Balakrishnan Karthik, Xiao Yi, Chen Yuanhong, Dong Jixin
Eppley Institute for Research in Cancer and Allied Diseases, Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE 68198, USA.
Cancers (Basel). 2024 Dec 3;16(23):4049. doi: 10.3390/cancers16234049.
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.
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.
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.
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.
识别基因表达谱的技术进步正被应用于一系列癌症的研究。本研究的目的是识别胰腺导管腺癌（PDAC）中差异表达的基因，并研究它们在肿瘤发生和转移中的潜在作用。
PDAC和非肿瘤组织样本的转录组谱来自基因表达综合数据库（GEO），这是一个公共数据库。使用GEO2R工具从这些谱中进一步获取差异表达基因。
在本研究中，共有68个基因来自三个或更多转录组谱中上调的PDAC基因，并被视为PDAC基因集。在分子特征数据库（MSigDB）中对鉴定出的PDAC基因集进行本体研究，结果显示这些基因参与细胞外基质，并与PDAC肿瘤发生中的细胞粘附过程相关。基因集富集分析显示基因集具有更高的富集分数。此外，使用STRING数据库对鉴定出的基因集进行蛋白质-蛋白质相互作用研究。基于功能k均值聚类，本研究确定了以下三个功能聚类组：细胞外基质/细胞粘附、代谢和黏液分泌相关蛋白组。受试者工作特征（ROC）曲线显示，这些聚类基因在预测PDAC肿瘤发生和转移方面具有更高的特异性和敏感性。此外，聚类基因的表达影响PDAC患者的总生存率。使用癌症基因组图谱（TCGA）数据库验证了表达水平与临床病理特征之间的关联。
总体而言，本研究中鉴定出的基因似乎在PDAC发展中至关重要，可作为胰腺癌治疗的潜在诊断和预后靶点。
