Identification of Lactate-Related Subgroups and Prognostic Model in Triple-Negative Breast Cancer

Identification of Lactate-Related Subgroups and Prognostic Model 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 Identification of Lactate-Related Subgroups and Prognostic Model in Triple-Negative Breast Cancer ShanShan Huang, LinYu Wu, Yu Qiu, Yi Xie, Hao Wu, YingQing Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3037116/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jul, 2023 Read the published version in Journal of Cancer Research and Clinical Oncology → Version 1 posted 5 You are reading this latest preprint version Abstract Background Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer that exhibits elevated glycolytic capacity. Lactate, as a byproduct of glycolysis, is considered a major oncometabolite that plays an important role in oncogenesis and remodeling of the tumor microenvironment. However, the potential roles of lactate in TNBC are not yet fully understood. In this study, our goal was to identify prognosis-related lactate genes (PLGs) and construct a lactate-related prognostic model (LRPM) for TNBC. Methods First, we applied lactate-related genes to classify TNBC samples using hierarchical clustering algorithm. Then, we performed the log-rank analysis and the least absolute shrinkage and selection operator (LASSO) analysis to screen PLGs and construct the LRPM. The biological functions of the identified PLGs in TNBC were inverstigated using CCK8 assay and clone formation assay. Finally, we constructed a nomogram based on the lactate-risk score (LRS) and tumor clinical stage. We used operating characteristic (ROC) curve and decision curve analysis (DCA) to evaluate the predictive capability of the nomogram. Results Our results showed that the TNBC samples could be classified into two subgroups with different survival probabilities. Three genes (NDUFAF3, CARS2 and FH), which can suppress TNBC cell proliferation, were identified as PLGs. Moreover, the LRPM and nomogram exhibited excellent predictive performance for TNBC patient prognosis. Conclusion we have developed a novel LRPM that enables risk stratification and identification of poor molecular subtypes in TNBC patients, showing great potential in clinical practice. triple-negative breast cancer lactate prognostic model nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Breast cancer is a prevalent cause of cancer-related mortality in females on a global scale, ranking second among all other causes 1 . The mortality rate of breast cancer has been declining over the past few decades dut to the widespread use of chemotherapy, endocrine therapy, and targeted therapy 2 . Nevertheless, triple negative breast cancer (TNBC), which is characterized by the lack of expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (Her2), remains the most challenging subtype to treat 3 . Neither endocrine therapy nor Her2-targeted therapy is effective for TNBC, and chemotherapy remains the primary treatment. Recently, immune therapy such as immune-checkpoint inhibitors (ICI) has shown promising results in advanced- stage TNBC 4 . However, a subset of TNBC patients still suffer from chemotherapy resistance, low response and efficacy of immune therapy. Consequently, exploring potential biomarkers for treatment response judgment and prognostic prediction has significant clinical value in the individualized treatment of TNBC. Upregulation of glycolysis is a nearly universal characteristic of primary and metastatic cancers 5 . Lactate, a byproduct of glycolysis, has recently garnered attention due to its implication in immunity, and research on the interactions between lactate and the tumor microenvironment is rapidly growing. Robert A. Gatenby proposed that tumor cells can alter the local microenvironment in a manner that is harmless to itself but fatal to other competing cells through persistent aerobic glycolysis. As a result, these cells have a powerful growth advantage, which promotes tumor proliferation and invasion 5 . Previous studies have provided evidence that the accumulation of lactate undermines tumor immune surveillance and leads to poor antitumor immunity by inhibiting the cytotoxic activity of NK cells 8 , CD8 + T cells 9 and cytotoxic T cells (CTLs) 10 , or promoting apoptosis of naive T cells 11 . Additionally, Chen P et al. reported that tumor-derived lactate mediates the functional polarization of tumor-associated macrophages 12 and promotes breast cancer metastasis 13 . Meanwhile, several other studies have revealed that lactate is associated with an increased incidence of metastasis and poor outcome in various cancers, such as cervical cancer 6 , head and neck cancers 7 . Nonetheless, the exact contribution of lactate and lactate-associated genes (LRGs) in TNBC subtype identification and prognostic prediction is still unclear. In this study, we collected transcriptomic data and corresponding clinical information of TNBC patients from The Cancer Genome Atlas Project (TCGA) cohort and Fudan University Shanghai Cancer Center (FUSCC) cohort. Firstly, we systematically analyzed the expression profiles of LRGs and identified prognosis-related lactate genes (PLGs) in TNBC. Then, we constructed a robust lactate-related prognosis model (LRPM) and divided TNBC patients into high-risk and low-risk groups according to the median lactate-risk score (LRS). We also investigated the associations between LRS and immune signature, immune infiltration as well as drug sensitivity. Furthermore, we developed a nomogram that combined the LRPM and tumor clinical stage to quickly assess the prognosis of TNBC patients. Our study sheds light on the significant role of lactate in TNBC. 2. Materials and Methods 2.1. Dataset Source and Preprocessing RNA transcriptome sequencing data and the corresponding clinical information of breast cancers (BRCA) were acquired from The Cancer Genome Atlas (TCGA) via USCS Xena ( https://xenabrowser.net/datapages/ ) in accordance with the website’s guidance. To select TNBC samples from BRCA, patients who were immunohistochemically negative for ER, PR and HER2 were included, resulting in 116 patients being enrolled in the TCGA-TNBC cohort. Additionally, RNA sequencing data, somatic mutation profile and clinical information of 298 TNBC patients treated at the Department of Breast Surgery at Fudan University Shanghai Cancer Center (FUSCC), designated the FUSCC-TNBC cohort, were downloaded from the National Omics Data Encyclopedia (NODE) ( https://www.biosino.org/node/ project/detail/OEP000155) [ 37 ] . Table 1 displays relevant grouping information and clinicopathological characteristics. The METABRIC dataset were downloaded from the cBioPortal ( http://www.cbioportal.org/ ) following the website’s guidance. The GSE76250 and GSE18864 cohorts were downloaded from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ) according to the website’s guidance. 2.2. Collection of LRGs We obtained a total of 287 genes associated with lactate metabolism from the Molecular Signatures Database (MSigDB; https://www.gsea-msigdb.org/gsea/msigdb/ ). Nine human gene sets were included using the search keyword “lactic”, and after deleting duplicates, 287 lactate-related genes were identified . 2.3. Analysis of LRGs in TNBC Principal component analysis (PCA) was conducted and visualized using the “FactoMineR” and “factoextra” package in R. Differences in LRG expression and lactate metabolic enzyme expression between TNBC and normal samples were analyzed using the “limma” package in R, and the results were included in Supplement Table S3 - S4 . The cut-off criterion were p 2. Somatic mutation profiles of TNBC patients ( Supplement Table S5 - S6 ) were analyzed and visualized using Hiplot online tools ( https://hiplot.com.cn/home/index.html ) according to the website’s guidance. 2.4. Clustering analysis Hierarchical Clustering was performed using the “cluster” package in R. Survival analysis of distinct subtypes of patients was analyzed and visualized using the “survival” and “survminer” package in R. To better understand the biological functions related to distinct clusters, Gene Ontology (GO) annotation and Gene Set Enrichment Analysis (GSEA) were performed using an online tool ( https://www.bioinformatics.com.cn/ ). The results were included in Supplement Table S7 -S11 , and some of the important results in functional enrichment analysis were visualized via Hiplot online tools. 2.5. Construction and Validation of the Lactate-Related Prognostic Model (LRPM) Based on log-rank analysis ( Supplement Table S12 ), Five LRGs (NDUFAF3, NDUFS7, NDUFB8, CARS2, and FH) were identified. Subsequently, using the “glmnet” package in R, three LRGs (NDUFAF3, CARS2 and FH) were selected to construct the LRPM via least absolute shrinkage and selection operator (LASSO) analysis. The lactate-risk score (LRS) was calculated for each patient using the following formula : LRS = mRNAgene1×coefficientsgene1 + mRNAgene2×coefficientsgene2 + mRNAgene3×coefficientsgene3. Then, the patients were divided into two groups based on the median value of LRS, and survival analysis was used to evaluate the prognostic value of this model. The performance of the model was also assessed using receiver operating characteristic (ROC) curve analysis, which was performed using the “timeROC” package in R. 2.6. Plasmid construction and transfection The coding regions of NDUFAF3, FH and CARS2 coding regions were individually tagged with FLAG and cloned into empty loading plasmids (pSin-EF2-puro) to obtain the overexpression plasmids pSin-EF2-puro-NDUFAF3- FLAG, pSin-EF2-puro-FH-FLAG and pSin-EF2-puro-CARS2-FLAG. For transient transfection, the human TNBC cell lines HCC1806 was cultured in RPMI-1640 (Invitrogen) medium supplemented with 8% foetal bovine serum (FBS, Gibico). HCC1806 cells were transfected with the overexpression plasminds using Neofect (Cat#TF20121201) and harvested for RT-qPCR to determine the efficiency of gene exoressioon. 2.7. RNA extraction and RT-qPCR Total RNA was extracted from TNBC cells using RNA-Quick Purification Kit (ESscience). Reverse transcription was performed using the Reverse Transcriptase Kit (Promega). Quantitative PCR reaction were conducted on a LightCycler 480 System (Roche) with SYBR qPCR Master mix (Vazyme). Relative gene expression was normalized to GAPDH expression. The primer sequences were as follows: NDUFAF3 forward:5`-GCTTTTCCCTCTTCTGGTTGCTG-3` NDUFAF3 reverse:5`-GTTGAAGGTGGCACAGGCATTG-3` FH forward:5`-CCGCTGAAGTAAACCAGGATTATG-3` FH reverse:5`-ATCCAGTCTGCCATACCACGAG-3` CARS2 forward:5`-CGAGAAGTCCTGCTGTGTTTGG-3` CARS2 reverse:5`-ACCACACCATGCAAGGTAGCCT-3` 2.8. Cell proliferation assays For CCK8 assays, HCC1806 cells were seeded into 96-wells plates at a density of 1000 cells per well. A volume of 10ul of Cell Counting Kit-8 (CCK8,TargetMol) was added to each well on the indicated days and incubation at 37℃for 2h. Then, the absorbance at 450nm was detected using a spectrophotometer. For colony formation assay, single-cell suspensions were seeded into 6-well plates (800 cells per well) and then incubation 14 days. The plates were fixed with methanol for 30min and then stained with crystal violet for 1h. Colonies containing more than 50 cells were counted using ImageJ. 