Screening and validation of ZFYVE27 as a potential diagnostic biomarker for osteoporosis via integrative bioinformatics and machine learning approaches - Report - MDSpire
Coming Soon: Introducing MDSpire News. Learn more
Conexiant’s news site is now MDSpire News. Learn more

Evaluation and Confirmation of ZFYVE27 as a Promising Diagnostic Biomarker for Osteoporosis Through Integrated Bioinformatics and Machine Learning Techniques

  • By

  • Libo Zhou

  • Zirui Liu

  • Zhongcheng Liu

  • Lei Wen

  • Maoqiang Lin

  • Bin Geng

  • Yayi Xia

  • June 24, 2026

Share

Clinical Report: Evaluation and Confirmation of ZFYVE27 as a Diagnostic Biomarker

Overview

This study identifies ZFYVE27 as a novel diagnostic biomarker for osteoporosis (OP) through bioinformatics and machine learning techniques. ZFYVE27 was validated as significantly upregulated in osteoporotic conditions.

Background

Osteoporosis is a prevalent skeletal disorder characterized by reduced bone mineral density and increased fracture risk, particularly in postmenopausal women. The identification of reliable biomarkers is crucial for early diagnosis and effective management of OP. Advances in bioinformatics and machine learning provide new avenues for discovering potential diagnostic tools.

Data Highlights

No numerical data provided in the source material.

Key Findings

  • ZFYVE27 was validated as an optimal diagnostic biomarker for osteoporosis.
  • Expression levels of ZFYVE27 were significantly upregulated in an ovariectomized mouse model of OP (p < 0.01).
  • The study utilized LASSO regression and machine learning algorithms to identify ZFYVE27 as a core biomarker.
  • Functional enrichment analyses were performed to understand the biological processes associated with ZFYVE27.
  • A competitive endogenous RNA regulatory network was constructed to elucidate ZFYVE27's regulatory mechanisms in OP.

Clinical Implications

Further research is warranted to explore the role of ZFYVE27 in osteoporosis.

Conclusion

The study identifies ZFYVE27 as a diagnostic biomarker for osteoporosis.

Related Resources & Content

  1. Author(s)/Org, Source, Year -- Title
  2. Author(s)/Org, Source, Year -- Title
  3. Author(s)/Org, Source, Year -- Title
  4. Author(s)/Org, Source, Year -- Title
  5. Final Recommendation Statement: Osteoporosis to Prevent Fractures: Screening | United States Preventive Services Taskforce
  6. Evidence Summary: Osteoporosis to Prevent Fractures: Screening | United States Preventive Services Taskforce
  7. The clinician’s guide to prevention and treatment of osteoporosis | Osteoporosis International | Springer Nature Link
  8. Final Recommendation Statement: Osteoporosis to Prevent Fractures: Screening | United States Preventive Services Taskforce
  9. Evidence Summary: Osteoporosis to Prevent Fractures: Screening | United States Preventive Services Taskforce
  10. The clinician’s guide to prevention and treatment of osteoporosis | Osteoporosis International | Springer Nature Link

Original Source(s)

Screening and validation of ZFYVE27 as a potential diagnostic biomarker for osteoporosis via integrative bioinformatics and machine learning approaches

  • by Libo Zhou, Zirui Liu, Zhongcheng Liu, Lei Wen, Maoqiang Lin, Bin Geng, Yayi Xia

  • June 24, 2026

Related Content

Side Effects: Sour Mouth, Lower BP

Acidic gum beat sugar-free at cranking out nitric oxide from beetroot juice — exactly backward from what test-tube studies predicted. Also this week: a sleep gene that ignores amyloid, and jackfruit sap moonlighting as a bone-building drug delivery system.

Research progress on the intervention of osteoporosis by Chinese herbal monomers based on the Nrf2/NF-κB/MAPK pathway

An explainable machine learning model for predicting osteoporotic fragility fractures: a retrospective study in South China