The notion of personalized medicine demands proper prognostic biomarkers to guide the optimal therapy for an invasive breast cancer patient. However, various risk prediction models based on conventional clinicopathological factors and emergent molecular assays have been frequently limited by either a low strength of prognosis or restricted applicability to specific types of patients. Therefore, there is a critical need to develop a strong and general prognosticator.

Background: The YTH domain family protein 3 (YTHDF3) is an important N6-methyladenosine (m⁶A) reader which is involved in multiple cancers. However, the biological role and mechanisms of action for YTHDF3 in triple-negative breast cancer (TNBC) remains to be elucidated. Methods: The expression of YTHDF3 in TNBC tissues was evaluated using The Cancer Genome Atlas (TCGA) database, BC-GenExMiner, and immunohistochemistry (IHC) staining. Cell migration, invasion, and epithelial-mesenchymal transition (EMT) were validated by wound healing assays, transwell assays, and Western blot (WB) analyses. The association between YTHDF3 and zinc finger E-box-binding homeobox 1 (ZEB1) was confirmed by Pearson correlation analysis. RNA-binding protein immunoprecipitation (RIP) assays and mRNA actinomycin stability analyses were applied to confirm whether YTHDF3 could interact with ZEB1in an m⁶A-dependent manner. Results: The expression of YTHDF3 was correlated with poorer disease-free survival (DFS) and overall survival (OS) in TNBC patients. Functional experiments indicated that YTHDF3 positively regulated cell migration, invasion, and EMT in TNBC cells. Moreover, ZEB1 was identified as a key downstream target for YTHDF3 and YTHDF3 could enhance ZEB1 mRNA stability in an m⁶A-dependent manner. Inhibition of YTHDF3 reduced migration, invasion, and EMT, all of which were reversed by rescue experiments overexpressing ZEB1. Conclusions: The findings herein confirmed that the YTHDF3/ZEB1 axis plays an important role in the progression and metastasis of TNBC. YTHDF3 is a promising prognosis biomarker and potential therapeutic target for patients with TNBC.

Background: Collagen fibers play an important role in tumor initiation, progression, and invasion. Our previous research has already shown that large-scale tumor-associated collagen signatures (TACS) are powerful prognostic biomarkers independent of clinicopathological factors in invasive breast cancer. However, they are observed on a macroscale and are more suitable for identifying high-risk patients. It is necessary to investigate the effect of the corresponding microscopic features of TACS so as to more accurately and comprehensively predict the prognosis of breast cancer patients.Methods: In this retrospective and multicenter study, we included 942 invasive breast cancer patients in both a training cohort (n= 355) and an internal validation cohort (n= 334) from one clinical center and in an external validation cohort (n= 253) from a different clinical center. TACS corresponding microscopic features (TCMFs) were firstly extracted from multiphoton images for each patient, and then least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust features to build a TCMF-score. Finally, the Cox proportional hazard regression analysis was used to evaluate the association of TCMF-score with disease-free survival (DFS).Results: TCMF-score is significantly associated with DFS in univariate Cox proportional hazard regression analysis. After adjusting for clinical variables by multivariate Cox regression analysis, the TCMF-score remains an independent prognostic indicator. Remarkably, the TCMF model performs better than the clinical (CLI) model in the three cohorts and is particularly outstanding in the ER-positive and lower-risk subgroups. By contrast, the TACS model is more suitable for the ER-negative and higher-risk subgroups. When the TACS and TCMF are combined, they could complement each other and perform well in all patients. As expected, the full model (CLI+TCMF+TACS) achieves the best performance (AUC0.905, [0.873–0.938]; 0.896, [0.860–0.931]; 0.882, [0.840–0.925] in the three cohorts).Conclusion: These results demonstrate that the TCMF-score is an independent prognostic factor for breast cancer, and the increased prognostic performance (TCMF+TACS-score) may help us develop more appropriate treatment protocols.