2.9. Establishing and Assessing of a Nomogram To predict the 1-, 3-, 5-year survival rate of TNBC patients, we constructed a nomogram based on LRS and tumour clinical stage using the “regplot” package in R. The calibration curve was used to estimate the consistency between the predicted survival and actual survival, using the “calibrate” package in R. To evaluate the specificity and sensitivity of the nomogram, the time-dependent ROC curves and the decision cure analysis (DCA) were performed, using “timeROC”, and “ggDCA” package in R respectively. 2.10. Tumor microenvironment analysis We investigated the immune infiltration of distinct breast cancer subtypes using the EPIC and TIMER algorithms. The xCell algorithms were applied to discriminate immune cell phenotypes in two different risk groups, and the results were listed in Supplement Table S13-S15 . Additionally, we calculated the ESTIMATE score, which can indicate stromal cell and immune cell infiltration profiles, using the “estimate” package ( Supplement Table S16 ) and visualized the results using the “ggplot” package. Furthermore, we calculate TIED score, MSI score, Dysfunction score and Exclusion score in Tumor Immune Dysfunction and Exclusion (TIDE) ( http://tide.dfci.harvard.edu/login/ ) to evaluate the potential response to immunotherapy, and the results were listed in Supplement Table S17-18 . Finally, we conducted a correlation analysis among the expression of immune-related genes, LRSs and the expression of three PLGs, using the cor R package. The results were listed in Supplement Table S19 . 2.11. Drug sensitivity analysis We predicted the semi-inhibitory concentration (IC50) values of 198 drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database using the “OncoPredict” package in R ( Supplement Table S20 ). We conducted correlation analysis to clarify the relationship between drug sensitivity and LRS, as well as comparing the differences of IC50 values in two different lactate-risk groups. 2.12. Statistical Analysis All statistical analyses were conducted using R v4.2.2 software. Comparisons between two groups were calculated uisng the unpaired Student’s t-test with two-sided p -values less than 0.05 considered statistically significant. The p -value in multiple analyses was corrected using the False discovery rate (FDR). Correlation analyses were conducted by Pearson correlation test. 3. Results 3.1. Dysregulation of LRGs in TNBC We first extracted 287 LRGs from the MSigDB and then explored the correlation between LRGs mRNA expression levels and lactate metabolomic abundance in the FUSCC-TNBC cohort ( Supplementary Table S1 ). The results showed that three LRGs had positive correlations (correlation coefficient > 0.2) with lactate metabolomic abundance, while three LRGs had negative correlations (correlation coefficient< -0.2), reflecting the complexity of the lactate metabolism (Fig. 1 A, Supplementary Table S2 ). On the other hand, the PCA demonstrated the significant regulatory effects of LRGs and lactate metabolomic abundance in TNBC. It was feasible to discriminate TNBC from normal samples in the TCGA cohort based on the mRNA expression of LRGs, as well as in the FUSCC cohort based on the lactate metabolomic abundance (Fig. 1 B). We further explored the LRGs expression profiles in the TCGA-TNBC cohort. There were 48 LRGs that showed significant differential expression ( p < 0.05 and |log 2 FC| ≥1), with 35 being upregulated and 13 being downregulated (Fig. 1 C). In addition, we determined whether the mRNA expression levels of the lactate metabolic enzymes were dysregulated in TNBC. The results from three individual datasets (TCGA, METABRIC, GSE76250) showed that most lactate metabolic enzymes were upregulated in TNBC patients (Fig. 1 D). Furthermore, somatic mutation profiles of TNBC patients in the TCGA and FUSCC cohorts were analyzed and visualized. The results showed that both TNBC cohorts had low mutation rates (Fig. 1 E-F). 3.2. Identification of TNBC Subtypes and Their Biological Functions To better clarify the lactate metabolism characteristics in TNBC, we divided a total of 258 TNBC patients from the FUSCC-TNBC cohort into subgroup A (n = 189) and subgroup B (n = 69) using the hierarchical clustering algorithm based on the expression of LRGs. The LRGs expression profiles of the two subgroups were illustrated in a heatmap which showed significant differences (Fig. 2 A). Survival analysis revealed that patients in subgroup A had a worse prognosis in the previous 5 years than those in subgroup B (Fig. 2 B). In order to explore the heterogeneity of two subgroups, we performed functional enrichment analysis after identifying the differentially expressed genes (DEGs). We found that the cluster-related DEGs were mainly enriched in biological processes linked to immune response processes (Fig. 2 C). Moreover, GSEA results suggested that the subgroup A was significantly associated with several cancer-related pathways, which could potentially account for the varied clinical outcomes among the different lactate subgroups (Fig. 2 D). We further used the TIMER and EPIC algorithms to calculate the immune cell infiltration score. We found that the two clusters had distinct immune phenotypes, with the subgroup A exhibiting a significantly higher abundance of most immune cells compared to the subgroup B (Fig. 2 E-F). As a result, subgroup B, which had a higher expression of LRGs, could be considered an immune-desert phenotype. Building on these findings, we suggested that the lactate may have a crucial role to play in the immune microenvironment remodeling process in TNBC. 3.3. Identification of LRGs and Their Biological Functions As patients with different lactate metabolic profiles showed different immune statuses and prognoses, we believed it was necessary to establish a LRPM to assist in determining TNBC clinical treatment strategies. We identified five PLGs using log-rank analysis and performed LASSO to establish a LRPM composed of three PLGs (Fig. 3 A). Survival analyses indicated that patients with low expressions of the above three PLGs had poor prognosis (Fig. 3 B). To evaluate the three PLGs protein expression levels, we utilized a public database called the Human protein Atlas. The results showed that NDUFAF3 and FH were downregulated in breast cancers compared to normal tissues, while CARS2 showed no significant difference ( Supplementary Fig. 1A ). The protein expression profile in TNBC need further exploration. Since the three PLGs were downregulated in TNBC, we investigated whether they could inhibit TNBC cell proliferation. Overexpression of NDUFAF3, FH, and CARS2 in HCC1806 substantially decreased cell proliferation and the colony formation (Fig. 3 C-D, Supplementary Fig. 1B ). These results suggested that the downregulation of NDUFAF3, FH, and CARS2 played important role in TNBC. 3.4. Establishment of LRPM in TCGA Cohort and Validation in FUSCC Cohort Subsequently, we calculated the LRS for each patient based on the expression of the three PLGs (NDUFAF3, FH, CARS), and the patients were categorized into the high-risk group and the low-risk group by utilizing the median LRS as the dividing point. As expected, the high-risk group showed higher lactate abundance ( Supplementary Fig. 2 ). As shown in Fig. 4 A, patients with high LRS scores had shorter overall survival (OS) times than those with low LRS scores. Meanwhile, as shown in Fig. 4 B, the LRS plot and survival status plot demonstrated that the number of TNBC patients with dead status gradually increased with increasing LRS, and the heatmap revealed that the three PLGs were significantly downregulated in the high-risk group. Additionally, the time-dependent ROC analyses showed that the areas under the curve (AUCs) value were 0.668, 0.707, 0.777, 0.647 for 1-, 3-, 5- and 10-year survival, respectively (Fig. 4 C). To further validate the performance of the LRPM in different populations, we designated the FUSCC-TNBC cohort as the validation cohort. We calculated the LRS of each patient using the same formula and separated TNBC patients into the high-risk group and the low-risk group using the median LRS as the cut-off point. Similarly, TNBC patients with high-risk scores had poorer OS than those with low-risk scores (Fig. 4 D). The LRS, survival status and expression levels of the three PLGs of every TNBC patient were displayed in Fig. 4 E. Moreover, the AUCs were 0.979, 0.613, 0.583, 0.797 for 1-, 3-, 5- and 10-year survival, respectively (Fig. 4 F). The above results indicate an excellent predictive performance of the LPRM in TNBC. 3.5. Construction and Assessment of a Clinical Nomogram We then performed multivariate COX analysis combining the LRS with clinical characteristics, including age and tumor clinical stage. The results showed that LRS acted as a prognostic risk factor for TNBC independent of clinical characteristics ( Supplementary Fig. 3A ). Additionally, we explored the relationships between the LRS and clinical characteristics of TNBC patients and noted that patients over 55 years old and those with advanced tumor stages had a higher probability of being categorized as the high-risk group ( Supplementary Fig. 3B-C ). Furthermore, our analysis revealed that the high-risk group patients were more likely to suffer from breast cancer with advanced N stage, indicating more lymph node metastases ( Supplementary Fig. 3D ). However, no significant differences were observed between patients stratified by the T stage and M stage (data not shown). Moreover, we discovered that LRS was linked to the response to neoadjuvant chemotherapy of TNBC patients in GSE18864 cohort ( Supplementary Fig. 3E ), patients in the high-risk group were more likely to have chemotherapy no-response, as indicated by the miller-payne response grade of 0–1. Overall, these results demonstrate that LRS acts as an independent risk factor in TNBC and suggest that the LRS can be used to construct a clinical nomogram to help predict the prognosis of TNBC patients. For improve the clinical application of LRS, we developed a nomogram to predict 1-year, 3-year and 5-year OS by integrating LRS and tumor clinical stage in the TCGA cohort (Fig. 5 A). To internally validate the nomogram, we conducted a calibration plot. The results demonstrated outstanding consistency between the predicted values generated by the nomogram and the actual 1-, 3-, and 5-year OS (Fig. 5 B). In addition, we evaluated the predictive efficiency of the nomogram by calculating the AUCs for 1-, 3- and 5-year OS. The result showed that the nomogram had higher efficiency than other clinical factors such as age, tumor clinical stage and LRS, with AUCs of 0.83, 0.88 and 0.75,respectively (Fig. 5 C). Moreover, we conducted decision curve analysis (DCA) to evaluate the predicted performance of age, tumor clinical stage, LRS and nomogram. As shown in Fig. 5 D, the nomogram exhibited a better clinical benefit. Overall, these results indicate that the nomogram has high accuracy for predicting the prognosis of TNBC patients and may aid clinical management. 