterns for the 11 candidate genes across the 21 TNBC cell lines. All analyses were performed in Python 3.8. The following packages were used: pandas for data handling and preprocessing, numpy for numerical operations, and matplotlib and seaborn for data visualization. These analyses allowed us to validate \u003cem\u003ein silico\u003c/em\u003e GEM-predicted essential genes using independent experimental CRISPR datasets and further prioritize RFK as potential TNBC-specific vulnerabilities.\u003c/p\u003e\n\u003ch3\u003eGene expression analysis\u003c/h3\u003e\n\u003cp\u003eGene expression profiles were obtained from the UCSC Xena Toil RNA-seq Recompute dataset (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://toil.xenahubs.net\u003c/span\u003e\u003cspan address=\"https://toil.xenahubs.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which provides uniformly processed and batch-corrected RNA-sequencing data from the cancer genome atlas breast invasive carcinoma cohort (TCGA-BRCA) and the genotype-tissue expression (GTEx) project. The Toil pipeline performs transcript alignment using STAR and quantification with RSEM, followed by batch correction across TCGA and GTEx samples, enabling direct cross-cohort comparisons [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For independent validation, two publicly available breast cancer microarray datasets (GSE76250 and GSE45827) were retrieved from the gene expression omnibus (GEO; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). PAM50 molecular subtype annotations for TCGA-BRCA samples were obtained from the UCSC Xena database. Tumors were categorized into basal-like and non-basal-like groups, the latter comprising luminal A, luminal B, HER2-enriched, and normal-like subtypes. Gene expression comparisons were performed across four groups: basal-like tumors, non-basal-like tumors, TCGA adjacent-normal tissues, and GTEx normal breast tissues.\u003c/p\u003e\u003cp\u003eFerroptosis-related genes were selected based on previously reported experimental evidence for their role in ferroptosis regulation and were categorized as ferroptosis-protective (e.g., \u003cem\u003eGPX4, SLC40A1\u003c/em\u003e) or ferroptosis-promoting genes (e.g., \u003cem\u003eTFRC\u003c/em\u003e, \u003cem\u003eACSL4\u003c/em\u003e).\u003c/p\u003e\u003cp\u003eFor the GSE76250 and GSE45827 datasets, statistical analysis between the two groups was conducted using a two-tailed unpaired Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test. Because the GSE datasets involved only two groups, two-sample comparisons were performed with a two-tailed unpaired Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test (a standard approach generally robust to modest deviations from normality). For the TCGA-BRCA and GTEx data, normality was assessed using the Shapiro\u0026ndash;Wilk or D\u0026rsquo;Agostino\u0026ndash;Pearson test (according to sample size), and homogeneity of variances was evaluated with Levene\u0026rsquo;s test across the four groups. Because both assumptions were violated (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), nonparametric methods were applied: overall group differences were analyzed using the Kruskal\u0026ndash;Wallis test, and post hoc pairwise comparisons were conducted using the Mann\u0026ndash;Whitney U test with Bonferroni correction for multiple testing. All tests were two-tailed, and statistical significance was defined as an adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All statistical analyses and data visualizations were performed in Python 3.8 using pandas, numpy, scipy, matplotlib, and seaborn.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSurvival analysis\u003c/h2\u003e\u003cp\u003eRelapse-free survival (RFS) and overall survival (OS) analyses were conducted using the KM-Plotter tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kmplot.com\u003c/span\u003e\u003cspan address=\"http://kmplot.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which integrates gene expression and clinical outcome data from multiple breast cancer cohorts. As KM-Plotter supports OS analysis for RNA-seq but not RFS in breast cancer cohorts, we evaluated OS using RNA-seq data (GSE96058) and RFS using microarray data aggregated from multiple GEO datasets within the platform [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For genes with multiple Affymetrix probe sets, the Jetset algorithm was applied to select the optimal \u0026ldquo;best probe\u0026rdquo; for analysis. Analyses were restricted to patients with basal-like tumors, as defined by PAM50 classification. As an exception, OS for GPX4 was assessed using microarray data because, in the RNA-seq dataset, dichotomizing GPX4 expression into \u0026ldquo;high\u0026rdquo; and \u0026ldquo;low\u0026rdquo; yielded highly unbalanced group sizes, making the comparison unreliable; thus, GPX4 OS was derived from the microarray cohorts. Kaplan\u0026ndash;Meier curves were generated, and hazard ratios (HRs) with 95% confidence intervals and log-rank \u003cem\u003ep\u003c/em\u003e-values were reported.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGeneration of stable RFK knockdown cell lines\u003c/h3\u003e\n\u003cp\u003eTo generate stable RFK knockdown cell lines, lentiviral particles encoding shRNA targeting human RFK (shRFK; #TRCN0000037599, sequence: CCCATATTACAAGAATACGAA; Sigma-Aldrich) were transduced into TNBC cells. Control cells were transduced with empty vector (pLKO.1-puro). Stable clones were selected using puromycin and established through single-cell cloning.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMouse models and\u003c/b\u003e \u003cb\u003ein vivo\u003c/b\u003e \u003cb\u003eexperiments\u003c/b\u003e\u003c/p\u003e\u003cp\u003e All animal experiments were conducted in compliance with institutional guidelines and were approved by the institutional animal care and use committee of the national cancer center (IACUC-NCC-24-1053). Four-week-old female BALB/c nude mice (ORIENT, Gyeonggi-do, Korea) were used for \u003cem\u003ein vivo\u003c/em\u003e experiments. To evaluate the effect of RFK knockdown, MDA-MB-231 (5 \u0026times; 10⁶) or MDA-MB-468 (1 \u0026times; 10⁷) control cells or shRFK-transduced cells were suspended in 100 \u0026micro;L of PBS and orthotopically injected into the mammary fat pads of mice. Tumor volumes were measured every three days using digital calipers. After five weeks, mice were euthanized and tumors were collected for imaging, weighing, and immunohistochemistry (IHC) analysis. To assess the therapeutic potential of roseoflavin, parental MDA-MB-231 or MDA-MB-468 cells were injected using the same protocol. When tumor volumes reached approximately 250 mm\u0026sup3;, mice were randomized into two groups to receive either vehicle (saline) or roseoflavin (30 mg/kg/day) via daily intraperitoneal injection for 30 days. At the study endpoint, tumors were excised for imaging, weighing, and IHC evaluation.\u003c/p\u003e\n\u003ch3\u003eCell proliferation assay\u003c/h3\u003e\n\u003cp\u003eStable RFK-knockdown TNBC cells and corresponding control cells were seeded into 96-well plates at a density of 5 \u0026times; 10\u0026sup3; cells per well and cultured for 24, 48, or 72 h. At each time point, 5 \u0026micro;L of EZ-CytoX reagent (#EZ-3000P, DoGen, Seoul, Korea) was added to each well and incubated at 37\u0026deg;C for 3 h in the dark. Optical density (OD) was then measured at 450 nm using a microplate reader (Infinite 200 PRO, Tecan Group Ltd., Switzerland). To evaluate the effect of pharmacological RFK inhibition, parental TNBC cells were seeded into 96-well plates (5 \u0026times; 10\u0026sup3; cells/well) and allowed to attach for 24 h. Cells were then treated with increasing concentrations of roseoflavin (0, 0.05, 0.1, 0.5, 1, 2, or 5 \u0026micro;M) for 48 h, followed by the addition of EZ-CytoX reagent as described above.