Combined SNPs sequencing and allele specific proteomics capture reveal functional causality underpinning the 2p25 prostate cancer susceptibility locus.
Gong-Hong WeiDandan DongPeng ZhangMengqi LiuYu WeiZixian WangWenjie XuQixiang ZhangYao ZhuQin ZhangXiayun YangJing ZhuLiang WangPublished in: Research square (2024)
Genome wide association studies (GWASs) have identified numerous risk loci associated with prostate cancer, yet unraveling their functional significance remains elusive. Leveraging our high-throughput SNPs-seq method, we pinpointed rs4519489 within the multi-ancestry GWAS-discovered 2p25 locus as a potential functional SNP due to its significant allelic differences in protein binding. Here, we conduct a comprehensive analysis of rs4519489 and its associated gene, NOL10, employing diverse cohort data and experimental models. Clinical findings reveal a synergistic effect between rs4519489 genotype and NOL10 expression on prostate cancer prognosis and severity. Through unbiased proteomics screening, we reveal that the risk allele A of rs4519489 exhibits enhanced binding to USF1, a novel oncogenic transcription factor (TF) implicated in prostate cancer progression and prognosis, resulting in elevated NOL10 expression. Furthermore, we elucidate that NOL10 regulates cell cycle pathways, fostering prostate cancer progression. The concurrent expression of NOL10 and USF1 correlates with aggressive prostate cancer characteristics and poorer prognosis. Collectively, our study offers a robust strategy for functional SNP screening and TF identification through high-throughput SNPs-seq and unbiased proteomics, highlighting the rs4519489-USF1-NOL10 regulatory axis as a promising biomarker or therapeutic target for clinical diagnosis and treatment of prostate cancer.
Keyphrases
- prostate cancer
- genome wide
- radical prostatectomy
- high throughput
- single cell
- dna methylation
- transcription factor
- genome wide association
- cell cycle
- poor prognosis
- mass spectrometry
- binding protein
- copy number
- cell proliferation
- machine learning
- label free
- long non coding rna
- radiation therapy
- small molecule
- risk assessment
- adverse drug
- high density
- genome wide identification