3.6. Associations between Lactate-Risk Score and Immune Infiltration Previous studies have highlighted the significant role of lactate in the remodelling of the tumor immune microenvironment. To explore the relationship between LRS and immune infiltration, we performed GSEA analysis using the LRS-correlated genes and calculated the immune cell infiltrate score by utilizing the xCell algorithm. Our findings revealed that signaling pathways involved in the tumor immunosuppressive microenvironment and inflammatory response were significantly enriched in the high-risk group (Fig. 6 A). In addition, the infiltration of CD4 + T cells notably decreased in TNBC of the high-risk group (Fig. 6 C). Notably, our results also indicated that LRS was significantly correlated with the modulation of the tumor microenvironment modulation, such as angiogenesis, myogenesis and epithelial mesenchymal transition (Fig. 6 B). Correspondingly, the infiltration of cancer- associated fibroblasts and hematopoietic stem cells showed a meaningful increase in the high-risk group (Fig. 6 D). In addition, it was observed that the high-risk group displayed a greater degree of stromal score and ESTIMATE score, but a reduced level of tumor purity in comparison to the low-risk group (Fig. 6 E). Our finding suggested that lactate is a crucial factor in remodeling immune microenvironment within the tumor. To further investigate the relationship between LRS and ICI response, we calculated the TIDE score online and found that the high-risk group in FUSCC-TNBC cohort showed a raised TIED score, Dysfunction score and Exclusion score, but no significant difference in microsatellite instability (MSI) score ( Supplementary Fig. 4A ). Similarly, the high-risk group in TCGA-TNBC cohort displayed a greater degree of Dysfunction scores, but lower microsatellite instability (MSI) scores ( Supplementary Fig. 4B ). These differences imply that individuals in the high-risk group may not respond well to ICI therapy and that LRS could be utilized as a predictive marker of immunotherapy responsiveness. Additionally, we examined the relationship between immune-related genes [ 38 ] expression and LRS, as well as three PLGs expression ( Supplementary Fig. 4C ). We observed no significant correlation between LRS/PLGs and classic checkpoint molecules, except for FH which was negatively correlated with some immune-inhibitory molecules such as VEGFE and TGFB1. This implies that FH may act as a hub gene for predicting immunotherapy response in TNBC. 3.7. Associations between Lactate-Risk Score and Drug Susceptibility To investigate whether there was correlation between LRS and drug sensitivity, we utilized the “OncoPredict” package to predict the IC50 values of 198 drugs in the Drug Sensitivity in Cancer (GDSC) database. We observed a significant correlation between the LRS and several common anticancer drugs, including Fluorouracil, Alpelisib, Fulvestrant and Olaparib (Fig. 7 A-B). Furthermore, we compared the IC50 values between the two risk groups. The reslust showed that the the low-isk group exhibited enhanced sensitivity to several common chemotherapeutic drugs for TNBC treatment, such as Cisplatin, Docetaxel, Gemcitabine and Paclitaxel (Fig. 7 C). These findings suggest that LRS can help identify suitable patients for appropriate therapy and provide a practical tool for TNBC treatment decision-marking. 4. Discussion Breast cancer is generally considered a poorly immunogenic malignancy 14 , but this is no always the case. Considerable evidence shows that compared to other subtypes of breast cancer, TNBC has a higher tumor mutation burden (TMB), frequent copy number changes (CNS) and more genetic instability 15 . As a result, TNBC should be a leading candidate for potential breast cancer immunotherapies 16 . A growing number of clinical trials aims to explore the role of immunotherapy in TNBC, but inconsistent results have been observed following different therapy strategies and tumor-stage patients 17 . The results of IMpassion130 study shows that atezolizumab combined with paclitaxel prolongs progression-free survival (PFS) in PD-L1-positive metastatic TNBC patients 18 . Additionally, the KEYNOTE-355 study in metastatic TNBC patients demonstrates a meaningful improvement in PFS with pembrolizumab combined chemotherapy versus placebo combined chemotherapy 19 . However, the KEYNOTE-119 study reveals that the pembrolizumab does not significantly improve overall survival (OS) in metastatic TNBC patients 20 . Since the efficacy of single-agent immune therapy in TNBC is low and varies among individuals 17 , identifying novel biomarkers that can predict immunotherapy response in TNBC is a high priority for clinical development. Lactate plays an important role in various diseases including tumor 21 . The main feature of metabolic reprogramming is aerobic glycolysis and accumulation of lactate in tumor microenvironment, which plays an essential role in the oncogenesis and immunosupression 22 . The LRGs which abnormally expressed in TNBC were able to discriminated TNBC from normal samples, and the lactate metabolic enzymes (such as SLC2A1, SLC16A1 and GAPDH) were upregulated in TNBC compared to normal samples. This indicates the existence of lactate metabolic reprogramming in TNBC. We then divided TNBC patients into two subtypes based on the expression of LRGs. The glycolysis signaling pathway was significantly enriched in cluster B, which had lower proportion of B cells, CD4 + T cells and CD8 + T cells. These findings suggest the link between lactate and immunity signature, and LRGs might server as a novel biomarker for predicting response to immunotherapy in TNBC. In addition, previous reports have demonstrated that the gene signature based on LRGs has a robust and effective predictability in Kidney Renal Clear Cell Carcinoma 23 , Hepatocellular Carcinoma 24 and Lung Adenocarcinoma 25 . Here, we constructed the first LRPM in TNBC. Three PLGs, including NDUFAF3, CARS2 and FH, were identified and used to construct the LPRM. NDUFAF3 (NADH: ubiquinone oxidoreductase complex assembly factor 3) is a mitochondrial respiratory chain complex I assembly protein. Mitochondrial complex I deficiency is the most common disorder of the oxidative phosphorylation system, and the deficiency of the NDUFAF3 has been identified as a cause of several mitochondrial disease 26,27 . Similarly, CARS2 (Cysteinyl-tRNA Synthetase 2), a mitochondrial aminoacyl-tRNA synthetase and novel cysteine persulfide synthase, has been reported to be correlated with defects in mitochondrial function and mitochondrial translation, such as mitochondrial epileptic encephalopathy 28,29 . The function and specific roles of NDUFAF3 and CARS2 in cancer remain unclear. However, our study revealed that NDUFAF3 and CARS2 might act as protective factors in TNBC. FH (Fumarate Hydratase) is one of the Krebs cycle genes, and its loss-of-function mutation results in the epigenetic alterations and contributes to tumorigenesis 30 . Transcriptional downregulation of FH has also been found in colorectal cancer and clear cell carcinoma 31 . Clinically, FH-deficient renal cell carcinoma is characterised by early metastasis and poor outcome 32 . Consistent with previous studies, our study demonstrated that FH loss was correlated with poor outcome in TNBC patients. We found that TNBC patients with different LRS had distinct immune components. The high-risk group was characterized by a reduced ratio of antitumor immune cells but a higher proportion of TME components and ESTIMATE score. Previous studies have shown that the tumor-derived lactate is a key mediator in the tumour microenvironment. It has been reported that lactate can enter endothelial cells and stimulate the NF-kB/IL-8 pathway, promoting tumor angiogenesis 33 . Furthermore, cancer-associated fibroblasts can secrete lactate to modulate acquired drug resistance in a NF-kB dependent manner 34 . Notably, we observed a significant enrichment of inflammatory-response related signaling pathways within the high-risk group. This observation is intriguing since a recent study has indicated that lactate has the capacity to stimulate a chronic inflammatory process by triggering a series of intracellular signals in inflammatory disease 35 . Additionally, it has been reported that lactate, mediated by the upregulated lactate transporter SLC5A12, inhibits CD4 + T cell motility in inflamed tissues, and blockade of SLC5A12 diminishes the disease severity in an arthritis model 36 . Using the TIED algorithm, we estimated the potential response of ICI therapy in two different LRS groups and found that the low LRS might exhibit sensitivity to ICI therapy. Further exploration is needed to determine the correlation between LRS and other immunotherapy in TNBC. Furthermore, our research revealed that patients with a high-risk score displayed elevated IC50 values of common chemotherapeutic drugs, including Cisplatin, Docetaxel, Gemcitabine and Paclitaxel. These findings suggest that LRS could potentially serve as a marker for predicting chemotherapy and immunotherapy response. In summarize, we identified three independent PLGs and constructed a novel LRPM for TNBC patients. The LRPM in our study shows promise in improving prognostic prediction accuracy and the ability to assess drug therapy response. Declarations Acknowledgments We have been very appreciative of the Department of Breast Surgery of Fudan University Shanghai Cancer Center (FUSCC) for their public data. Author contributions Conceptualization, X.X and Y.L; data curation, S.H and L.W; formal analysis, S.H and L.W; funding acquistion, X.X; methodology, S.H and Y.Q; project administration Y.X and H.W; supervision, X.X and Y.L; visualization, S.H; writing-orginal draft, S.H and L.W; writing-review and editing, Y.L and X.X; All authors have read and agreed to the published version of the manuscript. Funding This research was supported by National Natural Science Foundation of China (No.81974444, Xinhua Xie). Data availability All data can be obtained from the pubic database. Conflict of Interest The authors declare no conflict of interest. Ethics approval and consent to participate Not applicable. References Giaquinto AN, Sung H, Miller KD, et al. Breast Cancer Statistics, 2022. CA Cancer J Clin. 2022;72(6):524-541. doi:10.3322/caac.21754 Pondé NF, Zardavas D, Piccart M. Progress in adjuvant systemic therapy for breast cancer. Nat Rev Clin Oncol. 2019;16(1):27-44. doi:10.1038/s41571-018-0089-9 Waks AG, Winer EP. Breast Cancer Treatment: A Review. JAMA. 2019;321(3):288-300. doi:10.1001/jama.2018.19323 Bianchini G, Balko JM, Mayer IA, et al. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat Rev Clin Oncol. 2016;13(11):674-690. doi:10.1038/nrclinonc.2016.66 Gatenby RA, Gillies RJ. Why do cancers have high aerobic glycolysis?. 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Published 2022 Feb 17. doi:10.3389/fimmu.2022.818984 Li Y, Mo H, Wu S, Liu X, Tu K. A Novel Lactate Metabolism-Related Gene Signature for Predicting Clinical Outcome and Tumor Microenvironment in Hepatocellular Carcinoma. Front Cell Dev Biol. 2022;9:801959. Published 2022 Jan 3. doi:10.3389/fcell.2021.801959 Zhao F, Wang Z, Li Z, et al. Identifying a lactic acid metabolism-related gene signature contributes to predicting prognosis, immunotherapy efficacy, and tumor microenvironment of lung adenocarcinoma. Front Immunol. 2022;13:980508. Published 2022 Oct 7. doi:10.3389/fimmu.2022.980508 Saada A, Vogel RO, Hoefs SJ, et al. Mutations in NDUFAF3 (C3ORF60), encoding an NDUFAF4 (C6ORF66)-interacting complex I assembly protein, cause fatal neonatal mitochondrial disease. Am J Hum Genet. 2009;84(6):718-727. doi:10.1016/j.ajhg.2009.04.020 Baertling F, Sánchez-Caballero L, Timal S, et al. Mutations in mitochondrial complex I assembly factor NDUFAF3 cause Leigh syndrome. Mol Genet Metab. 2017;120(3):243-246. doi:10.1016/j.ymgme.2016.12.005 Fujii S, Sawa T, Motohashi H, Akaike T. Persulfide synthases that are functionally coupled with translation mediate sulfur respiration in mammalian cells. Br J Pharmacol. 