Breast cancer (BRCA) is the most frequent female malignancy and is potentially life threatening. Moreover, BRCA has one of the highest lethality rates among the female malignant tumors (Siegel et al., 2019; Siegel et al., 2020; Siegel et al., 2021). The BRCA incidence rates continue to increase by about 0.5% per year (Siegel et al., 2020; Siegel et al., 2021). Currently, dozens of treatments, including surgery, hormonal therapy, radiation therapy, and chemotherapy, are used to manage female BRCA. Nevertheless, most patients with BRCA are still at risk of having adverse outcomes, even for those who receive therapy in the early stage of the disease (Ciriello et al., 2015). In recent years, researchers have dedicated increased efforts to proving that the AAM is dramatically associated with BRCA development (Cha et al., 2018; Deyu Zhang et al., 2021; Morotti et al., 2021) and have demonstrated that BRCA is infiltrated by many types of immune cells and own a high level of immunogenicity, illustrating the hypothesis that immune cell infiltration plays an indispensable role in the clinical prognosis of BRCA (Lehmann et al., 2011; Loi et al., 2014; Denkert et al., 2018; Pruneri et al., 2018). Moreover, the tumor immune microenvironment participates significantly in the development of BRCA. Tumor-infiltrating lymphocytes have been shown to be correlated with the outcome of this disease (Pruneri et al., 2018).

Tumor-infiltrating lymphocytes have been shown to be correlated with the outcome of this disease (Adams et al., 2014; Ali et al., 2016; Stanton and Disis, 2016; Lee et al., 2018).

Altered metabolism is a hallmark of cancer, and the reprogramming of the energy metabolism has historically been acknowledged as a general phenomenon underlying tumors (Pei-Hsuan Chen et al., 2019; Faubert et al., 2020; Wang et al., 2018). One of the best-known alternative theories on cancer development is the “Warburg effect,” which consists of the continued activation of aerobic glycolysis in cancer cells (Hanahan and Weinberg, 2011). Furthermore, the AAM has been shown to be strongly correlated with the evolution and progression of human malignancies. Glutamine, serine, and glycine, for instance, are vital nutrients for tumor growth and maintenance. Moreover, myc overexpression influences the cellular glutamine levels by activating the transcription of GLS1 and the glutamine transporter SLC1A5 (Gao et al., 2009). In turn, phosphoglycerate dehydrogenase (PHGDH), which is a crucial enzyme in the serine synthesis pathway, is greatly upregulated in breast cancer cells (Dias et al., 2019). Similarly, immune cells require amino acids as a vital source of nutrition. The AAM can regulate immune effector protein activity (Kelly and Pearce, 2020). For example, activated CD8⁺ T cells exhibit higher levels of Slc7a5 and Slc1a5 on the cell surface compared with naïve CD8⁺ T cells. Activated T cells need a considerable amount of amino acids to maintain growth by improving transporter expression (Kelly and Pearce, 2020). Recent studies have demonstrated that the combination of a glutamine antagonist with anti-PD-1 therapy had a more obvious anti-tumor effect than did the anti-PD-1 therapy alone, and did not cause immune cell failure (Leone et al., 2019). In addition, the combination of the ladiratuzumab vedotin LIV-1 directed which is a transmembrane protein with zinc transporter and metalloproteinase activity (Nagayama et al., 2020) and anti-PD-1 therapy have been highlighted in breast cancer, particularly Triple negative breast cancer (TNBC) (Rizzo et al., 2022). There are many TNBC patients who benefit from immune checkpoint blockade therapy (Rizzo et al., 2021; Rizzo and Ricci, 2021).

