The cornerstone of early diagnosis and immunotherapy of prostate cancer:screening characteristic genes

Abstract

Background: Prostate cancer (PCA) has become a common malignant tumor globally, posing a substantial risk to the health of middle-aged and elderly men. However, there is still a lack of effective strategies for early detection and treatment of prostate cancer. The introduction of gene therapy in recent years has shown promise as a potential approach for cancer diagnosis and treatment. Methodology & Theoretical Orientation: The training set data GSE45016, GSE46602, and GSE69223 from the Gene Expression Omnibus (GEO) dataset, along with validation training set data GSE17951, were utilized. Differentially expressed genes (DEGs) between normal individuals and tumor patients were identified by combining the training set data. Subsequent analyses including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were conducted on the DEGs. WGCNA analysis was then performed on the gene expression matrix to identify module genes highly correlated with PCA, followed by the application of the LASSO algorithm to obtain PCA candidate genes. The candidate genes were validated using the area under the receiver operating characteristic (ROC) curve (AUC) to determine key feature genes. Finally, the relationship between key characteristic genes and immune cells was explored. Findings: A total of 54 DEGs were identified, with 26 down-regulated genes and 28 up-regulated genes. The GO function analysis revealed enrichment in processes such as 'establishment of protein localization to membrane' and 'protein targeting to membrane'. KEGG analysis showed enrichment in pathways like 'eutrophil degranulation', 'neutrophil activation involved in immune response', and 'regulation of cell morphogenesis'. GSEA analysis highlighted enrichment in pathways like 'CTRL_VS_ACT_IL4 AND ANTI_IL12_12H_CD4_TCELL_DN'. Through WGCNA and LASSO regression analysis, key characteristic genes MARCKSL1, TMTC4, and TTLL12 were identified, with AUC values greater than 0.8 in both the training and validation sets, and were found to be closely associated with immune cell infiltration. Conclusion & Significance: MARCKSL1, TMTC4, and TTLL12 emerge as crucial genes in the process of PCA, showing significant relevance to immune cell infiltration.this study offers valuable clinical insights into the diagnosis and treatment of prostate cancer through the identification of specific genes associated with the disease.