Starving Cancer: How Glucose Restriction Enhances Tamoxifen sensitivity in Breast Cancer

Starving Cancer: How Glucose Restriction Enhances Tamoxifen sensitivity in 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 Starving Cancer: How Glucose Restriction Enhances Tamoxifen sensitivity in Breast Cancer Haider Yabr Lafta, Hossein Fallahi, Majeed Arsheed Sabbah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7668101/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: Breast cancer cells rewire their metabolism to thrive on high glucose, and elevated blood sugar is linked to therapy resistance. We investigated whether glucose restriction can reprogram breast cancer cell behavior and enhance the efficacy of tamoxifen, a cornerstone endocrine therapy. Methods: Using estrogen receptor-positive MCF7 cells, we integrated gene expression profiling with protein–protein interaction and gene regulatory network analyses under three conditions: low-glucose (metabolic starvation), tamoxifen treatment, and their combination. Results: Glucose starvation alone triggered a broad transcriptional reprogramming (697 differentially expressed genes, DEGs) involving cell-cycle regulation and chromatin remodeling, while tamoxifen alone altered a smaller gene set (201 DEGs) linked to proliferation and apoptotic pathways. Strikingly, combined glucose restriction and tamoxifen induced a massive gene expression shift (1,294 DEGs), far exceeding either treatment alone and indicating a synergistic anti-cancer response. Network analysis revealed distinct but overlapping molecular networks: starvation preferentially upregulated DNA replication and mitotic cell-cycle modules, tamoxifen enriched for pathways suppressing cell proliferation and protein synthesis, and the combination uniquely engaged cell division and chromatin-organization networks. We identified six hub proteins and 44 genes consistently regulated across all conditions, pointing to a core stress-response program. Transcription factor analysis further uncovered 54 key regulators common to all treatments and an expanded set of master regulators (12 differentially expressed transcription factors) activated only under combined treatment, underscoring novel gene regulatory interactions behind the enhanced response. Conclusion: Our findings reveal new molecular insights into how glucose deprivation potentiates tamoxifen’s anti-tumor effects. This study underscores the interplay between cancer metabolism and hormone therapy, suggesting that targeting metabolic vulnerabilities can amplify treatment efficacy and offering a robust gene-network framework for advancing breast cancer metabolism research. Breast cancer MCF7 Network analysis differentially expressed genes Transcription factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Metabolic factors have a profound influence on cancer development and progression, and emerging evidence points to elevated blood glucose as a key contributor. Cancer cells characteristically rewire their metabolism to depend on high rates of glucose uptake and glycolysis – an observation dating back to Warburg’s early work on tumor metabolism [1, 2]. This “Warburg effect” implies that a high-glucose environment can fuel tumor growth and survival by providing abundant energy and biomass. Consistent with this idea, experimental studies confirm that high extracellular glucose can directly accelerate the proliferation of breast cancer cells and enhance their invasive capacity [3-5]. In essence, an elevated glycemic state creates a pro-tumor microenvironment that may hasten breast cancer progression. Clinical and epidemiological data further reinforce the connection between glucose homeostasis and breast cancer outcomes. Women with diabetes or chronic hyperglycemia have a higher incidence of breast cancer and often experience poorer survival rates than normoglycemic individuals [6-8]. For example, breast cancer patients presenting with elevated fasting blood glucose at diagnosis face significantly greater risks of tumor recurrence and distant metastasis compared to those with normal glycemic levels. One cohort study found that peridiagnostic high fasting glucose independently predicted worse short- and long-term outcomes in breast cancer patients, whereas maintaining healthy blood sugar was associated with improved prognosis. Indeed, some have suggested that long-term control of hyperglycemia could beneficially impact breast cancer outcomes [9, 10]. These observations have raised interest in whether correcting metabolic abnormalities – such as lowering circulating glucose – might slow tumor growth or enhance the effectiveness of cancer therapies. One approach to counteract the tumor-promoting effects of excess glucose is through dietary interventions like caloric restriction and fasting. Numerous preclinical studies have shown that sustained calorie restriction can reduce the incidence and progression of tumors in animal models [11, 12], although chronic dieting is often impractical for cancer patients. More feasible are short-term fasting regimens (or fasting-mimicking diets), which can dramatically lower systemic glucose and insulin/IGF-1 levels, thereby creating a metabolic state less conducive to tumor growth [13, 14]. Notably, combining periodic fasting with chemotherapy has yielded promising results in model systems: fasting-treated mice exhibit enhanced tumor sensitivity to chemotherapeutic drugs while normal cells are protected from toxicity, an effect attributed to a differential stress response [15]. Recent research extends this paradigm to hormone therapy. In hormone receptor–positive breast cancer models, cycles of fasting or low-calorie diet significantly boosted the efficacy of endocrine treatments (tamoxifen or fulvestrant), producing greater tumor regression and even overcoming acquired drug resistance. Intriguingly, fasting also mitigated tamoxifen’s side effect of endometrial hyperplasia in these models [16]. Early clinical studies indicate that such dietary interventions are safe and potentially beneficial: initial trials in breast cancer patients reported that fasting or fasting-mimicking diets are well-tolerated and may improve therapy tolerability, warranting further investigation in larger studies [17, 18]. Together, these findings suggest that modulating a patient’s nutritional/metabolic state (for instance, through fasting) can synergize with standard treatments to improve anticancer efficacy. Tamoxifen is a cornerstone of treatment for estrogen receptor–positive breast cancer, and its mechanism of action exemplifies the nuanced behavior of selective endocrine therapies. As a selective estrogen receptor modulator (SERM), tamoxifen binds to the estrogen receptor (ER) and induces a conformational change that blocks estrogen-driven transcription in breast tissue [19, 20]. Specifically, the tamoxifen–ER complex prevents the recruitment of coactivator proteins and instead facilitates the assembly of corepressor complexes at estrogen-responsive gene promoters, thereby shutting down proliferative signals that would otherwise be stimulated by estrogen [21, 22]. In breast cancer cells, tamoxifen thus functions as an ER antagonist to inhibit tumor growth, while in other tissues (for example, bone or the endometrium) it can act as a partial agonist – reflecting its tissue-selective hormonal effects [23, 24]. This dual activity underlies tamoxifen’s benefits and side effects (such as its anti-resorptive effect on bone and pro-estrogenic effect on uterine tissue). Clinically, tamoxifen has dramatically improved outcomes in ER-positive breast cancer, with about five years of adjuvant tamoxifen therapy reducing recurrence rates and breast cancer mortality by roughly one-third [25]. Its success underscores the importance of fully understanding tamoxifen’s mechanism and the factors that modulate its activity. Despite the evident links between metabolism and breast cancer, the precise molecular mechanisms by which hyperglycemia influences tumor behavior remain incompletely understood [26-28]. In particular, it is unclear how elevated glucose levels might reprogram breast cancer gene expression or interact with signaling pathways to affect disease progression and treatment response. To address this knowledge gap, the present study conducts a meta-analysis of gene expression profiles to elucidate the effects of blood glucose levels on breast cancer. By integrating data from gene expression profiling experiments, we aim to identify key genes and pathways modulated by hyperglycemia in breast cancer cells, shedding light on how metabolic stressors drive tumor-promoting changes at the molecular level [29, 30]. Clarifying these mechanisms is not only biologically important but also clinically relevant: such insights could point to new biomarkers or therapeutic targets and inform adjuvant strategies (such as dietary or pharmacologic glycemic control) to enhance the efficacy of standard treatments [31, 32]. In summary, this study seeks to bridge metabolic and molecular cancer research, offering a deeper understanding of how blood glucose levels impact breast cancer and how this knowledge can be leveraged to improve patient outcomes. Material and methods Data Collection and Gene Expression Analysis For this study, we analyzed publicly available gene expression data obtained from the Gene Expression Omnibus (GEO) database under accession number GSE121378. Our primary focus was to investigate the impact of blood glucose levels and tamoxifen treatment on breast cancer cells (MCF7). The dataset included three experimental conditions: low-glucose (starvation) treatment, tamoxifen exposure, and a combination of both treatments, each with four biological replicates. To identify differentially expressed genes (DEGs) across these conditions, we employed GEO2R ( http://www.ncbi.nlm.nih.gov/geo/geo2r/ ), an online tool designed for gene expression analysis. DEGs were filtered based on a p-value threshold of 0.05, and only genes with a log2 fold-change (FC) greater than ± 1 were retained for further investigation. To ensure data accuracy, we manually reviewed the gene lists, removing duplicate entries, ambiguous gene symbols, and missing annotations. This resulted in refined DEG lists for each experimental condition, which were subsequently used in downstream analyses. Protein-Protein Interaction (PPI) Network Analysis To explore the interactions between proteins encoded by the identified DEGs, we constructed protein-protein interaction (PPI) networks using the STRING database. The DEG lists were submitted to STRING, and the resulting interaction networks were analyzed using Cytoscape (version 3.4.0) and Gephi (version 0.9.1) for visualization and network characterization [33]. To determine key regulatory genes, we identified hub genes by selecting the top 5% of nodes with the highest degree of connectivity within each network. Additionally, to detect functional subnetworks, we applied ClusterONE 1.0, a Cytoscape plugin, using the following parameters: minimum cluster size of 5 with density threshold set to 0.6 and significance level was considered to be at p < 0.05. From the identified subnetworks, we selected the two largest sub-clusters per experimental group for further analysis. Finally, we examined the overlap between subnetworks from all three conditions to identify common proteins shared across treatments. Transcription Factor (TF) Analysis Since transcription factors (TFs) play a crucial role in regulating gene expression, we sought to identify TFs associated with our DEG lists. For this, we used the ChEA database, which compiles protein-DNA interactions obtained from ChIP-X assays. TFs with a p-value below 0.05 were considered statistically significant. Following this, we identified differentially expressed transcription factors (DE-TFs) and examined their regulatory influence on the DEG lists. Using network analysis, we constructed TF-target interaction networks and performed centrality analysis to determine the most influential TFs within the network [34]. Gene Ontology (GO) Enrichment Analysis To classify the identified DEGs based on their involvement in biological processes and pathways, we performed Gene Ontology (GO) enrichment analysis using the DAVID database. DEGs were separately analyzed based on their expression pattern (upregulated vs. downregulated genes) to determine their distinct functional roles. Only GO terms with a p-value below 0.05 were considered for further interpretation. From the statistically significant results, we selected