PHGDH and LEP serve as prognostic markers associated with B cell and responses to immunotherapy in triple-negative breast cancer

PHGDH and LEP serve as prognostic markers associated with B cell and responses to immunotherapy in triple-negative breast cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article PHGDH and LEP serve as prognostic markers associated with B cell and responses to immunotherapy in triple-negative breast cancer Ruifang Yang, Zhengli Zhou, Rui Chen, Zeyu Zhang, Changjing Zuo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6997049/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background. Triple-negative breast cancer (TNBC) is one of the most aggressive and prevalent cancers in women. This study aimed to identify target genes by integrating glutamine metabolism and cancer-immunity interactions in TNBC. Methods. Data were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO). Glutamine-related genes were extracted from the Gene Set Enrichment Analysis (GSEA) Database. Differential analysis and ESTIMATE were used to find immune-related and glutamine-related genes. The clusters were established by consensus clustering. Mechanistic insights were investigated through Gene set enrichment analysis (GSEA), ESTIMATE, epic, ssGSEA and weighted gene co-expression network analysis (WGCNA). Expression of immunomodulatory factors was used to assess immunotherapy response. Single cell RNA seq and RT-qPCR were used to validate the expression of PHGDH and LEP. Results. Analysis of The Cancer Genome Atlas (TCGA) dataset revealed that leptin (LEP) and phosphoglycerate dehydrogenase (PHGDH) are closely associated with immunity and glutamine metabolism in TNBC. Based on the expression profiles of LEP and PHGDH, TNBC samples were classified into two distinct clusters. Univariate logistic regression analysis demonstrated that clusters significantly influence TNBC prognosis. Gene set enrichment analysis (GSEA) highlighted potential pathways, showing that Cluster 2 correlates positively with immune cell infiltration and exhibits reduced oncogenic pathway activity. Utilizing Weighted gene co-expression network analysis (WGCNA), we identified a module strongly linked to immune response and clusters, along with eight B cell-associated genes. Notably, Cluster 2 displayed elevated expression of immunomodulatory factors, suggesting enhanced responsiveness to immunotherapy. Validation using the GSE76275 dataset confirmed these findings. Additionally, single-cell analysis revealed PHGDH expression in B cells within TNBC. The expression of PHGDH and LEP was validated in TNBC cell lines by RT-qPCR. Conclusion. These results suggested that LEP and PHGDH serve as prognostic markers associated with B cells and improved immunotherapy outcomes in TNBC. TNBC PHGDH LEP B cell immunotherapy single-cell analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. INTRODUCTION Breast cancer accounts for 32 percent of newly diagnosed cancers among women in 2025 [ 1 ]. TNBC has the highest recurrence and mortality rates among breast cancer subtypes [ 2 ]. The identification of subgroups with specific molecular features has led to multiple new targeted therapies for TNBC. Immunotherapy has advanced rapidly, yet only a minority of cancer patients achieve long-term benefits. Among breast cancer subtypes, TNBC patients derive the greatest immunotherapy benefit and exhibit higher immunogenicity than others [ 3 , 4 ]. While immunotherapy for TNBC is currently limited to single agents, combining PD1 inhibitors with chemotherapy reduces recurrence, and inhibitors targeting other immune checkpoints are under study [ 5 ]. Among multiple biomarkers, TIM3, OX40, and tumor mutational burden (TMB) are frequently used to predict immunotherapy response [ 6 ]. Developing a multi-dimensional therapeutic strategy, accurately assessing prognosis, and improving survival rates for TNBC patients remains a significant challenge. Therefore, it is important to develop novel prognostic features to accurately predict treatment response and prognosis. Cancer immunology has shifted in recent years from an emphasis on T cells to an emerging focus on B cells [ 7 ]. B lymphocytes play a crucial role in the tumor immune microenvironment, especially in adaptive immunity. The function of B cells is thought to include the production of antibodies, presentation of antigens, release of cytokines and cytotoxic effector molecules [ 8 ]. Previous study indicated that tumor-infiltrating B cells can enhance T cell-mediated anti-tumor immunity [ 7 ]. And it is well-known that when B cells are present, positive prognostic impact of T cells is stronger [ 9 ]. In certain tumors, B cells actively produce antibodies that target tumor-associated antigens [ 10 ]. In the pre-treatment samples of the lung adenocarcinoma cohort, B cells were associated with positive outcomes of immunotherapy [ 11 ]. Glutamine, the most prevalent amino acid in plasma, is a crucial nutrient that supports cancer growth [ 12 ]. The growth and survival of TNBC cells were particularly dependent on glutamine [ 13 ]. Phosphoglycerate dehydrogenase (PHGDH) plays a role in glutamine metabolism and acts as the rate-limiting enzyme in the initial step of the serine biosynthesis pathway [ 14 ]. Previous studies have found that under glutamine starvation, gastric cancer cells upregulate PHGDH [ 15 ]. Research has indicated that PHGDH is a potential target for enhancing the effectiveness of standard chemotherapy in TNBC [ 16 ]. The synergistic effect of nuclear PHGDH and cMyc can reshape the immune microenvironment of liver cancer [ 17 ]. PHGDH is necessary for the formation of germinal centers and is a therapeutic target for MYC driven lymphoma [ 18 ]. The research results indicated that upregulation of PHGDH expression can inhibit M1 macrophage differentiation and pro-inflammatory cytokine levels, thereby reducing the disease activity of SLE [ 19 ]. Furthermore, leptin (LEP) plays a significant role in glutamine metabolism as a glutamine transporter. The levels of leptin and its receptors correlate with unfavorable outcomes in endometrial cancer [ 20 ]. Leptin promotes proliferation in different cancer cell types, such as breast [ 21 ], prostate [ 22 ], and ovarian [ 23 ] cells. Leptin can also lead to the recruitment and activation of macrophages in the tumor microenvironment, thereby promoting angiogenesis [ 24 ]. Leptin induces STAT3 activation, leading to metabolic reprogramming of effector T cells, which is associated with weakened effector T cell function and enhanced breast tumor development [ 25 ]. However, the underlying molecular mechanisms of LEP and PHGDH in TNBC remain inadequately understood. We stratified 137 TNBC samples into two clusters based on leptin (LEP) and PHGDH expression: Cluster 1 (low LEP/high PHGDH) with enriched DNA repair and oncogenic pathways, and Cluster 2 (high LEP/low PHGDH) showing robust immune infiltration. Cluster 2 demonstrated significant associations with B cells, tertiary lymphoid structures (TLS), and immunomodulatory factors, indicating superior immunotherapy responsiveness. WGCNA discovered a module linking glutamine metabolism to immune function, identifying 8 genes linked to B cells. Single-cell analysis confirmed PHGDH expression in B cells. These findings established LEP and PHGDH as prognostic biomarkers for B cell-mediated immunotherapy response in TNBC. 2. MATERIALS AND METHODS 2.1 Data Sources and Preprocessing The TCGA cohort was download from xena ( https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Breast%20Cancer%20(BRCA)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443 ). Clinical information was extracted from the clinical biospecimen core resource (BCR) XML files using the “TCGAbiolink” package. We filtered the breast cancer patients based on the criteria that the breast_carcinoma_estrogen_receptor_status,breast_carcinoma_progesterone_receptor_status, lab_proc_her2_neu_immunohistochemistry_receptor_status and lab_procedure_her2_neu_in_situ_hybrid_outcome_type were “Negative”, while the her2_immunohistochemistry_level_result = “0”. This process yielded a total of 163 TNBC patients. Among these cases, merely 139 were identified as the histological subtype “Infiltrating Ductal Carcinoma”. The detailed information of TNBC to clusters is shown in Table 1 . Table 1 Primer sequences of qRT-PCR. Genes Forward Primer (5'-3') Reverse Primer (5'-3') PHGDH CGCTGATGTCATCAACGCAG TGGCCAGGCACATGATCATT LEP CTATGTCCAAGCTGTGCCCA GAGACTGACTGCGTGTGTGA GAPDH GAAGGTGAAGGTCGGAGTC GAAGATGGTGATGGGATTTC The Gene Expression Omnibus (GEO) dataset GSE76275 [ 26 ] is sourced from the GPL570 platform, and identified 188 TNBC patients. Glutamine-related gene sets were established by querying 'glutamine' in Gene Set Enrichment Analysis (GSEA) ( https://www.gsea-msigdb.org ). 2.2 Differentially Expressed Analysis We conducted comparative transcriptomic analysis of TCGA datasets using “DESeq2” [ 27 ] to identify differentially expressed genes (DEGs) in TNBC versus normal breast tissues and non-TNBC subtypes. DEGs met significance thresholds (adjusted p 1.1) and were visualized via volcano plots ( “ggplot2” package [ 28 ] ). Subsequent Venn analysis pinpointed glutamine-related DEGs specifically dysregulated in TNBC tissues. 2.3 Immune Infiltration Analysis We identified 12 glutamine-associated DEGs through transcriptomic analysis. By integrating 123 glutamine-related genes from the Molecular Signatures Database (MSigDB)( https://www.gsea-msigdb.org/gsea/msigdb/index.jsp ), we applied IOBR (Immuno-Oncology Biological Research) [ 29 ] to deconvolute the TNBC microenvironment, which includes methods such as ESTIMATE, EPIC, and ssGSEA. ESTIMATE quantified stromal/immune components (threshold: |scores|>0.19), pinpointing PHGDH and LEP as key biomarkers. EPIC characterized cellular composition, while the single-sample gene set enrichment analysis (ssGSEA) evaluated infiltration levels of 10 major immune lineages. 2.4 Consensus Clustering on LEP and PHGDH Expression Acquiring PHGDH and LEP expression profiles, we employed the 'ConsensusClusterPlus' package to define TNBC molecular subtypes. [ 30 ]. 2.5 Alluvial diagram Alluvial diagrams generated with “ggalluvial” and “reshape2” packages [ 31 ] visualized TNBC cluster-stage distributions. 2.6 GSEA and GO Enrichment Analysis GSEA (p < 0.05) identified 10,313 differentially expressed genes, including immune-related pathways. Subsequent GO enrichment analysis of 24 prioritized hub genes (p/q-value thresholds = 0.05) revealed significant immune pathway associations. All analyses were conducted using the “GSEA” and “clusterProfiler” packages [ 32 , 33 ]. 