Breast cancer (BC) is a highly heterogeneous malignancy and remains the leading cause of cancer-related mortality among women worldwide. Although advances in molecular classification and targeted therapies have improved outcomes for certain subtypes, robust prognostic biomarkers applicable across clinical contexts are still lacking. The CRISPR-Cas9 system offers a powerful platform for identifying cancer cell vulnerabilities and may facilitate the development of clinically relevant prognostic models. We integrated genome-wide CRISPR-Cas9 screening data from the DepMap database with transcriptomic and clinical data from TCGA and GEO datasets to identify BC cell survival-dependent genes (CSDGs). CSDGs prognostic signature was constructed using univariate Cox regression, LASSO, and stepwise multivariate Cox regression analyses. The model was validated in internal and external cohorts. Functional enrichment analyses, including GO, KEGG, WGCNA, and GSEA, were performed to explore the biological mechanisms underlying the signature. Random forest analysis and functional experiments were conducted to investigate the role of key gene in CSDGs signature. A total of 1,622 CSDGs were identified, and a nine-gene prognostic CSDGs signature (BRD4, CHORDC1, COPZ1, HNRNPC, NUP43, RAD1, RBBP8, TUBA1B, and VPS28) was developed. This signature effectively stratified patients into high- and low-risk groups with significantly different overall survival, and its robustness was confirmed across multiple internal and external cohorts. High-risk patients exhibited a significant association with multiple adverse clinical features. A nomogram that combined the risk score with clinical variables showed robust predictive performance, and its C-index surpassed those of individual predictors, underscoring the enhanced accuracy of the integrated model. Functional analyses revealed enrichment of oncogenic pathways (e.g., MYC targets, G2/M checkpoint, mTORC1 signaling) in high-risk patients, while low-risk patients exhibited immune and hormone response signatures. CHORDC1 was identified as the most critical gene in the model. Knockdown of CHORDC1 significantly inhibited proliferation, migration, and invasion of BC cells. Transcriptomic profiling further linked CHORDC1 to oncogenic pathways, including EMT, mTORC1 signaling, and TNF-α/NF-κB signaling activation. We developed a CRISPR-Cas9 screening-based prognostic signature for BC that effectively stratifies patient risk and demonstrates robust predictive performance across cohorts. CHORDC1 was identified as a key oncogenic driver, promoting tumor progression via pathways such as EMT and mTORC1 signaling, highlighting its potential as a therapeutic target. These findings may contribute to the development of personalized prognostic tools and therapeutic strategies in BC.

Breast cancer is the most common cancer globally and often diagnosed at advanced stages in Indonesia. Metabolomic profiling has emerged as a promising approach for identifying biomarkers associated with breast cancer (BC). However, the specificity and clinical applicability of candidate metabolites remain under investigation. This study investigates untargeted plasma metabolomic profiles of breast cancer patients to find candidate metabolite signatures of breast cancer. Plasma samples from 24 breast cancer patients and 24 healthy controls (HC) were analyzed using untargeted Gas Chromatography-Mass Spectrometry (GC-MS). A machine learning (ML) approach was utilized to validate the metabolites. Differential metabolites were identified and analyzed to explore altered metabolic pathways associated with BC. Several metabolites, including D-glucose, citric acid, lactic acid, L-hydroxyproline, and glutamic acid, were significantly different between BC and HC groups. Those metabolites correlated with arginine/proline metabolism, glycolysis, and alanine/aspartate/glutamate pathways. ML validation yielded favorable results for these metabolites as candidate metabolite signatures of breast cancer (AUC > 0.8, accuracy > 80%). Further subset analysis showed reduced dihydrouracil in late stage. Untargeted plasma metabolomic analysis combined with machine learning effectively identified a potential candidate metabolite signature for breast cancer. These findings improve understanding of breast cancer metabolic alterations and highlight promising pathways for early diagnosis. Nevertheless, further validation in larger, well-controlled studies is required to establish their diagnostic utility.

CLDN6 inhibits breast cancer growth by inducing autophagic cell death through SOX4 m6A modification.

