Identification of CXCL16 as a diagnostic biomarker for obesity and intervertebral disc degeneration based on machine learning.

Intervertebral disc degeneration (IDD) is the primary cause of neck and back pain. Obesity has been established as a significant risk factor for IDD. The objective of this study was to explore the molecular mechanisms affecting obesity and IDD by identifying the overlapping crosstalk genes associated with both conditions. The identification of specific diagnostic biomarkers for obesity and IDD would have crucial clinical implications. We obtained gene expression profiles of GSE70362 and GSE152991 from the Gene Expression Omnibus, followed by their analysis using two machine learning algorithms, least absolute shrinkage and selection operator and support vector machine-recursive feature elimination, which enabled the identification of C-X-C motif chemokine ligand 16 (CXCL16) as a shared diagnostic biomarker for obesity and IDD. Additionally, gene set variant analysis was used to explore the potential mechanism of CXCL16 in these diseases, and CXCL16 was found to affect IDD through its effect on fatty acid metabolism. Furthermore, correlation analysis between CXCL16 and immune cells demonstrated that CXCL16 negatively regulated T helper 17 cells to promote IDD. Finally, independent external datasets (GSE124272 and GSE59034) were used to verify the diagnostic efficacy of CXCL16. In conclusion, a common diagnostic biomarker for obesity and IDD, CXCL16, was identified using a machine learning algorithm. This study provides a new perspective for exploring the possible mechanisms by which obesity impacts the development of IDD.

Role of arachidonic acid metabolism in intervertebral disc degeneration: identification of potential biomarkers and therapeutic targets via multi-omics analysis and artificial intelligence strategies.

BACKGROUND: Intervertebral disc degeneration (IVDD) is widely recognized as the primary etiological factor underlying low back pain, often necessitating surgical intervention as the sole recourse in severe cases. The metabolic pathway of arachidonic acid (AA), a pivotal regulator of inflammatory responses, influences the development and progression of IVDD. METHODS: Initially, a comparative analysis was conducted to investigate the relationship between AA expression patterns and different stages of IVDD using single-cell sequencing (scRNA-seq) data. Additionally, three machine learning methods (LASSO, random forest, and support vector machine recursive feature elimination) were employed to identify hub genes associated with IVDD. Subsequently, a novel artificial intelligence prediction model was developed for IVDD based on an artificial neural network algorithm and validated using an independent dataset. The identified hub genes were further subjected to functional enrichment, immune infiltration, and Connectivity Map analysis. Moreover, external validation was performed using flow cytometry and real-time reverse transcription polymerase chain reaction analysis. RESULTS: Both scRNA-seq and bulk RNA-seq data revealed a positive correlation between the severity of IVDD and the AA metabolic pathway. They also revealed increased AA metabolic activity in macrophages and neutrophils, as well as enhanced intercellular communication with nucleus pulposus cells. Utilizing advanced machine learning algorithms, five hub genes (AKR1C3, ALOX5, CYP2B6, EPHX2, and PLB1) were identified, and an incipient diagnostic model was developed with an AUC of 0.961 in the training cohort and 0.72 in the validation cohort. An in-depth exploration of the functionality of these hub genes revealed their notable association with inflammatory responses and immune cell infiltration. Lastly, AH6809 was found to delay IVDD by inhibiting AKR1C3. CONCLUSIONS: This study offers comprehensive insights into potential biomarkers and small molecules associated with the early pathogenesis of IVDD. The identified biomarkers and the developed integrated diagnostic model hold great promise in predicting the onset of early IVDD. AH6809 was established as a therapeutic target for AKR1C3 in the treatment of IVDD, as evidenced by computer simulations and biological experiments.

Molecular characterization of cancer-intrinsic immune evasion genes indicates prognosis and tumour microenvironment infiltration in osteosarcoma.

Cancer-intrinsic immune evasion (IE) to cells is a critical factor in tumour growth and progression, yet the molecular characterization of IE genes (IEGs) in osteosarcoma remains underexplored. In this study, 85 osteosarcoma patients were comprehensively analyzed based on 182 IEGs, leading to the identification of two IE clusters linked to distinct biological processes and clinical outcomes. In addition, two IE clusters demonstrated diverse immune cell infiltration patterns, with IEGcluster A displaying increased levels compared to IEGcluster B. Moreover, an IE score was identified as an independent prognostic factor and nomogram may serve as a practical tool for the individual prognostic evaluation of patients with osteosarcoma. Finally, GBP1, a potential biomarker with high expression in osteosarcoma was identified. The findings of this study highlight the presence of two IE clusters, each associated with differing patient outcomes and immune infiltration properties. The IE score may serve to assess individual patient IE characteristics, enhance comprehension of immune features, and guide more efficacious treatment approaches.