2.7 Weighted Gene Coexpression Network Analysis (WGCNA) WGCNA [ 34 ] analysis of differentially expressed genes generated a scale-free coexpression network (soft threshold = 2), revealing associations between five modules, clusters, and ESTIMATE indices. Twenty-four hub genes were identified with module membership (MM) > 0.40 and gene significance (GS) > 0.39. 2.8 Gene‒Gene, Gene-ESTIMATE and Gene-ssGSEA association Protein-protein interactions were analyzed using STRING ( https://cn.string-db.org/ ) and cytoscape. Spearman correlations for gene–ESTIMATE, gene–gene and gene-ssGSEA interactions were calculated with the “corplot” package. 2.9 Processing of scRNA-seq Data [ 35 ] In this study, we analyzed two TNBC samples (GSM5354531/5354530) from scRNA-seq dataset GSE176078. Data quality assessment utilized “Seurat” and “Harmony”, while “SingleR” and “ggplot2” enabled cell-type annotation and visualization. 2.10 Cell culture and qRTPCR We used two human cell lines: MCF10A and SUM159PT. MCF10A is the normal mammary epithelial cell line and SUM159PT is the TNBC cell line. MCF10A was cultured in DMEM/F12 and SUM159PT was cultured in F12. Cells were maintained at 37°C / 5% CO2. We used GAPDH (glyceraldehyde- 3-phosphate dehydrogenase) as the reference gene, and the primer sequences are listed in Table 1 . Then, we performed qPCR under the specific cycling procedures: 95°C for 5 min, 40 cycles of 95°C for 15 s and 60°C for 60 s. Using the 2 − ΔΔCT method to calculate the mRNA expression levels. 2.11 Statistical Analysis Spearman's rank correlation analysis assessed variable associations, while chi-square tests analyzed categorical clinical data. Univariate logistic regression evaluated clinical and cluster relationships. Kaplan-Meier survival curves with log-rank tests determined prognostic significance. All analyses employed two-tailed hypothesis testing, with p < 0.05 defining statistical significance. 3. RESULTS 3.1 Identification of differentially expressed genes in TNBC The analytical framework is depicted in (Fig. 1 A). Using 'DESeq2' and 'Volcano' packages, we identified DEGs in TCGA datasets comparing 138 TNBC versus 960 non-TNBC samples (|log₂FC|≥1, adj.p < 0.05), revealing 3730 upregulated and 3305 downregulated genes (Fig. 2 A). Similarly, TNBC versus 114 normal tissues yielded 6421 upregulated and 4610 downregulated DEGs (Fig. 2 B). VennDiagram analysis intersected these datasets, identifying 3474 overlapping DEGs (2205 upregulated (Fig. 2 C); 1269 downregulated (Fig. 2 D)). Subsequently, we cross-referenced the 2205 upregulated and 1269 downregulated genes with glutamine-related genes, identifying a total of 12 candidate DEGs (Fig. 2 E). 3.2 Consensus Clustering of TNBC Based on PHGDH and LEP Expression Through the assessment of four ESTIMATE metrics, we obtained 2 genes (PHGDH and LEP) with an absolute value of the correlation coefficient between genes and ESTIMATE indices greater than 0.19 (Fig. 3 A). LEP positively correlated with Stromal/Immune/ESTIMATE Scores but negatively with Tumor Purity, while PHGDH exhibited inverse patterns (Fig. 3 A). In TCGA-TNBC versus adjacent tissues, LEP was significantly downregulated (p < 0.005, Fig. 3 B) and PHGDH upregulated (p < 0.005, Fig. 3 C). To further clarify the clinical relevance and biological characteristics of TNBC patients, we conducted ConsensusClusterPlus analysis stratified 137 TNBC samples into two molecular subtypes at optimal K = 2 (Fig. 3 D-F) : Cluster 1 (n = 55) and Cluster 2 (n = 82) (Fig. 3 G ) . These subtypes exhibit diametrically opposed PHGDH/LEP expression patterns, establishing them as key biomarkers for TNBC stratification. 3.3 Analysis of Clinical Characteristics To gain a deeper insight into the clinical features of the clusters, we analyzed the survival rate of TNBC patients in the TCGA. However, no statistically significant difference in overall survival between Cluster 1 and Cluster 2 ( Supplementary Fig. S1 A ). TAlluvial diagrams demonstrated distinct associations between clusters and TNM staging, particularly T-stage distribution ( Supplementary Fig. S1 B-D ). Baseline characteristics analysis confirmed significant T-stage disparity between clusters (p = 0.01, Table 2 ). Univariate logistic regression analysis indicated that T stage serves as a prognostic factor influencing clustering (p = 0.01, Table 3 ). The above data indicated that clusters can affect the prognosis of TNBC. Table 2 Clinical features of clusters. cluster1 cluster2 p value n 48 67 age (mean (SD)) 53.90 (10.44) 53.94 (11.38) 0.98 pathological.stage (%) 0.24 Stage1 5 (10.40) 15 (22.40) Stage2 37 (77.10) 41 (61.20) Stage3 6 (12.50) 10 (14.90) Stage4 0 (0.00) 1 (1.50) T.stage (%) 0.01 T1 6 (12.50) 23 (34.30) T2 41 (85.40) 38 (56.70) T3 0 (0.00) 5 (7.50) T4 1 (2.10) 1 (1.50) N.stage (%) 0.29 N0 36 (75.00) 39 (58.20) N1 7 (14.60) 18 (26.90) N2 3 (6.20) 7 (10.40) N3 2 (4.20) 3 (4.50) Table 3 Univariate logistic regression analysis for clustering (Cluster 2 vs Cluster 1) Variables Univariate Logistic Regression Odds Ratio (95% Confidence Interval) P value age 0.81(0.39–1.70) 0.58 T stage T2 vs T1 0.24(0.09–0.66) 0.01 T4 vs T1 0.26(0.01–4.81) 0.37 N stage N1 vs N0 2.37(0.89–6.35) 0.09 N2 vs N0 2.15(0.52–8.97) 0.30 N3 vs N0 1.38(0.22–8.77) 0.73 Pathological stage stage2 vs stage1 0.37(0.12–1.12) 0.08 stage3 vs stage1 0.56(0.13–2.32) 0.42 3.4 Identification of Immune-Associated Pathways via GSEA DEGs between clusters (|log₂FC|≥1, adj.p < 0.05) underwent GSEA to delineate functional disparities. GSEA-KEGG analysis revealed significant enrichment (FDR < 0.05) in immune pathways: primary immunodeficiency, cytokine-cytokine receptor interaction, Th1 and Th2 cell differentiation, B cell receptor signaling pathway, Natural killer cell mediated cytotoxicity and T cell receptor signaling pathway (Fig. 4 A-F). GSEA-GO analysis further confirmed immune-associated processes including dendritic cell migration, immune response-regulating signaling pathway, T cell proliferation and B cell mediated immunity (Fig. 4 G). These findings establish Cluster 2 as an immunologically active subtype. 3.5 Comparative Immune Microenvironment Profiling Immune infiltration analysis revealed significant disparities between clusters. To assess the immune association, we conducted ESTIMATE analysis on both clusters. ESTIMATE demonstrated Cluster 2 exhibited elevated Stromal, Immune, and ESTIMATE Scores (p < 0.01) but reduced Tumor Purity versus Cluster 1 (Fig. 5 A-D). We also conducted EPIC and ssGSEA to examine the extent of immune infiltration across the two clusters. EPIC quantification confirmed increased total immune infiltration and B cell abundance in Cluster 2 (Fig. 5 E). Concordantly, ssGSEA showed enhanced B cell infiltration and tertiary lymphoid structure (TLS) formation in Cluster 2 (Fig. 5 F ) . To further understand the biological mechanisms underlying the poorer outcomes of clusters, ssGSEA was performed using the tumor-associated gene sets. We found that Cluster 2 had suppressed oncogenic pathways (cell cycle) and impaired DNA damage repair mechanisms (mismatch repair (MMR), homologous recombination (HR), nucleotide excision repair (NER) and base excision repair (BER)) (Fig. 5 G). 3.6 PHGDH and LEP are associated with B cell in TNBC Transcriptomic analysis of TCGA data identified 1,066 upregulated and 239 downregulated DEGs in Cluster 2 vs. Cluster 1 (Fig. 6 A). WGCNA of these DEGs revealed five co-expression modules (Fig. 6 B-D). The turquoise module displayed positive associations with Cluster (R = 0.55, p = 4.00e-12), ESTIMATEScore (R = 0.50, p = 6.00e-10), StromalScore (R = 0.61, p = 2.00e-15), and ImmuneScore (R = 0.31, p = 2.00e-4), while showing a negative correlation with TumorPurity (R = -0.51, p = 3.00e-10) (Fig. 6 E). Screening the turquoise module (MM > 0.40, GS > 0.39) identified 24 hub genes (Fig. 7 A). GO enrichment confirmed the 24 hub genes have immune regulatory functions (Fig. 7 B). Among these genes, we found 8 genes (GAS7 [ 36 ], TBX15 [ 37 ], FGF7 [ 38 ], DKK2 [ 39 ], CXCL12 [ 40 ], VWF [ 41 ], ITIH5 [ 42 ] and S1PR1 [ 43 ]) were validated as critical regulators of B cell maturation, differentiation, infiltration and several additional functions. The gene expression correlation analysis also indicated that these genes exhibited positive correlation with LEP and negative correlation with PHGDH (Fig. 7 C). The protein‒protein analysis revealed direct interactions among FGF7, CXCL12, VWF, and S1PR1 (Fig. 7 D). Spearman correlation analysis indicated that all eight genes positively correlated with ESTIMATE Score but negatively with Tumor Purity (Fig. 7 E). Additionally, Spearman correlation between genes and ssGSEA confirmed their co-regulation with B cell infiltration (Fig. 7 F). These analyses establish Cluster 2 as a B cell-enriched TNBC subtype orchestrated by the PHGDH-LEP regulatory axis. 3.7 Immunotherapy Sensitivity Stratification by Molecular Subtype Elevated expression of immunomodulators predicts enhanced response to immune checkpoint blockade [ 23 ] [ 21 ]. In Cluster 2 versus Cluster 1, we observed significant upregulation of antigen present genes (HLA-DPA1, HLA-DQA1), cell adhesion molecules (ITGB2, SELP), costimulatory molecules (CD28), cytokine signaling (TGFB1, IL10), and immune checkpoint (BTLA) (Fig. 8 A-H). This coordinated overexpression of immunomodulatory machinery establishes Cluster 2 as a candidate for enhanced PD-1/PD-L1 inhibitor responsiveness. 3.8 Validation of Immune landscape in GSE76275 External validation using GSE76275 (n = 188) confirmed TNBC stratification into two clusters ( Supplementary Fig. S2 A-C ). The gene expression patterns concordant with TCGA. The expression levels of LEP were lower and of PHGDH were higher in Cluster 1 (n = 90), and the expression levels of LEP were higher and of PHGDH were lower in Cluster 2 (n = 98) in the GSE76275 (Fig. 9 A). Considering LEP and PHGDH displayed contrasting trends among the clusters, we performed a Spearman correlation analysis for PHGDH and LEP, which indicated a weak negative correlation (R^2 = 0.05, P = 2.00e-16) (Fig. 9 B). ESTIMATE analysis demonstrated Cluster 2 had elevated Stromal/Immune/ESTIMATE Scores but reduced Tumor Purity (Fig. 9 C-F). Both EPIC and ssGSEA confirmed enhanced B cell infiltration and tertiary lymphoid structure (TLS) formation in Cluster 2 (Fig. 9 G and Fig. 9 H). Oncogenic pathways (cell cycle) and DNA repair mechanisms (MMR/HR/NER/BER) were consistently suppressed versus Cluster 1 (Fig. 9 I). The eight B cell-regulatory genes (GAS7, TBX15, FGF7, DKK2, CXCL12, VWF, ITIH5 and S1PR1) are same as depicted in Fig. 7 C, which were positively correlated with LEP and negatively correlated with PHGDH (Fig. 10 A). Spearman correlation analysis of these genes and the four ESTIMATE indices (Fig. 10 B), along with ssGSEA (Fig. 10 C) demonstrated a positive association with the ESTIMATE Score and a negative association with Tumor Purity. Immunomodulator expression (HLA-DPA1/DQA1, ITGB2, SELP, CD28, TGFB1, IL10, BTLA) was significantly elevated in Cluster 2 (Fig. 11 A-H). This multi-platform validation establishes Cluster 2 as a conserved immunogenic subtype with enhanced therapeutic responsiveness. 