Bioinformatics-based analysis of amino acid metabolism-related features to predict clinical prognosis and immunotherapy response in triple-negative breast cancer

Bioinformatics-based analysis of amino acid metabolism-related features to predict clinical prognosis and immunotherapy response 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 Article Bioinformatics-based analysis of amino acid metabolism-related features to predict clinical prognosis and immunotherapy response in triple-negative breast cancer Yifan Zheng, Lin Li, Bing Lin, Yongxia Yang, Yongcheng Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3888711/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer associated with poor prognosis. In addition to the Warburg effect, amino acids and metabolites affect tumor development, are involved in modulating the tumor immune microenvironment (TME) and regulating the anti-tumor immune response. However, the relationship between amino acid metabolism and the clinical prognosis and immunotherapeutic response of triple negative breast cancer are still indistinct. We established a risk signature consisting of 12 genes by differential Analysis, univariate COX regression analysis and LASSO-COX analysis. The GEO cohort confirmed the validity of the risk signature. We used single-sample genomic enrichment analysis (ssGSEA), tumor mutation burden (TMB), and IC50 values of drugs to discover the relationship between the risk signature, immune status, and drug sensitivity in TNBC. We also verified the expression of the risk signature gene ALDH4A1 in tissues and cells by qPCR assay, and migration assay verified its role in TNBC cell invasion. Our study may provide new insights into amino acid metabolic therapy for the treatment of TNBC patients. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics triple-negative breast cancer amino acid metabolism immunity risk signature immunotherapy responses Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Triple-negative breast cancer (TNBC) is a type of breast cancer that does not express estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (Her-2) and accounts for approximately 15–20% of all breast cancers[ 1 ]. Histologically, TNBC is not significantly different from other types of breast cancer, all of which may present as invasive ductal carcinoma, lobular carcinoma, and medullary carcinoma. However, TNBC has a higher degree of malignancy, short survival, and higher recurrence rate than other types of breast cancer[ 2 ]. Due to the lack of these receptor targets, traditional endocrine therapy and targeted therapy are ineffective against TNBC, and chemotherapy has become the main treatment. Although with the development of immunotherapy, PARP inhibitors and PD-1/PD-L1 inhibitors for TNBC have achieved some clinical results[ 3 ]. However, precise therapeutic strategies for TNBC to bring more effective treatment options and better clinical prognosis are still the current research hotspots. Amino acid metabolic therapy is an emerging field in tumor therapy, which uses amino acids as targets of metabolic pathways to interfere with the metabolic processes of tumor cells and limit their metabolic demands to achieve therapeutic effects[ 4 ]. In contrast to normal cells, tumor cells take up large amounts of glucose and glutamine, also require carbon and nitrogen sources from amino acids other than glutamine, the most critical of which are essential amino acids that cannot be synthesized by the human body[ 5 ]. Tumor cells often exhibit the important feature of high uptake of essential amino acids, however, the mechanisms of how rapidly essential amino acids are transported and how essential amino acid metabolism contributes to tumor progression are unknown. L-asparaginase from Escherichia coli and Erwinia chrysanthemi , which hydrolyzes plasma L-asparagine and L-glutamine to inhibit cancer cell growth, is the only currently approved drug targeting amino acid metabolism for the treatment of acute lymphoblastic leukemia and lymphosarcoma[ 6 ]. For argininosuccinate synthase (ASS)-deficient tumors that are extremely dependent on extracellular arginine for their own needs, ADI-PEG20's arginine depletion strategy hydrolyzes plasma arginine to citrulline, which on the one hand depletes arginine to inhibit tumor growth and on the other hand allows citrulline to be used by normal cells to be converted to arginine unaffected. In contrast, BCT-100 converts arginine into ornithine and urea to exert similar anti-cancer effects[ 7 ]. Their efficacy has been confirmed in clinical trials. Tumor cells use SLC7A11 to obtain cystine from the environment for subsequent protein and glutathione synthesis. Whereas erastin and sorafenib block the function of SLC6A6, neither is specific and more selective inhibitors are needed to benefit patients[ 8 ]. While other amino acid metabolic targets such as IDO1, TDO2, PYCR1, GLDC, and SHMT2 are considered as potential therapeutic targets, but are still in preclinical studies[ 9 – 12 ]. However, studies applying targeted amino acid metabolic therapies to the treatment of triple-negative breast cancer are still relatively few, and the reprogramming of amino acid metabolism in triple-negative breast cancer is not yet known. In this study, we performed a systematic study to determine identify the expression of AAMRGs in normal and TNBC tissues. DEAAMRGs were also identified. Prognosis related DEAAMRGs were used to develop a prognostic model. The predictive ability of the model was confirmed by the GEO data. The model was used to divide TNBC patients into two subgroups with high and low risk, and the difference in immune microenvironment between the two groups was investigated, demonstrating that AAMRGs were associated with the immune microenvironment of TNBC. In addition, we screened that ALDH4A1 may play a potential role in the pathogenesis of TNBC. We verified the expression of ALDH4A1 in the collected clinical samples and cell lines. We also interfered with the expression of ALDH4A1 in human breast cancer cell lines MDA-MB-231 and BT549 by siRNA transfection, and verified the role of ALDH4A1 in TNBC by migration assay. Materials and Methods Data collection. The clinical information and the mRNA expression of 112 TNBC sample and 98 normal sample were obtained from the TCGA database (https://portal.gdc.cancer.gov/), and another 107 TNBC sample from the GEO cohort (GSE103091) were used as the verification set. We screened out human amino acid metabolism pathways and corresponding 372 genes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) (Supplementary Figures S1, S2). The data analysis flowchart is shown in Figure 1. Construction and Validation of the risk signature. The ‘DESeq2’ package was used to identify the differential expression genes (DEGs). | Log2FoldChange | >1 and p-adjust < 0.05 DEGs were considered statistically significant. Venn online software (http://bioinformatics.psb.ugent.be/webtools/Venn/) was then used to obtain DEAAMRGs in DEGs and AAMRGs. Based on the result of DEAAMRGs, univariate Cox analysis was used to select the genes significantly involved in prognosis risk. Then the genes were subjected to least absolute shrinkage and selection operator (LASSO) regression algorithm for feature selection, 10-fold cross-validation was used, and the R package ‘glmnet’ was used for the analysis. The risk signature consists of twelve genes and the risk score per each patient was calculated as: risk score = (0.5316 * expression value of SDS) − (1.9759 * expression value of SMYD3 ) + (0.7044 * expression value of PRDM16 ) − (0.1056 * expression value of PSAT1 ) + (0.1282 * expression value of ALDH4A1 ) − (0.1695 * expression value of GLDC ) − (0.0329 * expression value of MAT1A ) + (0.5444 * expression value of TH ) + (0.3893 * expression value of TDO2 ) + (0.3195 * expression value of GLYCTK ) − (0.3035 * expression value of GPT ) − (0.5323 * expression value of AMD1 ). The patients were classified into a high-risk group and a low-risk group via the upper tri-sectional quantile value. Independent prognostic analysis of risk characteristics and construction of nomogram We identified all independent prognostic factors by univariate and multivariate Cox regression analysis using the "survival" and "forestplot" R packages. Thereafter, we built a nomograms based on the results of multivariate regression analysis using the "rms" and "survivor" R packages, and also calculated the coordination index (C-index) of the prediction model and plotted calibration curves to assess the probability of OS at 1, 3, and 5 years. and 5-year survival probabilities. Tumor mutation burden analyses Tumor mutation burden (TMB) files with somatic mutation information were obtained from TCGA. TMB level differences between the two risk subgroups were estimated and shown using the "maftools", "ggpubr" and "ggplot2" R packages. The "limma", "ggpubr", "ggplot2" and "ggExtra" R packages were used to assess and show the correlation between risk scores and TMB scores. kaplan-Meier (KM) analysis was applied to analyze the differences in survival between groups with different TMB levels as well as risk status subgroups. The "Survival" and "survminer" R packages were used in this procedure. Analysis of tumor immune microenvironment To assess the immune activity, we used the ESTIMATE method to determine the immune score of each TNBC sample. Also, we used the single sample gene set enrichment analysis (ssGSEA) method to assess the activity of immune infiltrating cells as well as immune-related functions. The expression levels of 10 immune checkpoints and 19 human leukocyte antigens were also compared between the two groups. The "estimate" and "GSVA" R packages were used in this procedure. Validation of Protein Expressions The Human Protein Atlas (https://www.proteinatlas.org/) database was used to identify the expression of 10 AAMRGs in normal breast tissues and breast tumors. Single‑cell RNA‑seq analysis We collected single-cell RNA sequencing data of five primary breast cancer patients from the GSE180286 dataset. We analyzed single-cell sequencing data using the “Seurat” package. Quality control (QC) was first performed on the data by retaining cells with less than 10% mitochondrial gene content, and genes expressed in at least three cells within an expression range of 200 to 7500. We then identified highly variable genes for subsequent analysis, setting the highly variable genes number to 2000. The “Harmony” package was used to remove batch efects in the data from the five samples. We constructed cell clusters using the “FindClusters” and “FindNeighbors” functions and visualized them using the “t-SNE” method. Finally, we performed cell annotation based on the marker genes of different cell types. Cell culture and small-interfering RNA transfection The human breast cancer cell lines MCF-10A, MDA-MB-231 and BT-549 were procured from the cell bank of the Chinese Academy of Science. MDA-MB-231 and BT549 cells were cultured in high glucose DMEM medium, supplemented with 10% fetal bovine serum (FBS) and 1% Penicillin-Streptomycin Solution (P/S), in an incubator set at 37℃ with a 5% CO2 atmosphere. The sequences of siRNA targeting ALDH4A1 were cloned into MDA-MB-231 and BT-549 cells. Using GP-transfect-Mate (GenePharma, China), the siRNA transfection process was conducted as instructed by the manufacturer. The primer sequences are as follows: Table 1. Migration testing After cell transfection was completed, the serum-free cell suspension was spread evenly in the upper chamber of Transwell (Corning, USA), and DMEM medium containing 20% FBS was added to the lower chamber. After 8 hours of incubation, the upper chamber was fixed with 4% paraformaldehyde, and the cells that did not cross the polycarbonate membrane were gently scraped off with a cotton swab. After gentian violet staining and washing with PBS, cells at the bottom of the chambers were photographed in different fields of view using a microscope, and cells were counted using ImageJ. mRNA Expression Analysis We collected tumor samples from 20 triple-negative breast cancer patients and 10 adjacent normal tissues from the Department of Breast Care Surgery, The First Affiliated Hospital, Guangdong Pharmaceutical University. Total RNA was extracted from clinical samples and cells using TRIzol, after which it was reverse-transcribed into cDNA with a reverse transcription kit (Tsingke,China). The reaction conditions were set at 25℃ for 10 minutes, 50℃ for 15 minutes, and 85℃ for 5 minutes, followed by Real-time Quantitative PCR. The PCR amplification conditions were as follows: pre-denaturation at 95°C for 1 minute, denaturation at 95°C for 30 seconds, and annealing at 60°C for 20 seconds, repeated for 40 cycles. The relative expression of the target gene was calculated using the 2-△△Ct method. The primer sequences are as follows: Table 1. Statistical analysis Student's t-test was used to compare gene expression data between tumor samples and normal samples. Parametric distribution of Chi-square test or non-parametric distribution of Wilcoxon test was used for differences in proportions. All statistical analyses were performed with R software (version 4.1.2). p-values <0.05 were considered statistically significant. Results Identification of DEAAMRGs Between Normal and Tumor Tissues A total of 372 genes associated with amino acid metabolism were obtained from KEGG, and 5971 DEGs were identified. ssGSEA analysis showed large differences in amino acid metabolism in TNBC and normal tissues (Figure S1A). Then 143 DEAAMRGs were identified through Venn online tool (Figure2A). Among them, 72 AAMRGs were expressed up-regulated and 71 AAMRGs were expressed down-regulated in TNBC (Figure 2B). Uploading 143 DEAAMRGs to STRING online database constructed protein-protein interaction (PPI) network (Figure 2C). Core modules in PPI were identified using the MCODE plugin in cytoscape (Figure 2D). GO enrichment analysis showed that DEAAMRGs were enriched in biological processes such as amino acid metabolic process, alpha-amino acid metabolic process, small molecule catabolic process, cellular components such as mitochondrial matrix, peroxisome, heterochromatin and molecular function such as vitamin binding, pyridoxal phosphate binding, vitamin B6 binding (Figure 2E). KEGG enrichment analysis showed that DEAAMRGs were enriched in Tryptophan metabolism, Tyrosine metabolism, Arginine and proline metabolism pathways (Figure 2F). Construction and validation of the risk signature in the TCGA cohort In the TCGA cohort, univariate Cox analysis screened out thirteen genes related to the prognosis of TNBC ( p < 0.05) (Figure 3A). We further conducted a LASSO analysis to construct the risk signature (Figure 3B,C). The risk signature consists of twelve genes and the risk score per each patient was calculated as: risk score = (0.5316 * expression value of SDS) − (1.9759 * expression value of SMYD3 ) + (0.7044 * expression value of PRDM16 ) − (0.1056 * expression value of PSAT1 ) + (0.1282 * expression value of ALDH4A1 ) − (0.1695 * expression value of GLDC ) − (0.0329 * expression value of MAT1A ) + (0.5444 * expression value of TH ) + (0.3893 * expression value of TDO2 ) + (0.3195 * expression value of GLYCTK ) − (0.3035 * expression value of GPT ) − (0.5323 * expression value of AMD1 ).SMYD3, PSAT1, GLDC, MAT1A,GPT and AMD1 were protective factors. SDS, PRDM16, ALDH4A1, TH, TDO2 and GLYCTK were risk factors. Depending on upper tertile risk scores, we assigned TNBC patients to high-risk and low-risk subgroups (Figure 3D,E,F). A total of 107 TNBC samples were obtained from the GEO database (GSE103091) as the validation cohort. The GEO cohort was divided into high-risk and low-risk groups by the upper tertile risk scores (Figure 3G,H,I). ssGSEA results show differences in Lysine degradation, Phosphonate and phosphinate metabolism and Selenocompound metabolism between high and low risk groups (Figure S1B) In addition, the expression of 10 genes in the risk signature were validated in the HPA database (Figure S2). In the TCGA cohort, KM analysis showed that the high-risk group had a significantly worse prognosis than the low-risk group (Figure 4A). The accuracy of the risk signature was investigated by calculating the AUCs for 1, 3, and 5 years. And the AUCs for 1, 3, and 5 years were 0.934, 0.970, and 0.953, separately (Figure 4B). The AUC of the ROC curve for risk signature in the TCGA cohort was 0.893 (Figure 4C). In the GEO cohort, KM analysis showed that patients in the high-risk group had worse survival (Figure 4D). The AUCs for 1, 3, and 5 years were 0.692,0.636, and 0.604, respectively (Figure 4E). We analyzed the clinical correlation of risk signature and showed that the risk signature created were significantly correlated with pathological stage and pathological T-stage (Figure 4F-I). Independent prognostic analysis and construction of prognostic nomogram The results of the univariate Cox analysis combined with clinical factors showed that risk score, T, N, M, and stage were independent predictors (Figure 5A). In addition, multivariate Cox analysis showed that risk score were independent predictors (Figure 5B). Nomograms predicting 1-, 3-, and 5-year survival were created by incorporating stage and risk score based on the results of stepwise regression analysis (Figure 5C) and calibration graphs were drawn for 1-year, 3-year, and 5-year OS probabilities (Figure 5D). The C-index of the nomogram was 0.962. Based on the calibration curves, it is known that the predicted values of 1-year, 3-year and 5-year survival times are similar to the corresponding true survival times. These results indicate the good predictive power of this nomogram. Tumor mutation burden analyses The top ten genes in terms of mutation rate also differed greatly between the two groups (Figure 6A-D). Despite this, there is no significant correlation between TMB and subline scores (Figure 6E). To see if the risk signature or TMB was better at predicting survival, we divided the sample into high and low mutation subgroups based on the median TMB. There was no statistically significant difference between these two groups (Figure 6F). While the high-mutation-low-risk group had the highest survival rate, the low-mutation-high-risk group had the lowest survival rate (Figure 6G). This indicates that the predictive power of our model is stronger compared to TMB. Tumor microenvironment analysis The correlation between immune cell infiltration and immune function in the tumor microenvironment of two groups was assessed using the ssGSEA method. Immune function in the high-risk group Type II IFN response, Parainflammation, Type I IFN reponses, APC co stimulation, CCR, APC co inhibition, Checkpoint, T cell co -inhibition, HLA, T cell co stimulation, Cytolytic activity obtained higher ssGSEA scores (Figure 7A). Meanwhile, Macrophage, MDSC, Monocyte, Natural killer T cell, Neutrophil, Plasmacytoid dendritic cell, Regulatory T cell, T follicular helper cell, Type 1 T helper cell, among others obtained higher ssGSEA scores in the higher group (Figure 7B). In addition, we analyzed the expression of HLA genes and immune checkpoint genes in the high-risk and low-risk groups. The expression of both HLA and classical immune checkpoint genes was slightly higher in the high-risk group than in the low-risk group (Figure 7C,D), especially CTLA4, LAG3, LGALS9, SIRPA, TDO2, TIGIT, HLA-E, HLA-DRB5, HLA-DRB1, HLA-DRA, HLA-DQB1, HLA-DQA1, HLA-DPB1, HLA-DMB, and HLA-DMA were significantly higher than those in the low risk group, showing that the high-risk group showed a stronger immunosuppressive state. Therefore, TNBC patients in the high-risk group benefited more from immunotherapy. Prediction of drug sensitivity The results showed that TNBC patients in the low-risk group were more sensitive to Docetaxel, Epirubicin, Oxaliplatin, Paclitaxel and Cisplatin (Figure 8A-E). Validation of the role of ALDH4A1 in TNBC The intersection of prognosis-related genes (Figure 9A) and different gene sets screened for signature genes by LASSO analysis (Figure 9B) in the TCGA and GSE103091 datasets were all associated with one gene, ALDH4A1, implying its potentially important role in TNBC. KM curve confirmed shorter survival in the ALDH4A1 high expression group in TNBC (Figure 9C). It even plays a role in the development of breast cancer. Next, we used the GSE180286 dataset in analyzed the differences in ALDH4A1 at the single cell level. The results showed that ALDH4A1 was mainly expressed in epithelial cells. (Figure 9D-F). qPCR results showed that in our collected clinical samples, the expression level of ALDH4A1 in TNBC tissues was significantly lower than that in normal tissues adjacent to the cancer (Figure 9G). In addition, the expression level of ALDH4A1 in the TNBC cell lines MDA-MB-231 and BT-549 was also lower than that in the normal breast epithelial cell line MCF-10A (Figure 9H). To investigate the role of ALDH4A1 in TNBC, we inhibited the expression of ALDH4A1 in TNBC cell lines MDA-MB-231 and BT546 by transfection with siRNA. PCR confirmed that ALDH4A1 was successfully knocked down (Figure 9I-J). Migration results showed that inhibition of ALDH4A1 expression significantly inhibited the invasive ability of both TNBC cells (Figure 9K). These results indicated that ALDH4A1 plays an important role in TNBC. Discussion Triple-negative breast cancer, as a subtype of breast cancer, is prone to metastasis, recurrence, and poor prognosis due to the absence of ER, PR, and Her-2 receptors, rendering patients ineligible for endocrine and targeted therapies. The development of novel treatment strategies is imperative. Amino acid metabolism reprogramming has emerged as a significant discovery following the Warburg effect. During the course of tumorigenesis, tumor cells upregulate the expression of amino acid transporters and related enzymes to meet the demands for protein synthesis, energy production, nucleotide synthesis, and other processes essential for their rapid growth. Amino acid metabolism plays a closely intertwined role in the onset and progression of triple-negative breast cancer. These cells exhibit heightened metabolic activity, with distinct differences in energy metabolism and amino acid metabolism compared to normal cells, including the glutamine-glutamate cycle, arginine metabolism, and branched-chain amino acid metabolism. These alterations in metabolic pathways can impact the proliferation, metastasis, and drug resistance of triple-negative breast cancer cells. In this study, based on differential analysis and single-factor regression analysis, we have identified AAMRGs (Amino Acid Metabolism-Related Genes) associated with prognosis and established a risk feature composed of 12 AAMRGs, demonstrating outstanding predictive capabilities. These 12 AAMRGs are SDS, SMYD3, PRDM16, PSAT1, ALDH4A1, GLDC, MAT1A, TH, TDO2, GLYCTK, GPT, and AMD1. SET And MYND Domain Containing 3 (SMYD3) is a cytoplasmic lysine methyltransferase that is overexpressed in various cancers. It is considered a potential prognostic biomarker for prostate cancer, breast cancer, and colorectal cancer [ 13 – 15 ]. Studies indicate a significant upregulation of SMYD3 in breast cancer tissues, and knocking down SMYD3 can inhibit breast cancer cell proliferation. SMYD3 can also promote epithelial-mesenchymal transition (EMT) by regulating EMT-specific transcription factors and stromal genes controlled by TGF-β [ 16 ]. Histone-Lysine N-Methyltransferase PRDM16 (PRDM16) has been extensively studied in the context of brown fat decomposition. Our research aligns with previous studies as PRDM16 is significantly downregulated in kidney cancer and prostate cancer [ 17 ] compared to normal tissues. Overexpression of PRDM16 inhibits cell proliferation, migration, and invasion, while silencing PRDM16 produces the opposite effect. In kidney cancer, PRDM16 suppresses the expression of the gene encoding semaphorin 5B (SEMA5B) by inhibiting C-terminal binding proteins (CtBP1/2), and SEMA5B is a highly expressed HIF target gene in kidney cancer, promoting tumor growth [ 18 ]. Phosphoserine Aminotransferase 1 (PSAT1) is a critical enzyme in the serine synthesis pathway, catalyzing the conversion of 3-phosphohydroxypyruvate (3-PPyr) to phosphoserine (p-serine), which can be further utilized in downstream one-carbon and nucleotide metabolism. Research has indicated that PSAT1 is upregulated in triple-negative breast cancer compared to normal tissues, consistent with our findings. Loss of PSAT1 inhibits migration and invasion in triple-negative breast cancer but does not affect proliferation [ 19 ]. High PSAT1 expression is associated with poor prognosis in various cancers [ 20 ]. Mechanistically, overexpression of PSAT1 leads to the inhibition of cyclin D1 degradation, subsequently altering the activity of the Rb-E2F pathway, enhancing G1 phase progression and proliferation [ 21 ], ultimately promoting tumor progression [ 22 ]. Aldehyde Dehydrogenase Family 4 Member A1 (ALDH4A1) is part of the aldehyde dehydrogenase family and plays a role in the degradation of proline. Research on ALDH4A1 has primarily focused on cardiovascular diseases, where it is considered a potential biomarker and therapeutic target [ 23 ]. However, its role in tumorigenesis and progression remains relatively unexplored. Studies suggest that ALDH4A1 expression is reduced in colorectal cancer, leading to the accumulation of proline and supporting cell proliferation and survival [ 24 ]. Glycine decarboxylase (GLDC) acts through aminomethyl transferase to provide one-carbon units into the folate cycle after glycine cleavage. GLDC exhibits abnormal expression in tumors such as non-small cell lung cancer and ovarian cancer. In the early stages of tumorigenesis, high GLDC expression promotes one-carbon unit generation for nucleotide synthesis, driving tumor growth and correlating with increased mortality in patients [ 25 ]. Therefore, GLDC may serve as a potential therapeutic target to control tumor progression by targeting cancer stem cells. Methionine Adenosyltransferase 1A (MAT1A) is a crucial enzyme in cell metabolism, catalyzing the synthesis of the biological methyl donor S-adenosylmethionine (SAMe) by the reaction between methionine and adenosine triphosphate (ATP). MAT1A is identified as a biomarker in liver cancer, where its expression is significantly lower than in normal tissues, with levels decreasing as tumor grade increases. Silencing MAT1A results in the downregulation of dual-specificity phosphatase 1 (DUSP1), leading to a loss of control over extracellular signal-regulated kinase (ERK) signaling and promoting hepatocellular carcinoma progression [ 26 ]. However, our study indicates the opposite, with MAT1A expression levels higher in triple-negative breast cancer than in adjacent tissues, possibly linked to tumor heterogeneity. Tyrosine Hydroxylase (TH) is a gene that encodes a protein involved in the conversion of tyrosine to dopamine, playing a pivotal role in catecholamine synthesis, which is vital in the physiology of adrenergic neurons. Inhibition of the NF-κB pathway activation and reduction of TNF-α levels can upregulate TH expression [ 27 ]. Additionally, miR-375, when bound to the 3'-untranslated region of Sp1, negatively regulates TH expression [ 28 ]. Tyrosine hydroxylase uses tetrahydrobiopterin (BH4) as a cofactor to hydroxylate tyrosine, producing L-dopa and participating in amino acid metabolism pathways [ 29 ]. In a preclinical model of breast cancer, TH + sympathetic nerves are localized around the tumor, enhancing norepinephrine conversion and facilitating stress-induced tumor progression [ 30 ]. In pheochromocytoma, aberrations in the TH gene disrupt the feedback mechanism, resulting in damage to the body [ 31 ]. Tryptophan 2,3-Dioxygenase (TDO2) encodes an enzyme that plays a crucial role in the kynurenine pathway by catalyzing the first and rate-limiting step in tryptophan metabolism. Increased enzyme activity and kynurenine production may have a role in inhibiting anti-tumor immune responses in cancer [ 32 ]. TDO2 is considered the primary enzyme for tryptophan degradation, and tryptophan metabolites activate the aryl hydrocarbon receptor (AHR), enhancing tumor malignancy and suppressing anti-tumor immunity[ 9 ]. Kynurenine, a product of TDO2, can activate AHR, leading to the generation of tolerogenic dendritic cells and regulatory T cells, contributing to the tumor's immunosuppressive microenvironment[ 33 ]. Additionally, this pathway increases glycolysis, promoting cancer cell growth and CXCL5 secretion, which recruits macrophages to the tumor microenvironment[ 34 ]. In liver cancer cells, TDO2 promotes cancer cell migration and invasion through the Wnt5a pathway[ 35 ] . Adenosylmethionine Decarboxylase 1 (AMD1) encodes a critical rate-limiting enzyme for polyamine synthesis, impacting cell growth and tumorigenesis by increasing polyamine biosynthesis. Elevated intracellular polyamine levels can lead to the suppression of checkpoints that restrict growth, exerting carcinogenic effects[ 36 ]. In a murine model of neuroblastoma expressing the MYC gene, inhibiting AMD1 with the inhibitor SAM486 significantly reduces tumor incidence and extends the tumor's latency period[ 37 ]. Thus, the inhibition of the AMD1 gene may be an effective therapeutic approach for neuroblastoma. In prostate cancer, AMD1 upregulation activates mTORC1, which subsequently reinforces the metabolic program required for maintaining cancer cell growth and proliferation[ 38 ]. However, at present, there is no existing literature that precisely elucidates the relationship between the AAMRGs Serine Dehydratase (SDS), Glycerate Kinase (GLYCTK), and Glutamate-pyruvate Transaminase (GPT) and their association with tumor prognosis and immune evasion. In conclusion, the genes in the risk profile model we constructed may be involved in the development of triple-negative breast cancer, which provides a certain reference for further research on amino acid metabolic therapy. However, our study has some limitations, and further experiments are still needed to verify the role of these risk profile genes, especially ALDH4A1, in triple-negative breast cancer. Conclusion We constructed an amino acid metabolism risk profile consisting of 12 genes, which demonstrated excellent predictive ability in predicting the prognosis of triple-negative breast cancer. This work investigated the link between the immune microenvironment and amino acid metabolism. Further, we identified ALDH4A1 as a possible key gene involved in the reprogramming of amino acid metabolism in triple-negative breast cancer, and verified its role in invasive ability by migration experiments. Abbreviations BC- Breast cancer (BC) DEGs—Differentially Expressed Genes GO—Gene Ontology GSVA—Gene Set Variation Analysis KEGG—Kyoto Encyclopedia of Genes and Genomes OS—Overall Survival PCA—Principal Component Analysis ssGESA—single sample Gene Set Enrichment Analysis Declarations Acknowledgments We sincerely acknowledge TCGA database for providing their platforms and contributors for uploading their meaningful datasets. Ethics Statement Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Data will be made available on request. Funding This work was supported by the National Natural Science Foundation of China (No.22074024), Natural Science Foundation of Guangdong Province (No. 2023A1515012573), National Undergraduate Training Program for Innovation and Entrepreneurship & Student Research Training Program (No. 202310573005). Author Contributions Conception and design: WBH, RXZ. Development of methodology: YFZ, LL, BL, YCZ and YXY. Acquisition of data: YFZ and WBH. Analysis and interpretation of data (e.g., statistical analysis, bioinformatic, computational analysis): YFZ, LL and YFL. Writing, review, and/or revision of the manuscript: YFZ, LL, BL, YXY and YFL. 