一种新型肿瘤衍生外泌体基因特征可预测胰腺癌患者的预后。
A novel tumor-derived exosomal gene signature predicts prognosis in patients with pancreatic cancer.
作者信息
Wang Yang, Liang Chao, Liu Xinbo, Cheng Shu-Qun
机构信息
Department of Hepatopancreatobiliary Surgery, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China.
出版信息
Transl Cancer Res. 2024 Aug 31;13(8):4324-4340. doi: 10.21037/tcr-23-2354. Epub 2024 Aug 26.
BACKGROUND
Pancreatic cancer is a devastating disease with poor prognosis. Accumulating evidence has shown that exosomes and their cargo have the potential to mediate the progression of pancreatic cancer and are promising non-invasive biomarkers for the early detection and prognosis of this malignancy. This study aimed to construct a gene signature from tumor-derived exosomes with high prognostic capacity for pancreatic cancer using bioinformatics analysis.
METHODS
Gene expression data of solid pancreatic cancer tumors and blood-derived exosome tissues were downloaded from The Cancer Genome Atlas (TCGA) and ExoRBase 2.0. Overlapping differentially expressed genes (DEGs) in the two datasets were analyzed, followed by functional enrichment analysis, protein-protein interaction networks, and weighted gene co-expression network analysis (WGCNA). Using the least absolute shrinkage and selection operator (LASSO) regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was constructed based on the TCGA dataset, which was validated by an external validation dataset, GSE62452. The prognostic power of this gene signature and its relationship with various pathways and immune cell infiltration were analyzed.
RESULTS
A total of 166 overlapping DEGs were identified from the two datasets, which were markedly enriched in functions and pathways associated with the cell cycle. Two key modules and corresponding 70 exosomal DEGs were identified using WGCNA. Using LASSO Cox regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was built using six exosomal DEGs (, , , , , and ), which showed high predictive performance for prognosis in both the training and validation datasets. In addition, this prognostic signature is associated with the differential activation of several pathways, such as the cell cycle, and the infiltration of some immune cells, such as Tregs and CD8+ T cells.
CONCLUSIONS
This study established a six-exosome gene signature that can accurately predict the prognosis of pancreatic cancer.
背景
胰腺癌是一种预后较差的毁灭性疾病。越来越多的证据表明，外泌体及其所载物质有可能介导胰腺癌的进展，并且有望成为这种恶性肿瘤早期检测和预后评估的非侵入性生物标志物。本研究旨在通过生物信息学分析，从肿瘤来源的外泌体构建具有高胰腺癌预后预测能力的基因特征。
方法
从癌症基因组图谱（TCGA）和外泌体数据库ExoRBase 2.0下载实体胰腺癌肿瘤和血液来源外泌体组织的基因表达数据。分析两个数据集中重叠的差异表达基因（DEG），随后进行功能富集分析、蛋白质-蛋白质相互作用网络分析和加权基因共表达网络分析（WGCNA）。使用与预后相关的外泌体DEG的最小绝对收缩和选择算子（LASSO）回归，基于TCGA数据集构建肿瘤来源的外泌体基因特征，并通过外部验证数据集GSE62452进行验证。分析该基因特征的预后预测能力及其与各种通路和免疫细胞浸润的关系。
结果
从两个数据集中共鉴定出166个重叠的DEG，这些基因在与细胞周期相关的功能和通路中显著富集。使用WGCNA鉴定出两个关键模块和相应的70个外泌体DEG。使用与预后相关的外泌体DEG的LASSO Cox回归，利用6个外泌体DEG（、、、、、和）构建了肿瘤来源的外泌体基因特征，该特征在训练和验证数据集中均显示出对预后的高预测性能。此外，这种预后特征与细胞周期等几种通路的差异激活以及调节性T细胞和CD8 + T细胞等一些免疫细胞的浸润有关。
结论
本研究建立了一种可准确预测胰腺癌预后的六外泌体基因特征。
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