3.9 The Expression Dynamics of PHGDH and LEP in TNBC Given the overexpression of PHGDH in TNBC, we investigated the expression profiles of PHGDH at the single-cell level to delineate its cell type-specific expression landscape. Analysis of the TNBC samples identified nine distinct cell clusters (Fig. 12 A ) . Notably, PHGDH exhibited significant enrichment in B lymphocytes (Fig. 12 B-C). Then, we validated the expression level of PHGDH and LEP in TNBC cell lines by qRT-PCR (Fig. 12 D-E). The expression level of PHGDH and LEP in TNBC cell lines were consistent with previous results (Fig. 3 b-c ) . 4. DISCUSSION Our integrated analysis of TCGA, GSE76275 and GSE176078 cohorts establishes glutamine metabolic reprogramming as a cornerstone of TNBC microenvironmental heterogeneity. Through consensus clustering, we delineated two molecular subtypes with diametrically opposed PHGDH/LEP expression profiles: Cluster 1 (PHGDH high/LEP low): Enriched in oncogenic pathways and DNA damage repair mechanisms, correlating with advanced T stage and poorer prognosis; Cluster 2 (LEP high/PHGDH low): Characterized by robust B cell infiltration (ssGSEA), tertiary lymphoid structure formation, and coordinated overexpression of immunomodulators (HLA-DPA1/DQA1, ITGB2, SELP, CD28, TGFB1, IL10, BTLA). Critically, WGCNA revealed a PHGDH-LEP regulatory axis governing B cell functionality through eight hub genes (GAS7, TBX15[ 37 ], FGF7, DKK2, CXCL12, VWF, ITIH5 and S1PR1), with Cluster 2 exhibiting: enhanced immunogenicity, suppressed DNA repair capacity and elevated checkpoint molecule expression. This PHGDH low/LEP high phenotype demonstrated superior immunotherapy response potential across both cohorts. Our findings thus position PHGDH and LEP as master regulators of B cell-mediated antitumor immunity, providing a metabolic-immune signature for stratifying TNBC patients toward precision immunotherapy. Univariate logistic regression confirmed the prognostic significance of our molecular clusters. GSEA revealed pronounced enrichment of immune response pathways in Cluster 2, consistent with its immunogenic phenotype. Comprehensive immune deconvolution demonstrated Cluster 2 exhibited enhanced immune cells infiltration, among which the infiltration of B cells is the most stable. Conversely, Cluster 1 displayed significant upregulation of oncogenic drivers (cell cycle progression) and DNA damage repair pathways (MMR, HR, NER, BER). Critically, this molecular profile aligns with the immune-desert/excluded phenotypes observed in immunotherapy-resistant tumors [ 44 , 45 ], explaining Cluster 1's poorer prognosis. The inverse correlation between PHGDH expression and B cell infiltration establishes the PHGDH-LEP axis as a quantitative biomarker for stratifying TNBC patients' immunotherapy response and survival outcomes. B lymphocytes orchestrate adaptive and innate immunity through antigen presentation and cytokine signaling [ 46 ]. Our WGCNA identified an immune-related module containing eight hub genes (GAS7, TBX15, FGF7, DKK2, CXCL12, VWF, ITIH5, and S1PR1) that exhibit: positive correlation with LEP expression, negative correlation with PHGDH and co-regulation with B cell infiltration. Previous studies found that GAS7 and TBX15 modulate B cell differentiation [ 36 , 37 ]. FGF7 mediates B cell recruitment [ 47 ] and DKK2 regulates B cell development [ 39 ].Previous studies have evidenced that CXCL12 facilitates B cell cross-talk [ 48 ] and VWF, ITIH5 sustain B cell niche integrity [ 42 , 48 ]. And S1PR1 gates B cell trafficking [ 49 ]. These findings collectively demonstrate a PHGDH-LEP-B cell functional axis, where suppressed PHGDH and elevated LEP synergistically promote B cell-mediated antitumor immunity through coordinated gene network regulation. Recent breakthroughs in cancer immunotherapy have markedly improved antitumor responses and survival outcomes across malignancies [ 50 ]. Our study reveals that Cluster 2 (PHGDH low / LEP high ) exhibits coordinated upregulation of immunomodulators critical for therapeutic efficacy: antigen present molecules (HLA-DQA1, HLA-DPA1), cell adhesion molecules (TIGB2, SELP), costimulatory molecules (CD28), cytokine signaling (TGFB1, IL10) and checkpoint regulation (BTLA). This immunogenic phenotype aligns with emerging strategies targeting immune evasion mechanisms [ 51 ]. Critically, the PHGDH-LEP regulatory axis correlates with enhanced B cell infiltration, predicts improved ICB response and establishes a metabolic basis for TNBC immunotherapy stratification. Future studies should delineate how LEP-mediated signaling interfaces with PHGDH-dependent metabolism to orchestrate immunomodulatory pathways, potentially revealing novel combinational targets to overcome treatment resistance. Our consensus clustering-derived subtypes provide a robust framework for prognostic stratification in TNBC, with the PHGDH-LEP axis serving as a theranostic biomarker to predict immunotherapy response. While the identified association between PHGDH suppression, LEP elevation, and B cell infiltration offers mechanistic insights into glutamine metabolism-driven immunomodulation, two limitations warrant acknowledgment: firstly, the precise pathways through which PHGDH and LEP coordinate B cell functionality require further dissection; secondly, as this retrospective analysis may incur selection bias, prospective validation in multi-center cohorts incorporating immunotherapy response monitoring is essential. Nevertheless, this metabolic-immune signature lays the groundwork for developing PHGDH/LEP-targeted combination strategies to overcome TNBC immunotherapy resistance. 5. CONCLUSION Consensus clustering of TNBC based on LEP and PHGDH expression revealed two distinct molecular subtypes. Cluster 2 (LEP high /PHGDH low ) demonstrated significantly enhanced B cell infiltration and elevated immunomodulator expression compared to Cluster 1. This immunologically active phenotype correlated with improved immunotherapy response. Mechanistically, the inverse correlation between PHGDH expression and B cell abundance establishes the PHGDH-LEP axis as: a regulator of B cell-mediated antitumor immunity and a novel metabolic-immune biomarker in TNBC. These findings provide the first evidence that glutamine metabolic reprogramming directly coordinates B cell functionality, offering a therapeutic framework for combining metabolic modulators with immune checkpoint blockade. Declarations Acknowledgements Not applicable. Authors’ contributions C.Z. and R.Y. conceptualized the study. ZL.Z. and R.Y. conducted the formal analysis. ZY.Z. and R.Y. developed the methodology. ZL.Z. and R.Y. provided resources. ZL.Z. and R.Y. were responsible for software implementation. R.C. and ZY.Z. performed validation. C.Z. and R.Y. wrote the original draft, and R.Y. reviewed and edited the manuscript. Funding This work was supported by grants from the Major Research Plan of National Natural Science Foundation of China (Key Programme) (92359204) and Special Foundation for Emerging Interdisciplinary Field Research of Shanghai Municipal Health Commission (Grant No. 2022JC004). Data Availability The data accessed and utilized from The Cancer Genome Atlas Project and the Gene Expression Omnibus Repository, GSE76275 and GSE176078. Ethics approval and consent to participate All procedures are conducted in accordance with the principles expressed in the Helsinki Declaration. 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Supplementary Files FigS.docx 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6997049","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481734289,"identity":"44c1284b-39ed-460c-aa2d-3515bf34f5c6","order_by":0,"name":"Ruifang Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACfvb2AwcSKv7b2c9/fIA4LZI9ZxIPPDjDnGzAkJZAnBaDGw7GBx+2MTNuYMgxINJlNxgSDiSwsTGbM5z5eOMNg52cbgMBHYyzG4F+4eHhs2zs3Ww5hyHZ2OwAAS3MMgeAtkhIMDMc5t0mzcNwIHEbIS1sEgkGBxIMDBgbjvE8I04LD1hLQgLjhjM8bMRpkeA5A3TYgQPJkjPYjC3nGBDhF/vj7Yc//vx3wI5fgvnhjTcVdnIEtaBZSWzUIGkhVccoGAWjYBSMCAAAjk5H2HpGRtoAAAAASUVORK5CYII=","orcid":"","institution":"the First Affiliated Hospital of Naval Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ruifang","middleName":"","lastName":"Yang","suffix":""},{"id":481734290,"identity":"910747e9-725f-4011-8b81-751f4541850e","order_by":1,"name":"Zhengli Zhou","email":"","orcid":"","institution":"the First Affiliated Hospital of Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhengli","middleName":"","lastName":"Zhou","suffix":""},{"id":481734291,"identity":"ce399a71-2c58-47e4-8929-9213b5ec6e51","order_by":2,"name":"Rui Chen","email":"","orcid":"","institution":"the First Affiliated Hospital of Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Chen","suffix":""},{"id":481734292,"identity":"39ce59e3-41a9-4852-bc3a-fd6051073d36","order_by":3,"name":"Zeyu Zhang","email":"","orcid":"","institution":"the First Affiliated Hospital of Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zeyu","middleName":"","lastName":"Zhang","suffix":""},{"id":481734293,"identity":"b604dfaa-56b9-44e3-a4d9-3bbd089bb9ab","order_by":4,"name":"Changjing Zuo","email":"","orcid":"","institution":"the First Affiliated Hospital of Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Changjing","middleName":"","lastName":"Zuo","suffix":""}],"badges":[],"createdAt":"2025-06-28 10:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6997049/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6997049/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86301701,"identity":"d500c484-3421-4ca8-86f3-b087f84bc10a","added_by":"auto","created_at":"2025-07-09 06:29:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":335367,"visible":true,"origin":"","legend":"\u003cp\u003eThe study design. \u003cstrong\u003eA) \u003c/strong\u003eWorkflow of data processing and bioinformatics analysis, comprising three main modules, including gene screening and consensus clustering, weighted gene coexpression network analysis and analysis of immune infiltration, and GSE76275 external validation.