the top five biological processes with the highest number of associated DEGs. These processes were visualized and mapped within a network framework to provide a clearer understanding of their functional relationships. Results Differentially Expressed Genes (DEGs) Across Experimental Conditions To identify differentially expressed genes (DEGs) under various treatment conditions, we compared the gene expression profiles of treated samples to their respective controls. In the starvation condition, a total of 697 DEGs were identified, comprising 322 upregulated genes and 375 downregulated genes. For the tamoxifen-only treatment, we observed 201 DEGs, with 53 genes upregulated and 148 genes downregulated. Notably, in the combined tamoxifen and starvation condition, the number of DEGs increased substantially to 1,294 genes, with 598 upregulated and 696 downregulated genes (Fig. 1 a). To examine the degree of overlap among DEGs across these conditions, we generated a Venn diagram to visualize shared and unique gene expression changes (Fig. 1 b). As shown in Supplementary Table S1, we identified 44 common DEGs present across all three conditions. Among the shared DEGs, 39 genes were consistently downregulated, while 5 genes exhibited upregulation across all conditions (Fig. 1 c). Interestingly, no gene displayed opposing expression patterns—meaning that no DEG was upregulated in one condition and downregulated in another. Protein-Protein Interaction (PPI) Network Analysis To investigate functional interactions among differentially expressed genes (DEGs), we utilized the STRING database and analyzed the resulting networks using the ClusterONE plugin in Cytoscape. From the identified network modules, the two largest clusters were selected for further examination. In the starvation condition, the most prominent modules were associated with DNA replication and the mitotic cell cycle (Fig. 2 a). Meanwhile, in the tamoxifen-treated cells, key modules were linked to the negative regulation of cell proliferation and co-translational protein targeting to the membrane (Fig. 2 b). Interestingly, in the combined starvation and tamoxifen condition, the identified modules were primarily involved in cell division and chromatin organization, indicating shared regulatory pathways between the two treatments (Fig. 2 c). To further explore key regulatory proteins, we visualized the interaction networks using Gephi software, highlighting central hub nodes within each condition. A comparative analysis of the individual networks allowed us to identify common proteins shared across multiple conditions. Notably, six proteins were found to be consistently present in all three experimental conditions, suggesting their potential role in mediating responses to metabolic stress and endocrine therapy. Annotation and functional enrichment analysis Gene Ontology (GO) enrichment analysis was conducted to classify differentially expressed genes (DEGs) based on their involvement in biological processes, molecular functions, and pathways. Using the DAVID database, we identified key biological processes associated with both upregulated and downregulated genes across different experimental conditions. In the starvation condition, upregulated genes were predominantly linked to chromatin remodeling and cell division (Fig. 3 a), suggesting an adaptive response to metabolic stress. In contrast, downregulated genes were largely involved in transcriptional regulation by RNA polymerase II and apoptotic processes (Fig. 3 b), indicating potential suppression of gene expression and programmed cell death pathways. For cells treated with tamoxifen, upregulated genes were primarily associated with positive regulation of cell migration and the mitotic cell cycle (Fig. 3 c), reflecting the impact of tamoxifen on cellular proliferation and movement. Meanwhile, downregulated genes were significantly enriched in apoptotic processes (Fig. 3 d), suggesting tamoxifen-mediated alterations in cell survival mechanisms. Interestingly, in the combined starvation and tamoxifen condition, we observed a shared pattern where genes involved in cell division were consistently upregulated, whereas genes regulating transcription by RNA polymerase II were downregulated (Figs. 3 e and 3 f). These findings highlight overlapping molecular pathways influenced by both metabolic stress and endocrine therapy, providing insight into potential mechanisms underlying their combined effects on breast cancer cells. Prediction of TFs for the list of DEGs To identify transcription factors (TFs) that potentially regulate the differentially expressed genes (DEGs) across different conditions, we utilized the ChEA dataset available through the Enrichr server. This analysis allowed us to determine key regulatory TFs associated with gene expression changes in response to metabolic stress and tamoxifen treatment. Our findings revealed that in the starvation condition, a total of 91 TFs were predicted to interact with the identified DEGs, among which 5 were differentially expressed transcription factors (DE-TFs). In contrast, tamoxifen-treated cells exhibited a total of 71 predicted TFs, with only 1 DE-TF identified as a key regulator. When analyzing the combined starvation and tamoxifen condition, we found a total of 99 TFs, with 12 DE-TFs playing significant regulatory roles (Figs. 4a, 4b, and 4c). Furthermore, a comparative analysis of transcription factors across all three conditions revealed a set of 54 common TFs, suggesting shared regulatory mechanisms governing gene expression changes under metabolic and therapeutic stress. These findings highlight potential key regulators involved in the response to both glucose restriction and tamoxifen treatment, offering further insights into the molecular pathways influenced by these interventions. Discussion Glucose Availability and Breast Cancer Progression Our findings highlight the profound impact of glucose levels on breast cancer cell behavior at the molecular level. In conditions of glucose deprivation (fasting-mimicking), MCF7 cells underwent broad transcriptional reprogramming: hundreds of genes changed expression compared to normal-glucose controls. Notably, many genes were downregulated under low glucose (375 genes, versus 322 upregulated), indicating that nutrient stress curtails numerous cellular processes. These downregulated genes were enriched in functions related to RNA polymerase II–mediated transcription and apoptosis, suggesting that glucose-starved cells globally suppress gene expression and avoid cell death pathways (perhaps as an acute survival mechanism). Consistently, pro-apoptotic processes were dampened in low-glucose conditions, implying that breast cancer cells in an energy-poor environment may actively inhibit apoptosis to survive. This aligns with known biology: ample glucose can protect cancer cells from apoptosis by fueling antioxidant production (via NADPH and glutathione) that blocks apoptotic triggers​ [35]. Thus, removing glucose (as in our study) likely has the opposite effect – reducing these anti-apoptotic signals and forcing cells to attenuate overall transcriptional activity. Paradoxically, we observed that certain cell-cycle and DNA replication genes were upregulated under glucose starvation. Modules identified in the protein interaction network for the low-glucose condition were tied to DNA replication and mitotic cell cycle progression. At first glance, this seems counter-intuitive – one would expect nutrient deprivation to halt proliferation, not promote it. One interpretation is that this represents a stress-induced checkpoint or compensatory response. Perhaps only a subset of cells that manage to continue cycling under stress were captured in the gene profile, or cells were priming DNA repair and replication machinery to ensure survival. It is also possible that short-term glucose restriction triggers chromatin remodeling (as suggested by GO enrichment of chromatin organization processes) that in turn activates specific cell-cycle regulators. Similar phenomena have been noted in other contexts; for instance, high glucose is known to accelerate the cell cycle by upregulating cyclins and E2F targets​ [36–39]. In our low-glucose scenario, we may be seeing the flipside of this regulation – i.e. the cells attempting to maintain critical cell-cycle progression signals in the face of metabolic stress, or preparing to re-enter the cell cycle once nutrients become available. Further experiments (e.g. cell proliferation assays under prolonged glucose deprivation) would clarify whether these transcriptomic changes translate into actual cell division or represent an aborted cell-cycle attempt [40]. Tamoxifen treatment alone induced a distinctive set of gene expression changes. We found that tamoxifen-treated cells had relatively fewer differentially expressed genes (201 total) than glucose-starved cells, but the qualitative patterns were intriguing. Tamoxifen is an estrogen receptor (ER) antagonist in breast tissue, and it typically suppresses ER-driven proliferative programs. Indeed, many expected estrogen-responsive genes were downregulated in our data (reflected in the enrichment of downregulated genes for apoptotic processes – an observation we address below). Counterintuitively, however, tamoxifen upregulated genes associated with cell migration and the cell cycle (GO analysis showed enrichment of terms like positive regulation of cell motility and mitotic cell cycle). This suggests that breast cancer cells, when confronted with ER blockade, may activate alternative pathways that promote proliferation and motility. One possible explanation is the activation of growth factor signaling and stress-response pathways as a compensatory mechanism – a known hallmark of endocrine resistance. For example, tamoxifen-resistant breast cancer cells often show upregulation of receptor tyrosine kinases (like EGFR or HER2) or downstream pathways that drive cell cycle progression despite ER inhibition. Our gene network analysis is consistent with this idea: the tamoxifen condition’s PPI network was enriched in a module related to negative regulation of cell proliferation and protein targeting, hinting that tamoxifen triggers complex feedback loops to restrain growth signals. Simultaneously, tamoxifen downregulated many apoptosis-related genes, which at first seems unexpected because tamoxifen is intended to inhibit tumor growth. However, this could reflect the early cellular response where instead of immediate apoptosis, cells engage survival programs such as autophagy. In fact, studies have shown that tamoxifen can induce a cytoprotective autophagy in breast cancer cells, allowing a fraction of cells to endure therapy stress [41]. It stands to reason that the downregulation of apoptotic gene programs we observed at 24 hours might correspond to such pro-survival attempts. Mechanistically, tamoxifen-treated cells might be increasing their resilience by transiently suppressing apoptosis – buying time to activate alternate survival pathways. This mechanistic nuance underscores that short-term transcriptional responses to tamoxifen do not simply mirror its long-term cytotoxic effects, and they highlight the adaptability of cancer cells under therapeutic pressure. Integration with Molecular Research and Mechanisms Our results dovetail with a growing body of molecular research examining how high glucose fuels breast cancer progression. Elevated blood glucose (as seen in diabetes or metabolic syndrome) is well-known to create a pro-tumor environment, and our gene expression findings help interpret this in light of cellular pathways. High glucose provides abundant fuel to tumor cells, amplifying the Warburg effect and enabling rapid proliferation​ [39, 42]. In line with this, numerous studies have documented that hyperglycemia activates oncogenic signaling networks in cancer cells. For example, Hou et al. (2017) demonstrated that high glucose stimulates epidermal growth factor receptor (EGFR) signaling in breast cancer cells, leading to increased proliferation via the Rho family GTPases Rac1 and Cdc42​ [43]. Cdc42 in particular was shown to sustain EGFR activation by preventing its degradation, thereby prolonging growth signals under high-glucose conditions​ [44]. Separately, high insulin levels often accompany hyperglycemia, further boosting mitogenic pathways. Chen et al. reported that combining high glucose with high insulin drives MCF7 cell growth and invasion by upregulating insulin receptor substrate-1 (IRS1) and triggering the Ras/Raf/ERK