Prostate cancer is associated with a particular ethnicity and even each patient needs a specific clinical pathway process. The issue of personalized clinical pathway has not been identified yet and it is still lack of study. Genomic dataset of DNA sequences of prostate cancer patients can be utilized with Bioinformatics research pipeline to investigate and analysis personalized clinical pathway of each prostate patient.

The whole-genome sequencing (WGS) dataset was generated using Illumina sequencing technology, supported by a bioinformatics pipeline. Prostate tissue samples were collected from patients undergoing surgical resection for prostate cancer. Following DNA extraction and protein isolation, DNA quality was assessed and prepared for sequencing. Raw next-generation sequencing (NGS) data underwent quality evaluation via FastQC and Samtools. Somatic variants were identified using MuTect2, followed by functional annotation with ANNOVAR and Ensembl’s Variant Effect Predictor (VEP). The WGS data were aligned to the human reference genome (GRCh38), and SNP alterations were visualized using the Integrative Genomics Viewer (IGV). Subsequent genomic analyses integrated patient medical records to enhance diagnostic accuracy. All statistical analyses were conducted using R software.

Bioinformatics analysis identified genetic variations in multiple genes associated with prostate cancer pathogenesis. Diagnostic-related genes include SRD5A2 (rs1047303), CYP19A1 (rs1870050), BNC2 (rs16934641), and BRCA2 (rs144848). Therapeutic targets encompass SRD5A2 (rs1047303), CYP19A1 (rs743572 and rs2486758), CYB5A (rs10459592), and SLCO1B1 (rs4149056). Prognostic markers comprise SLCO1B3 (rs4149117), SLCO2B1 (rs12422149), ARRDC3 (rs2939244), FLT1 (rs9508016), SKAP1 (rs6054145), FBXO31 (rs7830622), BNC2 (rs16934641), TACC2 (rs3763763), ALPK1 (rs2051778), EGF (rs4444903), TGFBR2 (rs3087464), LSAMP (rs13088089), CCL17 (rs223899), PSMD7 (rs2387084), MON1B (rs284924), IRS2 (rs7986346), CASP3 (rs48622396), BMP5 (rs3734444), and ANRIL (rs10757278). These genetic variants will be analyzed in conjunction with patient medical records to develop an AI-driven algorithm. The integration of artificial intelligence is anticipated to enhance genomic accuracy, enabling more effective and personalized therapeutic strategies for prostate cancer.

Comprehensive genomic sequencing validates DNA sequence variations across multiple prostate cancer-associated genes. Artificial intelligence (AI) enhances diagnostic precision by integrating genomic data with patient medical records, enabling data-driven clinical decision-making and optimizing therapeutic strategies for prostate cancer management.