LncRNAs, which are RNA molecules with a length of about 200 nt, can adjust the expression of genes (Tao Zhang et al., 2021). With the exception of gene regulation, lncRNAs participate in numerous biological regulatory processes, including those involved in the appearance, development, and metastasis of tumors (Gupta et al., 2010). Moreover, lncRNAs have a great impact on the regulation of metabolism (Guo et al., 2021; Tan et al., 2021). lncRNAs can directly modulate the posttranslational modification of key metabolic enzymes, lncRNAs can also indirectly regulate metabolic pathways through posttranslational modifications (Tan et al., 2021). It has been proved by experiments that the lncRNA XLOC_006390 stablized c-Myc by preventing its ubiquitination, increasing the expression of glutamate dehydrogenase 1 (GDH1), and subsequently stimulating the production of alpha-ketoglutarate (α-KG). Excess α-KG supplied the tricarboxylic acid (TCA) cycle and facilitated glutamate metabolism, promoting pancreatic cancer growth (He et al., 2020). Meanwhile, LncRNA EPB41L4A-AS1 regulates glycolysis and glutaminolysis by mediating nucleolar translocation of HDAC2 (Liao et al., 2019). However, to date, AAM-related lncRNAs have not been used to predict overall survival (OS) in patients with BRCA. Whether amino acid metabolization-related lncRNAs participate in the immune regulation of BRCA remains ambiguous.

In this study, we used the TCGA BRCA database, from which genomic and transcriptome data (RNA-seq) are accessible. In cross-validation analyses, the dataset was stochastically divided into training and validation groups. Finally, nine lncRNAs that were correlated with BRCA outcomes were identified. According to the expression levels of these nine lncRNAs, a risk score prognostic model for BRCA was created according to the patients in the training cohort, and the vital prognostic values of this model were further acknowledged in the patients in the validation cohort and the patients in the whole cohort. The relationships between the risk score subtypes and immune checkpoints, the proportions of 22 immune cells [B cells naive, B cells memory, plasma cells, T cells CD8, T cells CD4 naive, T cells CD4 memory resting, T cells CD4 memory activated, T cells follicular helper, T cells gamma delta, T cells regulatory (Tregs), NK cells resting, NK cells activated, monocytes, macrophages M0, macrophages M1, macrophages M2, dendritic cells resting, dendritic cells activated, mast cells resting, mast cells activated, eosinophils, and neutrophils], and intrinsic molecular subtypes were also illustrated (Newman et al., 2015; Becht et al., 2016). Lastly, the difference between the two sets regarding sensitivity to chemotherapy was predicted.

In recent years, researchers have dedicated increased efforts toward proving that the amino acid metabolism (AAM) is dramatically associated with BRCA development (Cha et al., 2018; Deyu Zhang et al., 2021; Morotti et al., 2021). Glutamine, serine, glycine, etc. are critical nutrients for tumor growth and maintenance. Similarly, amino acids are vital nutrients for immune cells. For example, Glutaminolysis is a major energy-producing process for proliferating cells, including activated T cells (Newsholme et al., 1999), by supplying a-ketoglutarate (aKG) to the TCA cycle, via glutamate. Glutamine is used to promote LPS induction of IL-1 production by macrophages (Wallace and Keast, 1992). Activated NK cells use some glutamine to replenish TCA cycle intermediates and increase oxidative phosphorylation (Lam et al., 2016). One function of the reduced form of glutathione (GSH) in Treg cells is to restrict serine metabolism in order to maintain their suppressive function. Macrophages also utilize serine to generate glycine for GSH, needed for LPS-induced IL-1b mRNA expression (Rodriguez et al., 2019). Consequently, increasing attention has been paid to AAM in this context (Jones et al., 2018; Butler et al., 2021). As expected, recent studies have reported that the combination of a glutamine antagonist and anti-PD-1 therapy had a more obvious anti-tumor effect than did the anti-PD-1 therapy alone, and did not cause immune cell failure (Leone et al., 2019). Moreover, previous studies have produced prognostic models of genes related with amino acid metabolism in glioma and hepatoma via bioinformatics analysis (Liu et al., 2019; Zhao et al., 2021). In recent years, lncRNAs have been proved that they have a great impact on the regulation of metabolism (Guo et al., 2021; Tan et al., 2021). To date, however, AAM-related lncRNAs have not been used to predict OS in patients with BRCA. Whether amino acid metabolization-related lncRNAs participate in the immune regulation of BRCA remains ambiguous.