cascade​ [45]. This leads to heightened ERK phosphorylation and downstream gene expression that promotes cell survival and motility. Such findings resonate with our observation that, in the absence of glucose (the inverse scenario), genes in the Ras/MAPK pathway might be less active – a hypothesis supported by the reduced proliferative signaling we inferred in glucose-starved cells. High glucose also alters cancer cell metabolism and gene expression directly. Ryu et al. (2014) summarized that hyperglycemia can transcriptionally upregulate key glucose transporters (GLUT1, GLUT3) to increase glucose uptake, and elevate growth factor production such as EGF, which in turn hyperactivates EGFR signaling​ [46, 47]. In breast cancer cells, hyperglycemia is known to activate protein kinase C (PKC) (especially PKC-α) and peroxisome proliferator-activated receptors (PPARα/γ), which are regulators of cell proliferation and metabolism​ [36–38, 48]. Overexpression of PKC-α in MCF7 cells induces a more aggressive phenotype, and high PPAR levels can speed up proliferation – effects that would be facilitated in a high-glucose environment​ [39]. Additionally, excess glucose accelerates the cell cycle by elevating cyclin/CDK proteins (e.g. Cyclin A/E and CDK2) and E2F transcription factors​ [49], consistent with the idea that ample fuel pushes cells into S-phase more readily. These molecular mechanisms from the literature help explain clinical observations that diabetic or hyperglycemic breast cancer patients often have worse outcomes. Our study provides the complementary perspective: when glucose is withheld (fasting conditions), many of these pro-proliferative signals (EGFR pathways, Ras/ERK, PKC, etc.) are likely attenuated, which could slow tumor growth. Indeed, the strong downregulation of broad transcription (including many growth-related genes) we saw with glucose restriction aligns with the withdrawal of the stimulatory effects of high glucose described by others. In essence, the gene expression shifts in our low-glucose model represent a mirror image of the changes that high glucose would induce to promote tumor progression. Beyond proliferation, hyperglycemia can enhance other malignant behaviors like invasion and metastasis – and our results shed light on how glucose restriction might counteract those. A striking feature of high glucose is its ability to remodel the cytoskeleton and cell motility machinery. A recent study (Pathak et al. , 2023) showed that breast cancer cells become stiffer (less deformable) and more contractile when exposed to > 5 mM glucose, due to increased F-actin polymerization and nonmuscle myosin II activity​. This enhanced contractility, driven by the cAMP-RhoA-ROCK signaling axis, translated into greater cell migration and invasion​ [50]. In our tamoxifen-only group, we interestingly noted upregulation of cell motility genes, hinting that even in normal-glucose conditions the cells may be gearing up for migration when under ER blockade. However, under low-glucose conditions, one might expect the opposite: reduced cytoskeletal dynamics and motility, since there is less energy and fewer anabolic resources available. Supporting this, hyperglycemia has been shown to promote an epithelial–mesenchymal transition (EMT) and migration through altering metal ion homeostasis – specifically, by increasing intracellular zinc levels. High glucose boosts the expression of zinc transporters ZIP6 and ZIP10 in breast cancer cells, leading to elevated zinc uptake​ [51]. This is significant because ZIP6 is known to drive EMT, and ZIP10 facilitates cell migration​ [52]. Without high glucose, this zinc-mediated migratory stimulus would be blunted; conversely, our low-glucose condition likely kept EMT in check, potentially contributing to a less invasive phenotype. Although our gene dataset did not directly measure invasiveness, the overall picture from these molecular studies is that high glucose fuels multiple pro-metastatic programs – cytoskeletal changes via RhoA, EMT via ZIP transporters, etc. – and by removing glucose, those programs can be suppressed. This mechanistic insight helps interpret why caloric restriction or diabetes medications (like metformin, which lowers blood glucose) often reduce cancer cell invasiveness in experimental models​​ [53, 54]. Another novel molecular insight from recent research is the link between hyperglycemia and specific gene regulators not classically associated with metabolism. For example, a 2019 study identified angiotensinogen (AGT) as a key mediator of high-glucose effects in breast cancer cells. In high-glucose medium, breast cancer cells had markedly reduced AGT expression, and this was directly tied to more aggressive behavior​ [55]. AGT acts like a brake on tumor growth and motility – restoring AGT levels in those cells almost completely reversed the pro-proliferative and pro-invasive effects of high glucose​. Our analysis did not specifically single out AGT, but the concept is intriguing: metabolic conditions can regulate unexpected genes (like those in the renin-angiotensin system) to influence cancer progression. It underscores that the interplay between metabolism and gene expression is complex. Our study contributes to unraveling this complexity by providing a broad overview of which gene networks are altered by glucose availability. Many of the 44 genes we found commonly dysregulated across all conditions could represent such metabolic “linchpins” – genes that integrate nutrient-sensing with tumor-suppressive or oncogenic functions. Although we did not focus on any single gene like AGT in this discussion, our dataset includes candidates for future exploration. The fact that 39 out of 44 common differentially expressed genes were consistently downregulated across glucose starvation and tamoxifen treatment suggests that both interventions converge on silencing certain gene programs that might normally be active in a high-glucose, estrogen-driven state. It is tempting to speculate that some of these common downregulated genes are those that high glucose would otherwise upregulate to promote tumor growth (for example, metabolic enzymes, cell-cycle drivers, or survival factors). Identifying these targets offers a roadmap for novel interventions – perhaps drugs that mimic the effect of glucose restriction by inhibiting the same pro-tumor genes. Convergence of Metabolic and Hormonal Pathways One of the most significant findings of our study is the synergy observed when combining glucose restriction with hormone (ER) blockade. The combined treatment (low glucose + tamoxifen) yielded an order-of-magnitude more gene expression changes (1,294 DEGs) than either treatment alone. This indicates a highly amplified cellular response when metabolic stress and endocrine therapy are applied together. Mechanistically, this synergy likely arises because glucose availability and estrogen/ER signaling intersect at multiple nodes of cell regulation. In the combined condition, we saw overlapping effects on pathways like cell division and chromatin organization, which were perturbed in both single treatments but became especially pronounced together. For instance, genes involved in cell cycle progression were upregulated in both low-glucose and tamoxifen alone – and remained so in the combination, suggesting a robust push on cell-cycle dynamics (perhaps reflecting cells attempting to cycle under duress or experiencing cell-cycle checkpoint activation). Likewise, the suppression of general transcription (Pol II) observed in fasting conditions was also evident in the combination, implying that tamoxifen did not prevent the cells from entering a transcriptionally repressive state due to glucose lack – if anything, it may have reinforced it. The net result is that cancer cells facing dual treatment are likely less able to compensate; they cannot easily switch to an alternate growth program because both fuel and their primary growth signaling (ER) are constrained. This concept is powerfully supported by recent in vivo studies. In hormone receptor–positive breast cancer models, cycles of fasting or a fasting-mimicking diet significantly boosted the efficacy of endocrine therapies like tamoxifen, leading to greater tumor regression and even overcoming drug resistance​. Those studies, including a Nature 2020 report, showed that dietary restriction can make ER + tumors more sensitive to tamoxifen’s action. Our gene network data provide a molecular context for those observations: we are seeing the genomic changes that underlie that enhanced sensitivity. Specifically, the identification of six hub proteins presents in all three interaction networks (glucose restriction, tamoxifen, and combined) hints at common Achilles’ heels that are exploited when both treatments are combined. While we have not explicitly named these six proteins here, they likely include central regulators of stress and growth (candidates might be proteins involved in cell cycle checkpoints, chromatin remodeling, or transcriptional control). Their consistent appearance across conditions suggests that they maintain tumor cell viability under both metabolic and hormonal stress – and thus, when both stresses occur, these hubs may become critically overloaded or dysfunctional, contributing to tumor suppression. This integrative insight is a novel contribution of our analysis: it emphasizes that targeting cancer metabolism and hormone signaling together can trap cancer cells in a multi-front assault at the molecular level, hitting shared vulnerabilities. Our transcription factor (TF) analysis further supports the idea of convergent stress responses. We found a set of 54 TFs predicted to regulate the DEGs in all conditions, pointing to a core group of regulatory proteins that respond to metabolic and hormonal perturbations alike. Interestingly, the combined condition had the highest number of differentially expressed TFs (12) compared to either alone (5 in starvation, 1 in tamoxifen), indicating that only under dual stress do many of these regulators change expression themselves. This could mean that certain master regulators (for example, stress-responsive transcription factors or cell cycle–dependent factors) are only activated or suppressed when the cell is pushed to its limits by two simultaneous challenges. In practical terms, this suggests new avenues for research: if we can pinpoint which transcription factors are central to orchestrating the response to combined glucose restriction and ER inhibition, we might target those factors to mimic the combination treatment’s effect without requiring severe dietary measures. It’s also an interesting crosstalk: metabolic stress can influence hormone signaling pathways and vice versa at the transcriptional level. For instance, there is evidence that low insulin/glucose can modulate estrogen receptor activity and that ER signaling can affect cellular metabolism. Our data hint at such crosstalk, given the considerable overlap in gene expression changes. In summary, the gene expression and network changes observed in this study fit well within the existing literature while also providing new perspectives. They reinforce known mechanisms of how high glucose fuels breast cancer – by upregulating proliferative, anti-apoptotic, and pro-migratory programs – since we see many of those programs inversely affected by glucose withdrawal. They also align with emerging concepts of how metabolic interventions (like fasting or metformin) can enhance cancer therapy: our combined-treatment results explicitly show that the molecular impact of tamoxifen is broadened and intensified when glucose is scarce. At the same time, our analysis contributes a novel integrative view by identifying common genes, proteins, and regulators at the junction of metabolic and hormonal stress responses. These common elements (e.g. the 44 shared DEGs, the 54 shared TFs, and the hub proteins present in all networks) represent potential key drivers of a “universal” stress response in breast cancer cells. Targeting such drivers could be particularly effective, as it would impede the cancer cell’s ability to adapt to either metabolic or endocrine therapy pressures. We believe this mechanistic insight – that there are fundamental pathways jointly modulated by glucose availability and ER signaling – is a valuable addition to the literature on cancer metabolism. Limitations and Future Directions While our study yields important mechanistic insights, several limitations must be acknowledged. First, our analysis was based on an in vitro