2019;176(4):607-615. doi:10.1111/bph.14356 Coughlin CR 2nd, Scharer GH, Friederich MW, et al. Mutations in the mitochondrial cysteinyl-tRNA synthase gene, CARS2, lead to a severe epileptic encephalopathy and complex movement disorder. J Med Genet. 2015;52(8):532-540. doi:10.1136/jmedgenet-2015-103049 Xiao M, Yang H, Xu W, et al. Inhibition of α-KG-dependent histone and DNA demethylases by fumarate and succinate that are accumulated in mutations of FH and SDH tumor suppressors. Genes Dev. 2012;26(12):1326-1338. doi:10.1101/gad.191056.112 Schmidt C, Sciacovelli M, Frezza C. Fumarate hydratase in cancer: A multifaceted tumour suppressor. Semin Cell Dev Biol. 2020;98:15-25. doi:10.1016/j.semcdb.2019.05.002 Sun G, Zhang X, Liang J, et al. Integrated Molecular Characterization of Fumarate Hydratase-deficient Renal Cell Carcinoma. Clin Cancer Res. 2021;27(6):1734-1743. doi:10.1158/1078-0432.CCR-20-3788 Certo M, Tsai CH, Pucino V, Ho PC, Mauro C. Lactate modulation of immune responses in inflammatory versus tumour microenvironments. Nat Rev Immunol. 2021;21(3):151-161. doi:10.1038/s41577-020-0406-2 Pucino V, Certo M, Bulusu V, et al. Lactate Buildup at the Site of Chronic Inflammation Promotes Disease by Inducing CD4+ T Cell Metabolic Rewiring. Cell Metab. 2019;30(6):1055-1074.e8. doi:10.1016/j.cmet.2019.10.004 Végran F, Boidot R, Michiels C, Sonveaux P, Feron O. Lactate influx through the endothelial cell monocarboxylate transporter MCT1 supports an NF-κB/IL-8 pathway that drives tumor angiogenesis. Cancer Res. 2011;71(7):2550-2560. doi:10.1158/0008-5472.CAN-10-2828 Jin Z, Lu Y, Wu X, et al. The cross-talk between tumor cells and activated fibroblasts mediated by lactate/BDNF/TrkB signaling promotes acquired resistance to anlotinib in human gastric cancer. Redox Biol. 2021;46:102076. doi:10.1016/j.redox.2021.102076 Gong Y, Ji P, Yang YS, et al. Metabolic-Pathway-Based Subtyping of Triple-Negative Breast Cancer Reveals Potential Therapeutic Targets. Cell Metab . 2021;33(1):51-64.e9. doi:10.1016/j.cmet.2020.10.012 Zheng S, Zou Y, Tang Y, et al. Landscape of cancer-associated fibroblasts identifies the secreted biglycan as a protumor and immunosuppressive factor in triple-negative breast cancer. Oncoimmunology. 2022;11(1):2020984. Published 2022 Jan 3. doi:10.1080/2162402X.2021.2020984 Table Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.xlsx SupplementaryFigure1.pdf SupplementaryFigure2.pdf SupplementaryFigure3.pdf SupplementaryFigure4.pdf SupplementaryFigureLegends.docx supplementTable.zip Cite Share Download PDF Status: Published Journal Publication published 20 Jul, 2023 Read the published version in Journal of Cancer Research and Clinical Oncology → Version 1 posted Editorial decision: Accepted 09 Jul, 2023 Reviewers invited by journal 11 Jun, 2023 Editor assigned by journal 11 Jun, 2023 Submission checks completed at journal 08 Jun, 2023 First submitted to journal 08 Jun, 2023 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. 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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-3037116","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":208055949,"identity":"4e87779a-6c9c-4d7d-aafe-107aa9ec6d57","order_by":0,"name":"ShanShan Huang","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"ShanShan","middleName":"","lastName":"Huang","suffix":""},{"id":208055950,"identity":"8a493c4f-ba4b-4d4d-a2fe-09b145aa1bde","order_by":1,"name":"LinYu Wu","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"LinYu","middleName":"","lastName":"Wu","suffix":""},{"id":208055951,"identity":"7a4092d7-b358-47c3-a200-3a6a0f49c0c0","order_by":2,"name":"Yu Qiu","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Qiu","suffix":""},{"id":208055952,"identity":"6bc7826d-c9a6-4c1e-b4e1-27b00fb38bc8","order_by":3,"name":"Yi Xie","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Xie","suffix":""},{"id":208055953,"identity":"b5f51424-33a5-4afd-b7c3-619af3702d4a","order_by":4,"name":"Hao Wu","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wu","suffix":""},{"id":208055954,"identity":"79ad822b-1983-4de6-974b-abf610ccfa30","order_by":5,"name":"YingQing Li","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"YingQing","middleName":"","lastName":"Li","suffix":""},{"id":208055956,"identity":"21c98bf4-1940-47cb-93f1-8111f4c10378","order_by":6,"name":"XinHua Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDACZhCqIF3LGZItYmwjRbnBceZj0oXz6hLnz0h++IGh4p5dA/vZA3i1SDazJRvP3HY4ccONNGMJhjPFyQ08eQl4tfAz8xg+5t12IHGDRA4bA2NbQjKDBI8BXi1szPwfDvPOATmMWC1AWxgf8zYwJzbcgGixI6gF6BdjY55jh403nHlmLJFwJiGBjScHvxaD84efSfPU1MnObweG2IeKBHt+9jP4tcCAYwOITGBgSCQ6juwxGKNgFIyCUTAKYAAAErI8S31x4eYAAAAASUVORK5CYII=","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"XinHua","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2023-06-08 06:14:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3037116/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3037116/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00432-023-05171-6","type":"published","date":"2023-07-20T21:40:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":38404238,"identity":"2c64f7d4-d3b5-4aa4-8937-253b3cbcb107","added_by":"auto","created_at":"2023-06-12 14:44:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129020,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression profiles of lactate-related genes in TNBC:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eThe correlation between mRNA expression of lactate-related genes (LRGs) and lactate metabolomic abundance in FUSCC-TNBC cohort. \u003cstrong\u003e(B)\u003c/strong\u003e Principal component analysis of TNBC and normal samples in the TCGA-TNBC and FUSCC-TNBC cohorts. \u003cstrong\u003e(C)\u003c/strong\u003eExpression differences of LRGs between TNBC and normal samples in the TCGA cohort. The \u003cem\u003ey\u003c/em\u003e-axis represents the logarithm of fold changes of LRGs. \u003cstrong\u003e(D) \u003c/strong\u003eDiagram summarizing the metabolic genes involved in glycolysis. Bottom colors of each gene were depicted as the ratio of mRNA expression in TNBC samples of three datasets (TCGA, METABOLIC, GSE76250) compared to the normal samples. Red indicated upregulated genes and blue indicated downregulated genes. \u003cstrong\u003e(E-F)\u003c/strong\u003e Mutation frequency of LRGs in the TCGA cohort and FUSCC cohort.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3037116/v1/214c36d921dead455e7479aa.png"},{"id":38406918,"identity":"ee0321a9-3f48-427f-a393-acc2a46c7c51","added_by":"auto","created_at":"2023-06-12 14:52:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":194505,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of Lactate-Related Subgroups\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eThe FUCC-TNBC cohort was divided into two subtypes (subgroup A and subgroup B) based on expression levels of LRGs. Consensus matrix heatmap showed different expression profile of LRGs in the two subgroups, with samples dispalyed in columns. \u003cstrong\u003e(B)\u003c/strong\u003e Survival analysis of the two TNBC subtypes, revealing that patients in subgroup A had a lower 5-years survival probability (\u003cem\u003eP\u003c/em\u003e= 0.035). \u003cstrong\u003e(C)\u003c/strong\u003e GO pathway enrichment analysis in the two subgroups. The\u003cem\u003ex\u003c/em\u003e-axis represents the number of genes in the GO terms and the \u003cem\u003ey\u003c/em\u003e-axis represents the enriched GO terms including Biological Process (BP), Molecular Function (MF) and Cell Component (CC). The \u003cem\u003ep\u003c/em\u003e-value is \u0026lt; 0.05. \u003cstrong\u003e(D)\u003c/strong\u003eGSEA analysis in the two subgroups. The GLYCOLYSIS pathway was enriched in subgroup B, while several cancer-related pathways were enriched in subgroup A. The \u003cem\u003ep\u003c/em\u003e-value is \u0026lt; 0.05. \u003cstrong\u003e(E-F)\u003c/strong\u003e Boxplot showed the different abundance of infiltrating immune cell types in the two subgroups.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3037116/v1/bcebbadd8e516cb227d014b2.png"},{"id":38408380,"identity":"31c5ab3e-8781-4d05-bbbe-957b63616c73","added_by":"auto","created_at":"2023-06-12 15:00:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":304344,"visible":true,"origin":"","legend":"\u003cp\u003eIdentified PLGs in TNBC:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eCoefficient profiles of five PLGs (NDUFAF3, NDUFS7, NDUFB8, CARS2 and FH ) in the LASSO analysis and identification of the best parameter (lambda). \u003cstrong\u003e(B)\u003c/strong\u003eSurvival analysis based on the expression of three PLGs (NDUFAF3, FH and CARS2) included in lactate-related prognosis model (LPRM). The low expression of these PLGs is correlated with worse survival probability. \u003cstrong\u003e(C)\u003c/strong\u003e CCK8-assay of HCC1806 cells transfected with pSin-EF2-NDUFAF3, pSin-EF2-FH, pSin-EF2-CARS2 or empty vector. \u003cstrong\u003e(D)\u003c/strong\u003e Representative images (left) and quantification (right) of the colony formation assay in HCC1806 cells transfected with pSin-EF2-NDUFAF3, pSin-EF2-FH, pSin-EF2-CARS2 or empty vector. The above experiments were independently repeated at least three times.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3037116/v1/f928cde263b4b737920b2826.png"},{"id":38409645,"identity":"05e730f7-eaba-4902-b014-7da3fc72328b","added_by":"auto","created_at":"2023-06-12 15:08:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104101,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and validation of LRPM:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A, D)\u003c/strong\u003e Overall survival (OS) analysis of TNBC patients in the high-risk and low-risk groups. \u003cstrong\u003e(B, E)\u003c/strong\u003e Ranked dot plot showing the distribution of survival status and LRS of each patients, and a heatmap showing the expressions of three PLGs. Upper panel: The TNBC patients were divided according to the median LRS. Blue represents the low-risk group and red represents the high-risk group. Middle panel: The \u003cem\u003ey\u003c/em\u003e-axis represents the overall survival time of the patients divided into two groups at Upper panel. Lower panel: the expression profile of NDUFAF3, FH and CARS2 in the different risk groups. \u003cstrong\u003e(C, F)\u003c/strong\u003e The time-dependent ROC analysis of LPRM in predicting 1-, 3-, 5- and 10-years OS. The AUC value which represents the area under the curve represents the precision of LPRM.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3037116/v1/4181acd156af46fc7aad325e.png"},{"id":38404246,"identity":"df80d686-ab48-4571-9976-5605389d2678","added_by":"auto","created_at":"2023-06-12 14:44:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":102579,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and assessment of a clinical nomogram:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e The nomogram based on LRS and clinical stage was used for the prediction of overall survival of TNBC patients.\u003cstrong\u003e \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003e“score” represents a scoring scale for each factor and the “Total score” represents the sum of the scoring scale. The overall survival rate of 1-, 3- and 5-years was inferred according to the “Total score” and showen at lower panel.