Untargeted 1H NMR metabolomics offers a noninvasive means to identify biomarkers in breast cancer (BC) patients; however, metabolic signatures specific to Nigerian women remain poorly understood. This study aimed to identify plasma metabolomic and lipidomic biomarkers associated with BC in Nigerian patients, evaluate their diagnostic performance using machine learning (ML), and identify dysregulated metabolic pathways. This case-control study recruited 100 BC patients and 100 healthy controls from 4 Nigerian teaching hospitals. Plasma metabolites and lipids were profiled using 1H NMR spectroscopy and the Liposcale test. Multivariate and ML analyses revealed a clear distinction between BC and controls (PLS-DA accuracy: 92.4-94.4%). Twenty-four metabolites were significantly altered (FDR < 0.05), with decreased glycine and glutamine, and increased GlycA, GlycB, glucose, and ketone bodies. Lipoprotein profiling showed reduced small HDL, LDL, and large VLDL particles, alongside with increased HDL diameter. The random forest model achieved the best classification performance (AUC = 0.985) and identified 23 key biomarkers. Pathway analysis revealed 29 enriched metabolic pathways, including glyoxylate and dicarboxylate metabolism. Overall, these findings highlight distinct metabolic alterations in Nigerian BC patients and demonstrate the potential of combining NMR-based metabolomics with ML for population-specific, noninvasive BC diagnostics.

Identification and validation of an intratumor heterogeneity-related prognostic signature in triple-negative breast cancer: a study based on integrative machine learning and single-cell sequence.

Thermal signatures in breast cancer: Deciphering latent biomarkers through deep learning and explainable AI.

The field of reverse cardio-oncology examines how subclinical and overt cardiometabolic dysfunction-such as obesity-fuels breast cancer (BCa) risk and altered tumour biology through shared mechanisms such as chronic inflammation, hormonal dysregulation, and cellular senescence. Limitations of body mass index (BMI) have prompted the development of refined obesity phenotypes, including metabolically healthy vs unhealthy obesity and sarcopenic obesity that more accurately stratify BCa risk. Reverse cardio-oncology is conceptually distinguished from traditional cardio-oncology by focusing on how cardiometabolic impairment-even in the absence of manifest cardiovascular disease-increases BCa incidence and worsens prognosis. Within a common-soil framework, senescent adipose tissue is recognized as a key driver of breast tumour microenvironment remodelling through senescence-associated secretory phenotype (SASP), epigenetic reprogramming, and immunosenescence. Emerging translational strategies-including lifestyle modification, cardiometabolic therapies such as GLP-1 receptor agonists and SGLT2 inhibitors, and senolytic approaches-highlight opportunities to integrate cardiovascular and oncologic prevention and treatment in women with or at risk for BCa. Overall, this review synthesizes current knowledge on obesity's mechanistic links to BCa within a reverse cardio-oncology paradigm and provides a conceptual foundation for improved risk stratification and interdisciplinary clinical management.

Breast cancer prognosis remains challenging, and the emerging field of cancer neuroscience suggests that the nervous system plays a crucial yet underexplored role in tumor progression. This study aimed to construct and validate a novel prognostic signature for breast cancer based on genes involved in neural-tumor interactions. Differential expression analysis and univariate Cox regression were performed on the TCGA-BRCA dataset to identify genes associated with both cancer neuroscience and patient prognosis. A prognostic model was constructed using LASSO and multivariate Cox regression analyses. Its predictive performance was validated in external datasets. The immune microenvironment, tumor mutation burden, and immunotherapy response were compared between the high- and low-risk groups. Drug sensitivity was predicted using the oncoPredict algorithm. A 12-gene prognostic model was developed. Patients stratified into high- and low-risk groups showed significant survival differences in all cohorts. The signature demonstrated reliable predictive accuracy, with AUCs of 0.700, 0.744, and 0.759 for 1-, 3-, and 5-year survival in the TCGA dataset. The low-risk group exhibited a more immunologically active tumor microenvironment,suggesting a potentially better response to immunotherapy. Drug sensitivity analysis identified three compounds with lower predicted IC50 values in the high-risk group. This study establishes and validates a novel 12-gene cancer neuroscience-related prognostic model for breast cancer. This model not only effectively stratifies patient risk but also reveals distinct immune landscapes and predicts differential responses to immunotherapy and potential therapeutic agents. These findings offer new insights for prognostication and personalized treatment strategies in breast cancer.

Proteomic signatures in triple-negative breast cancer.

