Frontiers in immunology · 2026
A blood-based four-gene diagnostic signature for Kashin-Beck disease revealed by multi-cohort transcriptomic analysis and machine learning.
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.
Guo Minghui, Yang Kunkun, Liu Shizhang, Sun Zhengming, Wu Xueyuan, Li Xinpei, Wang Yuchao, Zhu Xinke, Ling Ming
Аннотация
BACKGROUND: Kashin-Beck disease (KBD) is an endemic osteoarthropathy characterized by growth retardation and progressive joint degeneration. However, its systemic molecular features in peripheral blood remain incompletely understood.
METHODS: Peripheral blood transcriptomic data from four independent cohorts were analyzed using differential expression analysis and weighted gene co-expression network analysis to identify KBD-associated gene sets. Multiple feature selection strategies and machine learning models were applied to construct and validate a blood-based diagnostic signature across cohorts. Immune cell composition was inferred by computational deconvolution, and transcription factor regulation, pathway enrichment, and genetic association data were integrated for biological interpretation.
RESULTS: A four-gene blood signature (C4B, AQP1, HBA2, and ACSL6) was identified, showing stable diagnostic performance across independent blood cohorts and preserved discriminatory capacity in cartilage tissue. Downstream analyses revealed that the diagnostic genes were associated with altered immune cell composition and immune- and metabolism-related pathways in peripheral blood.
CONCLUSIONS: This study defines a compact and interpretable blood-based transcriptomic signature for KBD and provides insight into its systemic immune-related molecular context, supporting its potential utility for disease identification and mechanistic investigation.
METHODS: Peripheral blood transcriptomic data from four independent cohorts were analyzed using differential expression analysis and weighted gene co-expression network analysis to identify KBD-associated gene sets. Multiple feature selection strategies and machine learning models were applied to construct and validate a blood-based diagnostic signature across cohorts. Immune cell composition was inferred by computational deconvolution, and transcription factor regulation, pathway enrichment, and genetic association data were integrated for biological interpretation.
RESULTS: A four-gene blood signature (C4B, AQP1, HBA2, and ACSL6) was identified, showing stable diagnostic performance across independent blood cohorts and preserved discriminatory capacity in cartilage tissue. Downstream analyses revealed that the diagnostic genes were associated with altered immune cell composition and immune- and metabolism-related pathways in peripheral blood.
CONCLUSIONS: This study defines a compact and interpretable blood-based transcriptomic signature for KBD and provides insight into its systemic immune-related molecular context, supporting its potential utility for disease identification and mechanistic investigation.