\u003cstrong\u003e (B)\u003c/strong\u003e Calibration curves were used to assess the accuracy of prognostic prediction. The \u003cem\u003ex\u003c/em\u003e-axis represents the OS predicted by the above nomogram and the \u003cem\u003ey\u003c/em\u003e-axis represents the true OS. \u003cstrong\u003e(C)\u003c/strong\u003e The Time-dependent ROC analysis of different predictors, including age, the above nomogram, LRS and clinical stage, to predict 1-, 3- and 5-year OS. The AUC value which represents the area under the curve represents the precision of each predictor. \u003cstrong\u003e(D) \u003c/strong\u003eThe decision curve analysis (DCA) of the above nomogram. The \u003cem\u003ey\u003c/em\u003e-axis represents the net benefit at different threshold of different predictors including age, the above nomogram, LRS and clinical stage.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-3037116/v1/b76af44891ab5ef404f01567.png"},{"id":38404252,"identity":"bf8d47b4-26c7-49e0-a30a-02e4f367f115","added_by":"auto","created_at":"2023-06-12 14:44:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":103191,"visible":true,"origin":"","legend":"\u003cp\u003eImmune cell infiltration in two risk groups:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A-B) \u003c/strong\u003eGSEA analysis of high-risk group and low-risk group. \u003cstrong\u003e(C-D)\u003c/strong\u003eHorizontal beanplots showed the different abundance of infiltrating immune cell types in high-risk and low-risk groups. \u003cstrong\u003e(E) \u003c/strong\u003eViolin plots were used to show the difference of the immunescore, stromal score, ESTIMATE score and tumor purity between high-risk and low-risk groups.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-3037116/v1/d1f345b8e81fa00a9a069647.png"},{"id":38406920,"identity":"aa5048e3-31cd-4f5e-8640-12eadd1aab5d","added_by":"auto","created_at":"2023-06-12 14:52:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":86419,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between LRS and drug susceptibility:\u003c/p\u003e\n\u003cp\u003e(A-B) The correlations between LRS and IC50 values. (C) Beanplots showing the high-risk group had higher IC50 values of common antitumor drugs in TNBC.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-3037116/v1/87332da062a94dca796cb808.png"},{"id":44734352,"identity":"7fea4176-5755-4b43-8fd5-08992b7996d9","added_by":"auto","created_at":"2023-10-16 22:17:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2549178,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3037116/v1/1282d070-770b-45bb-99c8-f73412b99215.pdf"},{"id":38409644,"identity":"b7e5c091-d910-4292-ad30-c150891f7d4e","added_by":"auto","created_at":"2023-06-12 15:08:15","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10442,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3037116/v1/e34c34ecf9a5f9603796a3e0.xlsx"},{"id":38404268,"identity":"e3be048b-c23d-4fef-be89-9c013ae43483","added_by":"auto","created_at":"2023-06-12 14:44:16","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":39266412,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3037116/v1/6acb604fe22af6ca9d6c68ae.pdf"},{"id":38404256,"identity":"0a8bbe6f-d4de-4673-affe-bf66b3fc1a19","added_by":"auto","created_at":"2023-06-12 14:44:15","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":225824,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3037116/v1/57c54efd6ceb007b221dd8fd.pdf"},{"id":38408382,"identity":"a01147ec-4953-47eb-a90f-287fb0ddaa4d","added_by":"auto","created_at":"2023-06-12 15:00:15","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":473006,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3037116/v1/afa01915812c9b6a07c0db38.pdf"},{"id":38406925,"identity":"724e9892-8056-47f4-8d2e-b30a1638e688","added_by":"auto","created_at":"2023-06-12 14:52:16","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":865052,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3037116/v1/67f174c44aa0f9e6ca7a749a.pdf"},{"id":38406923,"identity":"da2cd2aa-e06f-44fd-a7f0-ce08c80113e4","added_by":"auto","created_at":"2023-06-12 14:52:15","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":11567,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureLegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-3037116/v1/66732192860d3d44fc361ad6.docx"},{"id":38404261,"identity":"7c223a70-70ce-4708-9962-0be701cf9397","added_by":"auto","created_at":"2023-06-12 14:44:16","extension":"zip","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1437181,"visible":true,"origin":"","legend":"","description":"","filename":"supplementTable.zip","url":"https://assets-eu.researchsquare.com/files/rs-3037116/v1/f7966a483ea4f0202619f108.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Lactate-Related Subgroups and Prognostic Model in Triple-Negative Breast Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBreast cancer is a prevalent cause of cancer-related mortality in females on a global scale, ranking second among all other causes\u003csup\u003e1\u003c/sup\u003e. The mortality rate of breast cancer has been declining over the past few decades dut to the widespread use of chemotherapy, endocrine therapy, and targeted therapy\u003csup\u003e2\u003c/sup\u003e. Nevertheless, triple negative breast cancer (TNBC), which is characterized by the lack of expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (Her2), remains the most challenging subtype to treat\u003csup\u003e3\u003c/sup\u003e. Neither endocrine therapy nor Her2-targeted therapy is effective for TNBC, and chemotherapy remains the primary treatment. Recently, immune therapy such as immune-checkpoint inhibitors (ICI) has shown promising results in advanced-\u003c/p\u003e \u003cp\u003estage TNBC\u003csup\u003e4\u003c/sup\u003e. However, a subset of TNBC patients still suffer from chemotherapy resistance, low response and efficacy of immune therapy. Consequently, exploring potential biomarkers for treatment response judgment and prognostic prediction has significant clinical value in the individualized treatment of TNBC.\u003c/p\u003e \u003cp\u003eUpregulation of glycolysis is a nearly universal characteristic of primary and metastatic cancers\u003csup\u003e5\u003c/sup\u003e. Lactate, a byproduct of glycolysis, has recently garnered attention due to its implication in immunity, and research on the interactions between lactate and the tumor microenvironment is rapidly growing. Robert A. Gatenby proposed that tumor cells can alter the local microenvironment in a manner that is harmless to itself but fatal to other competing cells through persistent aerobic glycolysis. As a result, these cells have a powerful growth advantage, which promotes tumor proliferation and invasion\u003csup\u003e5\u003c/sup\u003e. Previous studies have provided evidence that the accumulation of lactate undermines tumor immune surveillance and leads to poor antitumor immunity by inhibiting the cytotoxic activity of NK cells\u003csup\u003e8\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e T cells\u003csup\u003e9\u003c/sup\u003e and cytotoxic T cells (CTLs)\u003csup\u003e10\u003c/sup\u003e, or promoting apoptosis of naive T cells\u003csup\u003e11\u003c/sup\u003e. Additionally, Chen P et al. reported that tumor-derived lactate mediates the functional polarization of tumor-associated macrophages\u003csup\u003e12\u003c/sup\u003e and promotes breast cancer metastasis\u003csup\u003e13\u003c/sup\u003e. Meanwhile, several other studies have revealed that lactate is associated with an increased incidence of metastasis and poor outcome in various cancers, such as cervical cancer\u003csup\u003e6\u003c/sup\u003e, head and neck cancers\u003csup\u003e7\u003c/sup\u003e. Nonetheless, the exact contribution of lactate and lactate-associated genes (LRGs) in TNBC subtype identification and prognostic prediction is still unclear.\u003c/p\u003e \u003cp\u003eIn this study, we collected transcriptomic data and corresponding clinical information of TNBC patients from The Cancer Genome Atlas Project (TCGA) cohort and Fudan University Shanghai Cancer Center (FUSCC) cohort. Firstly, we systematically analyzed the expression profiles of LRGs and identified prognosis-related lactate genes (PLGs) in TNBC. Then, we constructed a robust lactate-related prognosis model (LRPM) and divided TNBC patients into high-risk and low-risk groups according to the median lactate-risk score (LRS). We also investigated the associations between LRS and immune signature, immune infiltration as well as drug sensitivity. Furthermore, we developed a nomogram that combined the LRPM and tumor clinical stage to quickly assess the prognosis of TNBC patients. Our study sheds light on the significant role of lactate in TNBC.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Dataset Source and Preprocessing\u003c/h2\u003e\n \u003cp\u003eRNA transcriptome sequencing data and the corresponding clinical information of breast cancers (BRCA) were acquired from The Cancer Genome Atlas (TCGA) via USCS Xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003c/span\u003e) in accordance with the website\u0026rsquo;s guidance. To select TNBC samples from BRCA, patients who were immunohistochemically negative for ER, PR and HER2 were included, resulting in 116 patients being enrolled in the TCGA-TNBC cohort. Additionally, RNA sequencing data, somatic mutation profile and clinical information of 298 TNBC patients treated at the Department of Breast Surgery at Fudan University Shanghai Cancer Center (FUSCC), designated the FUSCC-TNBC cohort, were downloaded from the National Omics Data Encyclopedia (NODE) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.biosino.org/node/\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eproject/detail/OEP000155)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. \u003cstrong\u003eTable\u0026nbsp;1\u003c/strong\u003e displays relevant grouping information and clinicopathological characteristics. The METABRIC dataset were downloaded from the cBioPortal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbioportal.org/\u003c/span\u003e\u003c/span\u003e) following the website\u0026rsquo;s guidance. The GSE76250 and GSE18864 cohorts were downloaded from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e) according to the website\u0026rsquo;s guidance.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Collection of LRGs\u003c/h2\u003e\n \u003cp\u003eWe obtained a total of 287 genes associated with lactate metabolism from the Molecular Signatures Database (MSigDB; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/\u003c/span\u003e\u003c/span\u003e). Nine human gene sets were included using the search keyword \u0026ldquo;lactic\u0026rdquo;, and after deleting duplicates, 287 lactate-related genes were identified .\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Analysis of LRGs in TNBC\u003c/h2\u003e\n \u003cp\u003ePrincipal component analysis (PCA) was conducted and visualized using the \u0026ldquo;FactoMineR\u0026rdquo; and \u0026ldquo;factoextra\u0026rdquo; package in R. Differences in LRG expression and lactate metabolic enzyme expression between TNBC and normal samples were analyzed using the \u0026ldquo;limma\u0026rdquo; package in R, and the results were included in \u003cstrong\u003eSupplement Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e-\u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/strong\u003e. The cut-off criterion were \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;2. Somatic mutation profiles of TNBC patients (\u003cstrong\u003eSupplement Table \u003cspan class=\"InternalRef\"\u003eS5\u003c/span\u003e-\u003cspan class=\"InternalRef\"\u003eS6\u003c/span\u003e\u003c/strong\u003e) were analyzed and visualized using Hiplot online tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hiplot.com.cn/home/index.html\u003c/span\u003e\u003c/span\u003e) according to the website\u0026rsquo;s guidance.