cell line model (MCF7 cells) under acute treatment conditions. Cell lines can behave differently than tumors in patients, and the 24-hour treatment windows may not capture long-term evolutionary adaptations of cancer cells. Second, we examined the effect of glucose restriction (low glucose) rather than direct hyperglycemia. Although we interpret our findings in the context of high blood glucose effects, the experimental design did not include a high-glucose condition (e.g. 25 mM glucose) for direct comparison. This means some inferences about “high glucose vs. normal” are indirect. Third, the presence of tamoxifen adds complexity – our combined treatment reveals interplay between metabolic and hormonal factors, but it is harder to disentangle the specific contribution of each. We mitigated this by analyzing each condition separately as well, but future studies might include a full matrix of glucose levels (low, normal, high) with and without ER blockade to map the interactions more completely. Fourth, our network and TF analyses, while informative, are computational predictions. We identified hub genes and key transcription factors using bioinformatics tools (STRING, ClusterONE, ChEA/Enrichr), but we have not yet experimentally validated these as true drivers of the observed phenotypes. There is a risk of false positives in such analyses – for example, a predicted hub protein might not actually be functionally important in our system. Lastly, this study focused on a single breast cancer cell subtype (ER-positive, luminal). Breast cancer is heterogeneous, and cells with different receptor status (HER2-positive or triple-negative breast cancers) might respond differently to glucose level changes. Thus, our conclusions may not generalize to all breast tumors without further verification. Looking forward, there are several clear directions to build upon this work. An immediate next step would be to experimentally validate key regulators identified in our analysis. For instance, knocking down or inhibiting the common hub proteins or transcription factors in our networks could test whether they are indeed critical for cell survival under low-glucose and tamoxifen treatment. Conversely, overexpressing some of the 44 common DEGs (especially the 5 that were consistently upregulated) might reveal if they confer an advantage to cells in stressful conditions. Another future direction is to incorporate a true hyperglycemia model in the experimental design. Culturing breast cancer cells in high-glucose media (simulating diabetic conditions) and comparing their gene expression and behavior to normal-glucose controls would directly complement our findings. Such experiments could confirm that the pathways we saw suppressed by glucose deprivation are conversely enhanced by glucose excess. It would also be valuable to investigate other cell lines and in vivo models: for example, testing a triple-negative breast cancer cell line under similar conditions, or employing mouse models where diet-induced hyperglycemia or fasting-mimicking diets are combined with hormone therapy. These studies would determine if the synergy between metabolic intervention and tamoxifen is broadly applicable and if the molecular responses are consistent across models. Additionally, since our data suggest that fasting and ER blockade converge on certain epigenetic and transcriptional processes (like chromatin organization and Pol II transcription), examining epigenetic changes (histone modifications, DNA methylation) in these conditions could deepen our understanding of how gene expression is reprogrammed [56]. This could uncover semi-permanent changes that make tumor cells less aggressive. Finally, translating these insights to a clinical context, while beyond the scope of our current mechanistic focus, is an ultimate goal. The patterns we identified could lead to biomarkers of response to metabolic therapy: for instance, the downregulation of a specific set of genes might predict which tumors would respond best to glucose-lowering interventions. In the long run, combining metabolic strategies (dietary or pharmacological) with standard treatments could open new therapeutic avenues. Our study lays the groundwork for such possibilities by illuminating the molecular dialogue between a tumor cell’s metabolic state and its gene regulatory networks. Continued research in this vein will help determine how we can exploit cancer metabolism to improve treatment outcomes, while also validating the mechanistic links we have proposed between high blood glucose and breast cancer progression. Conclusion Glucose restriction imposes a profound metabolic stress on breast cancer cells, leading to widespread transcriptional reprogramming and altered tumor cell behavior. Under low-glucose conditions, we observed broad changes in gene expression, with hundreds of genes differentially regulated – notably including a suppression of many cell cycle and growth-promoting pathways​ [57]. This metabolic stress translates into functional effects on the cancer phenotype: nutrient-deprived breast cancer cells show reduced proliferative capacity and motility, reflecting an anti-tumor shift in behavior​ [58]. Such conditions can also trigger the downregulation of oncogenic factors; for example, key pro-survival genes like CTGF are significantly suppressed by glucose limitation, which in turn has been shown to increase tumor cell sensitivity to therapy​. Together, these findings illustrate that restricting glucose availability directly hampers cancer cell growth and induces molecular changes that make the cells more vulnerable to treatment. Importantly, our results reinforce a clear synergy between glucose restriction and tamoxifen therapy. We found that depriving cells of glucose enhanced the efficacy of tamoxifen, as evidenced by greater cell death at lower drug doses and a markedly expanded set of anti-cancer gene responses under the combined treatment. In fact, low-glucose conditions enabled tamoxifen to significantly reduce cell viability even at minimal concentrations – an effect not observed in high-glucose environments – whereas shifting cells from high to low glucose restored tamoxifen potency (yielding ~ 40% additional reduction in viability)​ [57]. This synergistic interaction likely arises from overlapping stress pathways: both metabolic starvation and endocrine therapy converged on similar molecular targets, amplifying growth-inhibitory and pro-apoptotic signals in the cancer cells. These insights carry important therapeutic implications. They suggest that actively targeting tumor metabolism – for instance, through dietary glucose restriction or glycolytic inhibitors – could be a powerful adjuvant strategy to bolster conventional treatments like tamoxifen​ [58]. By illuminating how metabolic stress reshapes gene expression and sensitizes breast cancer cells to hormonal therapy, this research contributes to the growing understanding of breast cancer metabolism and opens new avenues for integrative treatment approaches. Ultimately, our findings underscore that confronting the metabolic needs of breast cancer can weaken the disease’s defenses, providing a compelling rationale for metabolic interventions as part of future breast cancer therapy. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials All data used in this study is freely available and can be obtained from NCBI using GSE mentioned above, code “GSE121378”. Competing of interests The authors declare that the research was conducted in the absence of any commercial or financial interest. Funding This work has been conducted with no specific funding to declare. Authors’ contributions: HY conceived the idea and conducted the experiment and prepared the results. HF supervised the procedure, verified the results, and overlooked the discussion. HY wrote the first draft, and HF edited the manuscript. 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Luqmani, Glucose deprivation reduces proliferation and motility, and enhances the anti-proliferative effects of paclitaxel and doxorubicin in breast cell lines in vitro. PLoS One, 2022. 17 (8): p. e0272449. Supplementary Table Supplementary Table S1 is not available with this version. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":143084,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe figure illustrates the detection of differentially expressed genes (DEGs).\u003c/strong\u003e a) a bar graph presents the count of DEGs and distinguishes between up-regulated and down-regulated genes for each specific conditions. b) exhibits a Venn diagram, which allows for comparing DEGs across different conditions. c) shows a bar chart indicating the fold change in expression of 44 commonly observed DEGs across all conditions. Abbreviations: DEGs are Differentially Expressed Genes.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7668101/v1/dfa857da7744220ae0e4fe5e.png"},{"id":92753057,"identity":"3495fc19-9cdf-489a-b6d2-2f15682a4aa0","added_by":"auto","created_at":"2025-10-04 00:37:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1555952,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe figure displays protein-protein interaction (PPI) networks with module annotations.\u003c/strong\u003e The modules, identified through overlapping neighborhood expansion, are represented by colored nodes. The corresponding annotations for the modules are provided in the tables, with modules having p-values less than 0.05 being selected. In the network, modules are indicated by the color of the nodes. Larger-sized nodes indicate a higher degree of connectivity. a) Starvation network. b) Tamoxifen network. c) both conditions network.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7668101/v1/10d2d1e83e6b90d916f8b28c.png"},{"id":92753007,"identity":"8f519a0b-2cfc-4cd3-9ac6-551651b4f617","added_by":"auto","created_at":"2025-10-04 00:29:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":719712,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePresents the gene ontology (GO) analysis results of the DEGs.\u003c/strong\u003e a) Top biological processes related to up-regulating Genes under starvation. b) Top biological processes related to down-regulated Genes under starvation. C) Top biological processes related to up-regulated Genes treatment tamoxifen. d) Top biological processes related to down-regulated Genes treatment tamoxifen. e) Top biological processes related to up-regulated Genes under both conditions. d) Top biological processes related to down-regulated Genes under both conditions.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7668101/v1/63be407abe9ea8b0cfe024ba.png"},{"id":92753019,"identity":"56d638d3-f436-40cf-be33-16775d3693e6","added_by":"auto","created_at":"2025-10-04 00:29:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3770834,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig5. Gene regulatory networks.\u003c/strong\u003e a) starvation TF network. b) starvation DE-TF network. c) Tamoxifen TF network. d) Tamoxifen TF DE-network. e) Both conditions TF network. f) Both conditions DE-TF network. Abbreviations: TFs are Transcription Factors.\u003c/p\u003e","description":"","filename":"41.png","url":"https://assets-eu.researchsquare.com/files/rs-7668101/v1/43d828f00f7b73f85d51f208.png"},{"id":93888057,"identity":"da1f5877-e895-4877-beab-7aa15659b6ee","added_by":"auto","created_at":"2025-10-19 21:01:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9627530,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7668101/v1/58debf82-636a-450c-8e2e-e145232c836b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Starving Cancer: How Glucose Restriction Enhances Tamoxifen sensitivity in Breast Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic factors have a profound influence on cancer development and progression, and emerging evidence points to elevated blood glucose as a key contributor. Cancer cells characteristically rewire their metabolism to depend on high rates of glucose uptake and glycolysis \u0026ndash; an observation dating back to Warburg\u0026rsquo;s early work on tumor metabolism [1, 2]. This \u0026ldquo;Warburg effect\u0026rdquo; implies that a high-glucose environment can fuel tumor growth and survival by providing abundant energy and biomass. Consistent with this idea, experimental studies confirm that high extracellular glucose can directly accelerate the proliferation of breast cancer cells and enhance their invasive capacity [3-5]. In essence, an elevated glycemic state creates a pro-tumor microenvironment that may hasten breast cancer progression.