ABSTRACT Background Breast cancer is characterized by substantial molecular heterogeneity, and clinical features alone are insufficient for personalized management. This study aimed to develop a machine learning–based prognostic signature for breast cancer. Methods Ten independent breast cancer cohorts and 76 combinations of machine learning algorithms were used to identify the consensus machine learning‐derived prognosis signature (CMDPS). We then collected 62 published transcriptome signatures for comparison with CMDPS. The associations between CMDPS and the immune cell profile, multi‐omics alterations, and pharmacological landscape were further investigated. Results Nineteen genes consistently linked to survival across the 10 cohorts were identified through univariate analysis, with 76 algorithm combinations employed to determine the most reliable model. CMDPS was best at forecasting overall survival. In the cancer genome atlas breast invasive carcinoma (TCGA‐BRCA) cohort, CMDPS displayed a C‐index value of 0.696 (3‐year survival area under the curve: 0.769; hazard ratio: 5.065 [3.233–7.936]). Patients with high CMDPS scores had a dismal prognosis. Compared with clinical features and 62 published signatures, CMDPS displayed stronger robustness. Furthermore, an in‐depth examination of the CTRP and PRISM drug collections revealed that individuals with elevated CMDPS levels exhibited increased responsiveness to various widely used chemotherapy medications. In parallel, patients with a low CMDPS score exhibited more abundant immune cell infiltration. Conclusion CMDPS is a promising tool that has profound implications for optimizing the clinical management and personalized treatment of breast cancer.

Abstract Background: Microtubule inhibitors remain a standard chemotherapy in the management of HER2-negative metastatic breast cancer (MBC). CALGB (Alliance) 40502 was a phase 3 randomized study involving 799 patients with MBC receiving first-line chemotherapy, to determine the optimal chemotherapeutic agent among paclitaxel, nab-paclitaxel, or ixabepilone (with or without bevacizumab). Secondary analysis suggested inferior PFS with nab-paclitaxel specifically among patients with hormone receptor positive (HR+)/HER2-negative breast cancer. Correlative analysis demonstrated significant association of stromal tumor infiltrating lymphocytes (sTILs) with improved progression-free (PFS) and overall survival (OS) in CALGB40502. We hypothesized that immune activation would be associated with differential benefit among distinct microtubule agents. To address this, we evaluated the sTILs and RNA-based immune features association with clinical outcomes, including evaluation of nab-paclitaxel versus paclitaxel. Methods: To limit heterogeneity, analyses focused on pre-treatment primary tumor breast samples with central review/pathologist-enumerated sTILs in accordance with International TILs Working Group methods and RNAseq (n = 280). PAM50 molecular subtypes were assigned based on transcriptomic features. RNAseq data were used to generate immune deconvolution estimates from 5 distinct algorithms (CIBERSORT, xCell, ABIS, ConsensusTME, ImmuCellAI). 1,018 curated breast cancer gene expression signatures GES) were derived from previously published studies. Associations between sTILs and GES were evaluated with sTILs being a categorical variable with the thresholds &amp;lt;5% (low) and (≥5%) high. Results: In the evaluable population, basal-like PAM50 subtype was enriched for high sTILs category (p=1e-4), as anticipated. T-cell and B-cell immune signatures and immune deconvolution estimates were among most highly associated with sTILs. In addition to the expected prognostic association of high T-cell GES, high B-cell-related IgG signature score was also significantly associated with improved overall survival (OS) among triple-negative breast cancer (TNBC) in CALGB 40502 (HR = 0.50,95% CI:0.30-0.86, log-rank p=0.01) but not in HR+/HER2-. In an exploratory analysis of young patients (age at study entry &amp;lt;50 years), high sTILs were associated with improved OS among TNBC patients but worse OS among those with HR+/HER2- MBC (HR = 2.19, 95% CI:1.10-4.36, log-rank p=0.022). High (above median) RNA-based Xcell total immune score was significantly associated with worse OS in both CALGB 40502 and a validation dataset of primary breast cancers, METABRIC (OS HR = 2.12, 95% CI:1.01-4.45, log-rank p=0.042). To further investigate the clinical trial finding of inferior PFS for nab-paclitaxel compared with paclitaxel in patients with HR+/HER2- MBC, there was a significant enrichment of T-cell signatures associated with poor outcome in patients receiving nab-paclitaxel (Fisher exact p&amp;lt;0.001). Conclusion: In this translational analysis of sTILs and RNAseq from a large phase III clinical trial, sTILs and immune signatures were not uniformly associated with better outcome. Rather, high sTILs or immune signatures were associated with worse OS in young patients with HR+/HER2- breast cancer and higher T-cell signatures were associated with inferior PFS with nab-paclitaxel among HR+/HER2- MBC patients. These data reinforce the importance of context/subtype-specific interrogation of the tumor-immune microenvironment Support: U10CA180821, U10CA180882; https://acknowledgments.alliancefound.org. Bristol Myers Squibb (BMS); Clinicaltrials.gov Identifier: NCT00785291 Citation Format: E. K. Blige, K. V. Ballman, A. Michmerhuizen, M. Vater, L. A. Carey, A. H. Partridge, W. F. Symmans, M. A. Watson, C. M. Perou, D. G. Stover, H. S. Rugo. Rna-based immune features associated with benefit from distinct microtubule inhibitor therapy for metastatic her2-negative breast cancer: a post hoc analysis of the calgb 40502 (alliance) phase iii randomized clinical trial [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS1-13-17.

