Triple negative breast cancer (TNBC) lacks well-defined molecular targets and is highly heterogenous, making treatment challenging. Using gene expression analysis, TNBC has been classified into four different subtypes: basal-like immune-activated (BLIA), basal-like immune-suppressed (BLIS), mesenchymal (MES), and luminal androgen receptor (LAR). However, there is currently no standardized method for classifying TNBC subtypes. We attempted to define a gene signature for each subtype, and to develop a classification method based on machine learning (ML) for TNBC subtyping. In these experiments, gene expression microarray data for TNBC patients were downloaded from the Gene Expression Omnibus database. Differentially expressed genes unique to 198 known TNBC cases were identified and selected as a training gene set to train in seven different classification models. We produced a training set consisting of 719 DEGs selected from uniquely expressed genes of all four subtypes. The highest average accuracy of classification of the BLIA, BLIS, MES, and LAR subtypes was achieved by the SVM algorithm (accuracy 95–98.8%; AUC 0.99–1.00). For model validation, we used 334 samples of unknown TNBC subtypes, of which 97 (29.04%), 73 (21.86%), 39 (11.68%) and 59 (17.66%) were predicted to be BLIA, BLIS, MES, and LAR, respectively. However, 66 TNBC samples (19.76%) could not be assigned to any subtype. These samples contained only three upregulated genes (EN1, PROM1, and CCL2). Each TNBC subtype had a unique gene expression pattern, which was confirmed by identification of DEGs and pathway analysis. These results indicated that our training gene set was suitable for development of classification models, and that the SVM algorithm could classify TNBC into four unique subtypes. Accurate and consistent classification of the TNBC subtypes is essential for personalized treatment and prognosis of TNBC.

Triple-negative breast cancer (TNBC) presents an important clinical challenge, as it does not respond to endocrine therapies or other available targeting agents. FOXM1, an oncogenic transcriptional factor, has reported to be upregulated and associated with poor clinical outcomes in TNBC patients. In this study, we investigated the anti-cancer effects of FDI-6, a FOXM1 inhibitor, as well as its molecular mechanisms, in TNBC cells. Two TNBC cell lines, MDA-MB-231 and HS578T, were used in this study. The anti-cancer activities of FDI-6 were evaluated using various 2D cell culture assays, including Sulforhodamine B (SRB), wound healing, and transwell invasion assays together with 3D spheroid assays, mimicking real tumour structural properties. After treatment with FDI-6, the TNBC cells displayed a significant inhibition in cell proliferation, migration, and invasion. Increased apoptosis was also observed in the treated cells. In addition, we found that FDI-6 lead to the downregulation of FOXM1 and its key oncogenic targets, including CyclinB1, Snail, and Slug. Interestingly, we also found that the FDI-6/Doxorubicin combination significantly enhanced the cytotoxicity and apoptotic properties, suggesting that FDI-6 might improve chemotherapy treatment efficacy and reduce unwanted side effects. Altogether, FDI-6 exhibited promising anti-tumour activities and could be developed as a newly effective treatment for TNBC.

Background: Urinary extracellular vesicles (uEVs) have shown potential as non-invasive biomarkers for various cancers. However, research on the relationship between uEVs levels and cargo changes after breast cancer (BC) surgery remains limited. We investigated changes in uEVs levels and proteomes in patients with BC pre- and post-surgery. Methods: uEVs isolated from patients with BC (n=30) pre- and post-surgery were characterized using western blotting, transmission electron microscopy, nanoparticle tracking analysis, and proteomic analysis. Based on post- versus pre-surgery uEVs levels, patients were classified into increasing (n=8) and decreasing (n=22) trend groups. Results: Proteomic analysis revealed remarkable patterns of protein expression pre- and post-surgery in both groups, with 61 and 242 differentially expressed proteins (DEPs) in the increasing and decreasing trend groups, respectively. Downregulated proteins in the decreasing trend group were enriched in cell cycle pathways associated with cancer promotion, whereas upregulated proteins were related to the regulation of tissue development. In contrast, upregulated DEPs in the increasing trend group were involved in cancer cell proliferation, invasion, and migration. Conclusions: Changes in uEV proteomes may be related to pathological changes in patients with BC.

Background/Objectives: Current prophylactic human papillomavirus (HPV) vaccines rely on major capsid proteins (L1). Although highly immunogenic, L1-based immunity is clade-restricted owing to the hypervariability of HPV strains across different geographic regions. This restricts cross-protection against diverse, localized, and non-vaccine viral strains circulating in specific areas, leaving these populations vulnerable to regional genotypes. Methods: We aimed to design a cross-protective vaccine tailored to local viral diversity through evolutionary and immunoinformatic analyses of circulating HPV strains in Thailand. Both major (L1) and minor (L2) capsid proteins were analyzed to evaluate selective evolutionary pressures, structural sequence conservation, and cross-strain promiscuity of predicted B- and T-cell epitopes using the IEDB database. The predicted immunogenic profile of the combined L1/L2 proteins was further assessed using in silico immune response simulations. Results: Evolutionary analysis confirmed that although the L1 protein is under strict purifying selection, it exhibits high regional interclade variability. L1-only epitopes demonstrated restricted cross-strain conservation, creating structural blind spots against divergent regional variants. The minor capsid protein (L2) harbored highly conserved dual-action core peptides that are evolutionarily conserved across multiple HPV type. Incorporation of conserved L2 regions alongside L1 sequences may broaden the predicted epitope coverage and help address limitations associated with L1 variability. The proposed multi-targeted L1/L2 construct therefore represents a computational framework for the development of candidate cross-reactive HPV vaccines relevant to circulating Thai genotypes. However, experimental validation remains necessary to determine immunogenicity, cross-neutralization potential, and protective efficacy. Conclusion: Our findings highlight these conserved HPV epitopes as potential targets for future development of next-generation cross-subtype prophylactic vaccines with broader population coverage.