Based on WGCNA and machine learning studies, SMURF2 drives NSCLC malignant transformation, ferroptosis, and macrophage polarization by ubiquitinating SPP1
Clinical Snapshot
PICO Framework
| P — Population | Non-small cell lung cancer (NSCLC) cell lines and mouse models |
| I — Intervention | SMURF2-mediated ubiquitination and degradation of SPP1 |
| C — Comparator | Control conditions without SMURF2 manipulation or SPP1 overexpression rescue |
| O — Outcomes | Cell proliferation, invasion, apoptosis, ferroptosis, macrophage polarization, and tumor growth |
Bottom Line
This preclinical study identifies SMURF2 as a potential tumor suppressor in NSCLC through its ubiquitination and degradation of SPP1, affecting multiple cancer hallmarks including proliferation, invasion, ferroptosis, and immune modulation. While the multi-modal experimental approach provides mechanistic insights, the study lacks statistical detail and human validation. The findings suggest SMURF2-SPP1 axis as a potential therapeutic target, but significant clinical development would be required. Australian clinicians should note this represents early-stage research requiring substantial validation before clinical application. The comprehensive mechanistic investigation provides valuable insights into NSCLC biology, but translation to patient care remains distant.
Key Findings
P Value: Not reported
Effect Size: Not reported in abstract
Primary Outcome: SMURF2 ubiquitinated and degraded SPP1, inhibiting NSCLC progression
Nnt Or Sensitivity: Not applicable for mechanistic study
Confidence Interval: Not reported
Clinical Application
Early preclinical stage; requires extensive clinical validation before therapeutic application Relevant to Australian NSCLC patients; would require TGA approval for any therapeutic development and alignment with Cancer Australia guidelines Patients with non-small cell lung cancer, particularly those with SPP1 overexpression
Abstract
BACKGROUND: Non-small cell lung cancer (NSCLC) is a common type of lung cancer with poor prognosis and high mortality in advanced stages. Although secreted phosphoprotein 1 (SPP1) is associated with the progression of NSCLC, its specific mechanisms remain to be explored. METHODS: Through integrated bioinformatics analysis combining weighted gene co-expression network analysis (WGCNA), differential expression analysis, gene enrichment analysis with the Kyoto Encyclopedia of Genes and Genomes (KEGG)/Gene Ontology (GO), and machine learning techniques incorporating database predictions, core genes were identified. Experimental validation was conducted using Western blot, co-immunoprecipitation (Co-IP), cycloheximide (CHX) chase assay, Ubiquitin Immunoprecipitation (Ub-IP), colony formation, flow cytometry, transwell assay, reactive oxygen species (ROS) detection, and quantitative real-time PCR (qRT-PCR). The functional impact of SPP1 on tumor growth was further confirmed through mouse models and immunohistochemistry (IHC). RESULTS: Based on WGCNA and machine learning, SPP1 was identified as a core gene. SMURF2 ubiquitinated and degraded SPP1. Rescue experiments confirmed that SMURF2 inhibited proliferation and invasion, and promoted apoptosis in NSCLC cells, which were reversed by SPP1 overexpression. Furthermore, SMURF2 enhanced ferroptosis and modulated macrophage polarization via the same ubiquitination mechanism. Animal studies verified that SMURF2-mediated ubiquitination and degradation of SPP1 suppressed tumor growth. CONCLUSION: This study aims to investigate the role of SMURF2-mediated ubiquitination of SPP1 in inhibiting malignant progression, promoting ferroptosis, and modulating macrophage polarization in NSCLC, thereby providing a theoretical foundation and new insights for understanding and targeting NSCLC.
References
- 1.Cao, J., Yang, X., Guo, J., Zhang, S., Lu, Y., & Cai, F. (2026). Based on WGCNA and machine learning studies, SMURF2 drives NSCLC malignant transformation, ferroptosis, and macrophage polarization by ubiquitinating SPP1. Gene, 150093. https://doi.org/10.1016/j.gene.2026.150093
Related Research
The American journal of pathology
An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer
3 Aug 2026
Current opinion in chemical biology
Prediction of protein-protein interactions and co-complex models with deep learning
3 Aug 2026
European journal of radiology
A CT-based deep learning model to differentiate between benign and malignant adrenal lesions
3 Aug 2026
This content is for educational purposes for healthcare professionals only and does not constitute clinical advice. Clinical decisions should be based on individual patient assessment, current guidelines, and appropriate specialist consultation. Editorial Standards · Privacy Policy · Terms of Service