PO.BCS01.14 · 生物信息与计算
通过全转录组因果基因调控网络理解前列腺癌的分子机制
Understanding molecular mechanisms of prostate cancer via transcriptome-wide causal gene regulatory networks
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：前列腺癌仍是男性最常见的恶性肿瘤，也是全球癌症相关死亡的主要原因，预计2025年将有约313,780例新发病例。尽管筛查和治疗近期取得进展，但驱动前列腺癌的分子机制尚未完全阐明。来自同一患者的多组学数据日益增多，为揭示疾病通路和识别新治疗靶点提供了前所未有的机会。然而，整合这些大规模、多模态、异质的组学谱带来了巨大的统计学和计算挑战。
方法：转录组和基因组数据获取自GEO（GSE70768）中的前列腺肿瘤样本。经过预处理和质量控制后，该数据集包含来自90例患者的17,426个基因和272,564个单核苷酸多态性（SNP）。我们应用SIGNET识别工具变量（IV），为3,309个基因产生了7,806个基因-IV对。使用这些IV，我们随后应用SIGNET进行因果推断，并基于整合的基因组和转录组数据构建前列腺癌的全转录组基因调控网络，由100个自助抽样数据集支持。
结果：我们识别出1,840个在≥80%自助抽样数据集中被重复恢复的基因调控关系，其中369个出现在≥95%的构建中。在这些稳健的子网络中，我们检测到包括DDX51、PNPT1、FARSLB和IFI6在内的枢纽基因。使用来自癌症基因组图谱（TCGA）项目的数据，我们验证了IFI6与其预测靶点高度相关（相关系数0.52~0.90，p < 0.01）。IFI6是先天免疫的负调控因子，据报道在多种癌症中过表达，新出现的证据支持其在肿瘤发生和耐药中的作用。我们的发现提名IFI6为前列腺癌中的候选调控因子，值得进一步的功能研究以明确其在肿瘤增殖、转移、治疗反应和免疫肿瘤微环境中的作用。最后，对高自助抽样频率的顶级子网络进行的Ingenuity通路分析（IPA）凸显了几条显著通路，包括原发性免疫缺陷信号传导以及先天与适应性免疫细胞之间的通讯。
结论：使用来自前列腺癌组织的多组学数据结合全转录组因果推断，我们数据驱动的调控因子-靶点对检测为前列腺癌的分子机制提供了新见解，并可能最终促进个性化治疗策略的开发。
查看英文原文 English abstract
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.
利益披露 Disclosure
M. Zhang, None..
Z. Jiang, None..
X. Zi, None..
D. Liu, None..
Y. Li, None..
D. Zhang, None.