In contrast to previous methodological studies that directly investigated the relevance of miRNAs to diseases (e.g., breast cancer and lung cancer) by constructing similarity- or machine-learning-based models [32–35], the present study first identified DE-LMRMs by performing Pearson's correlation analysis between DEMs and DE-LMRGs and subsequently used three machine learning algorithms to finalize a diagnostic model for PCa consisting of seven signature miRNAs and constructed a lipid metabolism-related TF-miRNA‒mRNA network, providing novel targets for diagnosing and treating PCa. The gene discussed is TF; the disease is lung cancer.