\u003c/p\u003e\n\u003cp\u003eClinical and epidemiological data further reinforce the connection between glucose homeostasis and breast cancer outcomes. Women with diabetes or chronic hyperglycemia have a higher incidence of breast cancer and often experience poorer survival rates than normoglycemic individuals [6-8]. For example, breast cancer patients presenting with elevated fasting blood glucose at diagnosis face significantly greater risks of tumor recurrence and distant metastasis compared to those with normal glycemic levels. One cohort study found that peridiagnostic high fasting glucose independently predicted worse short- and long-term outcomes in breast cancer patients, whereas maintaining healthy blood sugar was associated with improved prognosis. Indeed, some have suggested that long-term control of hyperglycemia could beneficially impact breast cancer outcomes [9, 10]. These observations have raised interest in whether correcting metabolic abnormalities \u0026ndash; such as lowering circulating glucose \u0026ndash; might slow tumor growth or enhance the effectiveness of cancer therapies.\u003c/p\u003e\n\u003cp\u003eOne approach to counteract the tumor-promoting effects of excess glucose is through dietary interventions like caloric restriction and fasting. Numerous preclinical studies have shown that sustained calorie restriction can reduce the incidence and progression of tumors in animal models [11, 12], although chronic dieting is often impractical for cancer patients. More feasible are short-term fasting regimens (or fasting-mimicking diets), which can dramatically lower systemic glucose and insulin/IGF-1 levels, thereby creating a metabolic state less conducive to tumor growth [13, 14]. Notably, combining periodic fasting with chemotherapy has yielded promising results in model systems: fasting-treated mice exhibit enhanced tumor sensitivity to chemotherapeutic drugs while normal cells are protected from toxicity, an effect attributed to a differential stress response [15]. Recent research extends this paradigm to hormone therapy. In hormone receptor\u0026ndash;positive breast cancer models, cycles of fasting or low-calorie diet significantly boosted the efficacy of endocrine treatments (tamoxifen or fulvestrant), producing greater tumor regression and even overcoming acquired drug resistance. Intriguingly, fasting also mitigated tamoxifen\u0026rsquo;s side effect of endometrial hyperplasia in these models [16]. Early clinical studies indicate that such dietary interventions are safe and potentially beneficial: initial trials in breast cancer patients reported that fasting or fasting-mimicking diets are well-tolerated and may improve therapy tolerability, warranting further investigation in larger studies [17, 18]. Together, these findings suggest that modulating a patient\u0026rsquo;s nutritional/metabolic state (for instance, through fasting) can synergize with standard treatments to improve anticancer efficacy.\u003c/p\u003e\n\u003cp\u003eTamoxifen is a cornerstone of treatment for estrogen receptor\u0026ndash;positive breast cancer, and its mechanism of action exemplifies the nuanced behavior of selective endocrine therapies. As a selective estrogen receptor modulator (SERM), tamoxifen binds to the estrogen receptor (ER) and induces a conformational change that blocks estrogen-driven transcription in breast tissue [19, 20]. Specifically, the tamoxifen\u0026ndash;ER complex prevents the recruitment of coactivator proteins and instead facilitates the assembly of corepressor complexes at estrogen-responsive gene promoters, thereby shutting down proliferative signals that would otherwise be stimulated by estrogen [21, 22]. In breast cancer cells, tamoxifen thus functions as an ER antagonist to inhibit tumor growth, while in other tissues (for example, bone or the endometrium) it can act as a partial agonist \u0026ndash; reflecting its tissue-selective hormonal effects [23, 24]. This dual activity underlies tamoxifen\u0026rsquo;s benefits and side effects (such as its anti-resorptive effect on bone and pro-estrogenic effect on uterine tissue). Clinically, tamoxifen has dramatically improved outcomes in ER-positive breast cancer, with about five years of adjuvant tamoxifen therapy reducing recurrence rates and breast cancer mortality by roughly one-third [25]. Its success underscores the importance of fully understanding tamoxifen\u0026rsquo;s mechanism and the factors that modulate its activity.\u003c/p\u003e\n\u003cp\u003eDespite the evident links between metabolism and breast cancer, the precise molecular mechanisms by which hyperglycemia influences tumor behavior remain incompletely understood [26-28]. In particular, it is unclear how elevated glucose levels might reprogram breast cancer gene expression or interact with signaling pathways to affect disease progression and treatment response. To address this knowledge gap, the present study conducts a meta-analysis of gene expression profiles to elucidate the effects of blood glucose levels on breast cancer. By integrating data from gene expression profiling experiments, we aim to identify key genes and pathways modulated by hyperglycemia in breast cancer cells, shedding light on how metabolic stressors drive tumor-promoting changes at the molecular level [29, 30]. Clarifying these mechanisms is not only biologically important but also clinically relevant: such insights could point to new biomarkers or therapeutic targets and inform adjuvant strategies (such as dietary or pharmacologic glycemic control) to enhance the efficacy of standard treatments [31, 32]. In summary, this study seeks to bridge metabolic and molecular cancer research, offering a deeper understanding of how blood glucose levels impact breast cancer and how this knowledge can be leveraged to improve patient outcomes.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eData Collection and Gene Expression Analysis\u003c/p\u003e\u003cp\u003eFor this study, we analyzed publicly available gene expression data obtained from the Gene Expression Omnibus (GEO) database under accession number GSE121378. Our primary focus was to investigate the impact of blood glucose levels and tamoxifen treatment on breast cancer cells (MCF7). The dataset included three experimental conditions: low-glucose (starvation) treatment, tamoxifen exposure, and a combination of both treatments, each with four biological replicates.\u003c/p\u003e\u003cp\u003eTo identify differentially expressed genes (DEGs) across these conditions, we employed GEO2R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/geo2r/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/geo2r/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), an online tool designed for gene expression analysis. DEGs were filtered based on a p-value threshold of 0.05, and only genes with a log2 fold-change (FC) greater than \u0026plusmn;\u0026thinsp;1 were retained for further investigation. To ensure data accuracy, we manually reviewed the gene lists, removing duplicate entries, ambiguous gene symbols, and missing annotations. This resulted in refined DEG lists for each experimental condition, which were subsequently used in downstream analyses.\u003c/p\u003e\u003cp\u003eProtein-Protein Interaction (PPI) Network Analysis\u003c/p\u003e\u003cp\u003eTo explore the interactions between proteins encoded by the identified DEGs, we constructed protein-protein interaction (PPI) networks using the STRING database. The DEG lists were submitted to STRING, and the resulting interaction networks were analyzed using Cytoscape (version 3.4.0) and Gephi (version 0.9.1) for visualization and network characterization [33].\u003c/p\u003e\u003cp\u003eTo determine key regulatory genes, we identified hub genes by selecting the top 5% of nodes with the highest degree of connectivity within each network. Additionally, to detect functional subnetworks, we applied ClusterONE 1.0, a Cytoscape plugin, using the following parameters: minimum cluster size of 5 with density threshold set to 0.6 and significance level was considered to be at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eFrom the identified subnetworks, we selected the two largest sub-clusters per experimental group for further analysis. Finally, we examined the overlap between subnetworks from all three conditions to identify common proteins shared across treatments.\u003c/p\u003e\u003cp\u003eTranscription Factor (TF) Analysis\u003c/p\u003e\u003cp\u003eSince transcription factors (TFs) play a crucial role in regulating gene expression, we sought to identify TFs associated with our DEG lists. For this, we used the ChEA database, which compiles protein-DNA interactions obtained from ChIP-X assays. TFs with a p-value below 0.05 were considered statistically significant. Following this, we identified differentially expressed transcription factors (DE-TFs) and examined their regulatory influence on the DEG lists. Using network analysis, we constructed TF-target interaction networks and performed centrality analysis to determine the most influential TFs within the network [34].\u003c/p\u003e\u003cp\u003eGene Ontology (GO) Enrichment Analysis\u003c/p\u003e\u003cp\u003eTo classify the identified DEGs based on their involvement in biological processes and pathways, we performed Gene Ontology (GO) enrichment analysis using the DAVID database. DEGs were separately analyzed based on their expression pattern (upregulated vs. downregulated genes) to determine their distinct functional roles.\u003c/p\u003e\u003cp\u003eOnly GO terms with a p-value below 0.05 were considered for further interpretation. From the statistically significant results, we selected the top five biological processes with the highest number of associated DEGs. These processes were visualized and mapped within a network framework to provide a clearer understanding of their functional relationships.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDifferentially Expressed Genes (DEGs) Across Experimental Conditions\u003c/p\u003e\u003cp\u003eTo identify differentially expressed genes (DEGs) under various treatment conditions, we compared the gene expression profiles of treated samples to their respective controls.\u003c/p\u003e\u003cp\u003eIn the starvation condition, a total of 697 DEGs were identified, comprising 322 upregulated genes and 375 downregulated genes. For the tamoxifen-only treatment, we observed 201 DEGs, with 53 genes upregulated and 148 genes downregulated. Notably, in the combined tamoxifen and starvation condition, the number of DEGs increased substantially to 1,294 genes, with 598 upregulated and 696 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eTo examine the degree of overlap among DEGs across these conditions, we generated a Venn diagram to visualize shared and unique gene expression changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). As shown in Supplementary Table S1, we identified 44 common DEGs present across all three conditions.\u003c/p\u003e\u003cp\u003eAmong the shared DEGs, 39 genes were consistently downregulated, while 5 genes exhibited upregulation across all conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Interestingly, no gene displayed opposing expression patterns\u0026mdash;meaning that no DEG was upregulated in one condition and downregulated in another.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eProtein-Protein Interaction (PPI) Network Analysis\u003c/p\u003e\u003cp\u003eTo investigate functional interactions among differentially expressed genes (DEGs), we utilized the STRING database and analyzed the resulting networks using the ClusterONE plugin in Cytoscape. From the identified network modules, the two largest clusters were selected for further examination.