Biochemistry and biophysics reports · 2026
Screening of metabolic-related biomarkers linking intervertebral disc degeneration and type 2 diabetes based on comprehensive bioinformatics analysis and machine learning.
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Qiu Ba, Chen Tao, Hong Qi, Xie Xiaoxing, Xue Mintao, Xu Ning, Shi Changgui, Xu Guohua, Wang Weiheng, Sulaiman Muhammad, Xi Yanhai
Аннотация
BACKGROUND: Intervertebral disc degeneration (IVDD) is a prominent etiology of lower back pain. Type 2 diabetes (T2D), the most prevalent metabolic disorder, may expedite IVDD progression through mechanisms involving hyperglycemia, advanced glycation end products, and microvascular complications. We aim to identify the biomarkers for T2D and IVDD.
METHODS: We retrieved two datasets pertaining to IVDD and T2D respectively from the Gene Expression Omnibus (GEO) database. Initially, differential expression analysis was conducted to identify differentially expressed genes (DEGs) in each group. Subsequently, machine learning algorithms including Boruta algorithm, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) were utilized to explore co-biomarkers for IVDD and T2D by analyzing the intersection of DEGs sets between the two groups. The clinical diagnostic value of these biomarkers was evaluated using ROC curve. Additionally, we employed the CYBERSORT and Single-cell sequencing to investigate the infiltration of immune cells in two diseases. Finally, the expression of biomarkers and the correlation between BCAA and IVDD was experimentally validated.
RESULTS: AMBP, a shared biomarker of IVDD and T2D, were identified using differential analysis and machine learning algorithms. In addition, CYBERSORT analysis and single-cell sequencing results revealed significant correlations between AMBP and cellular immunity. Finally, experimental validation on clinical samples and HNPC cells confirmed upregulation of AMBP, and the correlation between BCAA metabolism and AMBP.
CONCLUSION: AMBP, as the shared biomarker between IVDD and T2D, has great clinical diagnostic value, which therefore may be a potential regulatory factor for two diseases.
METHODS: We retrieved two datasets pertaining to IVDD and T2D respectively from the Gene Expression Omnibus (GEO) database. Initially, differential expression analysis was conducted to identify differentially expressed genes (DEGs) in each group. Subsequently, machine learning algorithms including Boruta algorithm, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) were utilized to explore co-biomarkers for IVDD and T2D by analyzing the intersection of DEGs sets between the two groups. The clinical diagnostic value of these biomarkers was evaluated using ROC curve. Additionally, we employed the CYBERSORT and Single-cell sequencing to investigate the infiltration of immune cells in two diseases. Finally, the expression of biomarkers and the correlation between BCAA and IVDD was experimentally validated.
RESULTS: AMBP, a shared biomarker of IVDD and T2D, were identified using differential analysis and machine learning algorithms. In addition, CYBERSORT analysis and single-cell sequencing results revealed significant correlations between AMBP and cellular immunity. Finally, experimental validation on clinical samples and HNPC cells confirmed upregulation of AMBP, and the correlation between BCAA metabolism and AMBP.
CONCLUSION: AMBP, as the shared biomarker between IVDD and T2D, has great clinical diagnostic value, which therefore may be a potential regulatory factor for two diseases.