Kaplan-Meier plotter data analysis model in early prognosis of pancreatic cancer

Abstract

Intelligent data analysis methods provide helpful tools for cancer researchers to detect the prognosis of patients with specific diseases. Yet, very little information is known about the features of these models used in data analysis methods. In this study, we presented a new Kaplan-Meier plotter model with a better-combination of input features for early prognosis tasks of pancreatic cancer. Our new model integrates gender, race, and follow up the threshold to get better verification of genes of interest as prognostic markers for predicting cancer at early stages. Assessment is made for the developed model to examine the important role of the oncogene RablA in early prediction of pancreatic cancer on the standard clinical datasets from The Human Protein Atlas. Our results showed that overexpression of the oncogene Rab1A in pancreatic cancer plays a vital role in its early prognosis (p<0.05). The proposed model results were also verified using an independent dataset deposited in The Human Protein Atlas. Altogether, the experimental results highlight Rab1A potential role in cancer prognosis.