Understanding molecular mechanisms of prostate cancer via transcriptome-wide causal gene regulatory networks

Background: Prostate cancer remains the most common malignancy among men and a leading cause of cancer-related death worldwide, with an estimated 313,780 new cases expected in 2025. Despite recent advances in screening and treatment, the molecular mechanisms driving prostate cancer are not fully understood. The growing availability of multi-omics data from the same patients provides an unprecedented opportunity to reveal disease pathways and identify novel therapeutic targets. However, integrating these large-scale, multi-modal, and heterogeneous omics profiles poses substantial statistical and computational challenges. Methods: Transcriptomic and genomic data were obtained from prostate tumor samples in GEO (GSE70768). After pre-processing and quality control, the dataset included 17,426 genes and 272,564 single nucleotide polymorphisms (SNPs) from 90 patients. We applied SIGNET to identify instrumental variables (IVs), yielding 7,806 gene-IV pairs for 3,309 genes. Using these IVs, we then applied SIGNET to conduct causal inference and construct transcriptome-wide gene regulatory networks for prostate cancer based on the integrated genomic and transcriptomic data, supported by 100 bootstrap datasets. Results: We identified 1,840 gene regulations that were repeatedly recovered in ≥80% of the bootstrap datasets, of which 369 appeared in ≥95% of the constructions. Within these robust subnetworks, we detected hub genes including DDX51, PNPT1, FARSLB, and IFI6. Using data from the Cancer Genome Atlas (TCGA) project, we validated that IFI6 is highly correlated with its predicted targets (correlation coefficient 0.52 ~ 0.90, p < 0.01). IFI6 is a negative regulator of innate immunity and has been reported to be overexpressed in multiple cancers, with emerging evidence supporting its role in tumorigenesis and drug resistance. Our findings nominate IFI6 as a candidate regulator in prostate cancer, warranting further functional studies to define its role in tumor proliferation, metastasis, therapy responses, and the immune tumor microenvironment. Finally, Ingenuity Pathway Analysis (IPA) of top subnetworks with high bootstrap frequency highlighted several significant pathways, including primary immunodeficiency signaling and communication between innate and adaptive immune cells. Conclusion: Using multi-omics data from prostate cancer tissues coupled with transcriptome-wide causal inference, our data-driven detection of regulator-target pairs provides new insights into the molecular mechanisms of prostate cancer and may ultimately facilitate the development of personalized treatment strategies.