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLive cell counting\u003c/h2\u003e\u003cp\u003eStable RFK-knockdown and control TNBC cells were seeded into 6-well plates at a density of 1 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells per well and cultured for 48, 72, and 96 h. At each time point, cells were harvested using trypsin, resuspended in complete medium, and mixed with 0.4% trypan blue solution (#LS014-01, WELGENE). Viable and non-viable cells were quantified using a Luna II automated cell counter (Logos Biosystems, Inc., Anyang-si, Korea).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eClonogenic assay\u003c/h2\u003e\u003cp\u003eStable RFK-knockdown cells and control TNBC cells were seeded into 6-well plates at a density of 1,000 cells per well and cultured for 14 days, with medium refreshed every 3 days. For pharmacological inhibition experiments, parental TNBC cells were seeded at the same density and allowed to adhere for 24 h prior to treatment with roseoflavin (0, 5, or 10 \u0026micro;M) for 48 h. Following roseoflavin exposure, the medium was replaced with fresh drug-free medium and renewed every 3 days. At the end of incubation period, colonies were gently washed with PBS, fixed with 100% ethanol for 1 h, and stained with 0.5% crystal violet solution (#000C1066, Samchun Chemical, Seoul, Korea). Plates were washed with distilled water to remove excess dye and air-dried before obtaining the image results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eReactive oxygen species (ROS) analysis\u003c/h2\u003e\u003cp\u003eStable RFK-knockdown and control TNBC cells were seeded into 96-well black bottom plates at a density of 1 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells per well and cultured for 48 h. Cells were then stained with DCFDA (#C6827, Invitrogen, Waltham, USA) and incubated at 37\u0026deg;C in the dark for 30 min. Fluorescence was measured using a microplate reader at excitation/emission (Ex/Em) wavelengths of 485/535 nm. For pharmacological inhibition experiments, parental TNBC cells were seeded at the same density and allowed to adhere for 24 h, followed by treatment with roseoflavin (0, 5, or 10 \u0026micro;M) for 24 h. ROS levels were then measured as described above.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eQuantitative real-time polymerase chain reaction (qRT-PCR)\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted from cells using TRIzol reagent (#FATRR001, Invitrogen), and cDNA was synthesized using the PrimeScript RT reagent kit (#RR037A, TaKaRa, Kusatsu, Japan) according to the manufacturer\u0026rsquo;s instructions. qRT-PCR was conducted using SYBR Premix Ex Taq II (#RR820A, TaKaRa) on a CFX Maestro system (Bio-Rad Laboratories, Inc. CA, USA), and relative gene expression was analyzed using the ΔΔCt method. Primer sequences used were as follows: RFK (forward: 5\u0026prime;-ACTATGGTTGGGCCAGTGTT-3\u0026prime;; reverse: 5\u0026prime;-ATGGCCACATTGAGGATTTC-3\u0026prime;); GAPDH (forward: 5\u0026prime;-GAGTCAACGGATTTGGTCGT-3\u0026prime;; reverse: 5\u0026prime;-GACAAGCTTCCCGTTCTCAG-3\u0026prime;).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eWestern blotting\u003c/h2\u003e\u003cp\u003eStable RFK-knockdown and control TNBC cells (2 \u0026times; 10⁵ cells/well) were seeded into 6-well plates and cultured for 48 h under normal conditions. For pharmacological inhibition, parental TNBC cells (2 \u0026times; 10⁵ cells/well) were seeded and allowed to adhere for 24 h, then treated with roseoflavin (0, 5, or 10 \u0026micro;M) in serum-free medium for 48 h prior to lysis. Cells were lysed in ice-cold RIPA buffer (#9806s, Cell Signaling Technology, MA, USA) with protease inhibitor (#P3100-005, GenDEPOT, TX, USA) and phosphatase inhibitor (#04906845001, Roche, Basel, Switzerland). Lysates were sonicated on ice for 2 s and centrifuged at 13,000 rpm for 20 min at 4\u0026deg;C. Total protein concentration was determined using a BCA protein assay kit (#23225, Thermo Fisher Scientific, MA, USA). Equal amounts of protein (30 \u0026micro;g) were separated on 8%\u0026ndash;12% SDS-PAGE and transferred to PVDF membranes (#BSP0161, Pall Life Science, CA, USA). Membranes were blocked in 5% skim milk (#232100, BD Biosciences, NJ, USA) in 0.1% Tris-buffered saline with Tween-20 (TBST) for 1 h at room temperature (RT), and then incubated overnight at 4\u0026deg;C with primary antibodies diluted in 5% bovine serum albumin (BSA, #C0082-100, Research and Diagnostic Technology, USA). The following antibodies were used: RFK (#CAB9141, 1:1000, Assay Genie, Dublin, Ireland), cleaved caspase-7 (#9491S, 1:2000, Cell Signaling Technology), cleaved PARP (#9541S, 1:2000, Cell Signaling Technology), and β-actin (#PM-053,1:2000, MBL, Nagoya, Japan) as a loading control. After washing three times with TBST, membranes were incubated for 3 h at RT with HRP-conjugated secondary antibodies: anti-mouse (#GTX213111-01, 1:5000, GeneTex, CA, USA) or anti-rabbit (#GTX213110-01, 1:5000, GeneTex). Protein bands were visualized using an enhanced chemiluminescence (ECL) detection reagent (#RPN2235, Cytiva, Buckinghamshire, UK) and imaged using a ChemiDoc MP imaging system (Bio-Rad Laboratories, Inc.). Band intensity was quantified using ImageJ software (NIH, USA), and normalized to β-actin.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eFlow cytometry analysis\u003c/h2\u003e\u003cp\u003eCell samples were prepared as described in Western blotting. For apoptosis detection, cells were stained with Annexin V-FITC (#556420, BD Bioscience, CA, USA) and propidium iodide (PI; #556463, BD Bioscience) for 10 min at RT in the dark. Apoptotic cell populations were analyzed using a NovoCyte 2060R flow cytometry (Agilent Technologies, CA, USA) via FITC and PE channels. For lipid peroxidation analysis, cells were stained with 2 \u0026micro;M BODIPY 581/591-C11 dye (#D3861, Invitrogen, Thermo Fisher Scientific) at 37\u0026deg;C for 30 min in the dark. After staining, cells were washed, resuspended in PBS, and immediately analyzed by flow cytometry using the FITC channel to detect oxidized BODIPY fluorescence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eGSH/GSSG measurement\u003c/h2\u003e\u003cp\u003eRFK-knockdown/control or roseoflavin-treated cells (2 \u0026times; 10⁵ cells/well) were cultured under the same conditions as for Western blotting. Intracellular concentrations of GSH and GSSG were quantified using the GSH/GSSG assay kit (#ab138881, Abcam, Cambridge, UK). Fluorescence was measured at 490/520 nm using a microplate reader. GSH levels were directly proportional to fluorescence, and GSSG values were scaled by a factor of \u0026frac12; to reflect the two moles of GSH generated per GSSG reduction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eMalondialdehyde (MDA) quantification\u003c/h2\u003e\u003cp\u003eRFK-knockdown/control or roseoflavin-treated cells (2 \u0026times; 10⁵ cells/well) were cultured under the same conditions as for Western blotting. MDA levels were quantified using a lipid peroxidation assay kit (#ab118970, Abcam) according to the manufacturer\u0026rsquo;s instructions. Fluorescence was measured at 532/553 nm. MDA levels were normalized to total protein content and expressed as nmol/\u0026micro;g relative to control.