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Carracedo, mTORC1-dependent AMD1 regulation sustains polyamine metabolism in prostate cancer, Nature, 547 (2017) 109–113. Tables Table 1-1. The sequences of siRNA targeting ALDH4A1are as follows: Gene Sequence si1-ALDH4A1 sense-GGGUAAGACCGUGAUCCAATT antisense-UUGGAUCACGGUCUUACCCTT si2-ALDH4A1 sense- CCCAGAACCUGGACCGGUUTT antisense- AACCGGUCCAGGUUCUGGGTT Table 1-2. The primer sequences of ALDH4A1 are as follows: Gene Primer sequence ALDH4A1 F-CAGGGTAAGACCGTGATCCAA R- CCAGCTCCACCGCATACTTG Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif Figure S1. Analysis of pathways related to amino acid metabolism. FigureS2.tif Figure S2. Immunohistochemical results of ten signature genes in the HPA database in breast cancer tissues and normal tissues. 07Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3888711","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":273932141,"identity":"4dac5291-1ec4-474a-bff0-af54f918904f","order_by":0,"name":"Yifan Zheng","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Zheng","suffix":""},{"id":273932142,"identity":"e190fbb2-88a5-4062-8032-a96ad90740f8","order_by":1,"name":"Lin Li","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Li","suffix":""},{"id":273932143,"identity":"ed23c98b-fe4f-4ced-9967-a78ebaf26d07","order_by":2,"name":"Bing Lin","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Lin","suffix":""},{"id":273932144,"identity":"5f14307d-1257-47be-ac91-578d8b105cc5","order_by":3,"name":"Yongxia Yang","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Yongxia","middleName":"","lastName":"Yang","suffix":""},{"id":273932145,"identity":"df14a77c-32eb-4bcd-a81b-ea7e523acaf4","order_by":4,"name":"Yongcheng Zhang","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Yongcheng","middleName":"","lastName":"Zhang","suffix":""},{"id":273932146,"identity":"cd5cd9d5-51f0-4a84-8bdf-f22f2f352cf8","order_by":5,"name":"Yufeng Lin","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Yufeng","middleName":"","lastName":"Lin","suffix":""},{"id":273932147,"identity":"5481ef72-73a3-4a32-97d2-8df5952de51a","order_by":6,"name":"Wenbin Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIie2Rv0oDQRCH51hIlSPtHcEFH0AYWVCEEF9lF+HSnKKNXJHiJLCVYHug+BLC1XMsaHNqZ5NCfQFZsU3hxlRCboOdxX7lMB+/+QMQCPxDEPpAthjxQzOwjS34T5Xe/ErUVG0m4AF2TdKKlSL9CjOxNqp8BkGxXingU/aTY3KdmbqYgaT0Fjm+mHeXMuY75XrloDqRlDyNxIwB0VmNIr3O0ClHYo86BpvnSHiebWkWlZTWC3U1lEuFVO1TZM9El8zFxDeo9HBiNyukzXbFek4p0aXkG1JeP2RTuiMj64NJ7pe75Kck0bNL+2i+Fu6VOGjZp526i80nd9YWY96ldIF/aw8EAoHAb74BscRriCKEG/oAAAAASUVORK5CYII=","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":true,"prefix":"","firstName":"Wenbin","middleName":"","lastName":"Huang","suffix":""},{"id":273932148,"identity":"4fa3cfa0-c3d8-4aba-9a82-0366f7c53945","order_by":7,"name":"Rongxing Zhang","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Rongxing","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-01-22 18:21:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3888711/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3888711/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51513422,"identity":"1b13e9ab-5436-459b-b652-5ed2c58900a8","added_by":"auto","created_at":"2024-02-22 21:27:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7507029,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of this study.\u003c/p\u003e","description":"","filename":"Figure01.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3888711/v1/752200074fc4d1b17eced7b5.jpg"},{"id":51513423,"identity":"e281f12e-22fe-4b08-8da0-5877f0cb0bc2","added_by":"auto","created_at":"2024-02-22 21:27:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3749586,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of amino acid metabolism differential genes. (A) Venn diagram of the intersection of differential genes and AAMRGs. (B) Expression heatmap of 143 DEAAMRGs. (C) PPI constructed by 143 DEAAMRGs. (D) Core modules in the PPI. (E) Circle plot of GO enrichment analysis of 143 DEAAMRGs. (F) Circle plot of KEGG enrichment analysis of 143 DEAAMRGs.\u003c/p\u003e","description":"","filename":"Figure02.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3888711/v1/4753970d646997727239d09b.jpg"},{"id":51513431,"identity":"5a0e2a95-d955-40f0-bae0-e21d39a9c674","added_by":"auto","created_at":"2024-02-22 21:27:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2872776,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of risk signature. (A) Forest plots of 13 prognosis-related genes obtained by univariate cox analysis. (B,C) Construction of risk signature based on LASSO Cox analysis. (D,G). The plots of risk score in the TCGA cohort and the GEO cohort, respectively. (E,H) The distributions of patients' survival times and survival status in the TCGA cohort and the GEO cohort, respectively. (F,I) Heatmaps of the expression matrix of the signature genes in the TCGA cohort and the GEO cohort, respectively.\u003c/p\u003e","description":"","filename":"Figure03.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3888711/v1/d1b409c4ca5a7e4ab191d183.jpg"},{"id":51513426,"identity":"049ef205-89f9-4b5c-81c4-b0c835e88766","added_by":"auto","created_at":"2024-02-22 21:27:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1233103,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of risk signature. (A,D) KM curve in the TCGA cohort and the GEO cohort, respectively. (B,E) Time-dependent ROC curves in the TCGA cohort and the GEO cohort, respectively. (C) ROC curves in the TCGA cohort. (F-I) Comparison of risk scores for different T, N, Stage, and Age.\u003c/p\u003e","description":"","filename":"Figure04.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3888711/v1/2a3fb9a28e411c22530be324.jpg"},{"id":51513854,"identity":"d89a9b92-cce8-4641-aadb-29f661ccf989","added_by":"auto","created_at":"2024-02-22 21:35:03","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":936768,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of nomogram. (A,B) Forest plots for univariate COX and multivariate COX analyses based on risk score and the clinical factors. (C) Nomogram predicting 1-, 3- and 5-year survival in TNBC patients. (D) Calibration curves for the prediction of 1-, 3- and 5-year overall survival of TNBC patients\u003c/p\u003e","description":"","filename":"Figure05.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3888711/v1/5049a2d6ffb6fbf1aefdb138.jpg"},{"id":51513428,"identity":"2c361502-9f56-4587-92c7-bd753ecaf233","added_by":"auto","created_at":"2024-02-22 21:27:03","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2006040,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of Risk signature with TMB. (A, B) Waterfall plot of the top ten genes’ TMB status in the two groups. (C, D) Summary of the maf files of the two groups. (E) Correlation graph between TMB and riskScore. (F) KM curve of different TMB levels. (G) KM curve of different TMB and risk levels (H-H for TMB-high-risk-high. H-L for TMB-high-risk-low. L-H for TMB-low-risk-high. L-L for TMB-low-risk-low).\u003c/p\u003e","description":"","filename":"Figure06.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3888711/v1/37556cd62bed0cae81002982.jpg"},{"id":51513430,"identity":"7ce61a21-26d9-404e-b81f-e2394a7d75fc","added_by":"auto","created_at":"2024-02-22 21:27:03","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2575571,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of immune-related analyses in the two groups. (A)Heatmap of immune-related function scores. (B) Boxplot of the activity of immune infiltration cells in the two groups. (C) Comparison of immune checkpoint expression between the two groups. (D) Comparison of HLA-related gene expression between the two groups.\u003c/p\u003e","description":"","filename":"Figure07.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3888711/v1/75e9ea883a22564fc55b0443.jpg"},{"id":51513425,"identity":"af1627d0-9cdf-4a8d-b9b1-467d4e0e0b70","added_by":"auto","created_at":"2024-02-22 21:27:03","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":556508,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of drug sensitivity in the two groups.\u003c/p\u003e","description":"","filename":"Figure08.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3888711/v1/aafde0860b2d0791591735b4.jpg"},{"id":51513432,"identity":"05214070-4ff4-4918-a7b3-c7b00bcc0917","added_by":"auto","created_at":"2024-02-22 21:27:04","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3461168,"visible":true,"origin":"","legend":"\u003cp\u003eRole of ALDH4A1 in TNBC. (A) Venn diagram of intersection of prognosis-related genes in TCGA and GEO cohorts. (B) Venn diagram of intersection of signature genes in TCGA and GEO cohorts. (C) KM curve of ALDH4A1 in TNBC. (D) The annotation of celltypes in GSE180286. (E) Distribution map of ALDH4A1 expression. (F) Dot plot of the expression distribution of ALDH4A1. (G) Relative quantitative results of PCR for ALDH4A1 expression in TNBC and normal tissue adjacent to cancer. (H) Relative quantitative results of PCR for ALDH4A1 expression in MCF-10A, MDA-MB-231 and BT-549. (I) Validation of ALDH4A1 silencing in MDA-MB-231. (J) Validation of ALDH4A1 silencing in BT-549. (K) Invasion assay after silencing ALDH4A1 in TNBC cells.\u003c/p\u003e","description":"","filename":"Figure09.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3888711/v1/fd28d84cc59caa3c0fe713aa.jpg"},{"id":59006498,"identity":"d02a06b8-9799-4b67-a5fe-3c495a842369","added_by":"auto","created_at":"2024-06-25 08:19:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":25479684,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3888711/v1/7eca082f-1505-4280-a619-8534c6ce1af9.pdf"},{"id":51513427,"identity":"e9522da1-a648-4524-a4eb-56acf4a31f1d","added_by":"auto","created_at":"2024-02-22 21:27:03","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20817856,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. Analysis of pathways related to amino acid metabolism.