👤 Yoichi Tomioka

Paneth cell metaplasia (PCM), a metaplastic change associated with chronic inflammation in ulcerative colitis (UC), may be linked to UC-associated neoplasia (UCAN). However, no endoscopic method currently exists for detecting PCM. This study aimed to develop and validate a novel endoscopic staining technique-CV-SCAN-for identifying PCM and UCAN, and to explore the molecular characteristics of the stained areas. This retrospective observational study included 131 patients with UC undergoing surveillance colonoscopy. CV-SCAN involved spraying an ultra-diluted solution (0.006%) of crystal violet from the descending colon to the rectum. Biopsies were obtained from stained and non-stained areas and evaluated histologically and molecularly. RNA expression profiles were analyzed via microarray and real-time RT-PCR. The diagnostic performance of CV-SCAN for detecting PCM was assessed, along with its correlation with UCAN history. CV-SCAN visualized sharply demarcated, purple-stained areas corresponding to PCM or UCAN. PCM was significantly associated with a history of UCAN. Uniform, dark staining was characteristic of PCM, while UCAN showed heterogeneous staining with small round pits. CV-SCAN achieved a sensitivity of 81.3% and a specificity of 84.9% for PCM detection. Molecular analysis revealed upregulation of Paneth cell-specific (DEFA5, DEFA6), small intestinal (CCL25, APOC3), and UCAN-associated (IL17RC) genes, along with downregulation of SATB2 in stained areas. CV-SCAN is a novel and effective endoscopic staining method for detecting PCM and UCAN in patients with UC. It enables risk stratification through direct visualization of precancerous changes and may facilitate early detection and targeted surveillance. Show less

Lung cancer is one of the most common cancer and the leading cause of cancer-related death worldwide. Early detection of lung cancer can help reduce the death rate; therefore, the identification of potential biomarkers is crucial. Thus, this study aimed to identify potential biomarkers for lung cancer by integrating bioinformatics analysis and machine learning (ML)-based approaches. Data were normalized using the robust multiarray average method and batch effect were corrected using the ComBat method. Differentially expressed genes were identified by the LIMMA approach and carcinoma-associated genes were selected using Enrichr, based on the DisGeNET database. Protein-protein interaction (PPI) network analysis was performed using STRING, and the PPI network was visualized using Cytoscape. The core hub genes were identified by overlapping genes obtained from degree, betweenness, closeness, and MNC. Moreover, the MCODE plugin for Cytoscape was used to perform module analysis, and optimal modules were selected based on MCODE scores along with their associated genes. Subsequently, Boruta-based ML approach was utilized to identify the important genes. Consequently, the core genes were identified by the overlapping genes obtained from PPI networks, module analysis, and ML-based approach. The prognostic and discriminative power analysis of the core genes was assessed through survival and ROC analysis. We extracted five datasets from USA cohort and three datasets from Taiwan cohort and performed same experimental protocols to determine potential biomarkers. Four genes (LPL, CLDN18, EDNRB, MME) were identified from USA cohort, while three genes (DNRB, MME, ROBO4) were from Taiwan cohort. Finally, two biomarkers (EDNRB and MME) were identified by intersecting genes, obtained from USA and Taiwan cohorts. The proposed biomarkers can significantly improve patient outcomes by enabling earlier detection, precise diagnosis, and tailored treatment, ultimately contributing to better survival rates and quality of life for patients. Show less