The survival rate and prognosis of BC patients have improved dramatically in recent years, thanks in large part to the clinical application of ICIs. However, not all patients respond well to ICIs, and a sizable number of patients are either drug resistant or drug insensitive(Z. Yang, Sun, Lee, & Chan, 2022). According to reports, the TGF-β signaling pathway in TME contributes to immune evasion and ICIs resistance in cancer; hence, inhibiting TGF-β can improve the effectiveness of ICIs(Bai, Yi, Jiao, Chu, & Wu, 2019; Wu et al., 2022). TGF-β promotes the occurrence of BC metastasis by inducing EMT in this process, which is another important factor in patients' poor prognosis(Y. Yu et al., 2018). In conclusion, TGF-β signaling pathway plays a significant role in the occurrence, development, and treatment of BC, but its relationship with other factors in TME and its guiding significance for the prognosis of BC patients require further investigation.
First, the genes associated with prognostic OS were identified in this study using univariate COX regression analysis in the TCGA-BRCA dataset. These prognostic OS-related genes were then cross-referenced with 225 TSRGs. On the intersection genes, LASSSO regression analysis and multivariate COX regression analysis were continued, and the best prognostic model containing five genes (FUT8, IFNG, ID3, KLF10, and PARD6A) was finally determined.
Fucosyltransferase 8 (FUT8) is upregulated in many cancers because it promotes core fucosylation, which is important in cancer cell metastasis and immune evasion (Bastian, Scott, Elliott, & Munkley, 2021). Through core fucosylation, the process of FUT8 promoting BC cell transfer also promotes TGF-β signaling and EMT processes (Tu, Wu, Lin, Kannagi, & Yang, 2017). Numerous studies have shown that inhibiting FUT8 can slow the progression of BC and improve patient outcomes (Huang et al., 2021; Satoh, Iida, & Shitara, 2006). PARD6A is similar to FUT8 and is also associated with EMT during BC cell invasion (Viloria-Petit et al., 2009). IFNG, a gene that encodes IFN-γ, is important in anti-tumor immunity. IFN-γ can be released by TIICs to exert anti-tumor effects and can also induce the expression of PD-L1 to improve patient outcomes (Gao et al., 2018; Shihab et al., 2020). Through controlling the Smad signaling pathway, KLF10, a Krüppel-like zinc finger transcription factor, has been demonstrated to have a significant role in inducing apoptosis in TGF-β (Subramaniam, Hawse, Rajamannan, Ingle, & Spelsberg, 2010). ID3 promotes proliferation and invasion of human MCF-7 breast cancer cells (Chen et al., 2012). Importantly, KLF10 was regarded as a risk factor in our study, while FUT8 and PARD6A were deemed protective variables. We examined the expression levels of these five genes in normal tissue and BC tissue to further investigate the causes of this conundrum. The findings are in line with the findings of the previously published study; FUT8 and PARD6A are more expressed in tumors, but KLF10 is more highly expressed in normal tissues. Therefore, we conclude that the results in different BC risk groups may be influenced by the BC molecular subtype, resulting in some differences.
We investigated the potential link between TGF-β risk scores and TME of BC patients in this study. First, patients with low TGF-β risk scores had higher levels of gamma delta T cell (γδT cell) and T follicular helper cell (Tfh cell) infiltration in the TME, and the same conclusion was reached when normal and tumor tissues were compared. γδT cell plays an important role in both innate and adaptive immunity (Song, Wei, & Li, 2022). It, for example, recognizes tumor cells without the presence of major histocompatibility complex (MHC) antigens and kills tumor cells via powerful anti-inflammatory and cytotoxic effects (Morrow, Roseweir, & Edwards, 2019). Tfh cells promote anti-tumor immunity in a CD8+-dependent manner, and they are also important effector cells in anti-PD-1/PD-L1 therapy (Niogret et al., 2021). The correlation between TGF-β risk score and six immune checkpoints in TME was then compared, and the results showed that TGF-β risk score was negatively correlated with immune checkpoints in all datasets examined. Therefore, patients in the low TGF-β risk group were also more likely to have a good ICI treatment outcome, which was also confirmed by TIDE and IPS comparisons between the two groups Furthermore, the current general understanding of IFNG is that it can enhance the effect of anti-PD-1 therapy (M. Yu et al., 2021), and we found that IFNG expression was positively correlated with multiple immune checkpoints, including PDCD1. It should be noted that IFNG, which is mostly found in CD8 + T cells, is implicated in T cell immunity and cancer immunotherapy. The interferon gamma (IFNγ) generated by CD8 + T cells downregulates the expression of SLC3A2 and SLC7A11, resulting in a reduction in cystine absorption by tumor cells, causing lipid peroxidation and ferroptosis(Wang et al., 2019).
We simulated differences in the treatment effects of multiple drugs between high- and low-risk groups, implying that low-risk patients were more sensitive to ICIs. We also predicted that targeted chemotherapy agents would be used in both high- and low-risk groups to improve BC survival. Bortezomib, a proteasome inhibitor that inhibits myeloma growth and has an anabolic effect on bone, is an important treatment for BC bone metastases (Suvannasankha & Chirgwin, 2014). Everolimus, a rapamycin derivative, inhibits BC cell growth and aggressiveness via the PI3K/AKT/mTOR signaling pathway (Du et al., 2018). To circumvent tamoxifen resistance, the mTORC1/2 dual inhibitor (AZD8055) can downregulate HSPB8 (Shi et al., 2018). By blocking the insulin-like growth factor 1 receptor (IGF1R), BMS.754807 inhibits the proliferation of BC cells and increases their susceptibility to the chemotherapy drug cisplatin (O'Flanagan et al., 2016). By inhibiting the IL-6/STAT3 signaling pathway, sabutoclax effectively kills cancer stem cells in BC (Hu et al., 2018). There is currently no evidence to support the role of other drugs in the treatment of BC (CDK9.1, BI.2536, and Dihydrorotenone), and the relationship between these drugs and BC needs to be investigated further.
Our study had some limitations as well. First, all of our models were built retrospectively using open databases, and large, multi-center prospective studies are required to investigate their clinical application value. Second, because of differences in the data in the existing database, the ROC curve selection in the GSE42568 validation set validates the model's predictive ability at 1, 3, and 5 years rather than 3, 5, and 10 years. Third, our study only shows a preliminary link between TGF-β and TME in BC patients. So further investigation of their specific mechanisms will require in vivo or in vitro experiments in the future. Furthermore, more research and experiments are required to investigate BC's more nuanced classification and typing criteria.