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6997049/v1/a2a75300d9ac9d636b40944e.png"},{"id":86301705,"identity":"0a49fa58-4dad-4b3d-855d-4da70887555f","added_by":"auto","created_at":"2025-07-09 06:29:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":336371,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of differentially expressed genes in TNBC. Volcano plot of all genes \u003cstrong\u003e(A)\u003c/strong\u003e between 138 TNBC samples and 970 non-TNBC samples from TCGA, and \u003cstrong\u003e(B) \u003c/strong\u003ebetween 138 TNBC and 114 normal tissue samples. Red dots represent upregulated genes, and blue dots represent downregulated genes. \u003cstrong\u003e(C)\u003c/strong\u003e Venn diagram for overlapping upregulated genes and \u003cstrong\u003e(D)\u003c/strong\u003e downregulated genes in the two sets.\u003cstrong\u003e (E)\u003c/strong\u003e Venn diagram for overlapping upregulated genes, downregulated genes and glutamine-related genes.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6997049/v1/d627f20d797f79cf2b345958.png"},{"id":86302204,"identity":"26873111-7e64-4652-b9a2-38d9738e96ff","added_by":"auto","created_at":"2025-07-09 06:37:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":420073,"visible":true,"origin":"","legend":"\u003cp\u003eConsensus Clustering of TNBC Samples on Expression of PHGDH and LEP.\u003cstrong\u003e (A)\u003c/strong\u003e ESTIMATE analysis of 12 intersected genes. The absolute value of four ESTIMATE indices (Stromal Score, Immune Score, ESTIMATE Score and Tumor Purity)\u0026gt;0.19. Among them, red represented positive association, blue represented negative association, the darker the color, the stronger the association. \u003cstrong\u003e(B)\u003c/strong\u003e The expression of LEP in TNBC and adjacent normal tissues. \u003cstrong\u003e(C)\u003c/strong\u003e The expression of PHGDH in TNBC and adjacent normal tissues. Blue represents AdjN, red represents TNBC. ****p\u0026lt;0.001. \u003cstrong\u003e(D)\u003c/strong\u003e Consistency matrix heat map was shown through consistency clustering (k = 2). \u003cstrong\u003e(E)\u003c/strong\u003e The delta area helps to identificate the most optimal number of clusters, which was k=2. \u003cstrong\u003e(F)\u003c/strong\u003e Consistency score bar graph for subgroups with cluster counts between 2 and 9, and the optimal cluster counts was 2. \u003cstrong\u003e(G)\u003c/strong\u003e The TCGA TNBC was divided into two clusters according to the expression of LEP and PHGDH. The heatmap shows that LEP expressed lower and PHGDH expressed higher in Cluster 1 (n = 55), while LEP expressed higher and PHGDH expressed lower in Cluster 2 (n = 82).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6997049/v1/f3b34e30309cf6faa637ab59.png"},{"id":86301716,"identity":"ae6fe62d-2cf7-4299-a8e0-19e376e333e1","added_by":"auto","created_at":"2025-07-09 06:29:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":696431,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of Immune-Related Pathways by GSEA. \u0026nbsp;\u003cstrong\u003e(A-F)\u003c/strong\u003e Comparison of GSEA-GO and (g)GSEA-KEGG functional enrichment by differentially expressed genes in clusters.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6997049/v1/c3906616234fd361e12e8088.png"},{"id":86303162,"identity":"34108d56-d0be-4f9f-b375-79fb4c87f68b","added_by":"auto","created_at":"2025-07-09 06:45:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":406831,"visible":true,"origin":"","legend":"\u003cp\u003eComparing the characteristics of Immune Infiltration between Clusters. \u0026nbsp;\u003cstrong\u003e(A-D)\u003c/strong\u003e ESTIMATE analysis in Cluster 1 and Cluster 2. The Immune Score, Stromal Score and ESTIMATE Score of Cluster 2 were higher, and the tumor purity was lower comparing with Cluster 1.\u003cstrong\u003e (E)\u003c/strong\u003e EPIC analysis revealed that the portion of B cells in Cluster 2 was larger than Cluster 1. \u003cstrong\u003e(F)\u003c/strong\u003e ssGSEA revealed that Cluster 2 showed a higher infiltration extent of B cells and TLS. \u003cstrong\u003e(G)\u003c/strong\u003e ssGSEA revealed that DNA damage repair pathways and oncogenic pathways were upregulated in Cluster 1. Red represented Cluster 2, blue represented Cluster 1. ns, no significance, *p \u0026lt; 0.05, ***p \u0026lt; 0.005, and ****p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6997049/v1/f364de5772a06c06ef4568e8.png"},{"id":86301720,"identity":"f46eafeb-4601-4e1f-a319-be8a1cdc3e57","added_by":"auto","created_at":"2025-07-09 06:29:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":443987,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA to identify module genes associated with both clustering and immunity. \u003cstrong\u003e(A)\u003c/strong\u003eVolcano diagram showed the differential gene expression analysis between the Cluster 2 and Cluster 1. Red represented upregulated genes, blue represented downregulated genes, and black represented no change. \u003cstrong\u003e(B)\u003c/strong\u003e Scale independence analysis of soft power (soft powers=2). \u003cstrong\u003e(C)\u003c/strong\u003e Mean connectivity analysis of soft power (soft powers=2). \u003cstrong\u003e(D)\u003c/strong\u003e Gene dendrogram and module colours. \u003cstrong\u003e(E)\u003c/strong\u003eHeatmap among module eigengenes, Cluster and ESTIMATE results. We identified a module (turquoise) correlated with glutamine metabolism (Cluster, R =0.55, p =4.00e-12) and immunity (Immune Score, R =0.31, p =2.00e-4).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6997049/v1/10d5f4e20f30793c642e8588.png"},{"id":86301710,"identity":"f901f815-8351-4247-9ba9-92c22267fdb1","added_by":"auto","created_at":"2025-07-09 06:29:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":613366,"visible":true,"origin":"","legend":"\u003cp\u003eCluster 2 was positively correlated with B cell differentiation-associated genes. (A) Scatter diagram of module eigengenes in the turquoise module (MM \u0026gt;0.40 and GS \u0026gt;0.39), and through it we found 24 hub genes. (B) The GO analysis of hub genes. \u003cstrong\u003e(C) \u003c/strong\u003eAssociation between the expression of 8 hub genes and LEP or PHGDH. \u003cstrong\u003e(D) \u003c/strong\u003eProtein‒protein interaction network of 8 hub genes. \u003cstrong\u003e(E)\u003c/strong\u003eRelevance between hub genes (8 genes which among hub genes were essential for B cell development, differentiation, activation and other functions.) and results of four ESTIMATE indices or \u003cstrong\u003e(F)\u003c/strong\u003essGSEA. Red represented positive association, blue represented negative association, and the darker the color, the stronger the connection.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6997049/v1/0bb7fb04dbfaf587616b5946.png"},{"id":86301713,"identity":"4024a037-931f-4658-be2c-56beedb94b56","added_by":"auto","created_at":"2025-07-09 06:29:56","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":245219,"visible":true,"origin":"","legend":"\u003cp\u003eCompared the expression of immunomodulatory factors between the Cluster 1 and Cluster 2. Comparing the expression of\u003cstrong\u003e (A)\u003c/strong\u003e, HLA-DPA1 \u003cstrong\u003e(B)\u003c/strong\u003e, HLA-DQA1\u003cstrong\u003e (C)\u003c/strong\u003e, ITGB2 \u003cstrong\u003e(D)\u003c/strong\u003e, SELP\u003cstrong\u003e (E)\u003c/strong\u003e, CD28 \u003cstrong\u003e(F)\u003c/strong\u003e, TGFB1 \u003cstrong\u003e(G)\u003c/strong\u003e, IL10 \u003cstrong\u003e(H)\u003c/strong\u003e, BTLA between the Cluster 1 and Cluster 2. Red represented Cluster 2, blue represents Cluster 1. ****p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6997049/v1/8b2eb9e2db1581ced4a68947.png"},{"id":86301724,"identity":"379ea9e3-22c0-4f03-80e7-3eb3117f8675","added_by":"auto","created_at":"2025-07-09 06:29:57","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":536499,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of clustering in the GSE76275 dataset.\u003cstrong\u003e (A)\u003c/strong\u003e The GSE76275 dataset was divided into two clusters according to the expression of GLS and GOT2. The heatmap shows that LEP expressed low and PHGDH expressed high in Cluster 1 (n=558), while LEP expressed high and PHGDH expressed low in Cluster 2 (n=539). \u003cstrong\u003e(B)\u003c/strong\u003e Association between LEP and PHGDH expression.(\u003cem\u003eP \u003c/em\u003e= 2.00e-16,R^2=0.05).\u003cstrong\u003e(C-F)\u003c/strong\u003e ESTIMATE analysis in Cluster 1 and Cluster 2. The Stromal Score, Immune Score and ESTIMATE Score of Cluster 2 were higher, and the tumor purity was lower compared with Cluster 1. \u003cstrong\u003e(G)\u003c/strong\u003e EPIC analysis revealed that the portion of B cells in Cluster 2 was larger compared with that in Cluster 1. \u003cstrong\u003e(H) \u003c/strong\u003essGSEA revealed that Cluster 2 showed a larger infiltration extent of B cells and TLS compared with Cluster 1. \u003cstrong\u003e(I) \u003c/strong\u003essGSEA revealed that DNA damage repair pathways and oncogenic pathways were enriched in Cluster 1. Red represented Cluster 2, blue represented Cluster 1. ns, no significance, *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.005, and ****p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-6997049/v1/4729bb1612342d02dc4b158e.png"},{"id":86302212,"identity":"fc0d2441-74a8-4291-a7b4-a847f8d3b6ec","added_by":"auto","created_at":"2025-07-09 06:37:57","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":377439,"visible":true,"origin":"","legend":"\u003cp\u003eB cell-associated gene expression analysis in the GSE76275 dataset.\u003cstrong\u003e \u0026nbsp;(A)\u003c/strong\u003eAssociation between the expression of B cell-associated genes and LEP or PHGDH. \u003cstrong\u003e(B)\u003c/strong\u003e Association between 8 hub genes and the ESTIMATE indices (Stromal Score, Immune Score, ESTIMATE Score or Tumor Purity) or\u003cstrong\u003e (C)\u003c/strong\u003essGSEA. Red represented positive correlation, blue represented negative correlation. The darker the colour, the stronger the correlation.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-6997049/v1/5ceec772e63d220052d5df48.png"},{"id":86301734,"identity":"6ff05226-a29f-45a1-a568-8a456e366b37","added_by":"auto","created_at":"2025-07-09 06:29:57","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":241622,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the expression of immunomodulatory factors between the Cluster 1 and Cluster 2 in the GSE76275 dataset. Comparing the expression of\u003cstrong\u003e (A)\u003c/strong\u003e, HLA-DPA1 \u003cstrong\u003e(B)\u003c/strong\u003e, HLA-DQA1\u003cstrong\u003e (C)\u003c/strong\u003e, ITGB2 \u003cstrong\u003e(D)\u003c/strong\u003e, SELP\u003cstrong\u003e (E)\u003c/strong\u003e, CD28 \u003cstrong\u003e(F)\u003c/strong\u003e, TGFB1 \u003cstrong\u003e(G)\u003c/strong\u003e, IL10 \u003cstrong\u003e(H)\u003c/strong\u003e, BTLA between the Cluster 1 and Cluster 2. Red represented Cluster 2, blue represented Cluster 1. