Table_3_Identification of gene signatures related to hypoxia and angiogenesis in pancreatic cancer to aid immunotherapy and prognosis.xlsx

BackgroundOne of the most diverse tumors is pancreatic cancer (PC), which makes predicting the prognosis challenging. PC development is directly related to hypoxia, angiogenesis, and immunotherapy. It is still unclear how the three features are related.
MethodsThe Genotype-Tissue Expression (GTEx) and the Cancer Genome Atlas (TCGA) database were employed to obtain sequencing data for healthy pancreatic tissues and PC tissues, respectively. According to the constructed hypoxic prognostic model (HPM) and angiogenic prognostic model (APM), 4 subtypes of PC were identified. Hypoxia and angiogenesis prognostic model (HAPM) was established based on differentially expressed genes (DEGs) between high-angiogenesis/high-hypoxia (HH) and low-angiogenesis/low-hypoxia (LL) subgroups. Base on the median risk score, PC patients were separated into high-risk and low-risk groups, and clinical traits, prognosis, percentage of immune cell infiltration, PD-1 expression, and the fraction of T-cell depletion were compared between the groups. Finally, the predictive accuracy of the tumor immune dysfunction and rejection (TIDE) and tumor inflammatory signature (TIS) models, as well as HAPM, was compared.
ResultWe analyzed the mRNA sequencing data from 178 PC tissues and 171 normal pancreatic tissues to obtain 9527 DEGs. We discovered 200 genes linked with hypoxia and 36 genes involved with angiogenesis through the literature. We found the core genes related with hypoxia and angiogenesis in PC by intersecting the DEGs of the HH and LL subgroups with those of PC via WGCNA. IL-17 signaling pathway, ECM-receptor interactions, cytokine receptor interactions, etc. were all enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) results of core genes. HAPM has good predictive efficiency, according to an evaluation of KM survival curves and ROC curves. The external dataset also validated the model’s ability to anticipate outcomes. Patients in the high- and low-risk groups were compared for PD1 expression and T-cell exclusion scores, which suggested that the model might be used to forecast which PC patients might benefit from immunotherapy.
ConclusionsThe probable molecular processes connecting hypoxia and angiogenesis are described in this work, and a model is developed that may be utilized to forecast the prognosis for PC patients and the benefits of immunotherapy.

背景
胰腺癌（pancreatic cancer, PC）是最为异质性的肿瘤之一，其预后预测极具挑战性。胰腺癌的发生与缺氧、血管生成及免疫治疗直接相关，但三者之间的内在关联机制目前仍未明确。

方法
本研究分别从基因型-组织表达数据库（Genotype-Tissue Expression, GTEx）与癌症基因组图谱（The Cancer Genome Atlas, TCGA）获取健康胰腺组织与胰腺癌组织的测序数据。基于构建的缺氧预后模型（hypoxic prognostic model, HPM）与血管生成预后模型（angiogenic prognostic model, APM），鉴定出4种胰腺癌亚型。以高血管生成/高缺氧（high-angiogenesis/high-hypoxia, HH）与低血管生成/低缺氧（low-angiogenesis/low-hypoxia, LL）亚组间的差异表达基因（differentially expressed genes, DEGs）为基础，建立缺氧与血管生成预后模型（hypoxia and angiogenesis prognostic model, HAPM）。以风险评分中位数为分界阈值，将胰腺癌患者划分为高风险组与低风险组，比较两组间的临床特征、预后结局、免疫细胞浸润占比、PD-1表达水平以及T细胞耗竭比例。最后，本研究比较了肿瘤免疫功能异常与排斥模型（tumor immune dysfunction and exclusion, TIDE）、肿瘤炎症特征模型（tumor inflammatory signature, TIS）以及HAPM的预测准确性。

结果
本研究分析了178份胰腺癌组织与171份正常胰腺组织的mRNA测序数据，共筛选得到9527个差异表达基因。通过文献检索，我们获取了200个与缺氧相关的基因以及36个与血管生成相关的基因。通过加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA），将HH与LL亚组的差异表达基因与胰腺癌相关基因取交集，筛选得到胰腺癌中与缺氧及血管生成相关的核心基因。核心基因的京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）富集分析结果显示，IL-17信号通路、细胞外基质-受体相互作用、细胞因子受体相互作用等多条通路均显著富集。通过KM生存曲线与ROC曲线的评估，证实HAPM具有良好的预测效能；外部独立数据集同样验证了该模型的预后预测能力。对高、低风险组患者的PD-1表达水平与T细胞排斥评分进行比较后发现，该模型可用于预测胰腺癌患者能否从免疫治疗中获益。

结论
本研究阐明了连接缺氧与血管生成的潜在分子机制，并构建了可用于预测胰腺癌患者预后以及免疫治疗获益情况的模型。