The occurrence, development, and treatment of breast cancer have been extensively studied in the past 20 years (25). The classification based on traditional clinicopathological staging and biomarkers can make breast cancer treatment more accurate and effective (26,27). However, as a special subtype of TNBC, the standard treatment is still single, and there is no targeted treatment plan (28). Therefore, finding biomarkers with prognostic value for TNBC is an urgent requirement for precision treatment.
Gene mutations are the basis for the occurrence and development of tumors, and also play an essential role in the process of metastasis and drug resistance of tumors [9–11]. At present, the research on the mutant genes of TNBC mainly focuses on the highly mutated pathogenic genes such as TP53, BRCA1 and BRCA2 [16,17], while there are few studies on the genes with low mutation frequency. Studies have shown that TNBC is a disease with high mutation heterogeneity. Tumors of some patients have a small number of hidden pathways and a few mutations, while other patients' tumors contain extensive mutation burden and multi-pathway involvement . Therefore, we believe that single gene mutation and its regulatory factors may not be enough to reflect the impact on tumor prognosis. We hope to determine the factors and markers that regulate mutations through the different probability of gene mutation among TNBC samples.
The data of breast cancer mutations were downloaded from the TCGA database, and the frequency of mutated genes was counted for each sample. Our results showed that the mutation frequency of TNBC samples was significantly higher than that of nTNBC samples (Fig. 2c), and there were also significant differences in the mutation frequency between TNBC samples (Fig. 3a). We identified 558 differentially expressed genes that are believed to be regulatory signatures closely related to gene mutations. We used these differential genes for GO analysis, and the results showed that the enrichment in BP, CC and MF was closely related to immunity. By combining with survival data, we screened 20 genes for an unsupervised cluster. TNBC patients were divided into two subtypes, and there were significant differences in mutation frequency between the two subtypes (P < 0.01). Therefore, we named them as HM subtype and LM subtype, respectively, and demonstrated that the LM subtype prognosis is better than that of the HM subtype (P < 0.05). Through KEGG enrichment analysis of these two subtypes, we found that the HM subtype's enrichment pathway is related to gene mutation and gene repair. In contrast, the enrichment pathway of the LM subtype is closely related to immunity. According to the enrichment results of GO and KEGG pathways, we found that there was a close relationship between mutant differences and immunity. Can we assume that immunity is an essential factor in regulating gene mutations?
We then compared the levels of immune cell infiltration between the two subtypes. Our results showed that the proportion of macrophage M1, Tregs, CD8 T cells and CD4 T cells memory activated were relatively high in the LM subtype. The abundance of macrophage M0 and macrophage M2 was relatively high in the HM subtype. The role of macrophages in tumors is complex and bidirectional. Macrophage M1 initiates cytokines' production in the tumor microenvironment and promotes tumor cell destruction . At the same time, macrophage M2, especially tumor-associated macrophages, plays a vital role in tumor growth and metastasis [31,32]. Tregs maintain immune homeostasis by suppressing the immune response and antitumor effects in the tumor microenvironment . Thus, anti-immunotherapy with Tregs, such as Tregs' elimination, can improve immunotherapy efficacy . Activation of CD4 + T cell memory can promote the proliferation of effector T cells and enhance the anti-tumor immune response . CD8 + T cells are an essential component of the tumour immune response and play a key role in killing tumor cells [36. These results fully indicate that the LM subtype of TNBC has a more robust tumor killing function, and more Tregs may increase the chance of immune escape. The HM subtype appears to have greater immunodeficiency, leading to faster tumor progression and metastasis.
There are few studies on the correlation between somatic mutations of TNBC and immune cell infiltration. In other tumor studies, most results suggest that tumor mutation load is positively correlated with immune cell infiltration and serves as a predictor of immunotherapy [37–39]. However, our results showed a significant negative correlation between somatic mutations and immune cell infiltration in TNBC, which was similar to the effects of Anton Safonov et al .
According to the hypothesis of three stages between tumor and immunity , this theory can well explain our results. Different subtypes of TNBC are in different immune stages. The LM subtype has higher immune enrichment and immune checkpoint markers expression and has a better prognosis. In this subtype, tumor and immunity are in close balance. Cancer with an HM subtype lacking immune invasion, which has escaped immune surveillance and is no longer cleared by immune cell clones, has a worse prognosis.
These results suggest that immune checkpoint inhibitors may be an effective means of immune-monitoring in patients with immune-rich TNBC in equilibrium tumors. For TNBC patients with little or no immune infiltration, more sophisticated immunotherapy strategies may be required to reactivate the immune response against a population with clono-diverse tumors.
Through multivariate analysis, we identified mRNA signatures of gene mutation regulator (GMsig) containing six genes (Sema4b, IL22ra2, GPR25, TCF7, BACH2 and CLND5): Sema4b and CLND5 were the risk factors for TNBC; the other four mRNAs were the protective factors for TNBC. Through a literature search, no relevant studies of these six genes in TNBC were found. Sema4b, as a risk factor for TNBC, has not been widely studied in tumors. It has been reported to inhibit tumor progression in non-small cell lung cancer (NSCLC) . It has also been shown that lack of Sema4b leads to reduced astrocyte proliferation , similar to our results. Because SEMA4B is a gene we found that may have a mutation-regulating effect, and its expression is significantly associated with prognosis in patients with TNBC (Fig. 10a). Further literature review showed that gene mutations were positively correlated with prognosis in NSCLC , while high mutations in astrocytoma had poor prognosis [45, 46], similar to our results. The above evidence proves that, regardless of the relationship between mutation and prognosis, SEMA4B positively correlates with mutations. Therefore, we believe that SEMA4B is involved in regulating gene mutations in various malignant tumors. The effect of SEMA4B on prognosis is consistent with the impact of mutations on the prognosis of tumors.
We established a TNBC mutation regulation model using TCGA database, verified its accuracy using the ROC curve, and verified it using external data sets. However, the current research still has some limitations. In further studies, a large sample of the clinical cohort is still needed to validate the model. In addition, basic experiments are required to verify the association between tumor mutations and immune cell infiltration in TNBC. There are few studies on SEMA4B, and we have listed some evidence to prove that SEMA4B is involved in the regulation of mutation. The mechanism of regulating gene mutation is still needed to be further studied.