Integrates Differential Gene Expression analysis and deep learning for accurate and robust prostate cancer diagnosis

Abstract

The challenge of diagnosing complex diseases and increasing human lifespan is a pressing task. Traditional methods, relying on visual characteristics like ultrasound and angiography, often struggle to detect cancer in its early stages, limiting diagnostic accuracy due to the intricate and nonlinear nature of diseases. From the perspective of gene expression, detecting cancer offers a more robust and effective approach due to its ability to directly assess the genetic activity within cells. In this study, we present the development of a prostate cancer feature selection method based on differentially expressed genes (DEGs). Utilizing datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we meticulously curated data for both model training and testing, implementing stringent filtering criteria based on p-value and fold change. Our study identifies a panel of 220 genes with substantial potential for prostate cancer detection. We then construct an ANN model for the diagnosis of the disease, whose accuracy is 0.780.01, which is more effective than other models like Ridge Classifiers, Logistic Regression, Naive Bayes Regression and Decision Trees. The average accuracy of these classifiers is 0.730.01. Notably, these genes also demonstrate exceptional performance across other various classifiers, indicating their robustness and effectiveness without dependence on specific models. The credibility is validated by comparison to random genes, and adaptability by using pancreatic cancer data from GEO. The Gene Otology analysis also verifies the feasibility of such method. This panel establishes a solid foundation for advancing clinical diagnostics of prostate cancer. This framework holds potential to significantly transform prostate cancer screening by offering strong resilience and precision across multiple classification methods.