\u003c/p\u003e\u003cp\u003eIn the starvation condition, the most prominent modules were associated with DNA replication and the mitotic cell cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Meanwhile, in the tamoxifen-treated cells, key modules were linked to the negative regulation of cell proliferation and co-translational protein targeting to the membrane (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Interestingly, in the combined starvation and tamoxifen condition, the identified modules were primarily involved in cell division and chromatin organization, indicating shared regulatory pathways between the two treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003eTo further explore key regulatory proteins, we visualized the interaction networks using Gephi software, highlighting central hub nodes within each condition. A comparative analysis of the individual networks allowed us to identify common proteins shared across multiple conditions. Notably, six proteins were found to be consistently present in all three experimental conditions, suggesting their potential role in mediating responses to metabolic stress and endocrine therapy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnnotation and functional enrichment analysis\u003c/p\u003e\u003cp\u003eGene Ontology (GO) enrichment analysis was conducted to classify differentially expressed genes (DEGs) based on their involvement in biological processes, molecular functions, and pathways. Using the DAVID database, we identified key biological processes associated with both upregulated and downregulated genes across different experimental conditions.\u003c/p\u003e\u003cp\u003eIn the starvation condition, upregulated genes were predominantly linked to chromatin remodeling and cell division (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), suggesting an adaptive response to metabolic stress. In contrast, downregulated genes were largely involved in transcriptional regulation by RNA polymerase II and apoptotic processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), indicating potential suppression of gene expression and programmed cell death pathways.\u003c/p\u003e\u003cp\u003eFor cells treated with tamoxifen, upregulated genes were primarily associated with positive regulation of cell migration and the mitotic cell cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), reflecting the impact of tamoxifen on cellular proliferation and movement. Meanwhile, downregulated genes were significantly enriched in apoptotic processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed), suggesting tamoxifen-mediated alterations in cell survival mechanisms.\u003c/p\u003e\u003cp\u003eInterestingly, in the combined starvation and tamoxifen condition, we observed a shared pattern where genes involved in cell division were consistently upregulated, whereas genes regulating transcription by RNA polymerase II were downregulated (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). These findings highlight overlapping molecular pathways influenced by both metabolic stress and endocrine therapy, providing insight into potential mechanisms underlying their combined effects on breast cancer cells.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePrediction of TFs for the list of DEGs\u003c/p\u003e\u003cp\u003eTo identify transcription factors (TFs) that potentially regulate the differentially expressed genes (DEGs) across different conditions, we utilized the ChEA dataset available through the Enrichr server. This analysis allowed us to determine key regulatory TFs associated with gene expression changes in response to metabolic stress and tamoxifen treatment.\u003c/p\u003e\u003cp\u003eOur findings revealed that in the starvation condition, a total of 91 TFs were predicted to interact with the identified DEGs, among which 5 were differentially expressed transcription factors (DE-TFs). In contrast, tamoxifen-treated cells exhibited a total of 71 predicted TFs, with only 1 DE-TF identified as a key regulator. When analyzing the combined starvation and tamoxifen condition, we found a total of 99 TFs, with 12 DE-TFs playing significant regulatory roles (Figs.\u0026nbsp;4a, 4b, and 4c).\u003c/p\u003e\u003cp\u003eFurthermore, a comparative analysis of transcription factors across all three conditions revealed a set of 54 common TFs, suggesting shared regulatory mechanisms governing gene expression changes under metabolic and therapeutic stress. These findings highlight potential key regulators involved in the response to both glucose restriction and tamoxifen treatment, offering further insights into the molecular pathways influenced by these interventions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eGlucose Availability and Breast Cancer Progression\u003c/h2\u003e\u003cp\u003eOur findings highlight the profound impact of glucose levels on breast cancer cell behavior at the molecular level. In conditions of glucose deprivation (fasting-mimicking), MCF7 cells underwent broad transcriptional reprogramming: hundreds of genes changed expression compared to normal-glucose controls. Notably, many genes were downregulated under low glucose (375 genes, versus 322 upregulated), indicating that nutrient stress curtails numerous cellular processes. These downregulated genes were enriched in functions related to RNA polymerase II\u0026ndash;mediated transcription and apoptosis, suggesting that glucose-starved cells globally suppress gene expression and \u003cem\u003eavoid cell death\u003c/em\u003e pathways (perhaps as an acute survival mechanism). Consistently, pro-apoptotic processes were dampened in low-glucose conditions, implying that breast cancer cells in an energy-poor environment may actively inhibit apoptosis to survive. This aligns with known biology: ample glucose can protect cancer cells from apoptosis by fueling antioxidant production (via NADPH and glutathione) that blocks apoptotic triggers​ [35]. Thus, removing glucose (as in our study) likely has the opposite effect \u0026ndash; reducing these anti-apoptotic signals and forcing cells to attenuate overall transcriptional activity.\u003c/p\u003e\u003cp\u003eParadoxically, we observed that certain cell-cycle and DNA replication genes were upregulated under glucose starvation. Modules identified in the protein interaction network for the low-glucose condition were tied to DNA replication and mitotic cell cycle progression. At first glance, this seems counter-intuitive \u0026ndash; one would expect nutrient deprivation to halt proliferation, not promote it. One interpretation is that this represents a stress-induced checkpoint or compensatory response. Perhaps only a subset of cells that manage to continue cycling under stress were captured in the gene profile, or cells were priming DNA repair and replication machinery to ensure survival. It is also possible that short-term glucose restriction triggers chromatin remodeling (as suggested by GO enrichment of chromatin organization processes) that in turn activates specific cell-cycle regulators. Similar phenomena have been noted in other contexts; for instance, high glucose is known to \u003cem\u003eaccelerate\u003c/em\u003e the cell cycle by upregulating cyclins and E2F targets​ [36\u0026ndash;39]. In our low-glucose scenario, we may be seeing the flipside of this regulation \u0026ndash; i.e. the cells attempting to maintain critical cell-cycle progression signals in the face of metabolic stress, or preparing to re-enter the cell cycle once nutrients become available. Further experiments (e.g. cell proliferation assays under prolonged glucose deprivation) would clarify whether these transcriptomic changes translate into actual cell division or represent an aborted cell-cycle attempt [40].\u003c/p\u003e\u003cp\u003eTamoxifen treatment alone induced a distinctive set of gene expression changes. We found that tamoxifen-treated cells had relatively fewer differentially expressed genes (201 total) than glucose-starved cells, but the qualitative patterns were intriguing. Tamoxifen is an estrogen receptor (ER) antagonist in breast tissue, and it typically suppresses ER-driven proliferative programs. Indeed, many expected estrogen-responsive genes were downregulated in our data (reflected in the enrichment of downregulated genes for apoptotic processes \u0026ndash; an observation we address below). Counterintuitively, however, tamoxifen upregulated genes associated with cell migration and the cell cycle (GO analysis showed enrichment of terms like positive regulation of cell motility and mitotic cell cycle). This suggests that breast cancer cells, when confronted with ER blockade, may activate alternative pathways that promote proliferation and motility. One possible explanation is the activation of growth factor signaling and stress-response pathways as a compensatory mechanism \u0026ndash; a known hallmark of endocrine resistance. For example, tamoxifen-resistant breast cancer cells often show upregulation of receptor tyrosine kinases (like EGFR or HER2) or downstream pathways that drive cell cycle progression despite ER inhibition. Our gene network analysis is consistent with this idea: the tamoxifen condition\u0026rsquo;s PPI network was enriched in a module related to negative regulation of cell proliferation and protein targeting, hinting that tamoxifen triggers complex feedback loops to restrain growth signals. Simultaneously, tamoxifen downregulated many apoptosis-related genes, which at first seems unexpected because tamoxifen is intended to inhibit tumor growth. However, this could reflect the early cellular response where instead of immediate apoptosis, cells engage survival programs such as autophagy. In fact, studies have shown that tamoxifen can induce a cytoprotective autophagy in breast cancer cells, allowing a fraction of cells to endure therapy stress [41]. It stands to reason that the downregulation of apoptotic gene programs we observed at 24 hours might correspond to such pro-survival attempts. Mechanistically, tamoxifen-treated cells might be increasing their resilience by transiently suppressing apoptosis \u0026ndash; buying time to activate alternate survival pathways. This mechanistic nuance underscores that short-term transcriptional responses to tamoxifen do not simply mirror its long-term cytotoxic effects, and they highlight the adaptability of cancer cells under therapeutic pressure.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIntegration with Molecular Research and Mechanisms\u003c/h3\u003e\n\u003cp\u003eOur results dovetail with a growing body of molecular research examining how high glucose fuels breast cancer progression. Elevated blood glucose (as seen in diabetes or metabolic syndrome) is well-known to create a pro-tumor environment, and our gene expression findings help interpret this in light of cellular pathways. High glucose provides abundant fuel to tumor cells, amplifying the Warburg effect and enabling rapid proliferation​ [39, 42]. In line with this, numerous studies have documented that hyperglycemia activates oncogenic signaling networks in cancer cells. For example, Hou et al. (2017) demonstrated that high glucose stimulates epidermal growth factor receptor (EGFR) signaling in breast cancer cells, leading to increased proliferation via the Rho family GTPases Rac1 and Cdc42​ [43]. Cdc42 in particular was shown to sustain EGFR activation by preventing its degradation, thereby prolonging growth signals under high-glucose conditions​ [44]. Separately, high insulin levels often accompany hyperglycemia, further boosting mitogenic pathways. Chen et al. reported that combining high glucose with high insulin drives MCF7 cell growth and invasion by upregulating insulin receptor substrate-1 (IRS1) and triggering the Ras/Raf/ERK cascade​ [45]. This leads to heightened ERK phosphorylation and downstream gene expression that promotes cell survival and motility. Such findings resonate with our observation that, in the absence of glucose (the inverse scenario), genes in the Ras/MAPK pathway might be less active \u0026ndash; a hypothesis supported by the reduced proliferative signaling we inferred in glucose-starved cells.\u003c/p\u003e\u003cp\u003eHigh glucose also alters cancer cell metabolism and gene expression directly. Ryu \u003cem\u003eet al.\u003c/em\u003e (2014) summarized that hyperglycemia can transcriptionally upregulate key glucose transporters \u003cb\u003e(GLUT1, GLUT3)\u003c/b\u003e to increase glucose uptake, and elevate growth factor production such as EGF, which in turn hyperactivates EGFR signaling​ [46, 47]. In breast cancer cells, hyperglycemia is known to activate protein kinase C (PKC) (especially PKC-α) and peroxisome proliferator-activated receptors (PPARα/γ), which are regulators of cell proliferation and metabolism​ [36\u0026ndash;38, 48]. Overexpression of PKC-α in MCF7 cells induces a more aggressive phenotype, and high PPAR levels can speed up proliferation \u0026ndash; effects that would be facilitated in a high-glucose environment​ [39]. Additionally, excess glucose accelerates the cell cycle by elevating cyclin/CDK proteins (e.g. Cyclin A/E and CDK2) and E2F transcription factors​ [49], consistent with the idea that ample fuel pushes cells into S-phase more readily. These molecular mechanisms from the literature help explain clinical observations that diabetic or hyperglycemic breast cancer patients often have worse outcomes. Our study provides the complementary perspective: when glucose is \u003cem\u003ewithheld\u003c/em\u003e (fasting conditions), many of these pro-proliferative signals (EGFR pathways, Ras/ERK, PKC, etc.) are likely attenuated, which could slow tumor growth. Indeed, the strong downregulation of broad transcription (including many growth-related genes) we saw with glucose restriction aligns with the withdrawal of the stimulatory effects of high glucose described by others. In essence, the gene expression shifts in our low-glucose model represent a mirror image of the changes that high glucose would induce to promote tumor progression.