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.005, and ****p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-6997049/v1/7c678382a29d6247a6b8b133.png"},{"id":86302210,"identity":"49d3496e-67b3-490f-ac13-eb00be153540","added_by":"auto","created_at":"2025-07-09 06:37:57","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":390012,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression profiles of PHGDH and LEP in TNBC. \u003cstrong\u003e(A)\u003c/strong\u003e The UMAP plot shows the annotation of cell clusters. \u003cstrong\u003e(B)\u003c/strong\u003e UMAP of expression pattern of PHGDH in clusters.\u003cstrong\u003e (C)\u003c/strong\u003e Violin plot shows the expression of PHGDH in clusters. The mRNA expression level of PHGDH \u003cstrong\u003e(D)\u003c/strong\u003e and LEP \u003cstrong\u003e(E) \u003c/strong\u003ein cell lines detected by qRT-PCR.\u003cstrong\u003e \u003c/strong\u003e***p \u0026lt; 0.005, ****p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-6997049/v1/6ad54203913b39d177f88f82.png"},{"id":89013850,"identity":"b3842e55-df9d-42f4-af07-1c682defc50d","added_by":"auto","created_at":"2025-08-13 18:01:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6416585,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6997049/v1/70abc2b8-1f45-4e46-b8e9-0aa6d748b1bb.pdf"},{"id":86301702,"identity":"09ede800-8c7f-4554-b9b8-36b2d1e88f09","added_by":"auto","created_at":"2025-07-09 06:29:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":165317,"visible":true,"origin":"","legend":"","description":"","filename":"FigS.docx","url":"https://assets-eu.researchsquare.com/files/rs-6997049/v1/d9a0fe485a8336efec4ada40.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"PHGDH and LEP serve as prognostic markers associated with B cell and responses to immunotherapy in triple-negative breast cancer","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eBreast cancer accounts for 32 percent of newly diagnosed cancers among women in 2025 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. TNBC has the highest recurrence and mortality rates among breast cancer subtypes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The identification of subgroups with specific molecular features has led to multiple new targeted therapies for TNBC. Immunotherapy has advanced rapidly, yet only a minority of cancer patients achieve long-term benefits. Among breast cancer subtypes, TNBC patients derive the greatest immunotherapy benefit and exhibit higher immunogenicity than others [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While immunotherapy for TNBC is currently limited to single agents, combining PD1 inhibitors with chemotherapy reduces recurrence, and inhibitors targeting other immune checkpoints are under study [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among multiple biomarkers, TIM3, OX40, and tumor mutational burden (TMB) are frequently used to predict immunotherapy response [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Developing a multi-dimensional therapeutic strategy, accurately assessing prognosis, and improving survival rates for TNBC patients remains a significant challenge. Therefore, it is important to develop novel prognostic features to accurately predict treatment response and prognosis.\u003c/p\u003e\u003cp\u003eCancer immunology has shifted in recent years from an emphasis on T cells to an emerging focus on B cells [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. B lymphocytes play a crucial role in the tumor immune microenvironment, especially in adaptive immunity. The function of B cells is thought to include the production of antibodies, presentation of antigens, release of cytokines and cytotoxic effector molecules [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Previous study indicated that tumor-infiltrating B cells can enhance T cell-mediated anti-tumor immunity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. And it is well-known that when B cells are present, positive prognostic impact of T cells is stronger [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In certain tumors, B cells actively produce antibodies that target tumor-associated antigens [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In the pre-treatment samples of the lung adenocarcinoma cohort, B cells were associated with positive outcomes of immunotherapy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGlutamine, the most prevalent amino acid in plasma, is a crucial nutrient that supports cancer growth [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The growth and survival of TNBC cells were particularly dependent on glutamine [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Phosphoglycerate dehydrogenase (PHGDH) plays a role in glutamine metabolism and acts as the rate-limiting enzyme in the initial step of the serine biosynthesis pathway [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Previous studies have found that under glutamine starvation, gastric cancer cells upregulate PHGDH [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Research has indicated that PHGDH is a potential target for enhancing the effectiveness of standard chemotherapy in TNBC [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The synergistic effect of nuclear PHGDH and cMyc can reshape the immune microenvironment of liver cancer [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. PHGDH is necessary for the formation of germinal centers and is a therapeutic target for MYC driven lymphoma [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The research results indicated that upregulation of PHGDH expression can inhibit M1 macrophage differentiation and pro-inflammatory cytokine levels, thereby reducing the disease activity of SLE [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, leptin (LEP) plays a significant role in glutamine metabolism as a glutamine transporter. The levels of leptin and its receptors correlate with unfavorable outcomes in endometrial cancer [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Leptin promotes proliferation in different cancer cell types, such as breast [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], prostate [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and ovarian [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] cells. Leptin can also lead to the recruitment and activation of macrophages in the tumor microenvironment, thereby promoting angiogenesis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Leptin induces STAT3 activation, leading to metabolic reprogramming of effector T cells, which is associated with weakened effector T cell function and enhanced breast tumor development [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the underlying molecular mechanisms of LEP and PHGDH in TNBC remain inadequately understood.\u003c/p\u003e\u003cp\u003eWe stratified 137 TNBC samples into two clusters based on leptin (LEP) and PHGDH expression: Cluster 1 (low LEP/high PHGDH) with enriched DNA repair and oncogenic pathways, and Cluster 2 (high LEP/low PHGDH) showing robust immune infiltration. Cluster 2 demonstrated significant associations with B cells, tertiary lymphoid structures (TLS), and immunomodulatory factors, indicating superior immunotherapy responsiveness. WGCNA discovered a module linking glutamine metabolism to immune function, identifying 8 genes linked to B cells. Single-cell analysis confirmed PHGDH expression in B cells. These findings established LEP and PHGDH as prognostic biomarkers for B cell-mediated immunotherapy response in TNBC.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Sources and Preprocessing\u003c/h2\u003e\u003cp\u003eThe TCGA cohort was download from xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Breast%20Cancer%20(BRCA)\u0026amp;removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Breast%20Cancer%20(BRCA)\u0026amp;removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Clinical information was extracted from the clinical biospecimen core resource (BCR) XML files using the \u0026ldquo;TCGAbiolink\u0026rdquo; package. We filtered the breast cancer patients based on the criteria that the breast_carcinoma_estrogen_receptor_status,breast_carcinoma_progesterone_receptor_status, lab_proc_her2_neu_immunohistochemistry_receptor_status and lab_procedure_her2_neu_in_situ_hybrid_outcome_type were \u0026ldquo;Negative\u0026rdquo;, while the her2_immunohistochemistry_level_result = \u0026ldquo;0\u0026rdquo;. This process yielded a total of 163 TNBC patients. Among these cases, merely 139 were identified as the histological subtype \u0026ldquo;Infiltrating Ductal Carcinoma\u0026rdquo;. The detailed information of TNBC to clusters is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrimer sequences of qRT-PCR.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForward Primer (5'-3')\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReverse Primer (5'-3')\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePHGDH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCGCTGATGTCATCAACGCAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTGGCCAGGCACATGATCATT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLEP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCTATGTCCAAGCTGTGCCCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGAGACTGACTGCGTGTGTGA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGAPDH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGAAGGTGAAGGTCGGAGTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGAAGATGGTGATGGGATTTC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe Gene Expression Omnibus (GEO) dataset GSE76275 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] is sourced from the GPL570 platform, and identified 188 TNBC patients. Glutamine-related gene sets were established by querying 'glutamine' in Gene Set Enrichment Analysis (GSEA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Differentially Expressed Analysis\u003c/h2\u003e\u003cp\u003eWe conducted comparative transcriptomic analysis of TCGA datasets using \u0026ldquo;DESeq2\u0026rdquo; [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] to identify differentially expressed genes (DEGs) in TNBC versus normal breast tissues and non-TNBC subtypes. DEGs met significance thresholds (adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log2FC|\u0026gt;1.1) and were visualized via volcano plots ( \u0026ldquo;ggplot2\u0026rdquo; package [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] ). Subsequent Venn analysis pinpointed glutamine-related DEGs specifically dysregulated in TNBC tissues.