Clinicopathological Significance of Exosomal Proteins CD9 and CD63 and DNA Mismatch Repair Proteins in Prostate Adenocarcinoma and Benign Hyperplasia.

INTRODUCTION: Recently, it has been shown that exosomal biomarkers and DNA mismatch repair proteins (MMR) could play an important role in cancer risk stratification and prognosis assessment. The gold standard for prostate carcinoma (PCa) diagnosis is biopsy and histopathological examination. Thus, the complex evaluation of exosomal and MMR proteins could be beneficial for prostate cancer risk stratification and diagnostics. The aim of the current study was to evaluate and compare the expression of exosomal proteins CD9 and CD63 and MMR proteins in the tissue of patients with prostate benign hyperplasia (BPH) and PCa. METHODS: The study was retrospective. Altogether, 92 patients with PCa and 20 patients with BPH (control group) were enrolled in the study. Exosomal and MMR protein expression was analyzed by immunohistochemistry (IHC). The follow-up for each PCa patient in our study lasted till disease progression and/or a maximum of 5 years. RESULTS: Low-grade PCa was observed in 56 patients and high-grade PCa in 36 patients. CD63 expression was significantly higher in patients with high-grade PCa compared to those with low-grade PCa. CD9 expression was significantly downregulated in PCa patients compared to the control group. MMR protein expression deficiency was observed in 10 PCa patients. MMR proteins were maintained in all cases of BPH. The study found a negative correlation between MMR protein loss and PCa ISUP grade groups. Progression-free survival (PFS) in patients with MMR deficiency was significantly shorter than in patients with maintained MMR expression. CONCLUSIONS: CD9 protein expression was downregulated in PCa, compared to BPH, while CD63 protein expression was upregulated in high-grade PCa but downregulated in low-grade PCa. CD63 protein upregulation, CD9 downregulation, and loss of MMR protein characterized the shorter PFS of high-grade PCa patients. CD9, CD63, and MMR could be the routine immunohistochemical biomarkers for the diagnosis and risk stratification of PCa.

Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection.

BACKGROUND: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). METHODS: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. RESULTS: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. CONCLUSIONS: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications.