Objectives: Technological advances in identifying gene expression profiles are being applied to study an array of cancers. The goal of this study was to identify differentially expressed genes in pancreatic ductal adenocarcinoma (PDAC) and examine their potential role in tumorigenesis and metastasis. Methods: The transcriptomic profiles of PDAC and non-tumorous tissue samples were derived from the gene expression omnibus (GEO), which is a public repository. The GEO2R tool was used to further derive differentially expressed genes from those profiles. Results: In this study, a total of 68 genes were derived from upregulated PDAC genes in three or more transcriptomic profiles and were considered PDAC gene sets. The identified PDAC gene sets were examined in the molecular signatures database (MSigDB) for ontological investigation, which revealed that these genes were involved in the extracellular matrix and associated with the cell adhesion process in PDAC tumorigenesis. The gene set enrichment analysis showed greater enrichment scores for the gene sets. Moreover, the identified gene sets were examined for protein–protein interaction using the STRING database. Based on functional k-means clustering, the following three functional cluster groups were identified in this study: extracellular matrix/cell adhesion, metabolic, and mucus secretion-related protein groups. The receiver operating characteristic (ROC) curve revealed greater specificity and sensitivity for these cluster genes in predicting PDAC tumorigenesis and metastases. In addition, the expression of the cluster genes affects the overall survival rate of PDAC patients. Using the cancer genome atlas (TCGA) database, the associations between expression levels and clinicopathological features were validated. Conclusions: Overall, the genes identified in this study appear to be critical in PDAC development and can serve as potential diagnostic and prognostic targets for pancreatic cancer treatment.
目的：基因表达谱识别技术的进步正被应用于多种癌症的研究。本研究旨在识别胰腺导管腺癌（PDAC）中的差异表达基因，并探讨其在肿瘤发生和转移中的潜在作用。方法：从公共数据库基因表达综合数据库（GEO）中获取PDAC与非肿瘤组织的转录组谱，并利用GEO2R工具进一步分析得出差异表达基因。结果：本研究从三个及以上转录组谱中共同上调的PDAC基因中筛选出68个基因，定义为PDAC基因集。通过分子特征数据库（MSigDB）对这些基因集进行本体论分析，发现这些基因主要参与细胞外基质的构成，并与PDAC肿瘤发生过程中的细胞粘附过程相关。基因集富集分析显示这些基因集具有较高的富集分数。此外，利用STRING数据库对识别出的基因集进行蛋白质-蛋白质相互作用分析，基于功能k均值聚类方法，本研究确定了三个功能簇群：细胞外基质/细胞粘附相关蛋白群、代谢相关蛋白群以及粘液分泌相关蛋白群。受试者工作特征（ROC）曲线显示，这些簇群基因在预测PDAC肿瘤发生和转移方面具有较高的特异性和敏感性。同时，簇群基因的表达水平影响PDAC患者的总体生存率。通过癌症基因组图谱（TCGA）数据库，验证了基因表达水平与临床病理特征之间的关联。结论：总体而言，本研究所识别的基因在PDAC发展中起着关键作用，可作为胰腺癌治疗中潜在的诊断和预后靶标。
肿瘤(癌症)患者之家