Background: The NCCN clinical guidelines recommended core needle biopsy for breast lesions classified as Breast Imaging Reporting and Data System (BI-RADS) 4, while category 4A lesions are only 2-10% likely to be malignant. Thus, a large number of biopsies of BI-RADS 4A lesions were ultimately determined to be benign, and those unnecessary biopsies may incur additional costs and pains. However, it is important to emphasize that the current risk prediction model focuses primarily on the details and complex risk features of US or MG findings, which may be difficult to apply in order to benefit from the model. To stratify and manage BI-RADS 4A lesions effectively and efficiently, a more effective and practical predictive model must be developed. Methods: We retrospectively analyzed 465 patients with BI-RADS ultrasonography (US) category 4A lesions, diagnosed between January 2019 and July 2019 in Tianjin Medical University Cancer Institute and Hospital and National Clinical Research Center for Cancer. Univariate and multivariate logistic regression analyses were conducted to identify risk factors. To stratify and predict the malignancy of BI-RADS 4A lesions, a nomogram combining the risk factors was constructed based on the multivariate logistic regression results. In order to determine the predictive performance of our predictive model, we used the concordance index (C-index), calibration curve, and receiver operating characteristic (ROC), and the decision curve analysis (DCA) to assess the clinical benefits. Results: Based on our analysis, 16.3% (76 out of 465) of patients were pathologically diagnosed with malignant lesions, while 83.6% (389 out of 465) were diagnosed with benign lesions. According to univariate and multivariate logistic regression analysis, age (OR = 3.414, 95%CI:1.849-6.303), nipple discharge (OR = .326, 95%CI:0.157-.835), palpable lesions (OR = 1.907, 95%CI:1.004-3.621), uncircumscribed margin (US) (OR = 1.732, 95%CI:1.033-2.905), calcification (mammography, MG) (OR = 2.384, 95%CI:1.366-4.161), BI-RADS(MG) (OR = 5.345, 95%CI:2.934-9.736) were incorporated into the predictive nomogram (C-index = .773). There was good agreement between the predicted risk and the observed probability of recurrence. Furthermore, we determined that 153 was the best cutoff score for distinguishing between patients in the low- and high-risk groups. Malignant lesions were significantly more prevalent in high-risk patients than in low-risk patients. Conclusion: Based on clinical, US, and MG features, we present a predictive nomogram to reliably predict the malignancy risk of BI-RADS(US) 4A lesions, which may assist clinicians in the selection of patients at low risk of malignancy and reduce the number of false-positive biopsies.
Background Globally, breast cancer (BC) is the most common type of cancer and the second leading cause of cancer-related deaths among women. Early diagnosis and survival outcomes will be improved through the identification of modifiable risk factors and the development of better disease management strategies. There are many similarities between endometriosis and breast cancer, both in terms of risk factors and developmental characteristics. Thus, it would be beneficial to explore the common mechanisms behind the onset of BC and endometriosis to develop more effective intervention strategies in the future. In this study, bioinformatics was used to explore the key molecules and pathways that mediate the co-occurrence of BC and endometriosis. Method From the Gene Expression Omnibus (GEO) database, datasets for BC (GSE15852) and endometriosis (GSE5108) were downloaded. By using the GEO2R online tool, we were able to identify the differentially expressed genes (DEGs) between two diseases. Afterward, a protein-protein interaction network (PPI) was constructed based on DEG enrichment analysis. Additionally, the hub genes were identified using the STRING database and Cytoscape software. We investigated the relationship between hub gene expression levels and clinical expression, pathological stage, age, and prognosis. As a final step, transcription factor interaction, stemness score, and immune cell infiltration analysis were conducted on hub genes in BC. Results We identified 33 overlapping DEGs (18 downregulated genes and 15 upregulated genes) for further analysis. The significant functional pathways of DEGs were enriched in regulating the pluripotency of stem cells and the mis-regulation of transcription in cancer. Additionally, five key hub genes were identified, including HOXA10, PAX8, MSX1, FGFR1, and INHBA. Pathological stages, age, stemness score, and immune infiltration were associated with the expression level of hub genes. Conclusion A novel insight into the molecular mechanism of endometriosis complicated with BC is provided by the finding that HOXA10, PAX8, MSX1, FGFR1, and INHBA were hub genes for the co-occurrence of BC and endometriosis.
Background: Breast cancer (BC) has become the most common type of cancer and the second most common cause of cancer-related death. In comparison with other subtypes of breast cancer, triple-negative breast cancer (TNBC) is highly aggressive, more likely to metastasize, and has a shorter survival time. MiRNAs play an inhibitory or promoting role in cancer, and are involved in several cell signaling pathways, including growth, proliferation, differentiation, and survival. Vasculogenic mimicry (VM) is associated with invasive disease, tumor spread, metastasis, and poor prognosis. Additional research is needed to determine the mechanisms governing VM formation in TNBC. Methods: We measured RNA and protein expression using quantitative real-time PCR (RT-qPCR) and western blotting. Assays assessing cell proliferation were conducted with CCK-8, cell cycle, and colony formation. Cell migration and invasion were evaluated using transwells, scratch tests, and high-intensity imaging. Luciferase reporter assays were used to confirm miR-651-3p and CAV1 target relationships. Results: In TNBC, miR-651-3p was significantly overexpressed, implicating it as an oncogene. By inhibiting CAV1 transcriptional synthesis, miR-651-3p can enhance the activity of EGFR pathways, leading to promotion of TNBC proliferation, VM formation, and migration. Conclusion: It was determined that miR-651-3p/CAV1/EGFR axis could be a therapeutic target for TNBC in this study.
Background Because of the complexity and heterogeneity, therapeutic effect of breast cancer varies in each subtype, which is classified based on the molecular markers for estrogen or progesterone receptors and human epidermal growth factor 2. Thus, novel comprehensive and precise molecular indicators in breast carcinogenesis are urgently in need. Methods The expression of ZNF133 in breast cancer tissues was detected by immunohistochemical staining. The interaction between KAP1 complex and ZNF133 was identified by affinity purification-coupled mass spectrometry. The regulatory mechanisms were validated by luciferase reporter assay and co-immunoprepitation. The target genes of ZNF133 was determinged by chromatin immunoprecipitation-based deep sequencing. Gain-or-loss-of-function assays were used to identify the function and underlying mechanisms of ZNF133 in breast cancer. Cancer cell proliferation, invasion, and tumorigenesis of breast cancer cells were analyzed using cell counting assays, colony formation, transwell, and xenograft tumor models. Results Here we report that ZNF133, a zinc-finger protein, is negatively associated with advanced pathological staging and poor survival of breast carcinomas. Moreover, ZNF133 is a transcription repressor, physically associated with the KAP1 complex and transcriptionally represses a cohort of genes including L1CAM that are critically involved in cell proliferation and motility. We demonstrate that the ZNF133/KAP1 complex inhibits the proliferation and invasion of breast cancer cells in vitro and suppresses breast cancer growth and metastasis in vivo through dampening the transcription of L1CAM. Conclusion Our study ascertains the value of ZNF133 and L1CAM level in the diagnosis and prognosis of breast cancer, contributes to the deeper understanding of the regulation mechanism of ZNF133 for the first time, and provides a new therapeutic strategy and precise intervention target for breast cancer.
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