Breast cancer (BC) is a common malignant tumor with high incidence and mortality rates. Mitophagy refers to a selective form of autophagy that is believed to be closely related to the occurrence and progression of BC. Identifying the mitophagy-related sites associated with BC can help us gain a deeper understanding of the underlying mechanisms of BC, laying the foundation for early diagnosis and effective treatment of BC. RNA-seq expression data of BC were obtained from the GEO and TCGA databases. Differentially expressed genes were intersected with mitophagy-related genes from GeneCards to identify BC-associated mitophagy genes. Prognostic biomarkers were screened using Kaplan-Meier (K-M) survival and ROC analyses. Based on mitophagy-related gene expression and survival data, BC patients were classified into high- and low-risk subgroups for immune infiltration and GSEA analyses. Finally, IHC data from the HPA database and in vitro experiments, including siRNA-mediated knockdown, Western blot, CCK-8 proliferation assay, confocal microscopy and drug prediction were performed to validate the expression and biological functions of candidate biomarkers PBK and NEK2. Through dual validation of K-M survival analysis and ROC diagnosis-treatment efficacy analysis, we ultimately identified 9 mitophagy-related prognostic biomarkers for BC, and found their expression was significantly upregulated in BC tissues. In addition, the results showed that the degree of immune infiltration in the low-risk subgroup was considered higher than that in the high-risk subgroup. Inhibition of PBK and NEK2 will have an inhibitory effect on the proliferation of BC cell. Furthermore, clinicopathological analyses confirmed a genuinely higher risk in the high-risk subgroup, with PBK and NEK2 independently associated with risk stratification. This study elucidated the prognostic value, immune microenvironment characteristics, and molecular mechanisms of mitophagy in BC, and identified nine mitophagy-related biomarkers. Among them, PBK and NEK2 were experimentally confirmed to promote tumor cell proliferation, providing novel insights for early diagnosis and therapeutic strategies in breast cancer.

Fibrillarin (FBL), a core component of the C/D box small nucleolar ribonucleoprotein (snoRNP) complex, catalyzes the 2'-O-methylation (Nm) of the ribose 2'-hydroxyl moiety in ribosomal RNA (rRNA). Distinct Nm patterns contribute to ribosome heterogeneity, which is linked to selective translation of oncogenes. FBL dysregulation generates an aberrant Nm signature in triple-negative breast cancer (TNBC), the most aggressive breast cancer subtype. This study investigated the role of FBL in TNBC via translation-driven mechanisms. Our findings show that FBL knockdown impairs oncogenic traits, triggers metabolic stress, and reduces the translation efficiency of oncogenes, such as metastasis-associated protein 1 (MTA1), interleukin-1 receptor-associated kinase 1 (IRAK1), and thymosin beta 10 (TMSB10). RiboMethSeq confirmed that the rRNA Nm sites exhibited differential sensitivity to FBL depletion. Additionally, FBL knockdown led to alterations in 18S ribosome structure confirmed by SHAPE and specifically reduced RPS28 incorporation into ribosomes. Notably, silencing RPS28 also disrupted both the oncogenic phenotype and downregulated MTA1, IRAK1, and TMSB10 expression. These findings reveal a complex interplay between FBL, rRNA Nm modifications, and RPS28 in shaping oncogenic protein pools and ribosomal composition in TNBC, offering promising insights into therapeutic approaches targeting this aggressive cancer subtype.