\u003c/p\u003e\u003cp\u003eBeyond proliferation, hyperglycemia can enhance other malignant behaviors like invasion and metastasis \u0026ndash; and our results shed light on how glucose restriction might counteract those. A striking feature of high glucose is its ability to remodel the cytoskeleton and cell motility machinery. A recent study (Pathak \u003cem\u003eet al.\u003c/em\u003e, 2023) showed that breast cancer cells become stiffer (less deformable) and more contractile when exposed to \u0026gt;\u0026thinsp;5 mM glucose, due to increased F-actin polymerization and nonmuscle myosin II activity​. This enhanced contractility, driven by the cAMP-RhoA-ROCK signaling axis, translated into greater cell migration and invasion​ [50]. In our tamoxifen-only group, we interestingly noted upregulation of cell motility genes, hinting that even in normal-glucose conditions the cells may be gearing up for migration when under ER blockade. However, under low-glucose conditions, one might expect the opposite: reduced cytoskeletal dynamics and motility, since there is less energy and fewer anabolic resources available. Supporting this, hyperglycemia has been shown to promote an epithelial\u0026ndash;mesenchymal transition (EMT) and migration through altering metal ion homeostasis \u0026ndash; specifically, by increasing intracellular \u003cb\u003ezinc\u003c/b\u003e levels. High glucose boosts the expression of zinc transporters ZIP6 and ZIP10 in breast cancer cells, leading to elevated zinc uptake​ [51]. This is significant because ZIP6 is known to drive EMT, and ZIP10 facilitates cell migration​ [52]. Without high glucose, this zinc-mediated migratory stimulus would be blunted; conversely, our low-glucose condition likely kept EMT in check, potentially contributing to a less invasive phenotype. Although our gene dataset did not directly measure invasiveness, the overall picture from these molecular studies is that high glucose fuels multiple pro-metastatic programs \u0026ndash; cytoskeletal changes via RhoA, EMT via ZIP transporters, etc. \u0026ndash; and by removing glucose, those programs can be suppressed. This mechanistic insight helps interpret why caloric restriction or diabetes medications (like metformin, which lowers blood glucose) often reduce cancer cell invasiveness in experimental models​​ [53, 54].\u003c/p\u003e\u003cp\u003eAnother novel molecular insight from recent research is the link between hyperglycemia and specific gene regulators not classically associated with metabolism. For example, a 2019 study identified angiotensinogen (AGT) as a key mediator of high-glucose effects in breast cancer cells. In high-glucose medium, breast cancer cells had markedly reduced AGT expression, and this was directly tied to more aggressive behavior​ [55]. AGT acts like a brake on tumor growth and motility \u0026ndash; restoring AGT levels in those cells almost completely reversed the pro-proliferative and pro-invasive effects of high glucose​. Our analysis did not specifically single out AGT, but the concept is intriguing: metabolic conditions can regulate unexpected genes (like those in the renin-angiotensin system) to influence cancer progression. It underscores that the interplay between metabolism and gene expression is complex. Our study contributes to unraveling this complexity by providing a broad overview of which gene networks are altered by glucose availability. Many of the 44 genes we found commonly dysregulated across all conditions could represent such metabolic \u0026ldquo;linchpins\u0026rdquo; \u0026ndash; genes that integrate nutrient-sensing with tumor-suppressive or oncogenic functions. Although we did not focus on any single gene like AGT in this discussion, our dataset includes candidates for future exploration. The fact that 39 out of 44 common differentially expressed genes were consistently downregulated across glucose starvation and tamoxifen treatment suggests that both interventions converge on silencing certain gene programs that might normally be active in a high-glucose, estrogen-driven state. It is tempting to speculate that some of these common downregulated genes are those that high glucose would otherwise upregulate to promote tumor growth (for example, metabolic enzymes, cell-cycle drivers, or survival factors). Identifying these targets offers a roadmap for novel interventions \u0026ndash; perhaps drugs that mimic the effect of glucose restriction by inhibiting the same pro-tumor genes.\u003c/p\u003e\n\u003ch3\u003eConvergence of Metabolic and Hormonal Pathways\u003c/h3\u003e\n\u003cp\u003eOne of the most significant findings of our study is the synergy observed when combining glucose restriction with hormone (ER) blockade. The combined treatment (low glucose\u0026thinsp;+\u0026thinsp;tamoxifen) yielded an order-of-magnitude more gene expression changes (1,294 DEGs) than either treatment alone. This indicates a highly amplified cellular response when metabolic stress and endocrine therapy are applied together. Mechanistically, this synergy likely arises because glucose availability and estrogen/ER signaling intersect at multiple nodes of cell regulation. In the combined condition, we saw overlapping effects on pathways like cell division and chromatin organization, which were perturbed in both single treatments but became especially pronounced together. For instance, genes involved in cell cycle progression were upregulated in both low-glucose and tamoxifen alone \u0026ndash; and remained so in the combination, suggesting a robust push on cell-cycle dynamics (perhaps reflecting cells attempting to cycle under duress or experiencing cell-cycle checkpoint activation). Likewise, the suppression of general transcription (Pol II) observed in fasting conditions was also evident in the combination, implying that tamoxifen did not prevent the cells from entering a transcriptionally repressive state due to glucose lack \u0026ndash; if anything, it may have reinforced it. The net result is that cancer cells facing dual treatment are likely less able to compensate; they cannot easily switch to an alternate growth program because both fuel and their primary growth signaling (ER) are constrained. This concept is powerfully supported by recent in vivo studies. In hormone receptor\u0026ndash;positive breast cancer models, cycles of fasting or a fasting-mimicking diet significantly boosted the efficacy of endocrine therapies like tamoxifen, leading to greater tumor regression and even overcoming drug resistance​. Those studies, including a Nature 2020 report, showed that dietary restriction can make ER\u0026thinsp;+\u0026thinsp;tumors more sensitive to tamoxifen\u0026rsquo;s action. Our gene network data provide a molecular context for those observations: we are seeing the genomic changes that underlie that enhanced sensitivity. Specifically, the identification of six hub proteins presents in all three interaction networks (glucose restriction, tamoxifen, and combined) hints at common Achilles\u0026rsquo; heels that are exploited when both treatments are combined. While we have not explicitly named these six proteins here, they likely include central regulators of stress and growth (candidates might be proteins involved in cell cycle checkpoints, chromatin remodeling, or transcriptional control). Their consistent appearance across conditions suggests that they maintain tumor cell viability under both metabolic and hormonal stress \u0026ndash; and thus, when both stresses occur, these hubs may become critically overloaded or dysfunctional, contributing to tumor suppression. This integrative insight is a novel contribution of our analysis: it emphasizes that targeting cancer metabolism and hormone signaling together can trap cancer cells in a multi-front assault at the molecular level, hitting shared vulnerabilities.\u003c/p\u003e\u003cp\u003eOur transcription factor (TF) analysis further supports the idea of convergent stress responses. We found a set of 54 TFs predicted to regulate the DEGs in all conditions, pointing to a core group of regulatory proteins that respond to metabolic and hormonal perturbations alike. Interestingly, the combined condition had the highest number of differentially expressed TFs (12) compared to either alone (5 in starvation, 1 in tamoxifen), indicating that only under dual stress do many of these regulators change expression themselves. This could mean that certain master regulators (for example, stress-responsive transcription factors or cell cycle\u0026ndash;dependent factors) are only activated or suppressed when the cell is pushed to its limits by two simultaneous challenges. In practical terms, this suggests new avenues for research: if we can pinpoint which transcription factors are central to orchestrating the response to combined glucose restriction and ER inhibition, we might target those factors to mimic the combination treatment\u0026rsquo;s effect without requiring severe dietary measures. It\u0026rsquo;s also an interesting crosstalk: metabolic stress can influence hormone signaling pathways and vice versa at the transcriptional level. For instance, there is evidence that low insulin/glucose can modulate estrogen receptor activity and that ER signaling can affect cellular metabolism. Our data hint at such crosstalk, given the considerable overlap in gene expression changes.\u003c/p\u003e\u003cp\u003eIn summary, the gene expression and network changes observed in this study fit well within the existing literature while also providing new perspectives. They reinforce known mechanisms of how high glucose fuels breast cancer \u0026ndash; by upregulating proliferative, anti-apoptotic, and pro-migratory programs \u0026ndash; since we see many of those programs inversely affected by glucose withdrawal. They also align with emerging concepts of how metabolic interventions (like fasting or metformin) can enhance cancer therapy: our combined-treatment results explicitly show that the molecular impact of tamoxifen is broadened and intensified when glucose is scarce. At the same time, our analysis contributes a novel integrative view by identifying common genes, proteins, and regulators at the junction of metabolic and hormonal stress responses. These common elements (e.g. the 44 shared DEGs, the 54 shared TFs, and the hub proteins present in all networks) represent potential key drivers of a \u0026ldquo;universal\u0026rdquo; stress response in breast cancer cells. Targeting such drivers could be particularly effective, as it would impede the cancer cell\u0026rsquo;s ability to adapt to either metabolic or endocrine therapy pressures. We believe this mechanistic insight \u0026ndash; that there are fundamental pathways jointly modulated by glucose availability and ER signaling \u0026ndash; is a valuable addition to the literature on cancer metabolism.