CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题：Integrating tertiary lymphoid structure-associated genes into computational models to evaluate prognostication and immune infiltration in pancreatic cancer.
Integrating tertiary lymphoid structure-associated genes into computational models to evaluate prognostication and immune infiltration in pancreatic cancer.
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胰腺导管腺癌（PDAC）的特征是对所有治疗方式反应不佳且预后极差。多种实体癌中三级淋巴结构（TLSs）的存在具有重要的预后意义，凸显了肿瘤微环境与免疫细胞聚集之间复杂的相互作用。
然而，TLSs和免疫状态对PDAC预后的影响程度仍不完全清楚。在此，我们试图通过利用单细胞和批量转录组学来揭示PDAC中TLSs的独特特性，最终建立一个预测临床结局的风险模型。
我们分别使用了基于12基因（CCL2、CCL3、CCL4、CCL5、CCL8、CCL18、CCL19、CCL21、CXCL9、CXCL10、CXCL11和CXCL13）和9基因（PTGDS、RBP5、EIF1AY、CETP、SKAP1、LAT、CCR6、CD1D和CD79B）特征的TLS评分，并检查了它们在PDAC样本单细胞数据的细胞簇中的分布。选择这些簇中涉及的标志物，以癌症基因组图谱计划数据库作为训练队列，基因表达综合数据库作为验证队列，开发预后模型。
此外，我们比较了模型中高、低风险组之间的免疫浸润、药物敏感性以及富集和差异表达基因。因此，我们建立了一个风险模型，该模型对PADC患者的预后评估具有重要意义，低风险组和高风险组之间在免疫浸润和化疗敏感性方面存在显著差异。这种由TLS相关细胞标志基因建立的范式为探索潜在的免疫治疗提供了预后预测和一组新的治疗靶点。
Pancreatic ductal adenocarcinoma (PDAC) is characterized by poor response to all therapeutic modalities and dismal prognosis. The presence of tertiary lymphoid structures (TLSs) in various solid cancers is of crucial prognostic significance, highlighting the intricate interplay between the tumor microenvironment and immune cells aggregation.
However, the extent to which TLSs and immune status affect PDAC prognosis remains incompletely understood.
Here, we sought to unveil the unique properties of TLSs in PDAC by leveraging both single-cell and bulk transcriptomics, culminating in a risk model that predicts clinical outcomes.
We used TLS scores based on a 12-gene (CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, and CXCL13) and 9-gene (PTGDS, RBP5, EIF1AY, CETP, SKAP1, LAT, CCR6, CD1D, and CD79B) signature, respectively, and examined their distribution in cell clusters of single-cell data from PDAC samples. The markers involved in these clusters were selected to develop a prognostic model using The Cancer Genome Atlas Program database as the training cohort and Gene Expression Omnibus database as the validation cohort.
Further, we compared the immune infiltration, drug sensitivity, and enriched and differentially expressed genes between the high- and low-risk groups in our model.
Therefore, we established a risk model that has significant implications for the prognostic assessment of PADC patients with remarkable differences in immune infiltration and chemosensitivity between the low- and high-risk groups. This paradigm established by TLS-related cell marker genes provides a prognostic prediction and a panel of novel therapeutic targets for exploring potential immunotherapy.
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