BackgroundBreast cancer is a highly heterogeneous disease, and there is a continuing need for robust prognostic tools that can also inform therapeutic strategies. This study aimed to develop a novel prognostic signature for breast cancer by leveraging the multi-target philosophy of traditional Chinese medicine (TCM).MethodsWe systematically analyzed 221 TCM prescriptions for breast cancer to identify potential herb-related targets. A 20-gene herb-related risk score (HRS) was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression in The Cancer Genome Atlas (TCGA) breast cancer cohort (n=1,097). The model’s prognostic performance was validated in three independent cohorts: Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (n=1,466), GSE20685 (n=327), and GSE10886 (n=226). We further investigated the association of the HRS with the tumor immune microenvironment and potential drug sensitivity using bioinformatics algorithms.ResultsThe HRS served as an independent prognostic factor for overall survival (OS) [TCGA: hazard ratio (HR) =2.03, 95% confidence interval (CI): 1.25–3.30, P=0.004; METABRIC: HR =1.82, 95% CI: 1.59–2.10, P<0.001]. Time-dependent receiver operating characteristic analysis demonstrated consistent prognostic discrimination, with an area under the curve (AUC) for 3-year OS of 0.709 (95% CI: 0.657–0.761) in the TCGA cohort. A high HRS was significantly associated with an immunosuppressive microenvironment and reduced predicted response to immune checkpoint blockade. Furthermore, the signature identified several compounds (e.g., sirolimus, temsirolimus) with potential heightened sensitivity in high-risk patients.ConclusionsThis study developed and validated a novel TCM-derived gene signature that reliably stratifies breast cancer patients into distinct risk groups and is independently prognostic. While its clinical utility requires extensive validation in prospective studies before it can inform patient management, this novel gene signature may serve as a potential tool for individualized risk assessment and prognosis prediction, while also generating compelling hypotheses for guiding immunotherapy and targeted therapy strategies, contributing to the ongoing exploration of precision oncology in breast cancer.

BackgroundBreast cancer (BRCA) is one of the most prevalent malignant tumors in women worldwide, characterized by significant heterogeneity. Fatty acid metabolism (FAM) plays a crucial biological role in the initiation and progression of cancer. This study aims to identify novel, effective biomarkers related to FAM for improved risk stratification and treatment selection in BRCA patients.MethodsGene expression data from 1,217 BRCA patients were obtained from The Cancer Genome Atlas (TCGA) database. A comprehensive machine learning approach, incorporating ten different methods, was used to develop a FAM-related gene prognostic model (FAMGM). The Kaplan-Meier method and correlation analysis were employed to assess differences in overall survival (OS) and immune characteristics between high- and low-risk groups. External validation was performed using independent datasets. Single-cell RNA sequencing (scRNA-seq) data from 26 BRCA patients were analyzed, and the potential functions and mechanisms of the model genes were investigated using single-sample gene set enrichment analysis (ssGSEA), CellChat, and other algorithms. Finally, spatial transcriptomics (ST) analysis was conducted to examine the expression of model genes in the malignant regions of tumors.ResultsThe FAMGM, developed using CoxBoost and random survival forest (RSF) methods, was identified as the optimal prognostic model. FAMGM demonstrated stable and robust performance in predicting clinical outcomes for BRCA. The high-risk group showed poor survival prognosis, typically associated with advanced clinical stages, reduced immune cell infiltration, and increased tumor mutational burden (TMB). Model genes were predominantly enriched in macrophages and appeared to influence tumor progression through the upregulation of multiple signaling pathways. Additionally, these model genes exhibited higher expression in malignant tumor regions.ConclusionsFAMGM holds significant potential as a prognostic marker and could be used in the subsequent diagnosis, treatment, prognostic prediction, and mechanistic research of BRCA.

