Identification of Potential Molecular Mechanisms and Machine Learning Classification Model in Prostate Cancer Progression Based on GEO Datasets and TCGA Dataset

Prostate cancer is a leading cause of cancer-related morbidity and mortality. Identifying key genes involved in its progression is crucial for improving clinical outcomes.This paper used transcriptomic data from the GEO database to analyze differential expression and employed machine learning techniques to identify critical progression genes and develop a grading model. And the results were validated by TCGA dataset. This paper identified 802 key genes associated with prostate cancer progression. The molecular mechanisms are involving immune and cell growth pathways. The developed machine learning grading model demonstrated high AUCs, indicating its potential utility in clinical settings. Those key genes play important roles in prostate cancer.