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Clustering analysis\u003c/h2\u003e\n \u003cp\u003eHierarchical Clustering was performed using the \u0026ldquo;cluster\u0026rdquo; package in R. Survival analysis of distinct subtypes of patients was analyzed and visualized using the \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;survminer\u0026rdquo; package in R. To better understand the biological functions related to distinct clusters, Gene Ontology (GO) annotation and Gene Set Enrichment Analysis (GSEA) were performed using an online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn/\u003c/span\u003e\u003c/span\u003e). The results were included in \u003cstrong\u003eSupplement Table \u003cspan class=\"InternalRef\"\u003eS7\u003c/span\u003e-S11\u003c/strong\u003e, and some of the important results in functional enrichment analysis were visualized via Hiplot online tools.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Construction and Validation of the Lactate-Related Prognostic Model (LRPM)\u003c/h2\u003e\n \u003cp\u003eBased on log-rank analysis (\u003cstrong\u003eSupplement Table S12\u003c/strong\u003e), Five LRGs (NDUFAF3, NDUFS7, NDUFB8, CARS2, and FH) were identified. Subsequently, using the \u0026ldquo;glmnet\u0026rdquo; package in R, three LRGs (NDUFAF3, CARS2 and FH) were selected to construct the LRPM via least absolute shrinkage and selection operator (LASSO) analysis. The lactate-risk score (LRS) was calculated for each patient using the following formula : LRS\u0026thinsp;=\u0026thinsp;mRNAgene1\u0026times;coefficientsgene1\u0026thinsp;+\u0026thinsp;mRNAgene2\u0026times;coefficientsgene2\u0026thinsp;+\u0026thinsp;mRNAgene3\u0026times;coefficientsgene3. Then, the patients were divided into two groups based on the median value of LRS, and survival analysis was used to evaluate the prognostic value of this model. The performance of the model was also assessed using receiver operating characteristic (ROC) curve analysis, which was performed using the \u0026ldquo;timeROC\u0026rdquo; package in R.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6. Plasmid construction and transfection\u003c/h2\u003e\n \u003cp\u003eThe coding regions of NDUFAF3, FH and CARS2 coding regions were individually tagged with FLAG and cloned into empty loading plasmids (pSin-EF2-puro) to obtain the overexpression plasmids pSin-EF2-puro-NDUFAF3-\u003c/p\u003e\n \u003cp\u003eFLAG, pSin-EF2-puro-FH-FLAG and pSin-EF2-puro-CARS2-FLAG.\u003c/p\u003e\n \u003cp\u003eFor transient transfection, the human TNBC cell lines HCC1806 was cultured in RPMI-1640 (Invitrogen) medium supplemented with 8% foetal bovine serum (FBS, Gibico). HCC1806 cells were transfected with the overexpression plasminds using Neofect (Cat#TF20121201) and harvested for RT-qPCR to determine the efficiency of gene exoressioon.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7. RNA extraction and RT-qPCR\u003c/h2\u003e\n \u003cp\u003eTotal RNA was extracted from TNBC cells using RNA-Quick Purification Kit (ESscience). Reverse transcription was performed using the Reverse Transcriptase Kit (Promega). Quantitative PCR reaction were conducted on a LightCycler 480 System (Roche) with SYBR qPCR Master mix (Vazyme). Relative gene expression was normalized to GAPDH expression. The primer sequences were as follows:\u003c/p\u003e\n \u003cp\u003eNDUFAF3 forward:5`-GCTTTTCCCTCTTCTGGTTGCTG-3`\u003c/p\u003e\n \u003cp\u003eNDUFAF3 reverse:5`-GTTGAAGGTGGCACAGGCATTG-3`\u003c/p\u003e\n \u003cp\u003eFH forward:5`-CCGCTGAAGTAAACCAGGATTATG-3`\u003c/p\u003e\n \u003cp\u003eFH reverse:5`-ATCCAGTCTGCCATACCACGAG-3`\u003c/p\u003e\n \u003cp\u003eCARS2 forward:5`-CGAGAAGTCCTGCTGTGTTTGG-3`\u003c/p\u003e\n \u003cp\u003eCARS2 reverse:5`-ACCACACCATGCAAGGTAGCCT-3`\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8. Cell proliferation assays\u003c/h2\u003e\n \u003cp\u003eFor CCK8 assays, HCC1806 cells were seeded into 96-wells plates at a density of 1000 cells per well. A volume of 10ul of Cell Counting Kit-8 (CCK8,TargetMol) was added to each well on the indicated days and incubation at 37℃for 2h. Then, the absorbance at 450nm was detected using a spectrophotometer. For colony formation assay, single-cell suspensions were seeded into 6-well plates (800 cells per well) and then incubation 14 days. The plates were fixed with methanol for 30min and then stained with crystal violet for 1h. Colonies containing more than 50 cells were counted using ImageJ.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.9. Establishing and Assessing of a Nomogram\u003c/h2\u003e\n \u003cp\u003eTo predict the 1-, 3-, 5-year survival rate of TNBC patients, we constructed a nomogram based on LRS and tumour clinical stage using the \u0026ldquo;regplot\u0026rdquo; package in R. The calibration curve was used to estimate the consistency between the predicted survival and actual survival, using the \u0026ldquo;calibrate\u0026rdquo; package in R. To evaluate the specificity and sensitivity of the nomogram, the time-dependent ROC curves and the decision cure analysis (DCA) were performed, using \u0026ldquo;timeROC\u0026rdquo;, and \u0026ldquo;ggDCA\u0026rdquo; package in R respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e2.10. Tumor microenvironment analysis\u003c/h2\u003e\n \u003cp\u003eWe investigated the immune infiltration of distinct breast cancer subtypes using the EPIC and TIMER algorithms. The xCell algorithms were applied to discriminate immune cell phenotypes in two different risk groups, and the results were listed in \u003cstrong\u003eSupplement Table S13-S15\u003c/strong\u003e. Additionally, we calculated the ESTIMATE score, which can indicate stromal cell and immune cell infiltration profiles, using the \u0026ldquo;estimate\u0026rdquo; package (\u003cstrong\u003eSupplement Table S16\u003c/strong\u003e) and visualized the results using the \u0026ldquo;ggplot\u0026rdquo; package. Furthermore, we calculate TIED score, MSI score, Dysfunction score and Exclusion score in Tumor Immune Dysfunction and Exclusion (TIDE) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/login/\u003c/span\u003e\u003c/span\u003e) to evaluate the potential response to immunotherapy, and the results were listed in \u003cstrong\u003eSupplement Table S17-18\u003c/strong\u003e. Finally, we conducted a correlation analysis among the expression of immune-related genes, LRSs and the expression of three PLGs, using the cor R package. The results were listed in \u003cstrong\u003eSupplement Table S19\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e2.11. Drug sensitivity analysis\u003c/h2\u003e\n \u003cp\u003eWe predicted the semi-inhibitory concentration (IC50) values of 198 drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database using the \u0026ldquo;OncoPredict\u0026rdquo; package in R (\u003cstrong\u003eSupplement Table S20\u003c/strong\u003e). We conducted correlation analysis to clarify the relationship between drug sensitivity and LRS, as well as comparing the differences of IC50 values in two different lactate-risk groups.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e2.12. Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses were conducted using R v4.2.2 software. Comparisons between two groups were calculated uisng the unpaired Student\u0026rsquo;s t-test with two-sided \u003cem\u003ep\u003c/em\u003e-values less than 0.05 considered statistically significant. The \u003cem\u003ep\u003c/em\u003e-value in multiple analyses was corrected using the False discovery rate (FDR). Correlation analyses were conducted by Pearson correlation test.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Dysregulation of LRGs in TNBC\u003c/h2\u003e \u003cp\u003eWe first extracted 287 LRGs from the MSigDB and then explored the correlation between LRGs mRNA expression levels and lactate metabolomic abundance in the FUSCC-TNBC cohort (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The results showed that three LRGs had positive correlations (correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.2) with lactate metabolomic abundance, while three LRGs had negative correlations (correlation coefficient\u0026lt; -0.2), reflecting the complexity of the lactate metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). On the other hand, the PCA demonstrated the significant regulatory effects of LRGs and lactate metabolomic abundance in TNBC. It was feasible to discriminate TNBC from normal samples in the TCGA cohort based on the mRNA expression of LRGs, as well as in the FUSCC cohort based on the lactate metabolomic abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). We further explored the LRGs expression profiles in the TCGA-TNBC cohort. There were 48 LRGs that showed significant differential expression ( \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026ge;1), with 35 being upregulated and 13 being downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). In addition, we determined whether the mRNA expression levels of the lactate metabolic enzymes were dysregulated in TNBC. The results from three individual datasets (TCGA, METABRIC, GSE76250) showed that most lactate metabolic enzymes were upregulated in TNBC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Furthermore, somatic mutation profiles of TNBC patients in the TCGA and FUSCC cohorts were analyzed and visualized. The results showed that both TNBC cohorts had low mutation rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Identification of TNBC Subtypes and Their Biological Functions\u003c/h2\u003e \u003cp\u003eTo better clarify the lactate metabolism characteristics in TNBC, we divided a total of 258 TNBC patients from the FUSCC-TNBC cohort into subgroup A (n\u0026thinsp;=\u0026thinsp;189) and subgroup B (n\u0026thinsp;=\u0026thinsp;69) using the hierarchical clustering algorithm based on the expression of LRGs. The LRGs expression profiles of the two subgroups were illustrated in a heatmap which showed significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Survival analysis revealed that patients in subgroup A had a worse prognosis in the previous 5 years than those in subgroup B (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In order to explore the heterogeneity of two subgroups, we performed functional enrichment analysis after identifying the differentially expressed genes (DEGs). We found that the cluster-related DEGs were mainly enriched in biological processes linked to immune response processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Moreover, GSEA results suggested that the subgroup A was significantly associated with several cancer-related pathways, which could potentially account for the varied clinical outcomes among the different lactate subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). We further used the TIMER and EPIC algorithms to calculate the immune cell infiltration score. We found that the two clusters had distinct immune phenotypes, with the subgroup A exhibiting a significantly higher abundance of most immune cells compared to the subgroup B (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F). As a result, subgroup B, which had a higher expression of LRGs, could be considered an immune-desert phenotype. Building on these findings, we suggested that the lactate may have a crucial role to play in the immune microenvironment remodeling process in TNBC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Identification of LRGs and Their Biological Functions\u003c/h2\u003e \u003cp\u003eAs patients with different lactate metabolic profiles showed different immune statuses and prognoses, we believed it was necessary to establish a LRPM to assist in determining TNBC clinical treatment strategies. We identified five PLGs using log-rank analysis and performed LASSO to establish a LRPM composed of three PLGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Survival analyses indicated that patients with low expressions of the above three PLGs had poor prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). To evaluate the three PLGs protein expression levels, we utilized a public database called the Human protein Atlas. The results showed that NDUFAF3 and FH were downregulated in breast cancers compared to normal tissues, while CARS2 showed no significant difference (\u003cb\u003eSupplementary Fig.