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eMitochondrial membrane potential (MMP) assay\u003c/h2\u003e\u003cp\u003eRFK-knockdown/control or roseoflavin-treated cells (2 \u0026times; 10⁵ cells/well) were cultured under the same conditions as described in Western blotting. To assess MMP, cells were stained with 2 \u0026micro;M JC-1 dye (#ab113850, Abcam) at 37\u0026deg;C for 30 min, followed by washing with 1 \u0026times; PBS. Fluorescence was observed using a fluorescence microscope (Axio Observer 7, Carl Zeiss, Oberkochen, Germany) at 100\u0026times; magnification. MMP status was determined by the JC-1 fluorescence shift, with the red/green fluorescence intensity ratio used to indicate mitochondrial polarization\u0026ndash;higher ratios reflecting polarized (intact) mitochondria, and lower ratios indicating depolarized (damaged) mitochondria.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eImmunohistochemistry (IHC) analysis\u003c/h2\u003e\u003cp\u003eTumor tissues were harvested, fixed in 4% paraformaldehyde (PFA), and embedded in paraffin. Tissue sections were subjected to IHC staining using primary antibodies against RFK (#PA5-51222, 1:200, Thermo Fisher Scientific), 4-HNE (#MHN-020P, 1:200, JaICA, Japan), and Ki67 (#M7240, 1:100, Dako, Carpinteria, CA). Visualization was performed using 3,3-diaminobenzidine (DAB; #K3468, Dako), followed by hematoxylin counterstaining. Images were captured at 200\u0026times; magnification using a DM1000 LED Ergonomic system microscope (Leica, Wetzlar, Germany). Quantification was performed using ImageJ software, based on three representative fields per section, and presented as the percentage of positively stained area (% area).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eTUNEL assay\u003c/h2\u003e\u003cp\u003eApoptosis in paraffin-embedded tumor sections was assessed using the in situ cell death detection kit (#11684795910, Roche, Basel, Switzerland) following the manufacturer\u0026rsquo;s instructions. Sections were incubated with the TUNEL reaction mixture at 37\u0026deg;C for 1 h, followed by counterstaining with DAPI at RT for 30 s. Fluorescence images were acquired using a Zeiss Axio Observer 7 microscope at 200\u0026times; magnification. TUNEL-positive cells (green) and nuclei (blue) were quantified using ImageJ software from three representative fields per section.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using a GraphPad Prism 10.0 (GraphPad Software, CA, USA). Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Each experiment was independently repeated at least three times with one or more technical replicates per condition. Unless otherwise specified, for comparisons between two groups, a two-tailed unpaired Student\u0026rsquo;s \u003cem\u003et-\u003c/em\u003etest was used. For comparisons involving more than two groups, one-way or two-way analysis of variance (ANOVA) was applied as needed. Statistical significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Exact \u003cem\u003ep\u003c/em\u003e-values were reported unless \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, which is indicated as such. Only biologically relevant \u003cem\u003ep\u003c/em\u003e-values are shown, as specified in the figure legends.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eIntegrative Genome-Scale Metabolic Modeling and CRISPR Screening Identify\u003c/b\u003e \u003cb\u003eRFK\u003c/b\u003e \u003cb\u003eas a TNBC-Specific Essential Gene\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo identify functionally essential genes in TNBC, we constructed patient-specific GEMs for 115 TNBC patients using gene expression data in TCGA [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. \u003cem\u003eIn silico\u003c/em\u003e gene knockout simulations were then conducted on each model to predict essential genes. A union of 107 genes were defined as essential in at least one TNBC model, based on criteria described in the Methods section. We first assessed the essentiality frequency of each gene across the 115 TNBC models (Fig. S1). Notably, 85% (91/107) were essential in all models, suggesting they function as pan-essential housekeeping genes. To determine TNBC-specific essentiality, we expanded our analysis to include genome-scale knockout simulations across 9,911 patient-derived models representing 33 TCGA cancer types. Genes essential across all models from all 33 TCGA cancer types were considered pan-essential and excluded. This filtering yielded 16 candidate genes with potential TNBC-specific essentiality. The 91 genes essential in all TNBC models were also universally essential across all TCGA models (Fig. S2). Next, we quantified the essentiality frequency of each of the 16 candidates in all cancer types and calculated a TNBC specificity score, defined as the difference between essentiality frequency in TNBC models and the mean frequency across non-TNBC cancers. Among these, 11 genes with positive TNBC specificity scores-indicating higher essentiality in TNBC-were retained (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). These genes were subsequently validated using experimental gene dependency data from the DepMap project [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. To complement the simulation-based predictions, we analyzed CRISPR knockout screening data from the DepMap project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://depmap.org/portal\u003c/span\u003e\u003cspan address=\"https://depmap.org/portal\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Unlike raw gene effect scores, which indicate the magnitude of cell viability loss upon gene deletion but can be affected by experimental variability, DepMap provides gene dependency probabilities, which are normalized model-based estimates of gene essentiality. A gene dependency probability\u0026thinsp;\u0026gt;\u0026thinsp;0.5 is generally considered indicative of essentiality in a given cell line. Applying this threshold, we assessed the dependency of the 11 candidate genes across 21 TNBC cell lines. Three genes including \u003cem\u003eRFK\u003c/em\u003e, \u003cem\u003eFOSL1\u003c/em\u003e, and \u003cem\u003ePGM3\u003c/em\u003e were identified as essential in at least one TNBC cell line, with a dependency score exceeding 0.5. \u003cem\u003eRFK\u003c/em\u003e exhibited the highest mean dependency score (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Specifically, \u003cem\u003eRFK\u003c/em\u003e was identified as essential in 13 TNBC cell lines, \u003cem\u003eFOSL1\u003c/em\u003e in 12, and \u003cem\u003ePGM3\u003c/em\u003e in 5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Notably, \u003cem\u003eRFK\u003c/em\u003e exhibited essentiality across a broad spectrum of TNBC subtypes, including representative basal-like 1 (BL1) subtypes such as MDA-MB-468 and HCC1937, and basal-like 2 (BL2) subtype like SUM149PT and HCC1806. Furthermore, \u003cem\u003eRFK\u003c/em\u003e dependency was also observed in mesenchymal (M) and mesenchymal stem-like (MSL) subtypes, represented by CAL-51, CAL-120, MDA-MB-231, and MDA-MB-157, respectively [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Although \u003cem\u003eFOSL1\u003c/em\u003e is a well- characterized oncogene extensively implicated in TNBC progression and chemoresistance [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], \u003cem\u003eRFK\u003c/em\u003e was selected for further investigation in this study due to its novel and unexplored role in TNBC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFerroptosis-Sensitizing Transcriptomic and Prognostic Signatures in the Basal-like Subtype Support\u003c/b\u003e \u003cb\u003eRFK\u003c/b\u003e \u003cb\u003eas a TNBC-Specific Therapeutic Target\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further explore the clinical relevance of \u003cem\u003eRFK\u003c/em\u003e, we analyzed publicly available breast cancer datasets in GEO (GSE76250 and GSE45827) and found that \u003cem\u003eRFK\u003c/em\u003e expression was significantly elevated in TNBC tissues compared to normal breast tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). We next examined whether this upregulation was specific to a particular molecular subtype using