\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-3888711/v1/248ac4e5d1affdae6924b07c.tif"},{"id":51513433,"identity":"c53f10f1-8952-4345-88ad-c7a3fbae1d22","added_by":"auto","created_at":"2024-02-22 21:27:05","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24208668,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2. Immunohistochemical results of ten signature genes in the HPA database in breast cancer tissues and normal tissues.\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-3888711/v1/8232d239e103ee0753802d2a.tif"},{"id":51513853,"identity":"5859ff34-b320-4e8e-823e-f68d4a416cea","added_by":"auto","created_at":"2024-02-22 21:35:02","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13977,"visible":true,"origin":"","legend":"","description":"","filename":"07Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3888711/v1/d6033121bd12c0bf1dcc2eda.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bioinformatics-based analysis of amino acid metabolism-related features to predict clinical prognosis and immunotherapy response in triple-negative breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTriple-negative breast cancer (TNBC) is a type of breast cancer that does not express estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (Her-2) and accounts for approximately 15\u0026ndash;20% of all breast cancers[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Histologically, TNBC is not significantly different from other types of breast cancer, all of which may present as invasive ductal carcinoma, lobular carcinoma, and medullary carcinoma. However, TNBC has a higher degree of malignancy, short survival, and higher recurrence rate than other types of breast cancer[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Due to the lack of these receptor targets, traditional endocrine therapy and targeted therapy are ineffective against TNBC, and chemotherapy has become the main treatment. Although with the development of immunotherapy, PARP inhibitors and PD-1/PD-L1 inhibitors for TNBC have achieved some clinical results[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, precise therapeutic strategies for TNBC to bring more effective treatment options and better clinical prognosis are still the current research hotspots.\u003c/p\u003e \u003cp\u003eAmino acid metabolic therapy is an emerging field in tumor therapy, which uses amino acids as targets of metabolic pathways to interfere with the metabolic processes of tumor cells and limit their metabolic demands to achieve therapeutic effects[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In contrast to normal cells, tumor cells take up large amounts of glucose and glutamine, also require carbon and nitrogen sources from amino acids other than glutamine, the most critical of which are essential amino acids that cannot be synthesized by the human body[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Tumor cells often exhibit the important feature of high uptake of essential amino acids, however, the mechanisms of how rapidly essential amino acids are transported and how essential amino acid metabolism contributes to tumor progression are unknown.\u003c/p\u003e \u003cp\u003eL-asparaginase from \u003cem\u003eEscherichia coli\u003c/em\u003e and \u003cem\u003eErwinia chrysanthemi\u003c/em\u003e, which hydrolyzes plasma L-asparagine and L-glutamine to inhibit cancer cell growth, is the only currently approved drug targeting amino acid metabolism for the treatment of acute lymphoblastic leukemia and lymphosarcoma[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For argininosuccinate synthase (ASS)-deficient tumors that are extremely dependent on extracellular arginine for their own needs, ADI-PEG20's arginine depletion strategy hydrolyzes plasma arginine to citrulline, which on the one hand depletes arginine to inhibit tumor growth and on the other hand allows citrulline to be used by normal cells to be converted to arginine unaffected. In contrast, BCT-100 converts arginine into ornithine and urea to exert similar anti-cancer effects[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Their efficacy has been confirmed in clinical trials. Tumor cells use SLC7A11 to obtain cystine from the environment for subsequent protein and glutathione synthesis. Whereas erastin and sorafenib block the function of SLC6A6, neither is specific and more selective inhibitors are needed to benefit patients[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. While other amino acid metabolic targets such as IDO1, TDO2, PYCR1, GLDC, and SHMT2 are considered as potential therapeutic targets, but are still in preclinical studies[\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, studies applying targeted amino acid metabolic therapies to the treatment of triple-negative breast cancer are still relatively few, and the reprogramming of amino acid metabolism in triple-negative breast cancer is not yet known. In this study, we performed a systematic study to determine identify the expression of AAMRGs in normal and TNBC tissues. DEAAMRGs were also identified. Prognosis related DEAAMRGs were used to develop a prognostic model. The predictive ability of the model was confirmed by the GEO data. The model was used to divide TNBC patients into two subgroups with high and low risk, and the difference in immune microenvironment between the two groups was investigated, demonstrating that AAMRGs were associated with the immune microenvironment of TNBC. In addition, we screened that ALDH4A1 may play a potential role in the pathogenesis of TNBC. We verified the expression of ALDH4A1 in the collected clinical samples and cell lines. We also interfered with the expression of ALDH4A1 in human breast cancer cell lines MDA-MB-231 and BT549 by siRNA transfection, and verified the role of ALDH4A1 in TNBC by migration assay.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eData collection.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical information and the mRNA expression of 112 TNBC sample and 98 normal sample were obtained from the TCGA database (https://portal.gdc.cancer.gov/), and another 107 TNBC sample from the GEO cohort (GSE103091) were used as the verification set. We screened out human amino acid metabolism pathways and corresponding 372 genes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) (Supplementary Figures S1, S2). The data analysis flowchart is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction and Validation of the risk signature.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u0026lsquo;DESeq2\u0026rsquo; package was used to identify the differential expression genes (DEGs). | Log2FoldChange | \u0026gt;1 and p-adjust \u0026lt; 0.05 DEGs were considered statistically significant. Venn online software (http://bioinformatics.psb.ugent.be/webtools/Venn/) was then used to obtain DEAAMRGs in DEGs and AAMRGs. Based on the result of DEAAMRGs, univariate Cox analysis was used to select the genes significantly involved in prognosis risk. Then the genes were subjected to least absolute shrinkage and selection operator (LASSO) regression algorithm for feature selection, 10-fold cross-validation was used, and the R package \u0026lsquo;glmnet\u0026rsquo; was used for the analysis. The risk signature consists of twelve genes and the risk score per each patient was calculated as: risk score = (0.5316 * expression value of SDS) \u0026minus; (1.9759 * expression value of \u003cem\u003eSMYD3\u003c/em\u003e) + (0.7044 * expression value of \u003cem\u003ePRDM16\u003c/em\u003e) \u0026minus; (0.1056 * expression value of \u003cem\u003ePSAT1\u003c/em\u003e) + (0.1282 * expression value of \u003cem\u003eALDH4A1\u003c/em\u003e) \u0026minus; (0.1695 * expression value of \u003cem\u003eGLDC\u003c/em\u003e) \u0026minus; (0.0329 * expression value of \u003cem\u003eMAT1A\u003c/em\u003e) + (0.5444 * expression value of \u003cem\u003eTH\u003c/em\u003e) + (0.3893 * expression value of \u003cem\u003eTDO2\u003c/em\u003e) + (0.3195 * expression value of \u003cem\u003eGLYCTK\u003c/em\u003e) \u0026minus; (0.3035 * expression value of \u003cem\u003eGPT\u003c/em\u003e) \u0026minus; (0.5323 * expression value of \u003cem\u003eAMD1\u003c/em\u003e). The patients were classified into a high-risk group and a low-risk group via the upper tri-sectional quantile value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent prognostic analysis of risk characteristics and construction of nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified all independent prognostic factors by univariate and multivariate Cox regression analysis using the \u0026quot;survival\u0026quot; and \u0026quot;forestplot\u0026quot; R packages. Thereafter, we built a nomograms based on the results of multivariate regression analysis using the \u0026quot;rms\u0026quot; and \u0026quot;survivor\u0026quot; R packages, and also calculated the coordination index (C-index) of the prediction model and plotted calibration curves to assess the probability of OS at 1, 3, and 5 years. and 5-year survival probabilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor mutation burden analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumor mutation burden (TMB) files with somatic mutation information were obtained from TCGA. TMB level differences between the two risk subgroups were estimated and shown using the \u0026quot;maftools\u0026quot;, \u0026quot;ggpubr\u0026quot; and \u0026quot;ggplot2\u0026quot; R packages. The \u0026quot;limma\u0026quot;, \u0026quot;ggpubr\u0026quot;, \u0026quot;ggplot2\u0026quot; and \u0026quot;ggExtra\u0026quot; R packages were used to assess and show the correlation between risk scores and TMB scores. kaplan-Meier (KM) analysis was applied to analyze the differences in survival between groups with different TMB levels as well as risk status subgroups. The \u0026quot;Survival\u0026quot; and \u0026quot;survminer\u0026quot; R packages were used in this procedure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of tumor immune microenvironment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the immune activity, we used the ESTIMATE method to determine the immune score of each TNBC sample. Also, we used the single sample gene set enrichment analysis (ssGSEA) method to assess the activity of immune infiltrating cells as well as immune-related functions. The expression levels of 10 immune checkpoints and 19 human leukocyte antigens were also compared between the two groups. The \u0026quot;estimate\u0026quot; and \u0026quot;GSVA\u0026quot; R packages were used in this procedure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of Protein Expressions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Human Protein Atlas (https://www.proteinatlas.org/) database was used to identify the expression of 10 AAMRGs in normal breast tissues and breast tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle‑cell RNA‑seq analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe collected single-cell RNA sequencing data of five primary breast cancer patients from the GSE180286 dataset. We analyzed single-cell sequencing data using the \u0026ldquo;Seurat\u0026rdquo; package. Quality control (QC) was first performed on the data by retaining cells with less than 10% mitochondrial gene content, and genes expressed in at least three cells within an expression range of 200 to 7500. We then identified highly variable genes for subsequent analysis, setting the highly variable genes number to 2000. The \u0026ldquo;Harmony\u0026rdquo; package was used to remove batch efects in the data from the five samples. We constructed cell clusters using the \u0026ldquo;FindClusters\u0026rdquo; and \u0026ldquo;FindNeighbors\u0026rdquo; functions and visualized them using the \u0026ldquo;t-SNE\u0026rdquo; method. Finally, we performed cell annotation based on the marker genes of different cell types.