Metabolic pathways and key gene analysis of circulating tumor cells in pancreatic cancer based on GEO database

doi:10.3969/j.issn.1009-976X.2022.01.006

Abstract

Abstract: Objective To explore the critical metabolic pathway and gene of circulating tumor cells （CTCs） in pancreatic cancer. Methods CTCs related datasets were screened out from GEO database. To find out metabolism related differentially expressed genes （DEGs）, DEGs from datasets and metabolic genes from KEGG database were compared using R studio. KEGG pathway enrichment was performed by R while protein-protein interaction （PPI） network constructed by STRING and visualized by Cytoscape. Enriched KEGG pathway comparison was made between two CTCs datasets to obtain the key pathway and gene. Then, we analyzed the relationship between key gene and clinical characters and immune infiltration through TCGA and TIMER database. Results 834 and 1119 DEGs were selected from GSE118556 and GSE18670 separately. DEGs from the former were mainly enriched in cysteine and methionine metabolism, carbon metabolism and biosynthesis of cofactors. DEGs from the latter were mainly enriched in carbon metabolism, purine metabolism and glycerophospholipid metabolism. Among them, TKT （transketolase） upregulated significantly in carbon metabolism in both datasets. TKT was significantly associated with overall survival, neoplasm stage, and histological grade （P<0.05）. TKTL1 and TKTL2, which also encode transketolase isoenzymes, were associated with immune infiltration（P<0.05）. Conclusion Through bioinformatic analysis of pancreatic cancer CTCs datasets, carbon metabolism and TKT were found to be significant in the process of CTCs formation and maintaining, which provides the basis for further metastasis metabolic mechanism studies of pancreatic cancer.

Key words: pancreatic cancer, bioinformatic analysis, circulating tumor cells, metabolic reprogramming, immune infiltration

摘要： 目的探讨胰腺癌循环肿瘤细胞（CTCs）中起关键作用的代谢通路及关键基因。方法从GEO数据库筛选胰腺癌CTCs相关数据集,利用R studio软件筛选差异基因,与KEGG数据库的代谢相关基因比对,寻找代谢相关差异基因。对差异基因进行KEGG通路富集,使用STRING、Cytoscape进行蛋白相互作用网络分析和可视化。最后,对比两数据集富集的代谢通路,获得关键通路及基因,并利用TCGA和TIMER数据库分析关键基因与临床特征和免疫浸润的关系。结果分别从数据集GSE118556和GSE18670筛选出834个和1119个差异基因。前者基因主要富集到半胱氨酸和蛋氨酸代谢、一碳代谢和辅酶因子合成等代谢通路,后者基因主要富集到一碳代谢、嘌呤代谢和甘油磷脂代谢通路。其中转酮醇酶（TKT）在两个数据集的一碳代谢中均显著上调。TKT与总体生存期、肿瘤分期、组织学分级相关（P<0.05）。同样编码转酮醇酶同工酶的TKTL1和TKTL2与免疫浸润相关（P<0.05）。结论通过对胰腺癌CTCs数据集的生物信息学分析,发现一碳代谢和TKT可能在CTCs的形成和维持中起关键作用,为进一步研究胰腺癌转移的代谢机制提供一定的基础。

关键词: 胰腺癌, 生物信息学, 循环肿瘤细胞, 代谢重编程, 免疫浸润

CLC Number:

R735.9

LIN Xing-yi, TIAN Zhen-feng, PAN Le-le, SU Ming-xin, CHEN Yin-ting. Metabolic pathways and key gene analysis of circulating tumor cells in pancreatic cancer based on GEO database[J]. Lingnan Modern Clinics In Surgery, 2022, 22(01): 34-41.

林杏仪, 田振烽, 潘乐乐, 苏铭昕, 陈茵婷. 基于GEO数据库的胰腺癌循环肿瘤细胞代谢通路及关键基因研究[J]. 岭南现代临床外科, 2022, 22(01): 34-41.

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