PAM50-based classification in the TCGA breast cancer dataset. As the basal-like subtype largely overlaps with TNBC at the molecular level, we compared \u003cem\u003eRFK\u003c/em\u003e mRNA levels between basal-like and non-basal-like tumors. Interestingly, no significant difference was observed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), suggesting that \u003cem\u003eRFK\u003c/em\u003e overexpression may be a general feature of breast cancer rather than uniquely enriched in basal-like tumors. Although \u003cem\u003eRFK\u003c/em\u003e expression did not differ significantly between basal and non-basal breast cancer subtypes, basal-like tumors are known to exhibit heightened sensitivity to ferroptosis compared to other breast cancer subtypes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. To explore this, we investigated whether basal-like tumors exhibit a ferroptosis-sensitizing gene expression signature using a curated set of ferroptosis-related genes previously reported to be differentially regulated in basal-like breast cancer [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These genes were categorized into ferroptosis-protective (e.g., \u003cem\u003eGPX4, SLC40A1\u003c/em\u003e) or ferroptosis-promoting (e.g., \u003cem\u003eTFRC\u003c/em\u003e, \u003cem\u003eACSL4\u003c/em\u003e) groups based on their experimentally validated roles. Our analysis revealed that basal-like tumors exhibited a ferroptosis-prone transcriptional pattern driven by both impaired antioxidant defense and dysregulated iron/lipid metabolism. Expression of \u003cem\u003eGPX4\u003c/em\u003e, encoding glutathione peroxidase 4 (a GSH-dependent peroxidase that detoxifies lipid hydroperoxides), was significantly downregulated in basal-like tumors compared to non-basal-like subtypes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Decreased GPX4 expression is known to impair lipid peroxide detoxification and lower the threshold for ferroptotic cell death [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In terms of iron metabolism, basal-like tumors demonstrated upregulation of \u003cem\u003eTFRC\u003c/em\u003e (transferrin receptor 1), which promotes cellular iron uptake [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and downregulation of \u003cem\u003eSLC40A1\u003c/em\u003e (ferroportin), the key iron exporter [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), resulting in an anticipated increase in intracellular labile iron pools and enhanced susceptibility to iron-catalyzed lipid peroxidation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Furthermore, for lipid metabolism, \u003cem\u003eACSL4\u003c/em\u003e, a lipid-activating enzyme that incorporates polyunsaturated fatty acids (PUFAs) into membrane phospholipids, the primary substrates for lipid peroxidation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], was markedly upregulated in basal-like tumors compared to non-basal-like subtypes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eTaken together, these coordinated alterations in \u003cem\u003eGPX4\u003c/em\u003e, \u003cem\u003eTFRC\u003c/em\u003e, \u003cem\u003eSLC40A1\u003c/em\u003e, and \u003cem\u003eACSL4\u003c/em\u003e suggest that basal-like tumors harbor an intrinsic ferroptosis-sensitizing molecular profile, characterized by impaired peroxide detoxification and enhanced lipid peroxidation potential. These findings align with prior reports that TNBC, particularly the basal-like subtype, is intrinsically vulnerable to ferroptotic cell death due to concurrent dysregulation of glutathione metabolism, iron homeostasis, and PUFA-enriched phospholipid biosynthesis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Consistent with this notion, comparison of basal-like tumors with both normal and tumor-adjacent normal breast tissues revealed similar expression trends: \u003cem\u003eGPX4\u003c/em\u003e was downregulated, \u003cem\u003eTFRC\u003c/em\u003e was upregulated, and \u003cem\u003eSLC40A1\u003c/em\u003e was downregulated, while \u003cem\u003eACSL4\u003c/em\u003e showed no significant difference compared to normal tissue but was modestly decreased relative to adjacent-normal tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These data reinforce the hypothesis that TNBC exhibits a ferroptosis-sensitizing transcriptomic state not only relative to other subtypes but also compared to non-malignant breast tissues, supporting the rationale for exploiting RFK inhibition as a metabolic vulnerability in this context. Although \u003cem\u003eRFK\u003c/em\u003e expression was not significantly different between basal-like and non-basal-like breast cancer subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), Kaplan\u0026ndash;Meier analysis revealed that elevated \u003cem\u003eRFK\u003c/em\u003e expression in basal-like tumors was marginally associated with worse RFS (HR\u0026thinsp;=\u0026thinsp;1.24, p\u0026thinsp;=\u0026thinsp;0.051; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), whereas no such association was observed in other subtypes (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1, Fig. S3). This subtype-specific prognostic trend likely reflects the intrinsic ferroptosis sensitivity of basal-like tumors. While such sensitivity could typically promote tumor suppression via ferroptotic cell death, elevated \u003cem\u003eRFK\u003c/em\u003e expression may mitigate this vulnerability by sustaining redox homeostasis and reducing ferroptotic stress. In this context, \u003cem\u003eRFK\u003c/em\u003e may contribute to tumor survival and disease progression in TNBC. Taken together, these findings indicate that TNBC is characterized by a ferroptosis-prone transcriptomic profile and a distinct prognostic vulnerability marked by \u003cem\u003eRFK\u003c/em\u003e. Combined with prior evidence of RFK\u0026rsquo;s functional essentiality in TNBC cell models, our results support the therapeutic potential of targeting RFK to exploit the ferroptosis susceptibility of TNBC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eRFK Knockdown Suppresses TNBC Cell Proliferation and Induces Apoptosis\u003c/h2\u003e\u003cp\u003eTo evaluate the functional role of RFK in TNBC, we performed genetic knockdown of \u003cem\u003eRFK\u003c/em\u003e in two representative TNBC cell lines, MDA-MB-231 and MDA-MB-468 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B), which were selected based on their high dependency scores in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC. \u003cem\u003eRFK\u003c/em\u003e knockdown led to a significant reduction in cell proliferation under both short-term culture conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D) and long-term colony-forming conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Immunoblot analysis further revealed upregulation of apoptosis-related markers, and flow cytometry analysis confirmed a marked increase in apoptotic cell populations upon \u003cem\u003eRFK\u003c/em\u003e knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, G). These findings indicate that RFK is essential for TNBC cell survival and that its depletion triggers apoptotic cell death.