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell culture and small-interfering RNA transfection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe human breast cancer cell lines MCF-10A, MDA-MB-231 and BT-549 were procured from the cell bank of the Chinese Academy of Science. MDA-MB-231 and BT549 cells were cultured in high glucose DMEM medium, supplemented with 10% fetal bovine serum (FBS) and 1% Penicillin-Streptomycin Solution (P/S), in an incubator set at 37℃ with a 5% CO2 atmosphere. The sequences of siRNA targeting ALDH4A1 were cloned into MDA-MB-231 and BT-549 cells. Using GP-transfect-Mate (GenePharma, China), the siRNA transfection process was conducted as instructed by the manufacturer. The primer sequences are as follows: Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMigration testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter cell transfection was completed, the serum-free cell suspension was spread evenly in the upper chamber of Transwell (Corning, USA), and DMEM medium containing 20% FBS was added to the lower chamber. After 8 hours of incubation, the upper chamber was fixed with 4% paraformaldehyde, and the cells that did not cross the polycarbonate membrane were gently scraped off with a cotton swab. After gentian violet staining and washing with PBS, cells at the bottom of the chambers were photographed in different fields of view using a microscope, and cells were counted using ImageJ.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emRNA Expression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe collected tumor samples from 20 triple-negative breast cancer patients and 10 adjacent normal tissues from the Department of Breast Care Surgery, The First Affiliated Hospital, Guangdong Pharmaceutical University.\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted from clinical samples and cells using TRIzol, after which it was reverse-transcribed into cDNA with a reverse transcription kit (Tsingke,China). The reaction conditions were set at 25℃ for 10 minutes, 50℃ for 15 minutes, and 85℃ for 5 minutes, followed by Real-time Quantitative PCR. The PCR amplification conditions were as follows: pre-denaturation at 95\u0026deg;C for 1 minute, denaturation at 95\u0026deg;C for 30 seconds, and annealing at 60\u0026deg;C for 20 seconds, repeated for 40 cycles. The relative expression of the target gene was calculated using the 2-△△Ct method. The primer sequences are as follows: Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudent\u0026apos;s t-test was used to compare gene expression data between tumor samples and normal samples. Parametric distribution of Chi-square test or non-parametric distribution of Wilcoxon test was used for differences in proportions. All statistical analyses were performed with R software (version 4.1.2). p-values \u0026lt;0.05 were considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of DEAAMRGs Between Normal and Tumor Tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 372 genes associated with amino acid metabolism were obtained from KEGG, and 5971 DEGs were identified. ssGSEA analysis showed large differences in amino acid metabolism in TNBC and normal tissues (Figure S1A). Then 143 DEAAMRGs were identified through Venn online tool (Figure2A). Among them, 72 AAMRGs were expressed up-regulated and 71 AAMRGs were expressed down-regulated in TNBC (Figure 2B). Uploading 143 DEAAMRGs to STRING online database constructed protein-protein interaction (PPI) network (Figure 2C). Core modules in PPI were identified using the MCODE plugin in cytoscape (Figure 2D). GO enrichment analysis showed that DEAAMRGs were enriched in biological processes such as amino acid metabolic process, alpha-amino acid metabolic process, small molecule catabolic process, cellular components such as mitochondrial matrix, peroxisome, heterochromatin and molecular function such as vitamin binding, pyridoxal phosphate binding, vitamin B6 binding \u0026nbsp; (Figure 2E). KEGG enrichment analysis showed that DEAAMRGs were enriched in Tryptophan metabolism, Tyrosine metabolism, Arginine and proline metabolism pathways (Figure 2F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction and validation of the risk signature in the TCGA cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the TCGA cohort, univariate Cox analysis screened out thirteen genes related to the prognosis of TNBC (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) (Figure 3A). We further conducted a LASSO analysis to construct the risk signature (Figure 3B,C). The risk signature consists of twelve genes and the risk score per each patient was calculated as: risk score = (0.5316 * expression value of SDS) \u0026minus; (1.9759 * expression value of \u003cem\u003eSMYD3\u003c/em\u003e) + (0.7044 * expression value of \u003cem\u003ePRDM16\u003c/em\u003e) \u0026minus; (0.1056 * expression value of \u003cem\u003ePSAT1\u003c/em\u003e) + (0.1282 * expression value of \u003cem\u003eALDH4A1\u003c/em\u003e) \u0026minus; (0.1695 * expression value of \u003cem\u003eGLDC\u003c/em\u003e) \u0026minus; (0.0329 * expression value of \u003cem\u003eMAT1A\u003c/em\u003e) + (0.5444 * expression value of \u003cem\u003eTH\u003c/em\u003e) + (0.3893 * expression value of \u003cem\u003eTDO2\u003c/em\u003e) + (0.3195 * expression value of \u003cem\u003eGLYCTK\u003c/em\u003e) \u0026minus; (0.3035 * expression value of \u003cem\u003eGPT\u003c/em\u003e) \u0026minus; (0.5323 * expression value of \u003cem\u003eAMD1\u003c/em\u003e).SMYD3, PSAT1, GLDC, MAT1A,GPT and AMD1 were protective factors. SDS, PRDM16, ALDH4A1, TH, TDO2 and GLYCTK were risk factors.\u0026nbsp;Depending on upper tertile risk scores, we assigned TNBC patients to high-risk and low-risk subgroups (Figure 3D,E,F). A\u0026nbsp;total of 107 TNBC samples were obtained from the GEO database (GSE103091) as the validation cohort. The GEO cohort was divided into high-risk and low-risk groups by the upper tertile risk scores (Figure 3G,H,I). ssGSEA results show differences in Lysine degradation, Phosphonate and phosphinate metabolism and Selenocompound metabolism between high and low risk groups (Figure S1B)\u0026nbsp;In addition, the expression of 10 genes in the risk signature were validated in the HPA database (Figure S2).\u003c/p\u003e\n\u003cp\u003eIn the TCGA cohort, KM analysis showed that the high-risk group had a significantly worse prognosis than the low-risk group (Figure 4A). The accuracy of the risk signature was investigated by calculating the AUCs for 1, 3, and 5\u0026nbsp;years. And the AUCs for 1, 3, and\u0026nbsp;5 years were 0.934, 0.970, and 0.953, separately (Figure 4B).\u0026nbsp;The AUC of the ROC curve for risk signature in the TCGA cohort was 0.893 (Figure 4C). In the GEO cohort, KM analysis showed that patients in the high-risk group had worse survival (Figure 4D). The AUCs for 1, 3, and\u0026nbsp;5 years were 0.692,0.636, and 0.604, respectively (Figure 4E). We analyzed the clinical correlation of risk signature and showed that the risk signature created were significantly correlated with pathological stage and pathological T-stage (Figure 4F-I).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent prognostic analysis and construction of prognostic nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the univariate Cox analysis combined with clinical factors showed that risk score, T, N, M, and stage were independent predictors (Figure 5A). In addition, multivariate Cox analysis showed that risk score were independent predictors (Figure 5B). Nomograms predicting 1-, 3-, and 5-year survival were created by incorporating stage and risk score based on the results of stepwise regression analysis (Figure 5C) and calibration graphs were drawn for 1-year, 3-year, and 5-year OS probabilities (Figure 5D). The C-index of the nomogram was 0.962. Based on the calibration curves, it is known that the predicted values of 1-year, 3-year and 5-year survival times are similar to the corresponding true survival times. These results indicate the good predictive power of this nomogram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor mutation burden analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe top ten genes in terms of mutation rate also differed greatly between the two groups (Figure 6A-D). Despite this, there is no significant correlation between TMB and subline scores (Figure 6E). To see if the risk signature or TMB was better at predicting survival, we divided the sample into high and low mutation subgroups based on the median TMB. There was no statistically significant difference between these two groups (Figure 6F). While the high-mutation-low-risk group had the highest survival rate, the low-mutation-high-risk group had the lowest survival rate (Figure 6G). This indicates that the predictive power of our model is stronger compared to TMB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor microenvironment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation between immune cell infiltration and immune function in the tumor microenvironment