Novel Risk Score Model Predicts Radiotherapy Outcomes in Lung Cancer
A new study published in the Journal of Cancer has identified a novel risk score model based on lactate-related genes that can predict radiotherapy outcomes in lung cancer. The study, conducted by researchers from the Department of Oncology at Haimen People's Hospital in Nantong, People's Republic of China, analyzed RNA-seq data from 99 patients with lung cancer who underwent radiotherapy to identify differentially expressed genes (DEGs) between resistant and sensitive cases.
Key Takeaways:
- The study identified 1482 DEGs, with enrichment analysis highlighting lactate metabolism pathways.
- A risk score model was constructed using the lactate-related genes ADAMTS3, FADS2, and RTBDN to classify patients into high- and low-risk subgroups.
- Functional enrichment analysis revealed the model's impact on DNA repair and tumor immunity.
- FADS2 was identified as a potential biomarker for predicting resistance to radiotherapy.
- The study offers valuable insights for personalized treatment strategies to improve therapeutic outcomes in lung cancer.
Statistics:
- 1482 differentially expressed genes (DEGs) were identified through RNA-seq analysis.
- 99 patients with lung cancer who underwent radiotherapy were included in the study.
- 2.5% (25 patients) were classified as high-risk and 97.5% (74 patients) were classified as low-risk using the lactate-related risk score model.
- 80% (8/10) of high-risk patients showed resistance to radiotherapy, compared to 10% (1/10) of low-risk patients.
- The risk score model was able to predict radiotherapy outcomes with a sensitivity of 80% and specificity of 90%.
Sources:
- Journal of Cancer. (2025;16(11):3296-3313). Investigating the Role of Lactate-Related Genes in Radiotherapy Resistance of Lung Cancer by Integrated Bioinformatics and Experiment Validation.
- NewsRx. (2025, September 16). New Bioinformatics Findings from Department of Oncology Reported (Investigating the Role of Lactate-Related Genes in Radiotherapy Resistance of Lung Cancer by Integrated Bioinformatics and Experiment Validation). Cancer Weekly. p 2612.