Early detection of cancer can help patients with more effective treatments and result in better prognosis. Unfortunately, established cancer screening technologies are limited for use, especially for multi-cancer early detection. In this study, we described a serum-based platform integrating surface-enhanced Raman spectroscopy (SERS) technology with resampling strategy, feature dimensionality enhancement, deep learning and interpretability analysis methods for sensitive and accurate pan-cancer screening. Totally, 1655 early-stage patients with breast cancer (BC, n = 569), lung cancer (LC, n = 513), thyroid cancer (TC, n = 220), colorectal cancer (CC, n = 215), gastric cancer (GC, n = 100), esophageal cancer (EC, n = 38), and 1896 healthy controls (HC) were enrolled. The serum SERS spectra were obtained from each participant. Data dimension enhancement was conducted by heatmap transformation and continuous wavelet transform (CWT). The dimensionalization SERS spectral data were subsequently analyzed by residual neural network (ResNet) as convolutional neural network (CNN) algorithm. Class activation mapping (CAM) method was performed to elucidate the potential biological significance of spectral data classification. All participants were divided into a training set and a test set with a ratio of 7:3. The BorderlineSMOTE method was selected as the most appropriate resampling strategy and the deep neural network (DNN) model achieved desirable performance among all groups (accuracy rate: 93.15%, precision rate: 88:46%, recall rate: 85.68%, and F1-score: 86.98%), with the generated AUC values of 0.991 for HC, 0.995 for BC, 0.979 for LC, 0.996 for TC, 0.994 for CC, 0.982 for GC, and 0.941 for EC, respectively. Furthermore, the combination use of SERS spectra data and ResNet (form of heatmap) were also capable of effectively distinguishing different categories and making accurate predictions (accuracy rate: 94.75%, precision rate: 89.02, recall rate: 86.97, and F1-score: 87.88), with the AUC values of 0.996 for HC, 0.995 for BC, 0.988 for LC, 0.999 for TC, 0.993 for CC, 0.985 for GC, and 0.940 for EC, respectively. Additionally, strong wave number range of the spectral data was observed in the CAM analysis. Our study has offered a highly effective serum SERS-based approach for multi-cancer early detection, which might shed new light on cancer screening in clinical practice.

Background: Cytidine nucleotide triphosphate synthase 1 (CTPS1) is a CTP synthase which play critical roles in DNA synthesis. However, its biological regulation and mechanism in triple-negative breast cancer (TNBC) has not been reported yet. Methods: The expression of CTPS1 in TNBC tissues was determined by GEO, TCGA databases and immunohistochemistry (IHC). The effect of CTPS1 on TNBC cell proliferation, migration, invasion, apoptosis and tumorigenesis were explored in vivo and in vitro. In addition, the transcription factor Y-box binding protein 1 (YBX1) was identified by bioinformatics methods, dual luciferase reporter and chromatin immunoprecipitation (CHIP) assays. Pearson correlation analysis was utilized to assess the association between YBX1 and CTPS1 expression. Results: CTPS1 expression was significantly upregulated in TNBC tissues and cell lines. Higher CTPS1 expression was correlated with a poorer disease-free survival (DFS) and overall survival (OS) in TNBC patients. Silencing of CTPS1 dramatically inhibited the proliferation, migration, invasion ability and induced apoptosis of MDA-MB-231 and HCC1937 cells. Xenograft tumor model also indicated that CTPS1 knockdown remarkably reduced tumor growth in mice. Mechanically, YBX1 could bind to the promoter of CTPS1 to promote its transcription. Furthermore, the expression of YBX1 was positively correlated with CTPS1 in TNBC tissues. Rescue experiments confirmed that the enhanced cell proliferation and invasion ability induced by YBX1 overexpression could be reversed by CTPS1 knockdown. Conclusion: Our data demonstrate that YBX1/CTPS1 axis plays an important role in the progression of TNBC. CTPS1 might be a promising prognosis biomarker and potential therapeutic target for patients with triple-negative breast cancer.