Cesare Sala, Tiziano Lottini, Elena Lastraioli, Alessandro Ruffinatti Federico, Luca Visentin, Annarosa Arcangeli
Department of Experimental and Clinical Medicine, University of Florence, Viale GB Morgagni 50, 50134 Florence, Italy.
Complex Dynamics Study Centre (CSDC), University of Florence, 50100 Florence, Italy.
Data Brief. 2023 Mar 17;48:109069. doi: 10.1016/j.dib.2023.109069. eCollection 2023 Jun.
A bioinformatic approach was applied to evaluate the effect of treatment with Bevacizumab on the gene expression profile of colorectal adenocarcinoma cells. The transcriptomic profile of Bevacizumab-adapted HCT-116 (Bev/A) colorectal adenocarcinoma cells was determined and compared with that of the corresponding control cell line by Agilent microarray analysis. Raw data were preprocessed, normalized, filtered, and subjected to a differential expression analysis using standard R/Bioconductor packages (i.e., limma, RankProd). As consequence of Bevacizumab adaptation, 166 differentially expressed genes (DEGs) emerged, most of them (123) resulted downregulated and 43 overexpressed. The list of statistically significant dysregulated genes was used as an input for functional overrepresentation analysis using ToppFun web tool. Such analysis pointed at cell adhesion, cell migration, extracellular matrix organization and angiogenesis as the main dysregulated biological process involved in Bevacizumab-adaptation of HCT116 cells. In addition, gene set enrichment analysis was performed using GSEA, searching for enriched terms within the Hallmarks (H), Canonical Pathways (CP), and Gene Ontology (GO) gene sets. GO terms that showed significant enrichment included: transportome, vascularization, cell adhesion and cytoskeleton, extra cellular matrix (ECM), differentiation and epithelial-mesenchymal transition (EMT), inflammation and immune response. Raw and normalized microarray data were deposited in the Gene Expression Omnibus (GEO) public repository with accession number GSE221948.
采用生物信息学方法评估贝伐单抗治疗对大肠腺癌细胞基因表达谱的影响。通过安捷伦微阵列分析确定适应贝伐单抗的HCT-116（Bev/A）大肠腺癌细胞的转录组谱，并与相应对照细胞系进行比较。对原始数据进行预处理、标准化、过滤，并使用标准的R/Bioconductor软件包（即limma、RankProd）进行差异表达分析。由于适应贝伐单抗，出现了166个差异表达基因（DEG），其中大多数（123个）下调，43个上调。使用ToppFun网络工具将具有统计学意义的失调基因列表用作功能过度表达分析的输入。该分析指出细胞粘附、细胞迁移、细胞外基质组织和血管生成是参与HCT116细胞贝伐单抗适应的主要失调生物学过程。此外，使用GSEA进行基因集富集分析，在标志性（H）、经典途径（CP）和基因本体（GO）基因集中搜索富集的术语。显示出显著富集的GO术语包括：转运体、血管生成、细胞粘附和细胞骨架、细胞外基质（ECM）、分化和上皮-间质转化（EMT）、炎症和免疫反应。原始和标准化的微阵列数据已存入基因表达综合数据库（GEO）公共储存库，登录号为GSE221948。