of two groups was assessed using the ssGSEA method. Immune function in the high-risk group Type II IFN response, Parainflammation, Type I IFN reponses, APC co stimulation, CCR, APC co inhibition, Checkpoint, T cell co -inhibition, HLA, T cell co stimulation, Cytolytic activity obtained higher ssGSEA scores (Figure 7A). Meanwhile, Macrophage, MDSC, Monocyte, Natural killer T cell, Neutrophil, Plasmacytoid dendritic cell, Regulatory T cell, T follicular helper cell, Type 1 T helper cell, among others obtained higher ssGSEA scores in the higher group (Figure 7B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, we analyzed the expression of HLA genes and immune checkpoint genes in the high-risk and low-risk groups. The expression of both HLA and classical immune checkpoint genes was slightly higher in the high-risk group than in the low-risk group (Figure 7C,D), especially CTLA4, LAG3, LGALS9, SIRPA, TDO2, TIGIT, HLA-E, HLA-DRB5, HLA-DRB1, HLA-DRA, HLA-DQB1, HLA-DQA1, HLA-DPB1, HLA-DMB, and HLA-DMA were significantly higher than those in the low risk group, showing that the high-risk group showed a stronger immunosuppressive state. Therefore, TNBC patients in the high-risk group benefited more from immunotherapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction of drug sensitivity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results showed that TNBC patients in the low-risk group were more sensitive to Docetaxel, Epirubicin, Oxaliplatin, Paclitaxel and Cisplatin (Figure 8A-E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of the role of ALDH4A1 in TNBC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe intersection of prognosis-related genes (Figure 9A) and different gene sets screened for signature genes by LASSO analysis (Figure 9B) in the TCGA and GSE103091 datasets were all associated with one gene, ALDH4A1, implying its potentially important role in TNBC. KM curve confirmed shorter survival in the ALDH4A1 high expression group in TNBC (Figure 9C). It even plays a role in the development of breast cancer. Next, we used the GSE180286 dataset in analyzed the differences in ALDH4A1 at the single cell level. The results showed that ALDH4A1 was mainly expressed in epithelial cells. (Figure 9D-F). qPCR results showed that in our collected clinical samples, the expression level of ALDH4A1 in TNBC tissues was significantly lower than that in normal tissues adjacent to the cancer (Figure 9G). In addition, the expression level of ALDH4A1 in the TNBC cell lines MDA-MB-231 and BT-549 was also lower than that in the normal breast epithelial cell line MCF-10A (Figure 9H). To investigate the role of ALDH4A1 in TNBC, we inhibited the expression of ALDH4A1 in TNBC cell lines MDA-MB-231 and BT546 by transfection with siRNA. PCR confirmed that ALDH4A1 was successfully knocked down (Figure 9I-J). Migration results showed that inhibition of ALDH4A1 expression significantly inhibited the invasive ability of both TNBC cells (Figure 9K). These results indicated that ALDH4A1 plays an important role in TNBC.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTriple-negative breast cancer, as a subtype of breast cancer, is prone to metastasis, recurrence, and poor prognosis due to the absence of ER, PR, and Her-2 receptors, rendering patients ineligible for endocrine and targeted therapies. The development of novel treatment strategies is imperative. Amino acid metabolism reprogramming has emerged as a significant discovery following the Warburg effect. During the course of tumorigenesis, tumor cells upregulate the expression of amino acid transporters and related enzymes to meet the demands for protein synthesis, energy production, nucleotide synthesis, and other processes essential for their rapid growth. Amino acid metabolism plays a closely intertwined role in the onset and progression of triple-negative breast cancer. These cells exhibit heightened metabolic activity, with distinct differences in energy metabolism and amino acid metabolism compared to normal cells, including the glutamine-glutamate cycle, arginine metabolism, and branched-chain amino acid metabolism. These alterations in metabolic pathways can impact the proliferation, metastasis, and drug resistance of triple-negative breast cancer cells.\u003c/p\u003e \u003cp\u003eIn this study, based on differential analysis and single-factor regression analysis, we have identified AAMRGs (Amino Acid Metabolism-Related Genes) associated with prognosis and established a risk feature composed of 12 AAMRGs, demonstrating outstanding predictive capabilities. These 12 AAMRGs are SDS, SMYD3, PRDM16, PSAT1, ALDH4A1, GLDC, MAT1A, TH, TDO2, GLYCTK, GPT, and AMD1.\u003c/p\u003e \u003cp\u003eSET And MYND Domain Containing 3 (SMYD3) is a cytoplasmic lysine methyltransferase that is overexpressed in various cancers. It is considered a potential prognostic biomarker for prostate cancer, breast cancer, and colorectal cancer [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Studies indicate a significant upregulation of SMYD3 in breast cancer tissues, and knocking down SMYD3 can inhibit breast cancer cell proliferation. SMYD3 can also promote epithelial-mesenchymal transition (EMT) by regulating EMT-specific transcription factors and stromal genes controlled by TGF-β [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHistone-Lysine N-Methyltransferase PRDM16 (PRDM16) has been extensively studied in the context of brown fat decomposition. Our research aligns with previous studies as PRDM16 is significantly downregulated in kidney cancer and prostate cancer [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] compared to normal tissues. Overexpression of PRDM16 inhibits cell proliferation, migration, and invasion, while silencing PRDM16 produces the opposite effect. In kidney cancer, PRDM16 suppresses the expression of the gene encoding semaphorin 5B (SEMA5B) by inhibiting C-terminal binding proteins (CtBP1/2), and SEMA5B is a highly expressed HIF target gene in kidney cancer, promoting tumor growth [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePhosphoserine Aminotransferase 1 (PSAT1) is a critical enzyme in the serine synthesis pathway, catalyzing the conversion of 3-phosphohydroxypyruvate (3-PPyr) to phosphoserine (p-serine), which can be further utilized in downstream one-carbon and nucleotide metabolism. Research has indicated that PSAT1 is upregulated in triple-negative breast cancer compared to normal tissues, consistent with our findings. Loss of PSAT1 inhibits migration and invasion in triple-negative breast cancer but does not affect proliferation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. High PSAT1 expression is associated with poor prognosis in various cancers [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Mechanistically, overexpression of PSAT1 leads to the inhibition of cyclin D1 degradation, subsequently altering the activity of the Rb-E2F pathway, enhancing G1 phase progression and proliferation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], ultimately promoting tumor progression [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAldehyde Dehydrogenase Family 4 Member A1 (ALDH4A1) is part of the aldehyde dehydrogenase family and plays a role in the degradation of proline. Research on ALDH4A1 has primarily focused on cardiovascular diseases, where it is considered a potential biomarker and therapeutic target [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, its role in tumorigenesis and progression remains relatively unexplored. Studies suggest that ALDH4A1 expression is reduced in colorectal cancer, leading to the accumulation of proline and supporting cell proliferation and survival [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlycine decarboxylase (GLDC) acts through aminomethyl transferase to provide one-carbon units into the folate cycle after glycine cleavage. GLDC exhibits abnormal expression in tumors such as non-small cell lung cancer and ovarian cancer. In the early stages of tumorigenesis, high GLDC expression promotes one-carbon unit generation for nucleotide synthesis, driving tumor growth and correlating with increased mortality in patients [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, GLDC may serve as a potential therapeutic target to control tumor progression by targeting cancer stem cells.\u003c/p\u003e \u003cp\u003eMethionine Adenosyltransferase 1A (MAT1A) is a crucial enzyme in cell metabolism, catalyzing the synthesis of the biological methyl donor S-adenosylmethionine (SAMe) by the reaction between methionine and adenosine triphosphate (ATP). MAT1A is identified as a biomarker in liver cancer, where its expression is significantly lower than in normal tissues, with levels decreasing as tumor grade increases. Silencing MAT1A results in the downregulation of dual-specificity phosphatase 1 (DUSP1), leading to a loss of control over extracellular signal-regulated kinase (ERK) signaling and promoting hepatocellular carcinoma progression [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, our study indicates the opposite, with MAT1A expression levels higher in triple-negative breast cancer than in adjacent tissues, possibly linked to tumor heterogeneity.