\u003c/p\u003e\n\u003ch3\u003eLimitations and Future Directions\u003c/h3\u003e\n\u003cp\u003eWhile our study yields important mechanistic insights, several limitations must be acknowledged. First, our analysis was based on an \u003cem\u003ein vitro\u003c/em\u003e cell line model (MCF7 cells) under acute treatment conditions. Cell lines can behave differently than tumors in patients, and the 24-hour treatment windows may not capture long-term evolutionary adaptations of cancer cells. Second, we examined the effect of glucose restriction (low glucose) rather than direct hyperglycemia. Although we interpret our findings in the context of high blood glucose effects, the experimental design did not include a high-glucose condition (e.g. 25 mM glucose) for direct comparison. This means some inferences about \u0026ldquo;high glucose vs. normal\u0026rdquo; are indirect. Third, the presence of tamoxifen adds complexity \u0026ndash; our combined treatment reveals interplay between metabolic and hormonal factors, but it is harder to disentangle the specific contribution of each. We mitigated this by analyzing each condition separately as well, but future studies might include a full matrix of glucose levels (low, normal, high) with and without ER blockade to map the interactions more completely. Fourth, our network and TF analyses, while informative, are computational predictions. We identified hub genes and key transcription factors using bioinformatics tools (STRING, ClusterONE, ChEA/Enrichr), but we have not yet experimentally validated these as true drivers of the observed phenotypes. There is a risk of false positives in such analyses \u0026ndash; for example, a predicted hub protein might not actually be functionally important in our system. Lastly, this study focused on a single breast cancer cell subtype (ER-positive, luminal). Breast cancer is heterogeneous, and cells with different receptor status (HER2-positive or triple-negative breast cancers) might respond differently to glucose level changes. Thus, our conclusions may not generalize to all breast tumors without further verification.\u003c/p\u003e\u003cp\u003eLooking forward, there are several clear directions to build upon this work. An immediate next step would be to experimentally validate key regulators identified in our analysis. For instance, knocking down or inhibiting the common hub proteins or transcription factors in our networks could test whether they are indeed critical for cell survival under low-glucose and tamoxifen treatment. Conversely, overexpressing some of the 44 common DEGs (especially the 5 that were consistently upregulated) might reveal if they confer an advantage to cells in stressful conditions. Another future direction is to incorporate a true hyperglycemia model in the experimental design. Culturing breast cancer cells in high-glucose media (simulating diabetic conditions) and comparing their gene expression and behavior to normal-glucose controls would directly complement our findings. Such experiments could confirm that the pathways we saw suppressed by glucose deprivation are conversely enhanced by glucose excess. It would also be valuable to investigate other cell lines and in vivo models: for example, testing a triple-negative breast cancer cell line under similar conditions, or employing mouse models where diet-induced hyperglycemia or fasting-mimicking diets are combined with hormone therapy. These studies would determine if the synergy between metabolic intervention and tamoxifen is broadly applicable and if the molecular responses are consistent across models. Additionally, since our data suggest that fasting and ER blockade converge on certain epigenetic and transcriptional processes (like chromatin organization and Pol II transcription), examining epigenetic changes (histone modifications, DNA methylation) in these conditions could deepen our understanding of how gene expression is reprogrammed [56]. This could uncover semi-permanent changes that make tumor cells less aggressive. Finally, translating these insights to a clinical context, while beyond the scope of our current mechanistic focus, is an ultimate goal. The patterns we identified could lead to biomarkers of response to metabolic therapy: for instance, the downregulation of a specific set of genes might predict which tumors would respond best to glucose-lowering interventions. In the long run, combining metabolic strategies (dietary or pharmacological) with standard treatments could open new therapeutic avenues. Our study lays the groundwork for such possibilities by illuminating the molecular dialogue between a tumor cell\u0026rsquo;s metabolic state and its gene regulatory networks. Continued research in this vein will help determine how we can exploit cancer metabolism to improve treatment outcomes, while also validating the mechanistic links we have proposed between high blood glucose and breast cancer progression.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eGlucose restriction imposes a profound metabolic stress on breast cancer cells, leading to widespread transcriptional reprogramming and altered tumor cell behavior. Under low-glucose conditions, we observed broad changes in gene expression, with hundreds of genes differentially regulated \u0026ndash; notably including a suppression of many cell cycle and growth-promoting pathways​ [57]. This metabolic stress translates into functional effects on the cancer phenotype: nutrient-deprived breast cancer cells show reduced proliferative capacity and motility, reflecting an anti-tumor shift in behavior​ [58]. Such conditions can also trigger the downregulation of oncogenic factors; for example, key pro-survival genes like CTGF are significantly suppressed by glucose limitation, which in turn has been shown to increase tumor cell sensitivity to therapy​. Together, these findings illustrate that restricting glucose availability directly hampers cancer cell growth and induces molecular changes that make the cells more vulnerable to treatment.\u003c/p\u003e\u003cp\u003eImportantly, our results reinforce a clear synergy between glucose restriction and tamoxifen therapy. We found that depriving cells of glucose enhanced the efficacy of tamoxifen, as evidenced by greater cell death at lower drug doses and a markedly expanded set of anti-cancer gene responses under the combined treatment. In fact, low-glucose conditions enabled tamoxifen to significantly reduce cell viability even at minimal concentrations \u0026ndash; an effect not observed in high-glucose environments \u0026ndash; whereas shifting cells from high to low glucose restored tamoxifen potency (yielding\u0026thinsp;~\u0026thinsp;40% additional reduction in viability)​ [57]. This synergistic interaction likely arises from overlapping stress pathways: both metabolic starvation and endocrine therapy converged on similar molecular targets, amplifying growth-inhibitory and pro-apoptotic signals in the cancer cells. These insights carry important therapeutic implications. They suggest that actively targeting tumor metabolism \u0026ndash; for instance, through dietary glucose restriction or glycolytic inhibitors \u0026ndash; could be a powerful adjuvant strategy to bolster conventional treatments like tamoxifen​ [58]. By illuminating how metabolic stress reshapes gene expression and sensitizes breast cancer cells to hormonal therapy, this research contributes to the growing understanding of breast cancer metabolism and opens new avenues for integrative treatment approaches. Ultimately, our findings underscore that confronting the metabolic needs of breast cancer can weaken the disease\u0026rsquo;s defenses, providing a compelling rationale for metabolic interventions as part of future breast cancer therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\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\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study is freely available and can be obtained from NCBI using GSE mentioned above, code \u0026ldquo;GSE121378\u0026rdquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work has been conducted with no specific funding to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHY conceived the idea and conducted the experiment and prepared the results. HF supervised the procedure, verified the results, and overlooked the discussion. HY wrote the first draft, and HF edited the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWarburg, O., \u003cem\u003eOn the origin of cancer cells.\u003c/em\u003e Science, 1956. \u003cstrong\u003e123\u003c/strong\u003e(3191): p. 309-314.\u003c/li\u003e\n\u003cli\u003ePavlides, S., et al., \u003cem\u003eThe reverse Warburg effect: aerobic glycolysis in cancer associated fibroblasts and the tumor stroma.\u003c/em\u003e Cell cycle, 2009. \u003cstrong\u003e8\u003c/strong\u003e(23): p. 3984-4001.\u003c/li\u003e\n\u003cli\u003eHou, Y., et al., \u003cem\u003eHigh glucose levels promote the proliferation of breast cancer cells through GTPases.\u003c/em\u003e Breast Cancer: Targets and Therapy, 2017: p. 429-436.\u003c/li\u003e\n\u003cli\u003eZhao, J., et al., \u003cem\u003eSelenadiazole derivatives antagonize hyperglycemia-induced drug resistance in breast cancer cells by activation of AMPK pathways.\u003c/em\u003e Metallomics, 2017. \u003cstrong\u003e9\u003c/strong\u003e(5): p. 535-545.\u003c/li\u003e\n\u003cli\u003eQiu, J., Q. 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Fallahi, \u003cem\u003eThe Epigenetic Regulation of Quiescent in Stem Cells.\u003c/em\u003e Global Medical Genetics, 2023. \u003cstrong\u003e10\u003c/strong\u003e(04): p. 339-344.\u003c/li\u003e\n\u003cli\u003eAmbrosio, M.R., et al., \u003cem\u003eGlucose impairs tamoxifen responsiveness modulating connective tissue growth factor in breast cancer cells.\u003c/em\u003e Oncotarget, 2017. \u003cstrong\u003e8\u003c/strong\u003e(65): p. 109000-109017.\u003c/li\u003e\n\u003cli\u003eKhajah, M.A., S. Khushaish, and Y.A. Luqmani, \u003cem\u003eGlucose deprivation reduces proliferation and motility, and enhances the anti-proliferative effects of paclitaxel and doxorubicin in breast cell lines in vitro.\u003c/em\u003e PLoS One, 2022. \u003cstrong\u003e17\u003c/strong\u003e(8): p. e0272449.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Table","content":"\u003cp\u003eSupplementary Table S1 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"Breast cancer, MCF7, Network analysis, differentially expressed genes, Transcription factors","lastPublishedDoi":"10.21203/rs.3.rs-7668101/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7668101/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Breast cancer cells rewire their metabolism to thrive on high glucose, and elevated blood sugar is linked to therapy resistance. We investigated whether glucose restriction can reprogram breast cancer cell behavior and enhance the efficacy of tamoxifen, a cornerstone endocrine therapy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: Using estrogen receptor-positive MCF7 cells, we integrated gene expression profiling with protein–protein interaction and gene regulatory network analyses under three conditions: low-glucose (metabolic starvation), tamoxifen treatment, and their combination.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Glucose starvation alone triggered a broad transcriptional reprogramming (697 differentially expressed genes, DEGs) involving cell-cycle regulation and chromatin remodeling, while tamoxifen alone altered a smaller gene set (201 DEGs) linked to proliferation and apoptotic pathways. Strikingly, combined glucose restriction and tamoxifen induced a massive gene expression shift (1,294 DEGs), far exceeding either treatment alone and indicating a synergistic anti-cancer response. Network analysis revealed distinct but overlapping molecular networks: starvation preferentially upregulated DNA replication and mitotic cell-cycle modules, tamoxifen enriched for pathways suppressing cell proliferation and protein synthesis, and the combination uniquely engaged cell division and chromatin-organization networks. We identified six hub proteins and 44 genes consistently regulated across all conditions, pointing to a core stress-response program. Transcription factor analysis further uncovered 54 key regulators common to all treatments and an expanded set of master regulators (12 differentially expressed transcription factors) activated only under combined treatment, underscoring novel gene regulatory interactions behind the enhanced response.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: Our findings reveal new molecular insights into how glucose deprivation potentiates tamoxifen’s anti-tumor effects. This study underscores the interplay between cancer metabolism and hormone therapy, suggesting that targeting metabolic vulnerabilities can amplify treatment efficacy and offering a robust gene-network framework for advancing breast cancer metabolism research.\u003c/p\u003e","manuscriptTitle":"Starving Cancer: How Glucose Restriction Enhances Tamoxifen sensitivity in Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-04 00:29:06","doi":"10.21203/rs.3.rs-7668101/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":"65d493ed-bf58-4388-a0b1-09ac5bf8c868","owner":[],"postedDate":"October 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-29T12:38:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-04 00:29:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7668101","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7668101","identity":"rs-7668101","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}