Does study of integrated heterogeneous data reveal prognostic power of gene expression for breast cancer survival?
Our results indicate that we can obtain improved survival prediction using clinical data and gene expression data compared to prediction using only clinical data. We further conclude that we can obtain improved survival prediction using the RSF model instead of the Cox model. These results are significant because by incorporating more gene expression data with clinical features and using the RSF model, we could develop decision support systems that better utilize heterogeneous information to improve outcome prediction and decision making.
Thus, advanced NC findings were more frequently found in those patients with higher levels of AECA and their contemporary presence may consent to identify specific SSc subsets i.e., those with higher skin scores and cardiovascular involvement. These data suggest that AECA may have a role in the progression of the endothelial damage and their presence and titer should be considered as an adjunctive risk factor for a more severe disease. We also confirm the diagnostic and prognostic validity for NC in SSc, underlying the importance for an accurate capillaroscopic assessment. The contemporary assessment of these two diagnostic tools can be useful to better define different subset of SSc patients.