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eRFK Knockdown Induces Ferroptosis Through Oxidative Stress and Redox Imbalance\u003c/h2\u003e\u003cp\u003eIn addition to promoting apoptosis, \u003cem\u003eRFK\u003c/em\u003e knockdown triggered hallmark features of ferroptosis in TNBC cells. Lipid peroxidation levels were significantly elevated, as demonstrated by increased BODIPY-C11 oxidation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B) and malondialdehyde (MDA) accumulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Intracellular reactive oxygen species (ROS) levels were also elevated following \u003cem\u003eRFK\u003c/em\u003e depletion (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), and mitochondrial membrane potential (MMP) was markedly reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, F), indicating mitochondrial dysfunction. Furthermore, \u003cem\u003eRFK\u003c/em\u003e knockdown significantly decreased total and reduced glutathione (GSH) levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG), reflecting impaired redox homeostasis. Collectively, these findings indicate that \u003cem\u003eRFK\u003c/em\u003e depletion in TNBC cells promotes ferroptosis by elevating oxidative stress, impairing mitochondrial function, and disrupting glutathione-mediated redox homeostasis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRFK Knockdown Suppresses TNBC Tumor Growth and Induces Ferroptotic and Apoptotic Cell Death\u003c/b\u003e \u003cb\u003ein Vivo\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess the role of RFK in tumor progression \u003cem\u003ein vivo\u003c/em\u003e, we established orthotopic xenograft models by injecting control or RFK-knockdown TNBC cells into the mammary fat pads of immunodeficient mice. Tumors derived from RFK-depleted cells exhibited significantly slower growth, with markedly reduced tumor volume and weight compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). Importantly, mouse body weights remained stable throughout the experiment, indicating non-significant systemic toxicity from RFK depletion (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). To assess tumor cell death and proliferative activity, we performed immunohistochemical analyses and TUNEL assays. RFK expression was markedly reduced in tumors from the knockdown group, confirming efficient target silencing. Ki-67 staining showed a substantial decrease in proliferative activity, while 4-hydroxynonenal (4-HNE) levels, a marker of lipid peroxidation and ferroptosis, were significantly elevated. In parallel, TUNEL staining confirmed an increased number of apoptotic cells in RFK-deficient tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, F). These findings demonstrate that RFK promotes TNBC tumor growth \u003cem\u003ein vivo\u003c/em\u003e and that its depletion induces both ferroptotic and apoptotic cell death.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003ePharmacological Inhibition of RFK by Roseoflavin Induces Apoptosis in TNBC Cells\u003c/h2\u003e\u003cp\u003eRoseoflavin, a structural analog of riboflavin, competitively inhibits the biosynthesis of flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD), thereby functioning as an RFK inhibitor [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. To investigate the therapeutic potential of targeting RFK, we evaluated the effects of roseoflavin on TNBC cell proliferation. Roseoflavin treatment significantly suppressed the growth of MDA-MB-231 and MDA-MB-468 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), as further confirmed by a marked reduction in colony-forming ability (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, C). Additionally, Western blot analysis revealed elevated levels of apoptosis-related proteins, including cleaved caspase-7 and cleaved PARP (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, E). Flow cytometry analysis further demonstrated a substantial increase in apoptotic cell populations following roseoflavin treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, G). These findings suggest that pharmacological inhibition of RFK activity suppresses TNBC progression through the induction of apoptosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eRoseoflavin-Induced RFK Inhibition Triggers Ferroptosis in TNBC Cells\u003c/h2\u003e\u003cp\u003eTo determine whether pharmacological inhibition of RFK also induces ferroptosis, we treated TNBC cells with roseoflavin and evaluated ferroptosis-associated parameters. Roseoflavin treatment led to a significant increase in lipid peroxidation, as detected by BODIPY 581/591 C11 staining and elevated MDA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B). Intracellular ROS levels were markedly increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC), while MMP was significantly reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD), indicating mitochondrial dysfunction. Furthermore, intracellular levels of total glutathione and GSH were significantly decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE), suggesting impaired antioxidant capacity. Together, these findings indicate that roseoflavin-induced RFK inhibition promotes ferroptosis in TNBC cells by enhancing oxidative stress and disrupting redox homeostasis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePharmacological Inhibition of RFK by Roseoflavin Suppresses TNBC Tumor Growth\u003c/b\u003e \u003cb\u003eIn Vivo\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the therapeutic potential of RFK inhibition \u003cem\u003ein vivo\u003c/em\u003e, orthotopic xenograft models of TNBC were established by injecting MDA-MB-231 or MDA-MB-468 cells into the mammary fat pads of immunodeficient mice. Once tumors were established, mice were administered roseoflavin (30 mg/kg/day) or vehicle daily. Roseoflavin treatment significantly suppressed tumor growth, as evidenced by reduced tumor volume and weight compared to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u0026ndash;C). Notably, there were no significant changes in body weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD), and serum levels of liver toxicity markers, alanine aminotransferase (ALT) and aspartate aminotransferase (AST), remained within normal ranges (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE), indicating good tolerability. Immunohistochemical analysis of tumor tissues revealed a marked reduction in Ki-67 expression, indicating decreased proliferative activity, along with increased levels of 4-HNE, a marker of lipid peroxidation and ferroptosis. In addition, TUNEL staining demonstrated enhanced apoptotic cell death in roseoflavin-treated tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF, G). These findings suggest that pharmacological inhibition of RFK by roseoflavin not only suppresses TNBC tumor growth but also induces both ferroptosis and apoptosis \u003cem\u003ein vivo\u003c/em\u003e, without overt toxicity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTNBC is one of the most aggressive and therapeutically challenging subtypes of breast cancer, characterized by high recurrence rates, poor prognosis, and a lack of actionable molecular targets [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Targeted therapy options remain limited: PARP inhibitors provide benefit only for BRCA1/2-mutated patients, while immune checkpoint inhibitors (e.g., pembrolizumab) are approved for a subset of PD-L1-positive tumors [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Given the suboptimal efficacy of current treatments, there is an urgent need to develop new therapeutic strategies that engage alternative cell death pathways. In this regard, increasing attention has been paid toward non-apoptotic cell death pathways, especially ferroptosis, an iron-dependent regulated cell death driven by lipid peroxidation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Emerging evidence suggests that TNBC cells possess a unique metabolic dependency on iron handling and glutathione homeostasis, which may underlie their increased sensitivity to ferroptosis compared to other breast cancer subtypes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Several ferroptosis inducers, including GPX4 inhibitors, have shown selective lethality in TNBC models [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], positioning ferroptosis as a promising therapeutic vulnerability in this subtype. Consistent with this notion, Kaplan\u0026ndash;Meier analysis of breast cancer data in KM-Plotter revealed that higher \u003cem\u003eGPX4\u003c/em\u003e expression was significantly associated with worse RFS and OS specifically in basal-like tumors (Fig. S4). These findings further support the clinical relevance of GPX4-mediated ferroptosis resistance in TNBC and underscore the therapeutic potential of targeting ferroptosis in this subtype.