Genetic testing and molecular biomarkers · 2026
Identification and Validation of Transcriptomic Signatures in Inflammatory Bowel Disease with Metabolic Syndrome via Bioinformatics and Machine Learning.
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Yin Yu-Zhe, Yan Chao-Sheng, Wu Ling, Li Guo-Qing, Pei Xin-Yu, Xue Yu-Zheng, Ren Yi-Lin, Sun Hao-Wen, Sheng Ying-Yue
Аннотация
BACKGROUND: Inflammatory bowel disease (IBD) is a chronic, nonspecific inflammatory disorder affecting the gastrointestinal tract. The condition's pathology not only involves the digestive system but also can impact various organs and tissues throughout the body. Metabolic syndrome is a clinical syndrome characterized by obesity, insulin resistance, hypertension, and hyperlipidemia. Extensive research suggests a potential association between IBD and metabolic syndrome.
AIM: To seek biomarkers related to the diagnosis and treatment of IBD and metabolic syndrome.
METHODS: In this study, we utilized bioinformatics, integrated transcriptome analysis, and machine learning to develop a diagnostic model for the co-occurrence of IBD and metabolic syndrome. We applied two machine learning algorithms to select relevant features Least Absolute Shrinkage and Selection Operator and Random Forest (RF). Moreover, through external datasets and quantitative real-time polymerase chain reaction (qRT-PCR) experiments, we validated our findings.
RESULTS: We identified nine candidate gene-expression-based biomarkers (CCR6, CST7, LIMK2, TCF4, THBS3, CARS2, IFI27, LETM1, LOXL1) associated with immune and metabolic regulation that exhibited discriminatory potential for IBD complicated by metabolic syndrome. Furthermore, a column chart for diagnosing IBD in metabolic syndrome patients is provided.
CONCLUSION: Our study demonstrates that integrated bioinformatics and machine learning approaches can identify transcriptomic signatures associated with IBD and metabolic syndrome, providing candidate biomarkers for further validation.
AIM: To seek biomarkers related to the diagnosis and treatment of IBD and metabolic syndrome.
METHODS: In this study, we utilized bioinformatics, integrated transcriptome analysis, and machine learning to develop a diagnostic model for the co-occurrence of IBD and metabolic syndrome. We applied two machine learning algorithms to select relevant features Least Absolute Shrinkage and Selection Operator and Random Forest (RF). Moreover, through external datasets and quantitative real-time polymerase chain reaction (qRT-PCR) experiments, we validated our findings.
RESULTS: We identified nine candidate gene-expression-based biomarkers (CCR6, CST7, LIMK2, TCF4, THBS3, CARS2, IFI27, LETM1, LOXL1) associated with immune and metabolic regulation that exhibited discriminatory potential for IBD complicated by metabolic syndrome. Furthermore, a column chart for diagnosing IBD in metabolic syndrome patients is provided.
CONCLUSION: Our study demonstrates that integrated bioinformatics and machine learning approaches can identify transcriptomic signatures associated with IBD and metabolic syndrome, providing candidate biomarkers for further validation.