Search

This study aimed to evaluate the clinical utility of small breast epithelial mucin (SBEM) as a prognostic biomarker in an independent patient cohort. The paraffin-embedded tissues and clinicopathological data of 105 patients with breast cancer were collected, and the expression of SBEM in breast cancer samples was detected by immunohistochemical staining. The correlations between clinicopathological variables and the expression of SBEM were analyzed, and its significance as a prognostic indicator for breast cancer patients was determined. Immunohistochemical staining revealed that SBEM was expressed mostly in the cytomembrane and cytoplasm, with markedly increased SBEM expression (≥4 points on staining intensity) observed in 34 of 105 breast cancer tissues (32.4%). Elevated expression of SBEM was found to be significantly associated with larger tumor size (P = 0.002), more frequent lymph node metastasis (P = 0.029), advanced tumor node metastasis stage (P = 0.005), reduced expression of the progesterone receptor (PR) (P = 0.002), and a higher Ki-67 index (P = 0.006). Survival analysis indicated that patients with elevated SBEM expression had worse overall survival (OS) (5-year OS rate: 50.5 vs 93.9% for high and low SBEM expression, respectively, P < 0.001) and disease-free survival (DFS) (5-year DFS rate: 52.8 vs 81.7% for high and low SBEM expression, respectively, P = 0.001) rates than those with low expression of SBEM. Univariate and multivariate Cox analyses demonstrated that elevated expression of SBEM (hazard ratio [HR] = 1.994, 95% confidence interval [CI]: 1.008–3.945, P = 0.047), tumor size (HR = 2.318, 95% CI: 1.071–5.017, P = 0.033), and PR status (HR = 0.195, 95% CI: 0.055–0.694, P = 0.012) were independent predictors of OS in breast cancer patients. Elevated expression of SBEM was associated with both aggressive tumor characteristics and poor survival, indicating its potential as a useful prognostic biomarker for breast cancer patients.

Background: With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research., Objective: The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally., Methods: This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels., Results: To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers., Conclusions: The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention., (©Wenhao Qi, Xiaohong Zhu, Danni He, Bin Wang, Shihua Cao, Chaoqun Dong, Yunhua Li, Yanfei Chen, Bingsheng Wang, Yankai Shi, Guowei Jiang, Fang Liu, Lizzy M M Boots, Jiaqi Li, Xiajing Lou, Jiani Yao, Xiaodong Lu, Junling Kang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 08.08.2024.)