\u003c/p\u003e\u003cp\u003eTo identify metabolic vulnerabilities capable of triggering both apoptosis and ferroptosis, we employed an integrative approach combining GEM-based simulation and experimental validation. This approach revealed RFK as a TNBC-specific metabolic dependency. Patient-derived GEM simulations, constructed from transcriptomic data of TNBC tumors, predicted that RFK knockout would efficiently impair cell survival in this subtype. These predictions were subsequently validated \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiments. Genetic silencing or pharmacological inhibition of RFK using roseoflavin not only suppressed TNBC tumor growth but also induced hallmark features of apoptosis and ferroptosis. These included increased levels of cleaved caspase-7 and cleaved PARP, elevated ROS accumulation, glutathione depletion, lipid peroxidation, mitochondrial dysfunction, and ultimately, cell death. These dual phenotypic changes underscore the pivotal role of RFK in maintaining redox homeostasis and regulating multiple cell death pathways in TNBC.\u003c/p\u003e\u003cp\u003eSimulation-based gene knockout predictions using patient-derived GEMs allow for large-scale prediction of gene essentiality across heterogeneous tumor microenvironments modeled from patient-specific transcriptomes. However, a major limitation of this approach is the experimental impracticality of performing genome-wide CRISPR screening directly in primary human tumors. In contrast, CRISPR-based functional genomics screening in established cancer cell lines provides direct experimental evidence of gene dependency, but its physiological relevance is limited and may not fully recapitulate the complexity and interpatient heterogeneity of human tumors. To overcome these complementary limitations, we integrated patient-derived GEM-based predictions with CRISPR dependency datasets from the DepMap portal, thereby enhancing biological relevance through patient-level modeling and improving empirical validity \u003cem\u003evia\u003c/em\u003e experimental gene dependency. This integrative approach provided a converging line of evidence identifying RFK as an essential and druggable metabolic node in TNBC, underscoring its potential as a therapeutic target for precision oncology.\u003c/p\u003e\u003cp\u003eDespite its central role in flavin metabolism, the function of RFK in cancer biology remains poorly understood [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Prior studies in prostate cancer demonstrated that RFK overexpression conferred resistance to cisplatin and oxidative stress-inducing agents such as hydrogen peroxide and diamide. In contrast, RFK knockdown sensitized prostate cancer cells to these agents and induced apoptotic cell death. Mechanistically, RFK overexpression increased intracellular levels of FAD, FMN, and total glutathione, alongside the upregulation of antioxidant enzymes such as GR and glutathione S-transferase π (GSTπ). Furthermore, RFK expression was positively correlated with the Gleason score, implicating a role in oxidative stress resistance and malignant progression in prostate cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur transcriptomic and survival analyses identified that, beyond GPX4, additional ferroptosis-protective genes exhibit distinct expression and prognostic patterns in the basal-like subtype. Specifically, GCLM, the modifier subunit of glutamate\u0026ndash;cysteine ligase (the rate-limiting enzyme for glutathione biosynthesis) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], was significantly upregulated (Fig. S5A), as was SLC52A2 (Fig. S5B), encoding a riboflavin importer [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In contrast, ABCG2, which mediates riboflavin efflux [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], was downregulated in basal-like tumors compared to other subtypes (Fig. S5C). Notably, high expression of GCLM (HR\u0026thinsp;=\u0026thinsp;1.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) and SLC52A2 (HR\u0026thinsp;=\u0026thinsp;2.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0011) was associated with poorer overall survival specifically in basal-like tumors, but not in other subtypes (Fig. S6, 7). Mechanistically, these alterations likely represent compensatory adaptations to the heightened ferroptosis susceptibility observed in basal-like subtypes, which are characterized by reduced GPX4 expression, elevated levels of intracellular labile iron, and an abundance of PUFA-containing phospholipids. Upregulation of GCLM may serve to expand the intracellular glutathione pool, thereby fortifying antioxidant defenses and mitigating lipid peroxidation. Similarly, increased expression of SLC52A2, in combination with downregulation of ABCG2, is predicted to elevate intracellular riboflavin availability. This, in turn, enhances the production of FMN and FAD, critical cofactors required for glutathione biosynthesis enzymes, thereby strengthening redox buffering capacity. We propose that this adaptive remodeling, herein termed the riboflavin\u0026ndash;FMN/FAD\u0026ndash;glutathione axis, functions as a metabolic safeguard against ferroptotic stress and supports tumor cell survival in TNBC.\u003c/p\u003e\u003cp\u003ePrevious studies have shown that RFK activity is highly sensitive to the relative concentrations of its substrate, riboflavin [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In TNBC, increased riboflavin uptake (via SLC52A2) and reduced efflux (via ABCG2) likely lead to elevated intracellular riboflavin, thereby enhancing RFK activity and driving FMN/FAD production. This, in turn, supports glutathione-dependent redox buffering. Such a metabolic context implies that TNBC may be particularly dependent on RFK-mediated flavin cofactor synthesis to counter ferroptotic stress. This reliance offers a mechanistic rationale for the TNBC-specific prognostic association of RFK and highlights it as a selective therapeutic target in TNBC.\u003c/p\u003e\u003cp\u003eBeyond oncology, RFK has been widely studied for its role in antioxidant defense in neurodegenerative and ischemic diseases. In Parkinson\u0026rsquo;s disease models, both RFK and its metabolic product FMN have been shown to alleviate oxidative stress and prevent dopaminergic neurons from degeneration, emphasizing the neuroprotective role of riboflavin metabolism [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Similarly, in models of ischemic neuronal injury, RFK overexpression attenuated neuronal apoptosis and necrosis induced by oxygen-glucose deprivation, whereas RFK silencing exacerbated neuronal damage [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These findings reinforce the concept that RFK functions as a redox-regulating enzyme with broader cytoprotective effects across diverse pathological conditions.