Xu Qian, Wang Wei, Hu Hongyu, Ji Shujuan
Department of Anesthesiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China.
J Gastrointest Oncol. 2023 Feb 28;14(1):420-428. doi: 10.21037/jgo-23-94.
We aimed to identify cancer pain genes in pancreatic ductal adenocarcinoma (PDAC) using bioinformatic tools to provide evidence for pain treatment in PDAC patients.
The GSE50570 data were obtained from the high-throughput Gene Expression Omnibus (GEO) database and subsequently analyzed. A volcano map, principal component analysis (PCA) map, box plot, and heat map were drawn, and a Venn diagram was constructed by comparison with human secreted histone genes. The differentially expressed secreted histone genes in PDAC were obtained. Then, Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, followed by protein-protein interaction (PPI) network analysis and key genetic screening.
In comparison to normal samples, the expression of 81 secreted protein-related genes was downregulated, and the expression of 12 secreted protein-related genes was upregulated in PDAC. According to the GO and KEGG enrichment analysis results, these differentially expressed genes are mainly involved in the PI3K-Akt signaling pathway, protein digestion and absorption, extracellular matrix (ECM) receptor interaction, AGE-RAGE (advanced glycation endproducts-the Receptor of Advanced Glycation Endproducts) signaling pathway, relaxin signaling pathway, interleukin-17 (IL-17) signaling pathway, and transforming growth factor-β (TGF-β) signaling pathway, affecting the different manifestations of PDAC cancer pain. We used Cytoscape software to construct a protein interaction network of common differentially expressed genes and obtained three clusters with high scores. Our literature review found that several genes, including , , and , were directly related to cancer pain occurrence.
By data mining the PDAC tumor expression, dozens of differentially expressed genes were identified in this study, several of which have been associated with the frequency and severity of cancer pain. This study provides an important foundation for the pain treatment of PDAC tumor patients.
我们旨在利用生物信息学工具鉴定胰腺导管腺癌（PDAC）中的癌痛基因，为PDAC患者的疼痛治疗提供依据。
从高通量基因表达综合数据库（GEO）获取GSE50570数据并进行后续分析。绘制火山图、主成分分析（PCA）图、箱线图和热图，并与人类分泌型组蛋白基因进行比较构建维恩图。获取PDAC中差异表达的分泌型组蛋白基因。然后进行基因本体（GO）功能注释和京都基因与基因组百科全书（KEGG）分析，接着进行蛋白质-蛋白质相互作用（PPI）网络分析和关键基因筛选。
与正常样本相比，PDAC中有81个分泌蛋白相关基因的表达下调，12个分泌蛋白相关基因的表达上调。根据GO和KEGG富集分析结果，这些差异表达基因主要参与PI3K-Akt信号通路、蛋白质消化与吸收、细胞外基质（ECM）受体相互作用、晚期糖基化终产物-晚期糖基化终产物受体（AGE-RAGE）信号通路、松弛素信号通路、白细胞介素-17（IL-17）信号通路和转化生长因子-β（TGF-β）信号通路，影响PDAC癌痛的不同表现。我们使用Cytoscape软件构建了常见差异表达基因的蛋白质相互作用网络，并获得了三个高分簇。我们的文献综述发现，包括[具体基因名称缺失]、[具体基因名称缺失]和[具体基因名称缺失]在内的几个基因与癌痛发生直接相关。
通过对PDAC肿瘤表达数据挖掘，本研究鉴定出数十个差异表达基因，其中一些与癌痛的频率和严重程度相关。本研究为PDAC肿瘤患者的疼痛治疗提供了重要基础。