MicroRNAs are key post-transcriptional regulators in breast cancer, but their time-dependent dynamics and downstream oncogenic effects are not fully understood. miR-548c-3p has been proposed as a tumor suppressor, yet its temporal behavior and impact on cell cycle drivers remain unclear. This study investigated the time-dependent expression of miR-548c-3p and its post-transcriptional regulation of E2F3 and FOXM1 in MCF-7 breast cancer cells. Cells were analyzed at multiple time points (2–72 h) by quantitative real-time PCR to assess dynamic changes in miR-548c-3p, E2F3, and FOXM1 mRNA levels. Bioinformatic validation using TCGA-BRCA datasets and public platforms evaluated gene expression, promoter methylation, and prognostic significance. miR-548c-3p showed a progressive time-dependent decline, with the lowest levels at 72 h, whereas E2F3 and FOXM1 were significantly upregulated over time, supporting a post-transcriptional derepression mechanism. TCGA-based analyses confirmed overexpression and hypomethylation of E2F3 and FOXM1 in breast cancer, particularly in triple-negative tumors, and high expression of both genes was associated with poor survival. These findings indicate that time-dependent loss of miR-548c-3p contributes to E2F3 and FOXM1 activation through a post-transcriptional regulatory mechanism, highlighting this miRNA–oncogene axis as a potential prognostic signature and therapeutic target in breast cancer.

IntroductionBreast cancer is associated with significant restructuring of the gut ecosystem. Gut microbial composition and function may influence cancer development and progression through immune modulation, metabolic regulation, and inflammation-related pathways.MethodsUsing shotgun metagenomic sequencing of fecal samples from 38 stage I–III breast cancer patients and 36 age- and body mass index-matched healthy controls. Machine learning models were constructed to evaluate the diagnostic potential of integrated microbial and metabolic features.ResultsSignificant alterations were observed in gut microbiota composition, including depletion of beneficial taxa (Limosilactobacillus fermentum, Blautia sp.) and enrichment of Prevotella copri. Pathways involved in short-chain fatty acid and purine metabolism were reduced. The gut phageome exhibited structural changes and altered correlations with bacterial hosts. Predictive analysis revealed depletion of short-chain fatty acids (butyrate, propionate), purine intermediates (hypoxanthine, xanthine), and nicotinate in patients. A machine learning model integrating microbial and predicted metabolic features achieved an area under the curve values of 0.78 in the discovery cohort and 0.73 (recall = 0.74) in an independent validation cohort.DiscussionCoordinated gut microbiome, phageome, and metabolome alterations characterize breast cancer, offering potential non-invasive biomarkers and mechanistic insights for disease detection and intervention.

Breast cancer remains a major global health challenge with high incidence and mortality rates among women. Recent studies have highlighted the critical role of the tumor microenvironment, particularly cancer-associated fibroblasts (CAFs), in tumor progression. However, current understanding of CAFs heterogeneity and its implications for breast cancer diagnosis and treatment remains limited. This study aimed to identify and validate refined marker genes for CAFs and to develop a diagnostic model to improve breast cancer diagnosis and therapeutic strategies. We employed various machine learning algorithms to identify feature genes associated with CAFs. Based on these genes, we constructed a high-precision diagnostic model for breast cancer. Furthermore, through single-cell analysis, we delved into the heterogeneity of CAFs and predicted the sensitivity of different CAF subsets to specific drugs. To validate the expression of these characteristic genes, immunohistochemical (IHC) experiments were also conducted. This study used machine learning to identify FXYD1, SULF1, and TNXB as refined biomarkers for CAFs in breast cancer. Among these evaluated algorithms, the Random Forest algorithm distinctly stood out as the best due to its robust classification accuracy and stability. Single-cell analysis provided insights into the heterogeneity of CAFs between Luminal and non-Luminal breast cancer, thereby enhancing our understanding of the tumor microenvironment. Drug sensitivity predictions indicated that distinct CAF subsets responded differently to specific drugs, laying a solid foundation for the development of personalized breast cancer treatment strategies. Through IHC, the expression patterns of these three biomarkers were verified: FXYD1 was expressed in myoepithelial and fibroblasts in normal breast tissue but was significantly absent in breast cancer; SULF1 was upregulated in fibroblasts of breast cancer; while the expression of TNXB did not exhibit notable variations between normal and cancerous tissues. These findings not only highlight the crucial roles played by FXYD1, SULF1, and TNXB in the development of breast cancer, but also uncover the heterogeneity CAFs. Consequently, our research provides a fresh perspective and a solid theoretical basis for advancing both early and precise diagnostic methods, as well as tailored therapeutic strategies.