\u003c/p\u003e \u003cp\u003eTyrosine Hydroxylase (TH) is a gene that encodes a protein involved in the conversion of tyrosine to dopamine, playing a pivotal role in catecholamine synthesis, which is vital in the physiology of adrenergic neurons. Inhibition of the NF-κB pathway activation and reduction of TNF-α levels can upregulate TH expression [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, miR-375, when bound to the 3'-untranslated region of Sp1, negatively regulates TH expression [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Tyrosine hydroxylase uses tetrahydrobiopterin (BH4) as a cofactor to hydroxylate tyrosine, producing L-dopa and participating in amino acid metabolism pathways [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In a preclinical model of breast cancer, TH\u0026thinsp;+\u0026thinsp;sympathetic nerves are localized around the tumor, enhancing norepinephrine conversion and facilitating stress-induced tumor progression [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In pheochromocytoma, aberrations in the TH gene disrupt the feedback mechanism, resulting in damage to the body [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTryptophan 2,3-Dioxygenase (TDO2) encodes an enzyme that plays a crucial role in the kynurenine pathway by catalyzing the first and rate-limiting step in tryptophan metabolism. Increased enzyme activity and kynurenine production may have a role in inhibiting anti-tumor immune responses in cancer [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. TDO2 is considered the primary enzyme for tryptophan degradation, and tryptophan metabolites activate the aryl hydrocarbon receptor (AHR), enhancing tumor malignancy and suppressing anti-tumor immunity[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Kynurenine, a product of TDO2, can activate AHR, leading to the generation of tolerogenic dendritic cells and regulatory T cells, contributing to the tumor's immunosuppressive microenvironment[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, this pathway increases glycolysis, promoting cancer cell growth and CXCL5 secretion, which recruits macrophages to the tumor microenvironment[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In liver cancer cells, TDO2 promotes cancer cell migration and invasion through the Wnt5a pathway[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eAdenosylmethionine Decarboxylase 1 (AMD1) encodes a critical rate-limiting enzyme for polyamine synthesis, impacting cell growth and tumorigenesis by increasing polyamine biosynthesis. Elevated intracellular polyamine levels can lead to the suppression of checkpoints that restrict growth, exerting carcinogenic effects[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In a murine model of neuroblastoma expressing the MYC gene, inhibiting AMD1 with the inhibitor SAM486 significantly reduces tumor incidence and extends the tumor's latency period[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Thus, the inhibition of the AMD1 gene may be an effective therapeutic approach for neuroblastoma. In prostate cancer, AMD1 upregulation activates mTORC1, which subsequently reinforces the metabolic program required for maintaining cancer cell growth and proliferation[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, at present, there is no existing literature that precisely elucidates the relationship between the AAMRGs Serine Dehydratase (SDS), Glycerate Kinase (GLYCTK), and Glutamate-pyruvate Transaminase (GPT) and their association with tumor prognosis and immune evasion.\u003c/p\u003e \u003cp\u003eIn conclusion, the genes in the risk profile model we constructed may be involved in the development of triple-negative breast cancer, which provides a certain reference for further research on amino acid metabolic therapy. However, our study has some limitations, and further experiments are still needed to verify the role of these risk profile genes, especially ALDH4A1, in triple-negative breast cancer.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe constructed an amino acid metabolism risk profile consisting of 12 genes, which demonstrated excellent predictive ability in predicting the prognosis of triple-negative breast cancer. This work investigated the link between the immune microenvironment and amino acid metabolism. Further, we identified ALDH4A1 as a possible key gene involved in the reprogramming of amino acid metabolism in triple-negative breast cancer, and verified its role in invasive ability by migration experiments.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBC- Breast cancer (BC)\u003c/p\u003e\n\u003cp\u003eDEGs\u0026mdash;Differentially Expressed Genes\u003c/p\u003e\n\u003cp\u003eGO\u0026mdash;Gene Ontology\u003c/p\u003e\n\u003cp\u003eGSVA\u0026mdash;Gene Set Variation Analysis\u003c/p\u003e\n\u003cp\u003eKEGG\u0026mdash;Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eOS\u0026mdash;Overall Survival\u003c/p\u003e\n\u003cp\u003ePCA\u0026mdash;Principal Component Analysis\u003c/p\u003e\n\u003cp\u003essGESA\u0026mdash;single sample Gene Set Enrichment Analysis\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely acknowledge TCGA database for providing their platforms and contributors for uploading their meaningful datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Data will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No.22074024), Natural Science Foundation of Guangdong Province (No. 2023A1515012573), National Undergraduate Training Program for Innovation and Entrepreneurship \u0026amp; Student Research Training Program (No. 202310573005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: WBH, RXZ. Development of methodology: YFZ, LL, BL, YCZ and YXY. Acquisition of data: YFZ and WBH. Analysis and interpretation of data (e.g., statistical analysis, bioinformatic, computational analysis): YFZ, LL and YFL. Writing, review, and/or revision of the manuscript: YFZ, LL, BL, YXY and YFL. Administrative, technical, or material support: YFZ and WBH. Study supervision: RXZ. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eC. Luo, P. Wang, S. He, J. Zhu, Y. Shi, J. Wang, Progress and Prospect of Immunotherapy for Triple-Negative Breast Cancer, Front Oncol, 12 (2022) 919072.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ.Y. So, J. Ohm, S. Lipkowitz, L. Yang, Triple negative breast cancer (TNBC): Non-genetic tumor heterogeneity and immune microenvironment: Emerging treatment options, Pharmacol Ther, 237 (2022) 108253.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY. Qi, W. Zhang, R. Jiang, O. Xu, X. Kong, L. Zhang, Y. Fang, J. Wang, J. 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Castillo-Mart\u0026iacute;n, V. Serra, N. Beraza, A. Gentilella, G. Thomas, M. Azkargorta, F. Elortza, R. Farr\u0026agrave;s, D. Olmos, A. Efeyan, J. Anguita, J. Mu\u0026ntilde;oz, J.M. Falc\u0026oacute;n-P\u0026eacute;rez, R. Barrio, T. Macarulla, J.M. Mato, M.L. Martinez-Chantar, C. Cordon-Cardo, A.M. Aransay, K. Marks, J. Baselga, J. Tabernero, P. Nuciforo, B.D. Manning, K. Marjon, A. Carracedo, mTORC1-dependent AMD1 regulation sustains polyamine metabolism in prostate cancer, Nature, 547 (2017) 109\u0026ndash;113.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1-1. The sequences of siRNA targeting ALDH4A1are as follows:\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"463\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.16450216450217%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.83549783549783%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.16450216450217%\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.83549783549783%\"\u003e\n \u003cp\u003eSequence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.16450216450217%\"\u003e\n \u003cp\u003esi1-ALDH4A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.83549783549783%\"\u003e\n \u003cp\u003esense-GGGUAAGACCGUGAUCCAATT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.16450216450217%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.83549783549783%\"\u003e\n \u003cp\u003eantisense-UUGGAUCACGGUCUUACCCTT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.16450216450217%\"\u003e\n \u003cp\u003esi2-ALDH4A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.83549783549783%\"\u003e\n \u003cp\u003esense-\u003c/p\u003e\n \u003cp\u003eCCCAGAACCUGGACCGGUUTT\u003c/p\u003e\n \u003cp\u003eantisense-\u003c/p\u003e\n \u003cp\u003eAACCGGUCCAGGUUCUGGGTT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1-2. The primer sequences of\u0026nbsp;ALDH4A1 are as follows:\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"463\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.16450216450217%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.83549783549783%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.16450216450217%\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.83549783549783%\"\u003e\n \u003cp\u003ePrimer sequence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.16450216450217%\"\u003e\n \u003cp\u003eALDH4A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.83549783549783%\"\u003e\n \u003cp\u003eF-CAGGGTAAGACCGTGATCCAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"52.16450216450217%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.83549783549783%\"\u003e\n \u003cp\u003eR-\u003c/p\u003e\n \u003cp\u003eCCAGCTCCACCGCATACTTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"triple-negative breast cancer, amino acid metabolism, immunity, risk signature, immunotherapy responses","lastPublishedDoi":"10.21203/rs.3.rs-3888711/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3888711/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTriple negative breast cancer (TNBC) is an aggressive subtype of breast cancer associated with poor prognosis. In addition to the Warburg effect, amino acids and metabolites affect tumor development, are involved in modulating the tumor immune microenvironment (TME) and regulating the anti-tumor immune response. However, the relationship between amino acid metabolism and the clinical prognosis and immunotherapeutic response of triple negative breast cancer are still indistinct. We established a risk signature consisting of 12 genes by differential Analysis, univariate COX regression analysis and LASSO-COX analysis. The GEO cohort confirmed the validity of the risk signature. We used single-sample genomic enrichment analysis (ssGSEA), tumor mutation burden (TMB), and IC50 values of drugs to discover the relationship between the risk signature, immune status, and drug sensitivity in TNBC. We also verified the expression of the risk signature gene ALDH4A1 in tissues and cells by qPCR assay, and migration assay verified its role in TNBC cell invasion. Our study may provide new insights into amino acid metabolic therapy for the treatment of TNBC patients.\u003c/p\u003e","manuscriptTitle":"Bioinformatics-based analysis of amino acid metabolism-related features to predict clinical prognosis and immunotherapy response in triple-negative breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-22 21:26:57","doi":"10.21203/rs.3.rs-3888711/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c1b79c95-9768-4d88-983f-2049c4de0092","owner":[],"postedDate":"February 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28873439,"name":"Biological sciences/Cancer"},{"id":28873440,"name":"Biological sciences/Computational biology and bioinformatics"}],"tags":[],"updatedAt":"2024-06-25T08:11:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-22 21:26:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3888711","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3888711","identity":"rs-3888711","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}