Discussion
TNBC is a highly heterogeneous type of cancer ( Bianchini et al., 2016 ). Recent studies have classified TNBC based on different molecular or clinical subtypes and identified possible therapeutic targets, primarily based on transcriptomic subtypes ( Metzger-Filho et al., 2012 ; Garrido-Castro, Lin & Polyak, 2019 ; Hu et al., 2022 ). Cellular senescence promotes tumor regression through cell-non-autonomous and -autonomous mechanisms. Drugs inducing cancer cell senescence and modulating SASPs provide new targets for tumor therapy ( Calcinotto et al., 2019 ). However, no cellular senescence-based typing of TNBC has been established to date. In this study, we proposed cellular senescence molecular typing for the first time, and identified seven genes related to TNBC prognosis through analyses and established three molecular subtypes. Subsequently, DEGs among three molecular subtypes was adopted for the construction of risk model. Finally, a nonogram for clinical decision-making was designed.

Seven cellular senescence related genes exhibited different biological functions in TNBC or other cancer development. ETS2 is a key ETS transcriptional family member implicated in invasion and metastasis in TNBC ( Kollareddy & Martinez, 2021 ). A screening experiment conducted against CDK10 confirmed the ETS2 transcription factor as an interacting protein ( Guen et al., 2017 ). This finding indicated that ETS2 may also participate in cell cycle process. Serpin family E member 1 ( SERPINE1 ) is responsible for encoding plasminogen activator inhibitor 1, which is a primary inhibitor of tissue plasminogen activator ( Li et al., 2018 ). SERPINE1 has been detected in various cancer and involved in cancer invasion, migration, and angiogenesis ( Seker et al., 2019 ; Yang, Ma & Zhu, 2019 ). Also, SERPINE1 , a paclitaxel-resistant oncogene, is a putative target for TNBC treatment ( Zhang, Lei & Jing, 2020b ). FOS , a key regulator of TNBC cell proliferation and viability, is related to a poor patient prognosis ( Zhang, Lei & Jing, 2020a ). IL1A , a pro-inflammatory cytokine, enhances cell growth and invasiveness of TNBC cells. Abnormal IL1A induction correlates with a poor patient prognosis in TNBC ( You et al., 2021 ). IGFBP7 was reported to suppress breast tumor growth through the induction of apoptosis and senescence pathways ( Benatar et al., 2012 ). Overall, these findings disclosed the potential functions or the prognostic ability of selected hub genes and supported the reliability of our results.

TNBC is the most aggressive and heterogeneous subtype of breast cancer, and consists of several different microenvironmental phenotypes ( Xiao et al., 2019 ; Gruosso et al., 2019 ; Bareche et al., 2020 ). Previous studies have classified several molecular subtypes for TNBC. For instance, Lehmann and co-workers carried out clustering analysis and categorized TNBC patients into six subtypes, called basal-like 1, basal-like 2, immunomodulatory, luminal androgen receptor, mesenchymal, and mesenchymal stem-like subclusters ( Lehmann et al., 2011 ). Next, a transcriptome-wide analysis of Chinese TNBC samples clustered TNBC patients into four subtypes - BLIS, IM, LAR, and MES ( Tong et al., 2023 ). In this research, we classified TNBC patients into only three clusters based on senescence related characteristics, and our immunological analysis highlighted significant differences in TME among the three subtypes, which could support TNBC cellular senescence-based therapy and support clinical decision-making. Changes in the vital immune cells that make up the stroma around the tumor are correlated with tumor growth. TME metabolism changes are the result of the aberrant metabolic status of tumor cells ( Hinshaw & Shevde, 2019 ). The immune score produced by the ESTIMATE algorithm rates the immunological make-up and speed of tumor sample responses. Strong correlations exist between prognosis and tumor purity, or the proportion of malignant cells in the tumor tissue ( Aran, Sirota & Butte, 2015 ). Here, we used “ESTIMATE” to identify three subtypes in TME. Significant differences were found among the three subtypes, with patients in clusters 1 and 3 showing a high degree of immune infiltration. Actually, senescent cells secrete various factors which make uo senescence-relevant secretory phenotype, including pro-inflammatory factors (cytokines, chemokines, micro-RNAs) to induce inflammatory state ( Frasca et al., 2021 ).Therefore, in this work, TME-related cellular senescence characteristics are of interest in determining treatment strategies for TNBC and could be potential targets for individualized therapy.

We built a risk model based on genes in cellular senescence-related pathways and calculated risk scores based on four significant genes, including MMP28, CT83, ACP5, and KRT6A , and validated the relative expression levels of the four genes in TNBC cells. A previous study defined a subset of TNBC with poor prognosis based on the basal marker, KRT16 , the stem cell marker, WNT11 , and the EMT marker, MMP28 ( Yu et al., 2013 ), corroborating the rationality and validity of our gene selection approach. We constructed a risk model incorporating multiple genes, wherein MMP28 was a risk factor. EMT scores were calculated. Patients in cluster 2 had lower EMT scores than those in clusters 1 and 3, suggesting that the former were more likely to experience metastasis. CT83 is a gene specific to TNBC and its hypermethylation is oncogenic in breast cancer ( Chen et al., 2021 ). Overexpression of four genes, including ART3, FABP7, TTYH1, and CT83 correlated positively with patients’ life expectancy rates in TNBC ( p < 0.05) ( Zhong et al., 2020 ). This is supported by our study revealing the down-regulated expression of this gene in the high-risk group. A model comprising CT83 and FABP7 constructed using Cox regression, Kaplan–Meier, and ROC analyses correlated significantly with OS in TNBC. ACP5 has been shown to correlate with poor patient prognosis in cancers such as gastric and rectal cancers, and to correlate with malignant tumor progression by affecting malignant phenotypes such as cell proliferation and invasion ( Bian et al., 2019 ; An et al., 2021 ). In breast cancer-related studies, the TRAP / ACP5 /uteroferrin/purple acid phosphatase/PP5 signaling axis can act as a driver mediating breast cancer invasion and mediate cancer malignant progression at the cellular level ( Krumpel et al., 2015 ). This informs the present study, in which we reveal that this gene is up-regulated for expression in TNBC cells and promotes cell migration and invasive ability, providing a reference for subsequent work to uncover the mechanisms by which this gene affects TNBC progression at the cellular level. Ultimately, we identified potential biomarkers implicated in the onset and progression of TNBC. These genes may provide novel insights into the tumorigenesis of TNBC and serve as independent prognostic factors for TNBC. Taken together, these findings demonstrate the validity of the risk model developed in this study.

Some limitations exist in this study that should be stated prior to outlining the conclusions. Firstly, we could not demonstrate the difference and role of these subtypes in TNBC progression because of a lack of progression data such as tumor stage of the patients. Second, we only verified the role of the key signature gene associated with RiskScore in the regulation of the malignant phenotype of TNBC cells at the cellular level, but we did not verify the molecular mechanisms of the signature gene, such as influencing cytokine secretion to regulate cellular senescence, to corroborate the results of the bioinformatic analyses. Finally, rather than evaluating our cohort, the data were taken from available databases. As a result, there was still little evidentiary impact. Prospective studies are thus needed to validate the therapeutic and prognostic value of the established subtypes in TNBC.