- UAA Congress 2025

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Abstract

Abstract Title

Whole Genome Sequencing as a Comprehensive Tool for Diagnosis, Therapeutic Guidance, and Prognosis of Prostate Cancer to Advance Personalized Medicine in Indonesia

Presentation Type

Podium Abstract

Manuscript Type

Clinical Research

Abstract Category *

Oncology: Prostate

Author's Information

Number of Authors (including submitting/presenting author) *

No more than 10 authors can be listed (as per the Good Publication Practice (GPP) Guidelines).
Please ensure the authors are listed in the right order.

Co-author 1

Edvin Prawira Negara negara.edvin@gmail.com Universitas Brawijaya Department of Urology Malang Indonesia *

Co-author 2

Besut Daryanto urobes.fk@ub.ac.id Universitas Brawijaya Department of Urology Malang Indonesia -

Co-author 3

Kurnia Penta Seputra uropnt.fk@ub.ac.id Universitas Brawijaya Department of Urology Malang Indonesia -

Co-author 4

David Agustriawan david.agustriawan@umn.ac.id Indonesia International Institute for Life Science Faculty of Bioinformatic Jakarta Indonesia -

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Abstract Content

Introduction

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.

Materials and Methods

The Whole Genome Sequence Dataset is obtained from sequencing process using ilumina sequencing technology with Bioinformatics research pipeline. Prostate sample was taken from prostate cancer patient underwent prostate resection, followed by DNA extraction and protein sequencing. DNA was extracted, assessed for quality, and processed for sequencing. NGS was evaluated with FastQC and Samtools. Somatic variants were detected using MuTect2, and results were annotated with ANNOVAR and VEP. Data WGS was compared to Human GRCh38. SNP changes and positions were viewed through IGV. Data-data genomic selanjutnya dianalisis dengan data rekam medis pasien sehngga mendapatkan akurasi yang tinggi. All statistical analyses were performed using R software

Results

A bioinformatics analysis revealed the variation in the several genes that related to prostate cancer. Gen yang berhubungan dengan diagnostic meliputi SRD5A2 rs1047303, CYP19A1 rs1870050, BNC2 rs16934641, BRCA2 rs144848 berhubungan dengan ca prostat. Gen yang berhubungan dengan terapi SRD5A2 rs1047303, CYP19A1 (rs743572 dan rs2486758), CYB5a rs10459592 dan SLCO1B1 rs4149056. Gen yang berhubungan dengan prognostik meliputi SLCO1B3 rs4149117, SLCO2B1 rs12422149, ARRDC3 rs2939244, FLT1 rs9508016, SKAP1 rs6054145, FBXO31 rs7830622, BNC2 rs16934641, TACC2 rs3763763, ALPK1 rs2051778, EGF rs4444903, TFGBR2 rs3087464, LSAMP rs13088089, CCL17 rs223899 dan PSMD7 rs2387084, MON1B rs284924, IRS2 rs7986346, CASP3 rs48622396, BMP5 rs3734444, ANRIL rs10757278. Gen-gen ini akan dianalisis dengan rekam medis untuk membuat alogaritma AI. AI akan meningkatkan akurasi dari genomic sehingga penanganan BPH akan lebih efektif.

Conclusions

Complete sequence analysis confirms that DNA sequence variation in several genes related to prostate cancer. AI akan membantu menganalisis genomic dengan rekam medis sehingga meningkatkan akurasi dari analysis genomic

Keywords

AI Genomic, Bioinformatics, Next Generation Sequencing, Prostate Cancer

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