In this study, we explored for the first time the characteristics of AAM-related lncRNAs in BRCA and established a risk signature associated with OS. First, we screened AAM-associated DEGs between BRCA and normal breast tissues based on RNA-seq data. Furthermore, we established a robust and effective prognostic signature using univariate Cox, LASSO regression, and multivariate Cox analyses. Nine lncRNAs were included in this signature (LIPE-AS1, AC124067.4, LINC01655, AP005131.3, AC015802.3, USP30-AS1, SNHG26, AL589765.4). Moreover, 1,005 samples in total were stochastically split into training and validation cohorts at a 3:1 ratio, for building and validating the AAM-related lncRNA signature. The training set of 754 samples, the validation set of 251 samples, and the whole TCGA data set of 1,005 samples all showed the feasibility of this model.

Several recent studies have demonstrated that LIPE-AS1 is prominently expressed in BRCA and cervical squamous cell carcinoma and is associated with a higher survival rate (Xu et al., 2021; Wang et al., 2022). In turn, AC124067.4-hsa-miR-92b-3p (hsa-miR-589-5p)-PHYHIPL both decrease the MSI and TMB in COAD, thus reducing the risk of genome instability and alterations (Ren et al., 2021), whereas USP30-AS1 promotes mitochondrial quality control in glioblastoma cells (Wang et al., 2021), the USP30-AS1/miR-299-3p/PTP4A1 pathway aggravates the malignant progression of cervical cancer (Wang et al., 2021), and SNHG26 promotes the metastasis, growth, and cisplatin resistance of tongue squamous cell carcinoma through the PGK1/Akt/mTOR signaling pathway (Jiang et al., 2022). Nevertheless, no studies have investigated the prognostic value of LINC01655, AP005131.3, AC015802.3, and AL589765.4 in patients with BRCA or other malignancies. The current findings show for the first time that these four lncRNAs were associated with the prognosis of BRCA. The potential role of the four lncRNAs needs to be further explored.

To shed light on the immunologic nature of the risk subgroups, gene mutations were explored in the different risk subgroups. We found that the most frequent mutation type was missense mutation, followed by frameshift deletion and nonsense mutation, as reported previously (2012). The most significant difference in mutations between the groups was observed for TP53 mutations, which were more frequent in high-risk samples than in low-risk samples (77 vs. 64%). TP53 mutation is not only the single most significant genetic event in cancer, but also linked with poorer patient outcomes and more aggressive disease in many malignant tumors (Vousden and Prives, 2005), particularly BRCA (Olivier et al., 2006; Shahbandi et al., 2020). TP53 can affect the cancer cell cycle through the p53/TGF β signaling pathway. In a study of stage III breast cancer, patients with TP53 mutations were shown to have worse disease free survival (DFS) following treatment with paclitaxel (p = 0.007) (Chrisanthar et al., 2011). Olivier et al. noted in a study of 1794 breast cancer patients that those with tumors harboring TP53 mutations in exons 5–8 of the gene had a worse risk of dying of breast cancer within 10 years following surgery (p < 0.0001) (Olivier et al., 2006).

In addition, there was a higher rate of PIK3CA mutation in the high-risk subgroup compared with the low-risk subgroup, which could mean that tumor growth in high-risk BRCA cases is promoted through the PI3K–AKT signaling pathway (Xing et al., 2019). Hence, high-risk patients with high TP53 and PIK3CA mutations have a poorer prognosis than do low-risk patients with low TP53 and PIK3CA mutations, in accordance with our survival results. Next, we speculated that low-risk patients benefit more from the immune checkpoint inhibitor therapy, based on the results presented in Figure 8C, especially among patients with TNBC cancer in both groups (Lyons, 2019; Kwapisz, 2021).

