Abstract
Background
Lung cancer has a high morbidity and mortality rate with currently limited treatment options. There is an urgent need for prognostic markers to facilitate early diagnosis and improve survival rates. This study proposes lysosome-related genes as potential prognostic markers, as they play a significant role in the pathogenesis of lung cancer.
Methods
The study established a prognostic model using lysosome-related genes from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Univariate Cox regression and LASSO Cox regression analyses were utilized to identify and select relevant genes, and the model was then validated in an independent cohort of lung cancer patients. Further, immune cell infiltration scores, drug susceptibility, functional and pathway enrichment analyses were conducted to evaluate the model's predictive ability.
Results
The study identified 26 key lysosome-related genes and found that the high-risk group, as identified by the model, had a poorer overall survival rate. Additionally, the model demonstrated a good prediction accuracy for 1-, 3-, and 5- year prognosis in the training and validation cohorts. The model's risk score was identified as an independent prognostic factor, demonstrating its potential clinical relevance. Immune cell infiltration, tumor microenvironment analyses, and drug susceptibility predictions also provided significant insights.
Conclusion
The proposed model based on lysosome-related genes could be a potential tool for predicting the prognosis of lung cancer patients. It may facilitate early diagnosis, inform treatment plans, and improve overall survival rates. However, further research is required to establish its practical application in clinical settings.