Luo Qiang, Jiang Tingting, Xie Dacheng, Li Xiaojia, Xie Keping
Center for Pancreatic Cancer Research, The South China University of Technology School of Medicine, Guangzhou, Guangdong, China.
Center for Pancreatic Cancer Center, The South China University of Technology Comprehensive Cancer Center, Guangzhou, Guangdong, China.
Front Immunol. 2025 May 23;16:1559373. doi: 10.3389/fimmu.2025.1559373. eCollection 2025.
Pancreatic cancer (PC) remains a lethal malignancy with limited treatment options. The role of innate immune cell barrier-related genes in PC prognosis is poorly defined. This study aimed to identify prognostic biomarkers, develop a predictive model, and uncover novel targets for personalized therapy.
Innate immune cell barrier-related genes were curated from KEGG, ImmPort, MSigDB, and InnateDB. Differential expression analysis was performed using TCGA and GTEx datasets. Univariate Cox regression identified survival-associated genes. Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. Core genes were prioritized via the "mime1" package, and single-cell RNA sequencing (scRNA-seq) data explored UBASH3B's functional role.
352 differentially expressed genes of Innate immune cell barrier-related were identified, with NK cell pathways linked to PC immunity. Univariate Cox analysis revealed 8 protective and 84 risk genes. The RSF model (trained on risk genes) showed strong 3- and 5-year survival prediction. High-risk patients exhibited elevated tumor mutation burden (TMB), reduced NK/CD8+ T cell infiltration, and resistance to Erlotinib/Oxaliplatin but sensitivity to 5-Fluorouracil. Five key genes (ITGB6, COL17A1, MMP28, DIAPH3, UBASH3B) were highlighted. UBASH3B, a novel marker, correlated negatively with NK cell activation and mediated immune signaling and drug resistance.
This study established the CDRG-RSF model, a robust prognostic tool leveraging innate immune genes. UBASH3B's dual role in immune suppression and drug resistance highlights its potential for stratifying PC patients into tailored treatment groups. The findings underscore the importance of integrating machine learning with immune profiling to advance precision oncology for PC.
胰腺癌(PC)仍然是一种致命的恶性肿瘤,治疗选择有限。先天性免疫细胞屏障相关基因在PC预后中的作用尚不明确。本研究旨在识别预后生物标志物,开发预测模型,并发现个性化治疗的新靶点。
从KEGG、ImmPort、MSigDB和InnateDB中筛选先天性免疫细胞屏障相关基因。使用TCGA和GTEx数据集进行差异表达分析。单因素Cox回归确定与生存相关的基因。使用14种机器学习算法建立PC的预后模型,并通过长期生存指标、功能富集、免疫浸润分析和药物敏感性分析验证模型性能。通过“mime1”软件包对核心基因进行优先级排序,并利用单细胞RNA测序(scRNA-seq)数据探索UBASH3B的功能作用。
共鉴定出352个先天性免疫细胞屏障相关的差异表达基因,其中NK细胞途径与PC免疫相关。单因素Cox分析显示8个保护基因和84个风险基因。RSF模型(基于风险基因训练)显示出较强的3年和5年生存预测能力。高危患者表现出肿瘤突变负荷(TMB)升高、NK/CD8+ T细胞浸润减少,对厄洛替尼/奥沙利铂耐药,但对5-氟尿嘧啶敏感。突出了五个关键基因(ITGB6、COL17A1、MMP28、DIAPH3、UBASH3B)。新型标志物UBASH3B与NK细胞活化呈负相关,并介导免疫信号传导和耐药性。
本研究建立了CDRG-RSF模型,这是一种利用先天性免疫基因的强大预后工具。UBASH3B在免疫抑制和耐药性中的双重作用突出了其将PC患者分层为个性化治疗组的潜力。研究结果强调了将机器学习与免疫分析相结合以推进PC精准肿瘤学的重要性。