Wang Rui, Qin Guan-Hua, Jiang Yifei, Chen Fu-Xiang, Wang Zi-Han, Ju Lin-Ling, Chen Lin, Fu Da, Liu En-Yu, Zhang Su-Qing, Cai Wei-Hua
Department of Hepatobiliary Surgery, Affiliated Nantong Hospital 3 of Nantong University, Nantong, Jiangsu, China.
Medical School of Nantong University, Nantong, Jiangsu, China.
Front Immunol. 2025 Jul 4;16:1592416. doi: 10.3389/fimmu.2025.1592416. eCollection 2025.
Pancreatic cancer (PC) is marked by extensive heterogeneity, posing significant challenges to effective treatment. The tumor microenvironment (TME), particularly cancer-associated fibroblasts (CAFs), plays a critical role in driving PC progression. However, the prognostic and functional contributions of distinct CAF subtypes remain inadequately understood. Here, we introduce a novel 7-gene risk model that not only robustly stratifies PC patients but also unveils the unique role of PHLDA1 as a key mediator in tumor-stroma crosstalk.
By integrating single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA sequencing data, we comprehensively characterized the heterogeneity of CAFs in PC. We identified five CAF subtypes and focused on matrix CAFs (mCAFs), which were strongly associated with poor prognosis. A 7-gene mCAF-associated risk model was constructed using advanced machine learning algorithms, and the biological significance of PHLDA1 was validated through co-culture experiments and pan-cancer analyses.
Our multiomics analysis revealed that the novel 7-gene model (comprising USP36, KLF5, MT2A, KDM6B, PHLDA1, REL, and DDIT4) accurately predicts patient survival, immunotherapy response, and TME status. Notably, PHLDA1 was uniquely overexpressed in CAFs and correlated with the activation of key protumorigenic pathways, including EMT, KRAS, and TGF-β, underscoring its central role in modulating the crosstalk between CAFs and malignant ductal cells. Pan-cancer analysis further supported PHLDA1's prognostic and immunomodulatory significance across multiple tumor types.
Our study presents a novel 7-gene prognostic model that significantly enhances risk stratification in PC and identifies PHLDA1+ CAFs as promising prognostic biomarkers and therapeutic targets. These findings provide new insights into the TME of PC and open avenues for personalized treatment strategies.
胰腺癌（PC）具有广泛的异质性，给有效治疗带来了重大挑战。肿瘤微环境（TME），尤其是癌症相关成纤维细胞（CAFs），在推动PC进展中起着关键作用。然而，不同CAF亚型的预后和功能贡献仍未得到充分理解。在此，我们引入了一种新的7基因风险模型，该模型不仅能有力地对PC患者进行分层，还揭示了PHLDA1作为肿瘤-基质相互作用关键介质的独特作用。
通过整合单细胞RNA测序（scRNA-seq）、空间转录组学和批量RNA测序数据，我们全面表征了PC中CAFs的异质性。我们鉴定出五种CAF亚型，并聚焦于与预后不良密切相关的基质CAFs（mCAFs）。使用先进的机器学习算法构建了一个7基因mCAF相关风险模型，并通过共培养实验和泛癌分析验证了PHLDA1的生物学意义。
我们的多组学分析表明，新的7基因模型（包括USP36、KLF5、MT2A、KDM6B、PHLDA1、REL和DDIT4）能够准确预测患者生存、免疫治疗反应和TME状态。值得注意的是，PHLDA1在CAFs中特异性高表达，并与关键促肿瘤途径（包括EMT、KRAS和TGF-β）的激活相关，突显了其在调节CAFs与恶性导管细胞之间相互作用中的核心作用。泛癌分析进一步支持了PHLDA1在多种肿瘤类型中的预后和免疫调节意义。
我们的研究提出了一种新的7基因预后模型，该模型显著增强了PC中的风险分层，并将PHLDA1+ CAFs鉴定为有前景的预后生物标志物和治疗靶点。这些发现为PC的TME提供了新的见解，并为个性化治疗策略开辟了道路。
