BC has the highest incidence rate among all cancers globally, which causes tens of thousands of female deaths every year[4]. BC is characterized by tumor heterogeneity at the molecular level of tumor cells and the tumor microenvironment (TME)[51, 52]. Tumor heterogeneity complicates the aggressiveness and treatment of BC[53]. Recent studies have revealed lactate’s diverse roles in the TME. Although cancer cells have a sufficient oxygen supply, they still use glucose and produce lactate excessively, which could cause acidosis, angiogenesis, and immunosuppression[54]. In BC, lactate is correlated with the resistance to PI3K inhibitors[55]. In several cancers, lactate is essential in predicting prognosis, tumor microenvironment, and immune response[29, 30, 56]. However, the prognostic value of lactate in BC remains largely unknown. This is the first study investigating the role of lactate in predicting prognosis, immune status, and therapeutic response in BC.
We first identified 196 differential expression LRLs for further study. We used the univariate Cox regression analysis, LASSO, and multivariate Cox regression analysis to construct the LRLPS. Survival analysis and the time-dependent ROC curves confirmed the prognostic value and reliability of the LRLPS. The AUC of the risk score was higher than other clinicopathological characteristics, indicating the highest prognostic performance of the LRLPS. Subsequent univariate and multivariate Cox regression analyses further indicated the independent prognostic predict ability of the risk score. Stratified analysis showed that the LRLPS was suitable for patients in any clinical subgroups. Furthermore, the nomogram provided a powerful tool for clinicians to make decisions.
The GO/KEGG and GSEA indicated that the immune-related pathways differed between the two-risk groups. Previous research has demonstrated that lactate could regulate TMB. Through its ability to enhance the metabolic profile of the Treg and maintain acidity in the TME, lactate could enhance the immunosuppressive effect[57]. Excessive lactate inhibits T-cell proliferation, such as Natural killer, dendritic, and CD8 + T cells[58–60]. In addition, lactate could potentiate the anti-inflammatory effects by activating macrophages, promoting angiogenesis, tissue remodeling, and finally accelerating tumor growth and invasion[60]. Hence, we further explore the TIME through several algorithms. Tumor immune cell infiltration (TIICs) is a crucial component of the TIME. We calculated the levels of TIICs in BC with CIBERSORT. The high-risk group was enriched with the immunosuppressive immune cells, such as macrophages M0 and M2, which were also the critical members of EMT and cancer metastasis[61, 62]. Instead, CD4/8+ T cells, the vital factors in killing tumors and promoting immune response, were the main component in the low-risk group[41]. According to the ESTIMATE analysis, the low-risk group had a higher immune score and stromal content while lower tumor purity than the high-risk group.
Immunotherapy has been a new treatment modality in BC, especially for metastatic BC[63]. We further estimated the immunotherapy responses of the two risk groups. It is reported that ICIs antitumor relay on the CD8 + T cells, CD4 + T cells, and dendritic cells[64, 65]. The immune cell infiltration levels were positively correlated with the responsiveness to ICIs[66, 67]. As an essential biomarker for predicting cancer immunotherapy[68], the 27 ICPs expressed higher in the low-risk group. Therefore, we speculated that the low-risk group could respond better to immunotherapy and further verified the conclusion through TIDE and IPS analyses. All IPSs of CTLA4-/PD-1-, CTLA4 + /PD-1-, CTLA4-/PD-1 +, and CTLA4+/PD-1 + were higher in the low-risk group, indicted that the low-risk group had a better response to immunotherapy. Patients with high risk had the higher TMB in our study. Some research has indicated that TMB could act as a biomarker for predicting the response to ICIs[42, 43]. However, the predictive value varies among different cancers and might be insufficient in solid tumors[69]. Thus, ICIs could benefit low-risk patients, while other immunotherapy might be appropriate for high-risk patients. These results indicated the significant differences in the degree of immune cell infiltration and immunotherapy response between the two risk groups identified by lactate-related signature.
Regarding the seven LRLs in our signature, some have been studied before in other cancers. USP30-AS1 is involved in autophagy, proliferation, and apoptosis in acute myeloid leukemia, glioblastoma, and cervical cancer[70–72]. In our study, USP30-AS1 was positively correlated with the antitumor immune cells and the classic ICPs. These results indicated the potential role of USP30-AS1 in TME. C9orf163 could develop the tumor microenvironment through cytokine and chemokine signaling and might act as a tumor suppressor in anaplastic gliomas and pancreatic cancer[73, 74]. However, more research is required to clarify the molecular mechanism of the seven LRLs in BC.
In treating BC, chemotherapeutic drugs could reduce the tumor recurrence rate and be a primary treatment option for metastatic disease. However, chemo-resistance severely limited the clinical efficacy of chemotherapeutic drugs for BC patients[75]. Thus, we assessed the BC patients’ response to chemotherapy with the IC50 value. The pRRophetic showed that low-risk patients were more sensitive to the common chemotherapy drugs, such as 5-Fluorouracil, Sorafenib, Tamoxifen, Temozolomide, Temsirolimus, and Vinblastine. Furthermore, we performed the CellMiner database to predict the candidate small-molecule compounds. The results indicated that Ribavirin, Fulvestrant, SR16157, 8-Chloro-adenosine, and Methylprednisolone might benefit patients with high risk. In combination, these discoveries may provide BC patients with suitable treatment options.
However, there were still a few limitations to our study. We used the TCGA dataset for all analyses since other databases lacked the needed LRLs data, including the Gene Expression Omnibus (GEO) and METABRIC databases, which prevented us from verifying the results. Therefore, it is better to validate in a prospective cohort. Secondly, further studies on the biological functions of the seven LRLs are needed to be performed in vivo and in vitro.