Abstract
Objective Osteoarthritis (OA), a chronic ailment that leads to joint degeneration, could be prevented by dietary restriction (DR). This study investigated the molecular mechanisms related to DR-related genes (DRRGs) as potential biomarkers in OA.
Methods Transcriptome data of OA (GSE55235 and GSE89408) were retrieved from public databases. Differentially expressed genes (DEGs) from GSE55235 were cross-referenced with DRRGs to identify candidate genes. Machine learning techniques, expression validation, and receiver operating characteristic (ROC) curve analysis were utilized to pick out biomarkers. Functional enrichment analysis explored associated pathways, and the CIBERSORT algorithm assessed immune cell infiltration. Subsequently, an exploration was made into the associations between biomarkers and immune cells. Additionally, small-molecule compounds for treating OA were predicted and validated based on the biomarkers, and the magnitudes of biomarker expression were verified.
Results A sum of 43 DR-related candidate genes in OA was determined. Among them, FAM107A, RHOBTB1, and ZBTB16 were identified as biomarkers with strong predictive value for OA (AUC ≥ 0.7). Pathway enrichment analysis indicated that 3 biomarkers were significantly connected with the lysosome pathway. In terms of immune infiltration analysis, it revealed that the highest degree of positive correlation (r =0.72, P < 0.001) existed between FAM107A and CD8 T cells. Meanwhile, the most pronounced negative correlation (r=-0.74, P < 0.001) was found between RHOBTB1 and activated mast cells. Moreover, molecular docking analysis demonstrated that 4-(5-benzo(1,3) dioxol-5-yl-4-pyridin-2-yl-1H-imidazol-2-yl) benzamide, benzo(a)pyrene, and tetrachlorodibenzodioxin had good binding affinities with the biomarkers. In addition, Reverse transcription quantitative polymerase chain reaction (RT-qPCR) confirmed that, in the OA group, the expression levels of RHOBTB1 and ZBTB16 increased, whereas the level of FAM107A was decreased (P < 0.05).
Conclusion: This research identified 3 DR-related biomarkers in OA, emphasizing that they were involved in the pathogenesis of OA and had the potential to serve as therapeutic targets.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
The author(s) received no specific funding for this work.
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
The study was conducted in accordance with the Declaration of Helsinki. The research study has been approved by the Ethics Committee of Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University. The approval number and date of approval are as follows: [2025-0615-01] and [August 13th, 2025]. Participants in this study were provided with a clear and understandable explanation of the research objectives, procedures, potential risks, and benefits. They were informed that their participation is voluntary and that they have the right to withdraw from the study at any time. Participants were given the opportunity to ask questions and provided written informed consent prior to their involvement in the study.
I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.
Yes
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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
Yes
Data Availability
The datasets analysed in this study are available in the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/), including GSE89408 dataset.
