Current medicinal chemistry · 2025
Identification and Validation of NDRG2 as a Biomarker for Follicular Lymphoma.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhu Yidong, He Jun, Wei Rong
Аннотация
INTRODUCTION: Follicular lymphoma (FL) is the most prevalent form of indolent lymphoma, characterized by intermittent relapse and remission periods. This study aims to identify potential biomarker genes for FL and elucidate their roles in the disease.
METHODS: FL-related microarray datasets were downloaded from the Gene Expression Omnibus database. Differential expression and weighted gene co-expression network analyses (WGCNA) were conducted to identify potential hub genes. Various machine learning algorithms were applied to improve gene selection accuracy and predictive performance. Mendelian randomization (MR) analysis was carried out to identify genes with causal relationships to FL. Functional enrichment analysis was performed to explore the underlying mechanisms. Finally, the identified biomarker gene was validated in clinical samples using quantitative real-time PCR.
RESULTS: A total of 60 hub genes were identified through differential expression analysis and WGCNA. Subsequently, 11 characteristic genes were identified using machine learning algorithms. MR analysis revealed 173 genes with causal effects on FL, leading to the identification of one key co-expressed gene, NDRG family member 2 (NDRG2), as a potential biomarker for FL. NDRG2 demonstrated strong predictive performance. Functional enrichment analysis revealed significant associations between NDRG2 and immune-related pathways in FL. Validation in clinical samples confirmed the relevance of NDRG2 as a biomarker.
DISCUSSION: The integration of machine learning and MR successfully identified NDRG2 as a promising biomarker with a causal relationship to FL. Validation in clinical samples reinforced the reliability of these findings in clinical practice.
CONCLUSION: By combining machine learning, MR, and experimental validation, NDRG2 was identified and validated as a promising biomarker for FL.
METHODS: FL-related microarray datasets were downloaded from the Gene Expression Omnibus database. Differential expression and weighted gene co-expression network analyses (WGCNA) were conducted to identify potential hub genes. Various machine learning algorithms were applied to improve gene selection accuracy and predictive performance. Mendelian randomization (MR) analysis was carried out to identify genes with causal relationships to FL. Functional enrichment analysis was performed to explore the underlying mechanisms. Finally, the identified biomarker gene was validated in clinical samples using quantitative real-time PCR.
RESULTS: A total of 60 hub genes were identified through differential expression analysis and WGCNA. Subsequently, 11 characteristic genes were identified using machine learning algorithms. MR analysis revealed 173 genes with causal effects on FL, leading to the identification of one key co-expressed gene, NDRG family member 2 (NDRG2), as a potential biomarker for FL. NDRG2 demonstrated strong predictive performance. Functional enrichment analysis revealed significant associations between NDRG2 and immune-related pathways in FL. Validation in clinical samples confirmed the relevance of NDRG2 as a biomarker.
DISCUSSION: The integration of machine learning and MR successfully identified NDRG2 as a promising biomarker with a causal relationship to FL. Validation in clinical samples reinforced the reliability of these findings in clinical practice.
CONCLUSION: By combining machine learning, MR, and experimental validation, NDRG2 was identified and validated as a promising biomarker for FL.