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J. Mol. Pathol., Volume 6, Issue 2 (June 2025) – 2 articles

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9 pages, 1073 KiB  
Brief Report
Association of SEPT9 Gene Methylation with the Clinicopathologic Features and Fusobacterium nucleatum Infection in Colorectal Cancer Patients
by Siew-Wai Pang, Subasri Armon, Jack-Bee Chook, Kaik-Boo Peh, Suat-Cheng Peh and Sin-Yeang Teow
J. Mol. Pathol. 2025, 6(2), 8; https://doi.org/10.3390/jmp6020008 - 23 Apr 2025
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Abstract
Background/Objectives: Colorectal cancer (CRC) is a significant global health issue. The identification of methylated Septin 9 (mSEPT9) as a biomarker for CRC represents a significant advancement in cancer diagnostics. On the other hand, Fusobacterium nucleatum (FN) is one of the [...] Read more.
Background/Objectives: Colorectal cancer (CRC) is a significant global health issue. The identification of methylated Septin 9 (mSEPT9) as a biomarker for CRC represents a significant advancement in cancer diagnostics. On the other hand, Fusobacterium nucleatum (FN) is one of the most studied cancer-related microbes in CRC. This study provided cohort evidence on the association of mSEPT9 with clinicopathologic characteristics and FN infection in CRC patients. Methods: Paired formalin-fixed paraffin-embedded (FFPE) tissue DNA (cancerous and adjacent non-cancer tissues) of eighty-three CRC patients was collected. Methylation-specific qPCR targeting the v2 promoter region of mSEPT9 was carried out on bisulfite-converted FFPE DNA. For FN detection, a TaqMan probe-based method targeting the 16S rRNA gene was used. The differences in mSEPT9 levels and FN expression between cancer and non-cancer tissues were evaluated. Association studies between mSEPT9 in the tumor and relative mSEPT9 levels with FN infection and available clinical data were conducted. Results: Higher mSEPT9 levels were found in the cancerous tissue compared to non-cancerous tissue (p < 0.0001). High mSEPT9 levels in the tumor were significantly associated with older patients (p < 0.001) and larger tumor size (p = 0.048) but not with other clinicopathologic variables. In double-positive patients where mSEPT9 was detected in both cancerous and non-cancerous tissue, the expression fold-change in mSEPT9, calculated using the 2−ΔΔCT formula, was significantly higher in patients with tumor size equal to or greater than 5 cm (p = 0.042). High levels of mSEPT9 in tumor were not associated with FN infection. However, high levels of FN infection were associated with mSEPT9 (p < 0.021). Conclusions: High levels of mSEPT9 are found in CRC tumor tissue and are associated with older age and larger tumor size, while high levels of FN infection are associated with mSEPT9 in this single-center cohort study. Full article
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Review
Current Topics on the Integration of Artificial Intelligence in the Histopathological and Molecular Diagnosis of Uveal Melanoma
by Serena Salzano, Giuseppe Broggi, Andrea Russo, Teresio Avitabile, Antonio Longo, Rosario Caltabiano and Manuel Mazzucchelli
J. Mol. Pathol. 2025, 6(2), 7; https://doi.org/10.3390/jmp6020007 - 17 Apr 2025
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Abstract
Background: This review examines the expanding influence of artificial intelligence (AI) in the detection and management of uveal melanoma (UM). Methods: This work delves into the application of AI technologies such as machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs) [...] Read more.
Background: This review examines the expanding influence of artificial intelligence (AI) in the detection and management of uveal melanoma (UM). Methods: This work delves into the application of AI technologies such as machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs) in various diagnostic procedures, molecular profiling, and predictive analysis. Results: The discussion underscores AI’s potential to enhance diagnostic precision and efficiency. Particular focus is placed on its role in histopathological assessments of UM, where algorithms facilitate the analysis of whole-slide images (WSIs). AI contributes to more accurate tumor classification, assists in planning treatments, and improves the prediction of the prognostic indicators and molecular characteristics of the tumor. Conclusions: Despite these promising developments, this review acknowledges existing hurdles to AI implementation, including issues with data standardization and the interpretability of AI models. It emphasizes the need for further research to fully integrate AI into clinical workflows, ultimately aiming to improve patient care and outcomes. Full article
(This article belongs to the Special Issue Automation in the Pathology Laboratory)
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