\u003c/p\u003e\u003cp\u003eAs a key enzyme in riboflavin metabolism, RFK catalyzes the phosphorylation of riboflavin to generate FMN, which is further converted to FAD [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These flavin cofactors, FMN and FAD, are essential for the activity of more than 100 human flavoproteins, many of which are involved in mitochondrial energy production and oxidative stress responses [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Riboflavin has been shown to exert antioxidant effects by promoting mitochondrial integrity, maintaining glutathione homeostasis, and enhancing the activity of antioxidant enzymes such as glutathione peroxidase (GPx), superoxide dismutase (SOD), and catalase. Supplementation with riboflavin reduces oxidative stress markers such as MDA and protects against lipid peroxidation [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Notably, riboflavin deficiency induces apoptosis through multiple mechanisms, including redox imbalance, mitochondrial dysfunction, endoplasmic reticulum (ER) stress, and lipotoxicity [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. These effects align with the observed consequences of RFK depletion, which limits the availability of FMN and FAD and compromises antioxidant capacity.\u003c/p\u003e\u003cp\u003eIn the context of ferroptosis, several FMN- or FAD-dependent flavoproteins contribute to regulation of this non-apoptotic, iron-dependent cell death. These include GR, apoptosis-inducing factor mitochondria-associated 2 (AIFM2/FSP1), NAD(P)H:quinone oxidoreductase 1 (NQO1), and cytochrome P450 oxidoreductase (POR) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Among them, GR plays a critical role in maintaining intracellular glutathione levels, which are essential for the activity of GPX4, the enzyme responsible for reducing toxic lipid peroxides and blocking ferroptosis. Disruption of this GR-GPX4 axis leads to unchecked lipid peroxidation and ferroptotic death. Moreover, GR has been implicated in cancer aggressiveness and therapy resistance. For example, GR expression has been reported as a marker of malignancy in hepatocellular carcinoma [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and pharmacological inhibition of GR with carmustine promotes ferroptosis \u003cem\u003ein vivo\u003c/em\u003e and enhances the antitumor efficacy of sorafenib [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. GR is also required for colorectal cancer cell survival under acidic microenvironments, further emphasizing its importance in in redox adaptation [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, RFK was identified as a candidate therapeutic target through TNBC-specific genome-scale metabolic modeling. Functional experiments revealed that both genetic knockdown and pharmacological inhibition of RFK disrupted redox homeostasis in TNBC cells, leading to glutathione depletion, ROS accumulation, mitochondrial dysfunction, and enhanced lipid peroxidation, which are hallmarks of both apoptosis and ferroptosis. These findings highlight RFK as a dual regulator of redox signaling and cell death, linking its metabolic activity to the survival of TNBC cells. While our data support the tumor-suppressive effects of RFK inhibition, the detailed molecular pathways by which RFK governs ferroptosis and apoptosis remain to be elucidated. Future investigations will focus on identifying downstream effectors and interacting partners that mediate RFK-dependent cell fate decisions. Moreover, combinatorial strategies involving RFK inhibition alongside ferroptosis inducers, immune checkpoint blockade, or conventional chemotherapy may offer promising avenues to overcome therapeutic resistance and improve clinical outcomes in TNBC. Collectively, our findings highlight RFK as a metabolic vulnerability and a redox checkpoint in the ferroptosis-apoptosis axis of TNBC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAIFM2/FSP1\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eapoptosis-inducing factor mitochondria-associated 2 / ferroptosis suppressor protein 1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBSA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebovine serum albumin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDAB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e3,3-diaminobenzidine\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDepMap\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDependency Map\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDMEM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDulbecco\u0026rsquo;s Modified Eagle\u0026rsquo;s Medium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eER\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eestrogen receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eflavin adenine dinucleotide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFBA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eflux balance analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFBS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efetal bovine serum\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFMN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eflavin mononucleotide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene Expression Omnibus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGEMs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003egenome-scale metabolic models\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eglutathione reductase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGSM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003egenome-scale metabolic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGSH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eglutathione\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGSSG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eoxidized glutathione\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGSTπ\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eglutathione S-transferase-π\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGTEx\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenotype-Tissue Expression\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehazard ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIHC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eimmunohistochemistry\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ei.p.\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eintraperitoneal\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eknockout\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMDA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emalondialdehyde\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMILP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emixed integer linear programming\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMMP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emitochondrial membrane potential\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNQO1\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNAD(P)H:quinone oxidoreductase 1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eoptical density\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eoverall survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eprogrammed cell death\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePFA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eparaformaldehyde\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epropidium iodide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecytochrome P450 oxidoreductase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eprogesterone receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eriboflavin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRFK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eriboflavin kinase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRFS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003erelapse-free survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRoF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eroseoflavin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ereactive oxygen species\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRPMI 1640\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRoswell Park Memorial Institute 1640\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTBST\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTris-buffered saline with Tween-20\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTCGA-BRCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCancer Genome Atlas breast invasive carcinoma cohort\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTNBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etriple-negative breast cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTUNEL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eterminal deoxynucleotidyl transferase dUTP nick end labeling\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC) at the National Cancer Center (IACUC-NCC-24-1053).