\u0026nbsp;1A\u003c/b\u003e). The protein expression profile in TNBC need further exploration.\u003c/p\u003e \u003cp\u003eSince the three PLGs were downregulated in TNBC, we investigated whether they could inhibit TNBC cell proliferation. Overexpression of NDUFAF3, FH, and CARS2 in HCC1806 substantially decreased cell proliferation and the colony formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D, \u003cb\u003eSupplementary Fig.\u0026nbsp;1B\u003c/b\u003e). These results suggested that the downregulation of NDUFAF3, FH, and CARS2 played important role in TNBC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Establishment of LRPM in TCGA Cohort and Validation in FUSCC Cohort\u003c/h2\u003e \u003cp\u003eSubsequently, we calculated the LRS for each patient based on the expression of the three PLGs (NDUFAF3, FH, CARS), and the patients were categorized into the high-risk group and the low-risk group by utilizing the median LRS as the dividing point. As expected, the high-risk group showed higher lactate abundance (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, patients with high LRS scores had shorter overall survival (OS) times than those with low LRS scores. Meanwhile, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, the LRS plot and survival status plot demonstrated that the number of TNBC patients with dead status gradually increased with increasing LRS, and the heatmap revealed that the three PLGs were significantly downregulated in the high-risk group. Additionally, the time-dependent ROC analyses showed that the areas under the curve (AUCs) value were 0.668, 0.707, 0.777, 0.647 for 1-, 3-, 5- and 10-year survival, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eTo further validate the performance of the LRPM in different populations, we designated the FUSCC-TNBC cohort as the validation cohort. We calculated the LRS of each patient using the same formula and separated TNBC patients into the high-risk group and the low-risk group using the median LRS as the cut-off point. Similarly, TNBC patients with high-risk scores had poorer OS than those with low-risk scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The LRS, survival status and expression levels of the three PLGs of every TNBC patient were displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE. Moreover, the AUCs were 0.979, 0.613, 0.583, 0.797 for 1-, 3-, 5- and 10-year survival, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). The above results indicate an excellent predictive performance of the LPRM in TNBC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Construction and Assessment of a Clinical Nomogram\u003c/h2\u003e \u003cp\u003eWe then performed multivariate COX analysis combining the LRS with clinical characteristics, including age and tumor clinical stage. The results showed that LRS acted as a prognostic risk factor for TNBC independent of clinical characteristics (\u003cb\u003eSupplementary Fig.\u0026nbsp;3A\u003c/b\u003e). Additionally, we explored the relationships between the LRS and clinical characteristics of TNBC patients and noted that patients over 55 years old and those with advanced tumor stages had a higher probability of being categorized as the high-risk group (\u003cb\u003eSupplementary Fig.\u0026nbsp;3B-C\u003c/b\u003e). Furthermore, our analysis revealed that the high-risk group patients were more likely to suffer from breast cancer with advanced N stage, indicating more lymph node metastases (\u003cb\u003eSupplementary Fig.\u0026nbsp;3D\u003c/b\u003e). However, no significant differences were observed between patients stratified by the T stage and M stage (data not shown). Moreover, we discovered that LRS was linked to the response to neoadjuvant chemotherapy of TNBC patients in GSE18864 cohort (\u003cb\u003eSupplementary Fig.\u0026nbsp;3E\u003c/b\u003e), patients in the high-risk group were more likely to have chemotherapy no-response, as indicated by the miller-payne response grade of 0\u0026ndash;1. Overall, these results demonstrate that LRS acts as an independent risk factor in TNBC and suggest that the LRS can be used to construct a clinical nomogram to help predict the prognosis of TNBC patients.\u003c/p\u003e \u003cp\u003eFor improve the clinical application of LRS, we developed a nomogram to predict 1-year, 3-year and 5-year OS by integrating LRS and tumor clinical stage in the TCGA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). To internally validate the nomogram, we conducted a calibration plot. The results demonstrated outstanding consistency between the predicted values generated by the nomogram and the actual 1-, 3-, and 5-year OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). In addition, we evaluated the predictive efficiency of the nomogram by calculating the AUCs for 1-, 3- and 5-year OS. The result showed that the nomogram had higher efficiency than other clinical factors such as age, tumor clinical stage and LRS, with AUCs of 0.83, 0.88 and 0.75,respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Moreover, we conducted decision curve analysis (DCA) to evaluate the predicted performance of age, tumor clinical stage, LRS and nomogram. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, the nomogram exhibited a better clinical benefit. Overall, these results indicate that the nomogram has high accuracy for predicting the prognosis of TNBC patients and may aid clinical management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Associations between Lactate-Risk Score and Immune Infiltration\u003c/h2\u003e \u003cp\u003ePrevious studies have highlighted the significant role of lactate in the remodelling of the tumor immune microenvironment. To explore the relationship between LRS and immune infiltration, we performed GSEA analysis using the LRS-correlated genes and calculated the immune cell infiltrate score by utilizing the xCell algorithm. Our findings revealed that signaling pathways involved in the tumor immunosuppressive microenvironment and inflammatory response were significantly enriched in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). In addition, the infiltration of CD4\u003csup\u003e+\u003c/sup\u003e T cells notably decreased in TNBC of the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Notably, our results also indicated that LRS was significantly correlated with the modulation of the tumor microenvironment modulation, such as angiogenesis, myogenesis and epithelial mesenchymal transition (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Correspondingly, the infiltration of cancer- associated fibroblasts and hematopoietic stem cells showed a meaningful increase in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). In addition, it was observed that the high-risk group displayed a greater degree of stromal score and ESTIMATE score, but a reduced level of tumor purity in comparison to the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Our finding suggested that lactate is a crucial factor in remodeling immune microenvironment within the tumor.\u003c/p\u003e \u003cp\u003eTo further investigate the relationship between LRS and ICI response, we calculated the TIDE score online and found that the high-risk group in FUSCC-TNBC cohort showed a raised TIED score, Dysfunction score and Exclusion score, but no significant difference in microsatellite instability (MSI) score (\u003cb\u003eSupplementary Fig.\u0026nbsp;4A\u003c/b\u003e). Similarly, the high-risk group in TCGA-TNBC cohort displayed a greater degree of Dysfunction scores, but lower microsatellite instability (MSI) scores (\u003cb\u003eSupplementary Fig.\u0026nbsp;4B\u003c/b\u003e). These differences imply that individuals in the high-risk group may not respond well to ICI therapy and that LRS could be utilized as a predictive marker of immunotherapy responsiveness. Additionally, we examined the relationship between immune-related genes\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e expression and LRS, as well as three PLGs expression (\u003cb\u003eSupplementary Fig.\u0026nbsp;4C\u003c/b\u003e). We observed no significant correlation between LRS/PLGs and classic checkpoint molecules, except for FH which was negatively correlated with some immune-inhibitory molecules such as VEGFE and TGFB1. This implies that FH may act as a hub gene for predicting immunotherapy response in TNBC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Associations between Lactate-Risk Score and Drug Susceptibility\u003c/h2\u003e \u003cp\u003eTo investigate whether there was correlation between LRS and drug sensitivity, we utilized the \u0026ldquo;OncoPredict\u0026rdquo; package to predict the IC50 values of 198 drugs in the Drug Sensitivity in Cancer (GDSC) database. We observed a significant correlation between the LRS and several common anticancer drugs, including Fluorouracil, Alpelisib, Fulvestrant and Olaparib (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). Furthermore, we compared the IC50 values between the two risk groups. The reslust showed that the the low-isk group exhibited enhanced sensitivity to several common chemotherapeutic drugs for TNBC treatment, such as Cisplatin, Docetaxel, Gemcitabine and Paclitaxel (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). These findings suggest that LRS can help identify suitable patients for appropriate therapy and provide a practical tool for TNBC treatment decision-marking.