BackgroundThe complexities of nucleotide metabolism in breast cancer (BC) cells are not yet fully understood. A deeper exploration of the various tumor subpopulations and their interactions with the tumor microenvironment (TME) could provide important insights into these clinically relevant signaling pathways.MethodsWe integrated five distinct single-cell enrichment scoring methodologies to conduct a comprehensive enrichment analysis of BC cell populations. The analytical findings underwent subsequent validation using an independent single-cell cohort. Tumor cell clusters were categorized based on their average enrichment scores. Functional analyses were carried out using several tools, including CellChat, Monocle, CopyKAT, SCENIC, and CytoTRACE. The RCTD method was then employed to map the single-cell clusters onto spatial transcriptomics data, facilitating the evaluation of cellular dependencies and pathway activities to differentiate tumor cell subtypes. A prognostic framework was subsequently established using large-scale transcriptomic datasets, enabling prediction of immunotherapy responsiveness. Experimental validation further confirmed expression patterns of pivotal genes implicated in therapeutic outcomes.ResultsTumor cells exhibit significantly upregulated nucleotide metabolic activity, enabling their classification into two distinct subgroups: NUhighepi and NUlowepi. Cells within the NUhighepi subgroup demonstrate pronounced malignant phenotypes. Intercellular communication analysis performed with the stLearn platform revealed robust interactions between NUhighepi cells and fibroblasts. Supporting this finding, spatial transcriptomic analysis via MISTy revealed a distinct dependency of NUhighepi on fibroblasts. A robust prognostic model, developed using various machine learning algorithms, was able to predict survival outcomes and responses to immunotherapy. Furthermore, targeted drugs were identified for both the high and low scoring groups. Experimental investigations confirmed the expression of core genes in different breast cancer cells.DiscussionIn conclusion, we developed a nucleotide metabolism-derived prognostic signature for BC, with DCTPP1 highlighted as a promising biomarker and therapeutic target. These findings provide a valuable framework for early clinical intervention and show promising potential for predicting responses to immunotherapy in BC patients.

Background: Triple-negative breast cancer (TNBC) is an aggressive subtype with poor prognosis and resistance to conventional therapies, including radiotherapy. Cancer stem cells (CSCs) drive tumor initiation, metastasis, and therapy resistance in TNBC. Identifying pathways sustaining CSCs in radioresistant TNBC is key for targeted therapies. This study examines SRC proto-oncogene (SRC) and the signal transducer and activator of transcription 3 (STAT3) activation in radioresistance and CSC maintenance. Methods: A radioresistant MDA-MB-231 TNBC cell line (231RR) was developed and compared to the parental line for CSC activity and self-renewal. Western blotting assessed molecular changes; functional assays followed SRC and STAT3 inhibitor treatment. SRCY530F overexpression and hexokinase-2 (HK2) knockdown evaluated roles in CSC activity and signaling. Pathways were analyzed via metabolic assays, The Cancer Genome Atlas (TCGA) breast cancer datasets, and Harmonizome gene sets. Results: 231RR cells exhibited enhanced CSC traits and upregulated SRC/STAT3 signaling, with heightened sensitivity to SRC/STAT3 inhibitors. Forced expression of SRCY530F in parental cells boosted STAT3 activation and CSC activity. SRC/STAT3 inhibition reduced HK2 without impairing glycolysis. HK2 knockdown decreased MYC proto-oncogene (c-MYC) and octamer-binding transcription factor-4 (OCT4). Finally, the suppression of epidermal growth factor receptor (EGFR) activation by gefitinib resulted in the inhibition of the SRC/STAT3/HK2 axis. TCGA data linked SRC to glycolytic signatures in breast cancer. Conclusions: The EGFR/SRC/STAT3/HK2 axis drives radioresistance and CSC maintenance in TNBC via HK2 upregulation. HK2 promotes stemness mainly through non-metabolic means, not broad metabolic shifts. Targeting this pathway could overcome radioresistance and enhance TNBC outcomes.