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated during the current study. Materials or additional information used in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) (2022R1A2C2092053 and RS-2024-00431505) and by Korea Basic Science Institute (National research Facilities and Equipment Center) grant funded by the Ministry of Education (2021R1A6C101A442).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.L. performed the experiments, contributed to the experimental design, and drafted the manuscript. D.K. and T.K. conducted GEMs, Depmap analysis, interpreted related data, carried out bioinformatics analysis and co-wrote the manuscript. SA.K., I.M. and S.K. performed experiments, acquired data, contributed to data analysis. J.J. performed animal experiments and acquired data. S.Y. and Y.K. conceived and designed the study, supervised all aspects of the project, and secured funding. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.L. gratefully acknowledges financial support from the China Scholarship Council (CSC) Grant (201306120117). The authors also thank the Ewha Drug Development Research Core Center for providing analytical support using the NanoDrop spectrophotometer (NFEC-2023-03-286005), flow cytometer (NFEC-2019-03-254735), microplate reader (NFEC-2021-08-272460), fluorescence microscope (NFEC-2021-08-272462), and real-time PCR system(NFEC-2021-08-272451).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information \u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1 College of Pharmacy, Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, 03760, South Korea.\u003c/p\u003e\n\u003cp\u003eYi Liu ([email protected]);\u003c/p\u003e\n\u003cp\u003eSeul-Ah Kim ([email protected]);\u003c/p\u003e\n\u003cp\u003eInhye Moon ([email protected]);\u003c/p\u003e\n\u003cp\u003eSeojeong Kim ([email protected]);\u003c/p\u003e\n\u003cp\u003eYoungjoo Kwon ([email protected])\u003c/p\u003e\n\u003cp\u003e2 Standigm Inc., 6F, 182 Dogok-ro, Gangnam-gu, Seoul, Republic of Korea.\u003c/p\u003e\n\u003cp\u003eDongin Kim ([email protected])\u003c/p\u003e\n\u003cp\u003eTae Yong Kim ([email protected]);\u003c/p\u003e\n\u003cp\u003eSojeong Yun ([email protected])\u003c/p\u003e\n\u003cp\u003e3 Graduate Program in Innovative Biomaterials Convergence, Ewha Womans University, Seoul, 03760, Republic of Korea.\u003c/p\u003e\n\u003cp\u003eSeul-Ah Kim ([email protected]);\u003c/p\u003e\n\u003cp\u003eInhye Moon ([email protected]);\u003c/p\u003e\n\u003cp\u003eYoungjoo Kwon ([email protected])\u003c/p\u003e\n\u003cp\u003e4 National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang-si Gyeonggi-do, 10408, Republic of Korea.\u003c/p\u003e\n\u003cp\u003eJinhue Jeong ([email protected])\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F et al (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Cell Cycle 15(8):1125\u0026ndash;1133\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"College of Pharmacy, Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, 03760, South Korea.","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Triple-negative breast cancer (TNBC), riboflavin kinase (RFK), cell death, ferroptosis, Genome-scale metabolic models (GEMs), flavin cofactors","lastPublishedDoi":"10.21203/rs.3.rs-7709970/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7709970/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTriple-negative breast cancer (TNBC) is a highly aggressive and heterogeneous subtype of breast cancer lacking estrogen receptor, progesterone receptor, and HER2 expression. Due to the absence of actionable molecular targets, patients rely heavily on chemotherapy, often facingearly recurrence and poor prognosis. There is an urgent need for novel therapeutic strategies that utilize alternative cell death mechanisms beyond conventional apoptosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003cbr\u003e\nTo identify metabolic vulnerabilities specific to TNBC, we employed an integrative strategy combining genome-scale metabolic modeling based on patient transcriptomic data with CRISPR-Cas9 dependency datasets. Riboflavin kinase (RFK), an enzyme that converts riboflavin into FMN and FAD, was identified as a top-ranked candidate target. Functional validation was conducted via genetic knockdown and pharmacological inhibition using roseoflavin. Cellular proliferation was assessed by WST assay and crystal violet staining. Apoptosis and ferroptosis were evaluated by Annexin V/PI flow cytometry, western blotting, JC-1 and C11-BODIPY fluorescence, ROS and MDA assays, and glutathione quantification. \u003cem\u003eIn vivo\u003c/em\u003e efficacy was tested in orthotopic xenograft models using RFK-silenced TNBC cells or roseoflavin-treated mice. Immunohistochemical analyses (Ki67, 4-HNE, TUNEL) were used to assess tumor proliferation, ferroptosis, and apoptosis, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003cbr\u003e\nRFK suppression significantly inhibited TNBC cell proliferation \u003cem\u003ein vitro\u003c/em\u003eand \u003cem\u003ein vivo\u003c/em\u003e. Mechanistically, RFK loss reduced glutathione levels, increased intracellular ROS accumulation,and enhanced lipid peroxidation, resulting in mitochondrial dysfunction and concurrent induction of ferroptosis and apoptosis. In TNBC xenograft models, RFK knockdown or roseoflavin treatment markedly reduced tumor growth, enhanced lipid peroxidation, and increased cell death. Transcriptomic analyses suggest that TNBC tumors, exhibiting heightened ferroptosis susceptibility, may engage in metabolic reprogramming,characterized by upregulation of genes involved in riboflavin uptake, flavin cofactor biosynthesis, and glutathione synthesis, as a compensatory adaptation toenhance redox buffering capacity and resist ferroptotic stress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study identifiedRFK as a TNBC-specific metabolic vulnerability, regulatingredox homeostasis and cell death pathways. Targeting RFK represents a promising therapeutic strategy for TNBC, as it induced both ferroptosis and apoptosis. These findings underscore the potential of exploiting the riboflavin–FMN/FAD–glutathione axis as a redox metabolic checkpoint in ferroptosis-prone TNBC.\u003c/p\u003e","manuscriptTitle":"Targeting Riboflavin Kinase Induces Ferroptosis and Apoptosis in Triple-Negative Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-26 10:48:41","doi":"10.21203/rs.3.rs-7709970/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b320d02a-1386-4f46-905c-421ad704f2c3","owner":[],"postedDate":"September 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55312232,"name":"Cancer Biology"},{"id":55312233,"name":"Oncology"}],"tags":[],"updatedAt":"2025-09-26T10:48:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-26 10:48:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7709970","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7709970","identity":"rs-7709970","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}