Here, we concluded that the risk score had a slight positive relationship with the TMB (Figure 6D), which implies that the TMB can help explain why the risk score affects prognosis to a certain degree; however, other possible mechanisms may be involved in this relationship. Recent studies reported that TMB-high tumors not only did not exhibit a higher susceptibility to immune checkpoint blockade (ICB) vs. TMB-low tumors, but also exhibited a significantly lower susceptibility to ICB in BRCA, prostate cancer, etc. (McGrail et al., 2021). Interestingly, high expression levels of CTLA-4 and TIGIT were correlated with favorable prognosis in breast cancer (Fang et al., 2020). These are consistent with our results. Furthermore, we shed light on the tumor microenvironment and composition of immune cell infiltrates. The results of these analyses (Figures 7A,B) indicated that CD8⁺ T cells, plasma cells, naïve CD8⁺ T cells, M1 macrophages, memory B cells, and endothelial cells were more abundant in the low-risk subgroup, whereas M2 macrophages, M0 macrophages, and fibroblasts were more common in the high-risk subgroup. A substantial body of research has revealed that a high level of infiltration of T cells, especially cytotoxic CD8⁺ T cells, predicts a beneficial outcome (Bindea et al., 2013; Gentles et al., 2015; Fridman et al., 2017). Considering the fibroblasts in the high risk group were significantly higher than those in the low risk group (p < 0.001), we speculated that the high risk group were more abundant in cancer-associated fibroblast (CAF) compared to the low risk group. CAF is the main cell component of tumor microenvironment (TMB). Studies have shown that CAFs can assist the immune escape of breast cancer cells, promote proliferation, invasion and metastasis of breast cancer cells, inhibit immune response (Shimura et al., 2018; Gok Yavuz et al., 2019;Ivy X Chen et al., 2019) and inhibit T cell infiltration (Lei Li et al., 2021). This result is in accordance with our study.

In most malignancies, M2 macrophages, which are a significant subtype of macrophages, have been shown to correlate with increased tumor cell proliferation, development of an invasive phenotype, and chronic inflammation, and these cells have been correlated with a poor outcome in breast, gastric, ovarian, bladder, and prostate cancers (Josephs et al., 2015; Ruffell and Coussens, 2015; Fridman et al., 2017). Conversely, a high density of M1 macrophages seems to be correlated with acute inflammation and imply a favorable prognosis among patients with HCC, NSCLC, gastric, or ovarian cancers (Josephs et al., 2015; Ruffell and Coussens, 2015; Fridman et al., 2017). The results of our study support these conclusion.

Moreover, we found that the low-risk samples had a more robust ability for damage repair, whereas the high-risk samples had more immunosuppressive cells and signals and tumor and metastasis-related signals, which implies that the high-risk subgroup exhibited characteristics of immunosuppression and active tumor progression.

Furthermore, there were different proportions of the PAM50 subtypes in the two subsets (Figure 9A), as we found that the high-risk subgroup possessed more HER2+ samples, which are more invasive. In contrast, the low-risk subgroup possessed more luminal A samples, which are less invasive. In summary, we concluded that the low-risk subgroup was characterized by lower tumor aggressiveness and an active immune response, whereas the high-risk subgroup was characterized by higher tumor aggressiveness and an immune-suppressive response.

Finally, we learned that patients in the low-risk group were more responsive to treatment with gefitinib, epothilone B, and doxorubicin. In contrast, patients in the high-risk group were more responsive to treatment with docetaxel (Figures 9B–G). This phenomenon might provide valuable clinical treatment recommendations for high- and low-risk groups.

There are still some limitations to this study. First, the data was obtained only from a single TCGA dataset. The analysis of multiple datasets would have been more convincing. Second, the associations were analyzed solely by statistical analysis and were not validated experimentally. Lastly, when exploring the immune microenvironment, we did not illustrate the signalling pathways of the target genes at a deeper level. We should investigate the specific mechanisms of the AAM-related prognostic lncRNAs and immune cells in the future. There is still a long way to go to considerably optimise personalised immunotherapy management.

In conclusion, we attempted to shed light on the importance of AAM-associated lncRNAs in BRCA. The prognostic model built here might be acknowledged as an indispensable reference for predicting the outcome of patients with BRCA and help identify their immune and molecular characteristics. However, further studies are needed to illustrate this point.