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Immune Infiltration Analysis\u003c/h2\u003e\u003cp\u003eWe identified 12 glutamine-associated DEGs through transcriptomic analysis. By integrating 123 glutamine-related genes from the Molecular Signatures Database (MSigDB)( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we applied IOBR (Immuno-Oncology Biological Research) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] to deconvolute the TNBC microenvironment, which includes methods such as ESTIMATE, EPIC, and ssGSEA.\u003c/p\u003e\u003cp\u003eESTIMATE quantified stromal/immune components (threshold: |scores|\u0026gt;0.19), pinpointing PHGDH and LEP as key biomarkers. EPIC characterized cellular composition, while the single-sample gene set enrichment analysis (ssGSEA) evaluated infiltration levels of 10 major immune lineages.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Consensus Clustering on LEP and PHGDH Expression\u003c/h2\u003e\u003cp\u003eAcquiring PHGDH and LEP expression profiles, we employed the 'ConsensusClusterPlus' package to define TNBC molecular subtypes. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Alluvial diagram\u003c/h2\u003e\u003cp\u003eAlluvial diagrams generated with \u0026ldquo;ggalluvial\u0026rdquo; and \u0026ldquo;reshape2\u0026rdquo; packages [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] visualized TNBC cluster-stage distributions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 GSEA and GO Enrichment Analysis\u003c/h2\u003e\u003cp\u003eGSEA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) identified 10,313 differentially expressed genes, including immune-related pathways. Subsequent GO enrichment analysis of 24 prioritized hub genes (p/q-value thresholds\u0026thinsp;=\u0026thinsp;0.05) revealed significant immune pathway associations. All analyses were conducted using the \u0026ldquo;GSEA\u0026rdquo; and \u0026ldquo;clusterProfiler\u0026rdquo; packages [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e2.7 Weighted Gene Coexpression Network Analysis\u003c/b\u003e (WGCNA)\u003c/h2\u003e\u003cp\u003eWGCNA [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] analysis of differentially expressed genes generated a scale-free coexpression network (soft threshold\u0026thinsp;=\u0026thinsp;2), revealing associations between five modules, clusters, and ESTIMATE indices. Twenty-four hub genes were identified with module membership (MM)\u0026thinsp;\u0026gt;\u0026thinsp;0.40 and gene significance (GS)\u0026thinsp;\u0026gt;\u0026thinsp;0.39.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Gene‒Gene, Gene-ESTIMATE and Gene-ssGSEA association\u003c/h2\u003e\u003cp\u003eProtein-protein interactions were analyzed using STRING ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ) and cytoscape. Spearman correlations for gene\u0026ndash;ESTIMATE, gene\u0026ndash;gene and gene-ssGSEA interactions were calculated with the \u0026ldquo;corplot\u0026rdquo; package.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Processing of scRNA-seq Data [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/h2\u003e\u003cp\u003eIn this study, we analyzed two TNBC samples (GSM5354531/5354530) from scRNA-seq dataset GSE176078. Data quality assessment utilized \u0026ldquo;Seurat\u0026rdquo; and \u0026ldquo;Harmony\u0026rdquo;, while \u0026ldquo;SingleR\u0026rdquo; and \u0026ldquo;ggplot2\u0026rdquo; enabled cell-type annotation and visualization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Cell culture and qRTPCR\u003c/h2\u003e\u003cp\u003eWe used two human cell lines: MCF10A and SUM159PT. MCF10A is the normal mammary epithelial cell line and SUM159PT is the TNBC cell line. MCF10A was cultured in DMEM/F12 and SUM159PT was cultured in F12. Cells were maintained at 37\u0026deg;C / 5% CO2.\u003c/p\u003e\u003cp\u003eWe used GAPDH (glyceraldehyde- 3-phosphate dehydrogenase) as the reference gene, and the primer sequences are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Then, we performed qPCR under the specific cycling procedures: 95\u0026deg;C for 5 min, 40 cycles of 95\u0026deg;C for 15 s and 60\u0026deg;C for 60 s. Using the 2\u0026thinsp;\u0026minus;\u0026thinsp;ΔΔCT method to calculate the mRNA expression levels.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Statistical Analysis\u003c/h2\u003e\u003cp\u003eSpearman's rank correlation analysis assessed variable associations, while chi-square tests analyzed categorical clinical data. Univariate logistic regression evaluated clinical and cluster relationships. Kaplan-Meier survival curves with log-rank tests determined prognostic significance. All analyses employed two-tailed hypothesis testing, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 defining statistical significance.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Identification of differentially expressed genes in TNBC\u003c/h2\u003e\u003cp\u003eThe analytical framework is depicted in (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Using 'DESeq2' and 'Volcano' packages, we identified DEGs in TCGA datasets comparing 138 TNBC versus 960 non-TNBC samples (|log₂FC|\u0026ge;1, adj.p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), revealing 3730 upregulated and 3305 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Similarly, TNBC versus 114 normal tissues yielded 6421 upregulated and 4610 downregulated DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). VennDiagram analysis intersected these datasets, identifying 3474 overlapping DEGs (2205 upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC); 1269 downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD)). Subsequently, we cross-referenced the 2205 upregulated and 1269 downregulated genes with glutamine-related genes, identifying a total of 12 candidate DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Consensus Clustering of TNBC Based on PHGDH and LEP Expression\u003c/h2\u003e\u003cp\u003eThrough the assessment of four ESTIMATE metrics, we obtained 2 genes (PHGDH and LEP) with an absolute value of the correlation coefficient between genes and ESTIMATE indices greater than 0.19 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). LEP positively correlated with Stromal/Immune/ESTIMATE Scores but negatively with Tumor Purity, while PHGDH exhibited inverse patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In TCGA-TNBC versus adjacent tissues, LEP was significantly downregulated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) and PHGDH upregulated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). To further clarify the clinical relevance and biological characteristics of TNBC patients, we conducted ConsensusClusterPlus analysis stratified 137 TNBC samples into two molecular subtypes at optimal K\u0026thinsp;=\u0026thinsp;2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F) : Cluster 1 (n\u0026thinsp;=\u0026thinsp;55) and Cluster 2 (n\u0026thinsp;=\u0026thinsp;82) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e. These subtypes exhibit diametrically opposed PHGDH/LEP expression patterns, establishing them as key biomarkers for TNBC stratification.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Analysis of Clinical Characteristics\u003c/h2\u003e\u003cp\u003eTo gain a deeper insight into the clinical features of the clusters, we analyzed the survival rate of TNBC patients in the TCGA. However, no statistically significant difference in overall survival between Cluster 1 and Cluster 2 (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e A\u003c/b\u003e). TAlluvial diagrams demonstrated distinct associations between clusters and TNM staging, particularly T-stage distribution (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e B-D\u003c/b\u003e). Baseline characteristics analysis confirmed significant T-stage disparity between clusters (p\u0026thinsp;=\u0026thinsp;0.01, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Univariate logistic regression analysis indicated that T stage serves as a prognostic factor influencing clustering (p\u0026thinsp;=\u0026thinsp;0.01, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The above data indicated that clusters can affect the prognosis of TNBC.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical features of clusters.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecluster1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ecluster2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eage (mean (SD))\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.90 (10.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53.94 (11.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003epathological.stage (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStage1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (10.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (22.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStage2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (77.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (61.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStage3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (14.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStage4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT.stage (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (34.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (85.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (56.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (7.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN.stage (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (75.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39 (58.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (14.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (26.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (6.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (10.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (4.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (4.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eUnivariate logistic regression analysis for clustering (Cluster 2 vs Cluster 1)\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate Logistic Regression\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOdds Ratio (95% Confidence Interval)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.81(0.39\u0026ndash;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT2 vs T1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.24(0.09\u0026ndash;0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT4 vs T1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.26(0.01\u0026ndash;4.