Abstract
Background
The impact of mitochondrial and lysosomal co-dysfunction on breast cancer patient outcomes is unclear. The objective of this study is to develop a predictive machine learning (ML) model utilizing mitochondrial and lysosomal co-regulators in order to enhance the prognosis for individuals with BC.
Methods
Differences and correlations of mitochondrial and lysosome related genes were screened and validated. WGCNA and univariate Cox regression were employed to identify prognostic mitochondrial and lysosomal co-regulators. ML was utilized to further selected these regulators as mitochondrial and lysosome-related model signature genes (mlMSGs)and constructed models. The association between the immune and mlMSGs score was investigated through scRNA-seq. Finally, the expression and function of the key gene SHMT2 were confirmed through in vitro experiments.
Results
According to the C-index, the coxboost+ Survivor-SVM model was identified as the most suitable for predicting outcomes in BC patients. Subsequently, patients were stratified into high and low risk groups based on the model, which demonstrated strong prognostic accuracy. While the overall immunoinfiltration of immune cells was decreased in the high-risk group, it was specifically noted that B cell mlMSGs activity remained diminished in high-risk patients. Additionally, the study found that SHMT2 promoted the proliferation, migration, and invasion of BC cells.
Conclusion
This study shows that the ML model accurately predicts the prognosis of BC patients. Analysis conducted through the model has identified decreased B-cell immune infiltration and reduced mlMSGs activity as significant factors influencing patient prognosis. These results may offer novel approaches for early intervention and prognostic forecasting in BC.