Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Integrative Whole-Genome and Epigenome profiling of cfDNA in Familial Prostate Cancer: Insights from a Pilot Study

ABSTRACT: Prostate cancer (PCa) is one of the most common malignancies in men, with familial forms accounting for nearly 20% of cases. Early detection and risk stratification remain challenging, especially in individuals with a genetic predisposition. In this pilot study, we evaluated the feasibility and clinical relevance of an integrated multi-omic approach by performing whole-genome, strand-specific sequencing of circulating cell-free DNA (cfDNA) from eight patients with confirmed familial PCa. Through an integrated analysis pipeline, we identified 18,878 genetic variants, of which 2,276 were potentially pathogenic. Among these, 26 recurrent high-impact mutations—such as stop-gain and start-loss variants—were found in genes including MUC4, MCM9, and SKA3. Epigenomic profiling revealed widespread hypermethylation in these genes, suggesting transcriptional repression, while allele-specific methylation (ASM) was detected in TTC22, TEX51, WDR89, LAIR2, and SKA3, indicating functional interplay between somatic mutations and local epigenetic regulation. These findings demonstrate the potential of combining genetic and epigenetic data from cfDNA to uncover novel markers for improved stratification and personalized management of familial prostate cancer.

INSTRUMENT(S): Illumina NovaSeq 6000

ORGANISM(S): Homo sapiens

SUBMITTER: Genomix4Life Genomix4Life

PROVIDER: E-MTAB-15437 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:The highly intra-tumoral heterogeneity and complex cell origination of PCa greatly limited the significance of traditional bulk RNA sequencing in finding better biomarker for disease stratification. In the context of these sequencing strategies, significant gene expression differences in specific cell population might be normalized or even shaded by genes in those less “important” but large in amount cells. Tissue specimens based single-cell RNA sequencing holds great promise for identification of novel stratification biomarkers. However, the preparation of enough viable cells for sequencing, in particular for prostate cancer (PCa), is very challenging when using this technique. In the current study, we, for the first time, employed single-cell RNA sequencing of PCa cells isolated from cancerous prostate tissues of two patients and identified fifteen cell groups including three luminal clusters with different expression profiles. One of these luminal clusters had the highest copy number variation level and marker genes mainly enriched in PCa-related metabolic process, thereafter, was considered as the malignant luminal cells in PCa. Further analysis indicates that the fifth subgroup of these cells highly expressed genes significantly correlated with PCa initiation and early development. In addition, we found this subgroup highly expressed PCa prognosis biomarkers such as AMACR and PCA3. These findings suggest that this cell subgroup might be a critical cell population for PCa diagnosis and stratification. Moreover, by further deciphering the gene expression of this subgroup, we identified another marker gene, HPN, with a 0.930 area under the curve (AUC) score distinguishing normal tissue from PCa lesion using the RNA-seq data from The Cancer Genome Atlas (TCGA). This finding was further validated by immunostaining of HPN in PCa tissue array. The expression of HPN in PCa with Gleason score > 6 is significantly higher than those in Gleason score = 6. Taken together, we provide a valuable resource for interpreting the tumor heterogeneity in PCa, and a novel candidate marker for PCa management.

Dataset Information

Integrative Whole-Genome and Epigenome profiling of cfDNA in Familial Prostate Cancer: Insights from a Pilot Study

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