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN1 vs N0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.37(0.89\u0026ndash;6.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN2 vs N0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.15(0.52\u0026ndash;8.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN3 vs N0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.38(0.22\u0026ndash;8.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePathological stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003estage2 vs stage1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.37(0.12\u0026ndash;1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003estage3 vs stage1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.56(0.13\u0026ndash;2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Identification of Immune-Associated Pathways via GSEA\u003c/h2\u003e\u003cp\u003eDEGs between clusters (|log₂FC|\u0026ge;1, adj.p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) underwent GSEA to delineate functional disparities. GSEA-KEGG analysis revealed significant enrichment (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in immune pathways: primary immunodeficiency, cytokine-cytokine receptor interaction, Th1 and Th2 cell differentiation, B cell receptor signaling pathway, Natural killer cell mediated cytotoxicity and T cell receptor signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-F). GSEA-GO analysis further confirmed immune-associated processes including dendritic cell migration, immune response-regulating signaling pathway, T cell proliferation and B cell mediated immunity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). These findings establish Cluster 2 as an immunologically active subtype.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Comparative Immune Microenvironment Profiling\u003c/h2\u003e\u003cp\u003eImmune infiltration analysis revealed significant disparities between clusters. To assess the immune association, we conducted ESTIMATE analysis on both clusters. ESTIMATE demonstrated Cluster 2 exhibited elevated Stromal, Immune, and ESTIMATE Scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but reduced Tumor Purity versus Cluster 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-D). We also conducted EPIC and ssGSEA to examine the extent of immune infiltration across the two clusters. EPIC quantification confirmed increased total immune infiltration and B cell abundance in Cluster 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Concordantly, ssGSEA showed enhanced B cell infiltration and tertiary lymphoid structure (TLS) formation in Cluster 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. To further understand the biological mechanisms underlying the poorer outcomes of clusters, ssGSEA was performed using the tumor-associated gene sets. We found that Cluster 2 had suppressed oncogenic pathways (cell cycle) and impaired DNA damage repair mechanisms (mismatch repair (MMR), homologous recombination (HR), nucleotide excision repair (NER) and base excision repair (BER)) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.6 PHGDH and LEP are associated with B cell in TNBC\u003c/h2\u003e\u003cp\u003eTranscriptomic analysis of TCGA data identified 1,066 upregulated and 239 downregulated DEGs in Cluster 2 vs. Cluster 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). WGCNA of these DEGs revealed five co-expression modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-D). The turquoise module displayed positive associations with Cluster (R\u0026thinsp;=\u0026thinsp;0.55, p\u0026thinsp;=\u0026thinsp;4.00e-12), ESTIMATEScore (R\u0026thinsp;=\u0026thinsp;0.50, p\u0026thinsp;=\u0026thinsp;6.00e-10), StromalScore (R\u0026thinsp;=\u0026thinsp;0.61, p\u0026thinsp;=\u0026thinsp;2.00e-15), and ImmuneScore (R\u0026thinsp;=\u0026thinsp;0.31, p\u0026thinsp;=\u0026thinsp;2.00e-4), while showing a negative correlation with TumorPurity (R = -0.51, p\u0026thinsp;=\u0026thinsp;3.00e-10) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Screening the turquoise module (MM\u0026thinsp;\u0026gt;\u0026thinsp;0.40, GS\u0026thinsp;\u0026gt;\u0026thinsp;0.39) identified 24 hub genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). GO enrichment confirmed the 24 hub genes have immune regulatory functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Among these genes, we found 8 genes (GAS7 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], TBX15 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], FGF7 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], DKK2 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], CXCL12 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], VWF [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], ITIH5 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and S1PR1 [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]) were validated as critical regulators of B cell maturation, differentiation, infiltration and several additional functions. The gene expression correlation analysis also indicated that these genes exhibited positive correlation with LEP and negative correlation with PHGDH (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The protein‒protein analysis revealed direct interactions among FGF7, CXCL12, VWF, and S1PR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Spearman correlation analysis indicated that all eight genes positively correlated with ESTIMATE Score but negatively with Tumor Purity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Additionally, Spearman correlation between genes and ssGSEA confirmed their co-regulation with B cell infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). These analyses establish Cluster 2 as a B cell-enriched TNBC subtype orchestrated by the PHGDH-LEP regulatory axis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Immunotherapy Sensitivity Stratification by Molecular Subtype\u003c/h2\u003e\u003cp\u003eElevated expression of immunomodulators predicts enhanced response to immune checkpoint blockade [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In Cluster 2 versus Cluster 1, we observed significant upregulation of antigen present genes (HLA-DPA1, HLA-DQA1), cell adhesion molecules (ITGB2, SELP), costimulatory molecules (CD28), \u003cb\u003ecytokine signaling\u003c/b\u003e (TGFB1, IL10), and \u003cb\u003eimmune checkpoint\u003c/b\u003e (BTLA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-H). This coordinated overexpression of immunomodulatory machinery establishes Cluster 2 as a candidate for enhanced PD-1/PD-L1 inhibitor responsiveness.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Validation of Immune landscape in GSE76275\u003c/h2\u003e\u003cp\u003eExternal validation using GSE76275 (n\u0026thinsp;=\u0026thinsp;188) confirmed TNBC stratification into two clusters (\u003cb\u003eSupplementary Fig. S2 A-C\u003c/b\u003e). The gene expression patterns concordant with TCGA. The expression levels of LEP were lower and of PHGDH were higher in Cluster 1 (n\u0026thinsp;=\u0026thinsp;90), and the expression levels of LEP were higher and of PHGDH were lower in Cluster 2 (n\u0026thinsp;=\u0026thinsp;98) in the GSE76275 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Considering LEP and PHGDH displayed contrasting trends among the clusters, we performed a Spearman correlation analysis for PHGDH and LEP, which indicated a weak negative correlation (R^2\u0026thinsp;=\u0026thinsp;0.05, P\u0026thinsp;=\u0026thinsp;2.00e-16) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eESTIMATE analysis demonstrated Cluster 2 had elevated Stromal/Immune/ESTIMATE Scores but reduced Tumor Purity (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC-F). Both EPIC and ssGSEA confirmed enhanced B cell infiltration and tertiary lymphoid structure (TLS) formation in Cluster 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eG and Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eH). Oncogenic pathways (cell cycle) and DNA repair mechanisms (MMR/HR/NER/BER) were consistently suppressed versus Cluster 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eI).\u003c/p\u003e\u003cp\u003eThe eight B cell-regulatory genes (GAS7, TBX15, FGF7, DKK2, CXCL12, VWF, ITIH5 and S1PR1) are same as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, which were positively correlated with LEP and negatively correlated with PHGDH (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). Spearman correlation analysis of these genes and the four ESTIMATE indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB), along with ssGSEA (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC) demonstrated a positive association with the ESTIMATE Score and a negative association with Tumor Purity. Immunomodulator expression (HLA-DPA1/DQA1, ITGB2, SELP, CD28, TGFB1, IL10, BTLA) was significantly elevated in Cluster 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA-H).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis multi-platform validation establishes Cluster 2 as a conserved immunogenic subtype with enhanced therapeutic responsiveness.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.9 The Expression Dynamics of PHGDH and LEP in TNBC\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eGiven the overexpression of PHGDH in TNBC, we investigated the expression profiles of PHGDH at the single-cell level to delineate its cell type-specific expression landscape. Analysis of the TNBC samples identified nine distinct cell clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Notably, PHGDH exhibited significant enrichment in B lymphocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB-C). Then, we validated the expression level of PHGDH and LEP in TNBC cell lines by qRT-PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eD-E). The expression level of PHGDH and LEP in TNBC cell lines were consistent with previous results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-c\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eOur integrated analysis of TCGA, GSE76275 and GSE176078 cohorts establishes glutamine metabolic reprogramming as a cornerstone of TNBC microenvironmental heterogeneity. Through consensus clustering, we delineated two molecular subtypes with diametrically opposed PHGDH/LEP expression profiles: Cluster 1 (PHGDH high/LEP low): Enriched in oncogenic pathways and DNA damage repair mechanisms, correlating with advanced T stage and poorer prognosis; Cluster 2 (LEP high/PHGDH low): Characterized by robust B cell infiltration (ssGSEA), tertiary lymphoid structure formation, and coordinated overexpression of immunomodulators (HLA-DPA1/DQA1, ITGB2, SELP, CD28, TGFB1, IL10, BTLA). Critically, WGCNA revealed a PHGDH-LEP regulatory axis governing B cell functionality through eight hub genes (GAS7, TBX15[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], FGF7, DKK2, CXCL12, VWF, ITIH5 and S1PR1), with Cluster 2 exhibiting: enhanced immunogenicity, suppressed DNA repair capacity and elevated checkpoint molecule expression. This PHGDH low/LEP high phenotype demonstrated superior immunotherapy response potential across both cohorts. Our findings thus position PHGDH and LEP as master regulators of B cell-mediated antitumor immunity, providing a metabolic-immune signature for stratifying TNBC patients toward precision immunotherapy.