PO.BCS01.16 · 生物信息与计算
整合分析识别出ELF3-CREB1共激活是高危胰腺癌的预后驱动因素
Integrated analysis identifies ELF3-CREB1 co-activation as a prognostic driver of high-risk pancreatic cancer
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：确诊为胰腺导管腺癌（PDAC）的患者预后极差，五年生存率仅约13%。转录因子通过决定肿瘤侵袭性、治疗耐药性和临床结局，在PDAC的生存和预后中发挥重要作用。CREB1等关键调控因子此前已被证实与肿瘤进展和较短生存期相关。虽然已知CREB1信号通路驱动PDAC，但对与CREB1相关的活性转录因子（TF）及其临床相关性的系统性刻画仍然有限。在本研究中，我们旨在识别与CREB1相关的关键TF活性，并评估其在PDAC中的组合预后影响。
方法：我们利用VIPER（基于富集调控子的蛋白活性虚拟推断）算法结合高置信度DoRothEA调控子（A-C级）来推断182个TCGA-PAAD RNA-seq样本（178个原发肿瘤[TP]和4个正常组织[NT]）中的TF活性。使用limma进行的差异活性分析（DAA）识别出在TP与NT之间失调的TF。生存分析在TP队列上采用Kaplan-Meier和Cox比例风险模型，重点关注两个关键失调TF——CREB1和ELF3（ETS转录因子3）之间的相互作用。所有分析均使用R 4.3.0版本，包括GSVA（v1.50.5）和VIPER（v1.36.0）。差异表达和通路分析使用R包limma（v3.58.1）、GSVA（v1.50.5）、msigdbr、gplots和ggplot2完成。
结果：DAA识别出44个显著失调的TF（FDR < 0.05）。上皮谱系调控因子ELF3在肿瘤中显示出最高的激活水平，而淋巴调控因子PAX5（配对盒5）则被高度抑制。虽然单个TF的预后检验无统计学意义，但组合分析揭示了强烈的情境依赖效应。CREB1高活性与ELF3高活性同时存在定义了一个独特的侵袭性亚组，其中位总生存期显著更短（272天），相比之下最低风险组合为492天。Cox模型证实了这两个TF之间存在显著的协同相互作用（HR = 2.31，p = 0.049）。
结论：本分析揭示，高度激活的上皮驱动因子ELF3与CREB1协同作用，共同定义了PDAC中的高危预后特征。我们的发现强调，是TF网络而非单一因子对患者分层至关重要，并代表了PDAC中令人信服的、情境特异性的治疗靶点。
查看英文原文 English abstract
Background: Patients diagnosed with pancreatic ductal adenocarcinoma (PDAC) face a dismal prognosis, with only about 13% surviving five years. Transcriptional factors play a vital role in PDAC survival and prognosis by defining tumor aggressiveness, therapeutic resistance, and clinical outcome. Key regulators such as CREB1 have previously been linked with tumor progression and shorter survival. While CREB1 signaling driving PDAC is known, the systematic characterization of active Transcription Factor (TF) associated with CREB1, and their clinical relevance is limited. In this study, we aimed to identify critical TF activity associated with CREB1 and assess their combinatorial prognostic impact in PDAC.
Methods: We utilized the VIPER (Virtual Inference of Protein-activity by Enriched Regulon) algorithm with high-confidence DoRothEA regulons (A-C) to infer TF activity across 182 TCGA-PAAD RNA-seq samples (178 Primary Tumors [TP] and 4 Normal Tissues [NT]). Differential Activity Analysis (DAA) using limma identified TFs dysregulated between TP and NT. Survival analyses utilized Kaplan-Meier and Cox Proportional Hazards modeling on the TP cohorts, focusing on the interaction between two key dysregulated TFs, CREB1 and ELF3 (ETS transcription factor 3). R version 4.3.0 was used for all analyses, including GSVA (v1.50.5) and VIPER (v1.36.0). Differential expression and pathway analyses were performed using the R packages limma (v3.58.1), GSVA (v1.50.5), msigdbr, gplots, and ggplot2.
Results: DAA identified 44 significantly dysregulated TFs (FDR < 0.05). The epithelial lineage regulator ELF3 showed the highest activation in tumors, while the lymphoid regulator PAX5 (paired box 5) was highly repressed. Although single-TF prognostic tests were non-significant, combinatorial analysis revealed a strong context-dependent effect. The simultaneous High CREB1 and High ELF3 activity defined a uniquely aggressive subgroup with a markedly shorter median overall survival (272 days) compared to the lowest-risk combination (492 days). Cox modeling confirmed a significant synergistic interaction between the two TFs (HR =2.31, p = 0.049).
Conclusion: This analysis reveals that the highly activated epithelial driver ELF3 acts synergistically with CREB1 to define a high-risk prognostic signature in PDAC. Our findings underscore that TF networks, rather than single factors, are crucial for patient stratification and represent compelling, context-specific therapeutic targets in PDAC.
利益披露 Disclosure
P. Poorva, None..
R. Khurana, None..
V. Krishnamoorthy, None..
S. Jinka, None..
Y. Guo, None..
V. K. Gupta, None..
N. Nagathihalli, None.