Department of Molecular Medicine, School of Medicine, Birjand University of Medical Sciences, Birjand, Iran.
Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
Pathol Res Pract. 2023 Sep;249:154726. doi: 10.1016/j.prp.2023.154726. Epub 2023 Aug 2.
Pancreatic cancer is one of the highly invasive and the seventh most common cause of death among cancers worldwide. To identify essential genes and the involved mechanisms in pancreatic cancer, we used bioinformatics analysis to identify potential biomarkers for pancreatic cancer management. Gene expression profiles of pancreatic cancer patients and normal tissues were screened and downloaded from The Cancer Genome Atlas (TCGA) bioinformatics database. The Differentially expressed genes (DEGs) were identified among gene expression signatures of normal and pancreatic cancer, using R software. Then, enrichment analysis of the DEGs, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, was performed by an interactive and collaborative HTML5 gene list enrichment analysis tool (enrichr) and ToppGene. The protein-protein interaction (PPI) network was also constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and ToppGenet web based tool followed by identifying hub genes of the top 100 DEGs in pancreatic cancer using Cytoscape software. Over 2000 DEGs with variable log2 fold (LFC) were identified among 34,706 genes. Principal component analysis showed that the top 20 DEGs, including H1-4, H1-5, H4C3, H4C2, RN7SL2, RN7SL3, RN7SL4P, RN7SKP80, SCARNA12, SCARNA10, SCARNA5, SCARNA7, SCARNA6, SCARNA21, SCARNA9, SCARNA13, SNORA73B, SNORA53, SNORA54 might distinguish pancreatic cancer from normal tissue. GO analysis showed that the top DEGs have more enriched in the negative regulation of gene silencing, negative regulation of chromatin organization, negative regulation of chromatin silencing, nucleosome positioning, regulation of chromatin silencing, and nucleosomal DNA binding. KEGG analysis identified an association between pancreatic cancer and systemic lupus erythematosus, alcoholism, neutrophil extracellular trap formation, and viral carcinogenesis. In PPI network analysis, we found that the different types of histone-encoding genes are involved as hub genes in the carcinogenesis of pancreatic cancer. In conclusion, our bioinformatics analysis identified genes that were significantly related to the prognosis of pancreatic cancer patients. These genes and pathways could serve as new potential prognostic markers and be used to develop treatments for pancreatic cancer patients.
胰腺癌是全球侵袭性最高的癌症之一，也是第七大常见癌症死因。为了确定胰腺癌中的关键基因和相关机制，我们使用生物信息学分析来鉴定胰腺癌管理的潜在生物标志物。从癌症基因组图谱（TCGA）生物信息学数据库中筛选并下载了胰腺癌患者和正常组织的基因表达谱。使用 R 软件鉴定正常和胰腺癌基因表达谱之间的差异表达基因（DEGs）。然后，通过交互式和协作的 HTML5 基因列表富集分析工具（enrichr）和 ToppGene 对 DEGs 进行富集分析，包括基因本体论（GO）分析和京都基因与基因组百科全书（KEGG）途径分析。还使用 Search Tool for the Retrieval of Interacting Genes（STRING）数据库和基于 ToppGenet 的网络工具构建蛋白质-蛋白质相互作用（PPI）网络，然后使用 Cytoscape 软件确定胰腺癌中前 100 个 DEG 的枢纽基因。在 34706 个基因中鉴定出超过 2000 个具有可变对数倍（LFC）的 DEGs。主成分分析显示，前 20 个 DEGs 包括 H1-4、H1-5、H4C3、H4C2、RN7SL2、RN7SL3、RN7SL4P、RN7SKP80、SCARNA12、SCARNA10、SCARNA5、SCARNA7、SCARNA6、SCARNA21、SCARNA9、SCARNA13、SNORA73B、SNORA53、SNORA54，可能可以将胰腺癌与正常组织区分开来。GO 分析显示，前 DEGs 在基因沉默的负调控、染色质组织的负调控、染色质沉默的负调控、核小体定位、染色质沉默的调控和核小体 DNA 结合中更为丰富。KEGG 分析确定了胰腺癌与系统性红斑狼疮、酒精中毒、中性粒细胞胞外陷阱形成和病毒致癌之间的关联。在 PPI 网络分析中，我们发现不同类型的组蛋白编码基因作为胰腺癌发生的枢纽基因参与其中。总之，我们的生物信息学分析确定了与胰腺癌患者预后显著相关的基因。这些基因和途径可以作为新的潜在预后标志物，并用于开发胰腺癌患者的治疗方法。