\u003c/p\u003e\u003cp\u003eUnivariate logistic regression confirmed the prognostic significance of our molecular clusters. GSEA revealed pronounced enrichment of immune response pathways in Cluster 2, consistent with its immunogenic phenotype. Comprehensive immune deconvolution demonstrated Cluster 2 exhibited enhanced immune cells infiltration, among which the infiltration of B cells is the most stable. Conversely, Cluster 1 displayed significant upregulation of oncogenic drivers (cell cycle progression) and DNA damage repair pathways (MMR, HR, NER, BER). Critically, this molecular profile aligns with the immune-desert/excluded phenotypes observed in immunotherapy-resistant tumors [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], explaining Cluster 1's poorer prognosis. The inverse correlation between PHGDH expression and B cell infiltration establishes the PHGDH-LEP axis as a quantitative biomarker for stratifying TNBC patients' immunotherapy response and survival outcomes.\u003c/p\u003e\u003cp\u003eB lymphocytes orchestrate adaptive and innate immunity through antigen presentation and cytokine signaling [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Our WGCNA identified an immune-related module containing eight hub genes (GAS7, TBX15, FGF7, DKK2, CXCL12, VWF, ITIH5, and S1PR1) that exhibit: positive correlation with LEP expression, negative correlation with PHGDH and co-regulation with B cell infiltration. Previous studies found that GAS7 and TBX15 modulate B cell differentiation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. FGF7 mediates B cell recruitment [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and DKK2 regulates B cell development [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].Previous studies have evidenced that CXCL12 facilitates B cell cross-talk [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and VWF, ITIH5 sustain B cell niche integrity [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. And S1PR1 gates B cell trafficking [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. These findings collectively demonstrate a PHGDH-LEP-B cell functional axis, where suppressed PHGDH and elevated LEP synergistically promote B cell-mediated antitumor immunity through coordinated gene network regulation.\u003c/p\u003e\u003cp\u003eRecent breakthroughs in cancer immunotherapy have markedly improved antitumor responses and survival outcomes across malignancies [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Our study reveals that Cluster 2 (PHGDH \u003cem\u003elow\u003c/em\u003e / LEP \u003cem\u003ehigh\u003c/em\u003e) exhibits coordinated upregulation of immunomodulators critical for therapeutic efficacy: antigen present molecules (HLA-DQA1, HLA-DPA1), cell adhesion molecules (TIGB2, SELP), costimulatory molecules (CD28), cytokine signaling (TGFB1, IL10) and checkpoint regulation (BTLA). This immunogenic phenotype aligns with emerging strategies targeting immune evasion mechanisms [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Critically, the PHGDH-LEP regulatory axis correlates with enhanced B cell infiltration, predicts improved ICB response and establishes a metabolic basis for TNBC immunotherapy stratification. Future studies should delineate how LEP-mediated signaling interfaces with PHGDH-dependent metabolism to orchestrate immunomodulatory pathways, potentially revealing novel combinational targets to overcome treatment resistance.\u003c/p\u003e\u003cp\u003eOur consensus clustering-derived subtypes provide a robust framework for prognostic stratification in TNBC, with the PHGDH-LEP axis serving as a theranostic biomarker to predict immunotherapy response. While the identified association between PHGDH suppression, LEP elevation, and B cell infiltration offers mechanistic insights into glutamine metabolism-driven immunomodulation, two limitations warrant acknowledgment: firstly, the precise pathways through which PHGDH and LEP coordinate B cell functionality require further dissection; secondly, as this retrospective analysis may incur selection bias, prospective validation in multi-center cohorts incorporating immunotherapy response monitoring is essential. Nevertheless, this metabolic-immune signature lays the groundwork for developing PHGDH/LEP-targeted combination strategies to overcome TNBC immunotherapy resistance.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eConsensus clustering of TNBC based on LEP and PHGDH expression revealed two distinct molecular subtypes. Cluster 2 (LEP \u003cem\u003ehigh\u003c/em\u003e/PHGDH \u003cem\u003elow\u003c/em\u003e) demonstrated significantly enhanced B cell infiltration and elevated immunomodulator expression compared to Cluster 1. This immunologically active phenotype correlated with improved immunotherapy response. Mechanistically, the inverse correlation between PHGDH expression and B cell abundance establishes the PHGDH-LEP axis as: a regulator of B cell-mediated antitumor immunity and a novel metabolic-immune biomarker in TNBC. These findings provide the first evidence that glutamine metabolic reprogramming directly coordinates B cell functionality, offering a therapeutic framework for combining metabolic modulators with immune checkpoint blockade.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.Z. and R.Y. conceptualized the study. ZL.Z. and R.Y. conducted the formal analysis. ZY.Z. and R.Y. developed the methodology. ZL.Z. and R.Y. provided resources. ZL.Z. and R.Y. were responsible for software implementation. R.C. and ZY.Z. performed validation. C.Z. and R.Y. wrote the original draft, and R.Y. reviewed and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Major Research Plan of National Natural Science Foundation of China (Key Programme) (92359204) and Special Foundation for Emerging Interdisciplinary Field Research of Shanghai Municipal Health Commission (Grant No. 2022JC004).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data accessed and utilized from The Cancer Genome Atlas Project and the Gene Expression Omnibus Repository, GSE76275 and GSE176078.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures are conducted in accordance with the principles expressed in the Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consent to the publication of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Kratzer TB, Giaquinto AN et al. 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Molecular cancer. 2025; \u003cem\u003e24\u003c/em\u003e(1): 136.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"TNBC, PHGDH, LEP, B cell, immunotherapy, single-cell analysis","lastPublishedDoi":"10.21203/rs.3.rs-6997049/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6997049/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground.\u003c/b\u003e Triple-negative breast cancer (TNBC) is one of the most aggressive and prevalent cancers in women. This study aimed to identify target genes by integrating glutamine metabolism and cancer-immunity interactions in TNBC.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods.\u003c/b\u003e Data were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO). Glutamine-related genes were extracted from the Gene Set Enrichment Analysis (GSEA) Database. Differential analysis and ESTIMATE were used to find immune-related and glutamine-related genes. The clusters were established by consensus clustering. Mechanistic insights were investigated through Gene set enrichment analysis (GSEA), ESTIMATE, epic, ssGSEA and weighted gene co-expression network analysis (WGCNA). Expression of immunomodulatory factors was used to assess immunotherapy response. Single cell RNA seq and RT-qPCR were used to validate the expression of PHGDH and LEP.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults.\u003c/b\u003e Analysis of The Cancer Genome Atlas (TCGA) dataset revealed that leptin (LEP) and phosphoglycerate dehydrogenase (PHGDH) are closely associated with immunity and glutamine metabolism in TNBC. Based on the expression profiles of LEP and PHGDH, TNBC samples were classified into two distinct clusters. Univariate logistic regression analysis demonstrated that clusters significantly influence TNBC prognosis. Gene set enrichment analysis (GSEA) highlighted potential pathways, showing that Cluster 2 correlates positively with immune cell infiltration and exhibits reduced oncogenic pathway activity. Utilizing Weighted gene co-expression network analysis (WGCNA), we identified a module strongly linked to immune response and clusters, along with eight B cell-associated genes. Notably, Cluster 2 displayed elevated expression of immunomodulatory factors, suggesting enhanced responsiveness to immunotherapy. Validation using the GSE76275 dataset confirmed these findings. Additionally, single-cell analysis revealed PHGDH expression in B cells within TNBC.\u003c/p\u003e\u003cp\u003eThe expression of PHGDH and LEP was validated in TNBC cell lines by RT-qPCR.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion.\u003c/b\u003e These results suggested that LEP and PHGDH serve as prognostic markers associated with B cells and improved immunotherapy outcomes in TNBC.\u003c/p\u003e","manuscriptTitle":"PHGDH and LEP serve as prognostic markers associated with B cell and responses to immunotherapy in triple-negative breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-09 06:29:51","doi":"10.21203/rs.3.rs-6997049/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":"a078f6b7-28ab-427b-8a62-07cb80b6bb98","owner":[],"postedDate":"July 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-13T17:53:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-09 06:29:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6997049","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6997049","identity":"rs-6997049","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}