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBreast cancer is generally considered a poorly immunogenic malignancy\u003csup\u003e14\u003c/sup\u003e, but this is no always the case. Considerable evidence shows that compared to other subtypes of breast cancer, TNBC has a higher tumor mutation burden (TMB), frequent copy number changes (CNS) and more genetic instability\u003csup\u003e15\u003c/sup\u003e. As a result, TNBC should be a leading candidate for potential breast cancer immunotherapies\u003csup\u003e16\u003c/sup\u003e. A growing number of clinical trials aims to explore the role of immunotherapy in TNBC, but inconsistent results have been observed following different therapy strategies and tumor-stage patients\u003csup\u003e17\u003c/sup\u003e. The results of IMpassion130 study shows that atezolizumab combined with paclitaxel prolongs progression-free survival (PFS) in PD-L1-positive metastatic TNBC patients\u003csup\u003e18\u003c/sup\u003e. Additionally, the KEYNOTE-355 study in metastatic TNBC patients demonstrates a meaningful improvement in PFS with pembrolizumab combined chemotherapy versus placebo combined chemotherapy\u003csup\u003e19\u003c/sup\u003e. However, the KEYNOTE-119 study reveals that the pembrolizumab does not significantly improve overall survival (OS) in metastatic TNBC patients\u003csup\u003e20\u003c/sup\u003e. Since the efficacy of single-agent immune therapy in TNBC is low and varies among individuals\u003csup\u003e17\u003c/sup\u003e, identifying novel biomarkers that can predict immunotherapy response in TNBC is a high priority for clinical development.\u003c/p\u003e \u003cp\u003eLactate plays an important role in various diseases including tumor\u003csup\u003e21\u003c/sup\u003e. The main feature of metabolic reprogramming is aerobic glycolysis and accumulation of lactate in tumor microenvironment, which plays an essential role in the oncogenesis and immunosupression\u003csup\u003e22\u003c/sup\u003e. The LRGs which abnormally expressed in TNBC were able to discriminated TNBC from normal samples, and the lactate metabolic enzymes (such as SLC2A1, SLC16A1 and GAPDH) were upregulated in TNBC compared to normal samples. This indicates the existence of lactate metabolic reprogramming in TNBC. We then divided TNBC patients into two subtypes based on the expression of LRGs. The glycolysis signaling pathway was significantly enriched in cluster B, which had lower proportion of B cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells and CD8\u003csup\u003e+\u003c/sup\u003e T cells. These findings suggest the link between lactate and immunity signature, and LRGs might server as a novel biomarker for predicting response to immunotherapy in TNBC. In addition, previous reports have demonstrated that the gene signature based on LRGs has a robust and effective predictability in Kidney Renal Clear Cell Carcinoma\u003csup\u003e23\u003c/sup\u003e, Hepatocellular Carcinoma\u003csup\u003e24\u003c/sup\u003e and Lung Adenocarcinoma\u003csup\u003e25\u003c/sup\u003e. Here, we constructed the first LRPM in TNBC.\u003c/p\u003e \u003cp\u003eThree PLGs, including NDUFAF3, CARS2 and FH, were identified and used to construct the LPRM. NDUFAF3 (NADH: ubiquinone oxidoreductase complex assembly factor 3) is a mitochondrial respiratory chain complex I assembly protein. Mitochondrial complex I deficiency is the most common disorder of the oxidative phosphorylation system, and the deficiency of the NDUFAF3 has been identified as a cause of several mitochondrial disease\u003csup\u003e26,27\u003c/sup\u003e. Similarly, CARS2 (Cysteinyl-tRNA Synthetase 2), a mitochondrial aminoacyl-tRNA synthetase and novel cysteine persulfide synthase, has been reported to be correlated with defects in mitochondrial function and mitochondrial translation, such as mitochondrial epileptic encephalopathy\u003csup\u003e28,29\u003c/sup\u003e. The function and specific roles of NDUFAF3 and CARS2 in cancer remain unclear. However, our study revealed that NDUFAF3 and CARS2 might act as protective factors in TNBC. FH (Fumarate Hydratase) is one of the Krebs cycle genes, and its loss-of-function mutation results in the epigenetic alterations and contributes to tumorigenesis\u003csup\u003e30\u003c/sup\u003e. Transcriptional downregulation of FH has also been found in colorectal cancer and clear cell carcinoma\u003csup\u003e31\u003c/sup\u003e. Clinically, FH-deficient renal cell carcinoma is characterised by early metastasis and poor outcome\u003csup\u003e32\u003c/sup\u003e. Consistent with previous studies, our study demonstrated that FH loss was correlated with poor outcome in TNBC patients.\u003c/p\u003e \u003cp\u003eWe found that TNBC patients with different LRS had distinct immune components. The high-risk group was characterized by a reduced ratio of antitumor immune cells but a higher proportion of TME components and ESTIMATE score. Previous studies have shown that the tumor-derived lactate is a key mediator in the tumour microenvironment. It has been reported that lactate can enter endothelial cells and stimulate the NF-kB/IL-8 pathway, promoting tumor angiogenesis\u003csup\u003e33\u003c/sup\u003e. Furthermore, cancer-associated fibroblasts can secrete lactate to modulate acquired drug resistance in a NF-kB dependent manner\u003csup\u003e34\u003c/sup\u003e. Notably, we observed a significant enrichment of inflammatory-response related signaling pathways within the high-risk group. This observation is intriguing since a recent study has indicated that lactate has the capacity to stimulate a chronic inflammatory process by triggering a series of intracellular signals in inflammatory disease\u003csup\u003e35\u003c/sup\u003e. Additionally, it has been reported that lactate, mediated by the upregulated lactate transporter SLC5A12, inhibits CD4\u003csup\u003e+\u003c/sup\u003e T cell motility in inflamed tissues, and blockade of SLC5A12 diminishes the disease severity in an arthritis model\u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUsing the TIED algorithm, we estimated the potential response of ICI therapy in two different LRS groups and found that the low LRS might exhibit sensitivity to ICI therapy. Further exploration is needed to determine the correlation between LRS and other immunotherapy in TNBC. Furthermore, our research revealed that patients with a high-risk score displayed elevated IC50 values of common chemotherapeutic drugs, including Cisplatin, Docetaxel, Gemcitabine and Paclitaxel. These findings suggest that LRS could potentially serve as a marker for predicting chemotherapy and immunotherapy response.\u003c/p\u003e \u003cp\u003eIn summarize, we identified three independent PLGs and constructed a novel LRPM for TNBC patients. The LRPM in our study shows promise in improving prognostic prediction accuracy and the ability to assess drug therapy response.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have been very appreciative of the Department of Breast Surgery of Fudan University Shanghai Cancer Center (FUSCC) for their public data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, X.X and Y.L; data curation, S.H and L.W; formal analysis, S.H and L.W; funding acquistion, X.X; methodology, S.H and Y.Q; project administration Y.X and H.W; supervision, X.X and Y.L; visualization, S.H; writing-orginal draft, S.H and L.W; writing-review and editing, Y.L and X.X; All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis\u0026nbsp;research\u0026nbsp;was\u0026nbsp;supported\u0026nbsp;by\u0026nbsp;National\u0026nbsp;Natural\u0026nbsp;Science\u0026nbsp;Foundation\u0026nbsp;of\u0026nbsp;China\u0026nbsp;(No.81974444,\u0026nbsp;Xinhua\u0026nbsp;Xie).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data can be obtained from the pubic database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGiaquinto AN, Sung H, Miller KD, et al. 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Published 2022 Jan 3. doi:10.1080/2162402X.2021.2020984\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-research-and-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jocr","sideBox":"Learn more about [Journal of Cancer Research and Clinical Oncology](https://www.springer.com/journal/432)","snPcode":"432","submissionUrl":"https://submission.nature.com/new-submission/432/3","title":"Journal of Cancer Research and Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"triple-negative breast cancer, lactate, prognostic model, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-3037116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3037116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTriple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer that exhibits elevated glycolytic capacity. Lactate, as a byproduct of glycolysis, is considered a major oncometabolite that plays an important role in oncogenesis and remodeling of the tumor microenvironment. However, the potential roles of lactate in TNBC are not yet fully understood. In this study, our goal was to identify prognosis-related lactate genes (PLGs) and construct a lactate-related prognostic model (LRPM) for TNBC.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFirst, we applied lactate-related genes to classify TNBC samples using hierarchical clustering algorithm. Then, we performed the log-rank analysis and the least absolute shrinkage and selection operator (LASSO) analysis to screen PLGs and construct the LRPM. The biological functions of the identified PLGs in TNBC were inverstigated using CCK8 assay and clone formation assay. Finally, we constructed a nomogram based on the lactate-risk score (LRS) and tumor clinical stage. We used operating characteristic (ROC) curve and decision curve analysis (DCA) to evaluate the predictive capability of the nomogram.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur results showed that the TNBC samples could be classified into two subgroups with different survival probabilities. Three genes (NDUFAF3, CARS2 and FH), which can suppress TNBC cell proliferation, were identified as PLGs. Moreover, the LRPM and nomogram exhibited excellent predictive performance for TNBC patient prognosis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003ewe have developed a novel LRPM that enables risk stratification and identification of poor molecular subtypes in TNBC patients, showing great potential in clinical practice.\u003c/p\u003e","manuscriptTitle":"Identification of Lactate-Related Subgroups and Prognostic Model in Triple-Negative Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-12 14:44:10","doi":"10.21203/rs.3.rs-3037116/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2023-07-09T13:37:09+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-06-11T11:27:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-06-11T11:08:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-06-08T13:09:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cancer Research and Clinical Oncology","date":"2023-06-08T06:06:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-research-and-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jocr","sideBox":"Learn more about [Journal of Cancer Research and Clinical Oncology](https://www.springer.com/journal/432)","snPcode":"432","submissionUrl":"https://submission.nature.com/new-submission/432/3","title":"Journal of Cancer Research and Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"34a0d7d4-e7ec-4890-a468-27505b837b0c","owner":[],"postedDate":"June 12th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T21:58:53+00:00","versionOfRecord":{"articleIdentity":"rs-3037116","link":"https://doi.org/10.1007/s00432-023-05171-6","journal":{"identity":"journal-of-cancer-research-and-clinical-oncology","isVorOnly":false,"title":"Journal of Cancer Research and Clinical Oncology"},"publishedOn":"2023-07-20 21:40:17","publishedOnDateReadable":"July 20th, 2023"},"versionCreatedAt":"2023-06-12 14:44:10","video":"","vorDoi":"10.1007/s00432-023-05171-6","vorDoiUrl":"https://doi.org/10.1007/s00432-023-05171-6","workflowStages":[]},"version":"v1","identity":"rs-3037116","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3037116","identity":"rs-3037116","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}